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List of Papers
This thesis is based on the following papers, which are referred to in the text
by their Roman numerals.
Junaid M, Lapins M, Eklund M, Spjuth O, Wikberg JE (2010)
Proteochemometric modeling of the susceptibility of mutated variants of the
HIV-1 virus to reverse transcriptase inhibitors. PLoS ONE 5: 1-10.
Salaemae W, Junaid M, Angsuthanasombat C, Katzenmeier G (2010) Structure-guided mutagenesis of active site residues in the dengue virus twocomponent protease NS2B-NS3. J Biomed Sci 17: 68.
Junaid M, Chalayut C, Torrejon AS, Angsuthasombat C, Shutava I, Lapins
M, Wikberg JE, Katzenmeier G (2012) Enzymatic analysis of recombinant
Japanese encephalitis virus NS2B(H)-NS3pro with model peptide
substrates. PloS ONE (Accepted).
Prusis P, Junaid M, Petrovska R, Yahorava S, Yahorau A, Katzenmeier G,
Lapins M, Wikberg JE (2012) Design and evaluation of substrate-based
octapeptide and nonsubstrate-based tetrapeptide inhibitors of dengue virus
NS2B/NS3 proteases. (Manuscript).
Junaid M, Lapins M, Katzenmeier G, Cui T, Wikberg JE (2012) Design and
Evaluation of Dengue NS3 Protease Peptide Inhibitors. (Manuscript).
Reprints were made with permission from the respective publishers.
I have also contributed to the following article, which is not included in this
thesis. This publication reports further development of the research described in paper I.
Spjuth O, Eklund M, Lapins M, Junaid M, Wikberg JES (2010) Services for
prediction of drug susceptibility for HIV proteases and reverse
transcriptases at the HIV Drug Research Centre. Bioinformatics 27: 17191720.
Introduction ......................................................................................... 11
1.1 Modern Drug Discovery and Development (MDDD) Process ....... 11
1.2 Computational Methods in Drug Discovery and Design ................ 14
1.2.1 Virtual High-throughput Screening (V-HTS) ........................ 14
1.2.2 Structure-Based Drug Design (SBD) ..................................... 15
1.2.3 Quantitative Structure-Activity Relationship (QSAR) .......... 15
1.2.4 Statistical Molecular Design (SMD)...................................... 15
1.2.5 Proteochemometrics (PCM) .................................................. 16
1.3 Enzyme as Drug Target in Modern Medicine ................................. 20
1.4 The Virus in Modern Medicine ....................................................... 20
1.5 Retroviruses .................................................................................... 22
1.5.1 HIV/AIDS .............................................................................. 22
1.5.2 Life Cycle of HIV .................................................................. 25
1.5.3 HIV/AIDS Drug Targets and Drugs ...................................... 26
1.5.4 Highly Active Antiretroviral Therapy (HAART) .................. 29
1.5.5 HIV Drug Resistance: The Bottle Neck................................. 29
1.6 Flaviviruses ..................................................................................... 31
1.6.1 Dengue ................................................................................... 32
1.6.2 Japanese encephalitis (JE) ..................................................... 35
1.6.3 Life Cycle of Flavivirus ......................................................... 38
1.6.4 Flaviviral Drug Targets: NS2B-NS3 Protease ....................... 40
1.6.5 Drug Development for Flaviviral NS2B-NS3 Proteases ....... 42
Aims..................................................................................................... 44
Results Discussion ............................................................................... 45
3.1 PCM analysis of HIV mutants susceptibility to NRTIs (paper I) ... 45
3.2 Kinetics of DEN NS2B(H)-NS3pro mutants (paper II) ................. 46
3.3 JEV NS2B(H)-NS3pro substrate specificity (paper III) ................ 48
3.4 DEN NS2B(H)-NS3pro inhibitors design & evaluation
(papers IV & V) .............................................................................. 50
Conclusions and Perspectives .............................................................. 53
Acknowledgements.............................................................................. 55
References ........................................................................................... 57
o-aminobenzoic acid
Antibody-dependent enhancement
Absorption, distribution, metabolism, excretion,
Acquired immunodeficiency syndrome
Cross validation
Dengue virus
Dengue fever
Dengue hemorrhagic fever
Design of experiments
Dengue shock syndrome
External cross validation
Fragment-based design
Food and drug administration
Fold decrease in susceptibility
Feline leukemia virus
Genotypic resistance testing
Highly active anti-retroviral therapy
Human immunodeficiency virus type 1
HIV-1 Reverse transcriptase
Human T-cell leukemia virus
International Committee on Taxonomy of Viruses
Investigational new drug
Japanese encephalitis virus
Turnover number, Rate of catalysis
Inhibition constant
Michaelis constant
Leave-one-out cross validation
New drug application
Non-nucleoside reverse transcriptase inhibitors
Nucleos(t)ide reverse transcriptase inhibitors
Non-structural protein 2B hydrophilic sequence
Non-structural protein 3
NS3 protease
Polyacrylamide gel electrophoresis
Principal component analysis
Partial least squares projections to latent structures
Phenotypic resistance testing
Quantitative structure-activity relationship
Quantitative structure-property relationship
RNA-dependent RNA polymerase
Rough sets
Structure-based drug design
Statistical experimental design
Simian immunodeficiency virus
Statistical molecular design
Splicing over extension polymerase chain reaction
Support vector machine
Tick-borne encephalitis virus
Virtual high-throughput screening
Virtual phenotypic testing
West Nile virus
Yellow fever virus
1 Introduction
1.1 Modern Drug Discovery and Development
(MDDD) Process
Contrary to traditional trial-and-error based drug design which involves
testing of chemicals on tissues, organs or whole animals, the modern drug
discovery process exploits knowledge about drug targets, and therefore, is
referred to as rational drug design. Modern drug discovery is a lengthy,
costly and complex process usually consisting of three stages, namely,
discovery research, pre-clinical development and clinical development
(Figure 1). A brief overview of different drug discovery and development
phases is given below.
1) Drug Target Identification
A drug target is a key biomolecule involved in a particular functional (metabolic or signaling) pathway that is specific to a pathological condition or to
the survival or infectivity of a pathogen.
The revolution in automation and information technologies has made the
whole genome sequencing and proteome characterisation a routine task, and
consequently has opened windows to new worlds of potential drug targets.
2) Target Validation
The relevance of the target in a disease process or pathogenicity is a difficult
task to prove and which has no general approach. A plausible relevance of a
target maybe demonstrated by knock-out or knock-in animal models, sitedirected mutagenesis studies [paper II], use of ribozymes, or neutralising
antibodies, along with many other approaches.
3) Lead Discovery
A ‘lead’ is a chemical compound that has pharmacological or biological
activity, and has the potential to be optimised to a drug candidate.
Large libraries (106-107) of diverse substances may be tested on drug target using real (usually in vitro) or virtual (in silico) high-throughput screening [1]. Generating millions of compounds and screening in the wet-lab is
often practically unfeasible. Alternate approaches requiring fewer compounds include statistical molecular design (SMD) [paper V], structurebased design (SBD) [papers II, III], fragment-based design (FBD), pharmacophore-based design (PBD), quantitative structure- activity/property rela11
tionship (QSAR/QSPR) and proteochemometrics (PCM) [paper I] and are
methods of great importance that can be utilized in drug discovery processes.
4) Lead Optimisation
The structure of a ‘lead’ is used as a starting point for chemical modifications in order to obtain compounds with certain desired properties. These
properties include ease of synthesis, specificity, efficacy, adherence to Lipinski’s rules [2] and many other criteria. Various in vitro and in vivo methods
are used to test these properties [3]. Lead optimisation requires the simultaneous optimisation of multiple parameters and is thus a time consuming and
costly step.
4) Preclinical Development
Preclinical developments involve in vitro studies and animal trials. Different
formulations and wide ranging dosages of the compounds are introduced and
evaluated in order to obtain preliminary efficacy, general safety, toxicity
patterns and pharmacokinetic information. The compound reaching the preclinical stage is referred to as a ‘candidate drug’ and a candidate drug having
a good safety profile is called an ‘investigational new drug’ (IND).
An IND application, containing data about animal pharmacology and
toxicology studies, chemistry and manufacturing, clinical protocols and investigators, is filed at the Food and Drug Administration (FDA) for approval
of clinical trials.
5) Clinical Trials
Pharmaceutical clinical trials are commonly divided into 4 phases (Phase I–
IV). At the clinical drug development stage the selected IND passes through
three phases (Phase I–III). Phase I utilises a fairly small number (20–80) of
healthy volunteers, where the primary goal is to assess the safety of the agent
and to determine an acceptable dose for Phase II. Phase II normally utilises
about 30–300 patients, where the main goal is to initially assess an activity
of the drug against a disease. Only about 25% of the candidates that enter
clinical trials complete Phases I and II successfully and pass to Phase III. In
Phase III the candidate drug is administered to a larger group (hundreds to
thousands) of patients with the goal to demonstrate its safety and prove its
effectiveness over existing standard therapies. After successful completion
of a Phase III trial, a new drug application (NDA) is filed at regulatory authorities for approval to launch a new drug onto the market [4].
The Phase IV trials (also called pharmaco-epidemiologic studies or postmarketing surveillance) are designed to detect rare or long-term adverse
effects over a larger patient population and longer timescale than was possible during phases I–III of the clinical trials.
• Classical Biological Techniques (Molecular, Cellular)
• Advanced Techniques (Genomics, Proteomics, Bioinformatics)
• Knock in/out animal models
• 3D Structural studies (Real/Virtual)
• Interfering molecules (ribozymes, antibodies, etc.)
Identification •
High-throughput Screening (Real/Virtual)
Structure (Real/Virtual)-based Design
Statistical Molecular Design (SMD)
Optimization •
In Vitro/In vivo screening
Structure-based Design
QSAR/PCM Modeling
• In-vitro studies (Cell lines)
• Trials on animal populations
• Phase I (Healthy, 20-80), Safety, PK, PD, tolerability
• Phase II (Patients 20-300), Safety confirmation
• Phase III (Pateints, 100s-1000s), efficacy, compared with
Standard Drug
• Drug Registration & Marketing
• Post-marketing surveillance (Phase IV trial)
Figure 1. Overview of drug discovery and development process.
1.2 Computational Methods in Drug Discovery and
All existing drugs on the market together hit only about 500 drug targets,
representing less than 1% of potential targets of the presumed druggable
proteome. Similarly, the existing drugs (slightly over a thousand in number)
represent a tiny portion of the chemical space of potential drug-like molecules. Thus a large number of potential targets and a huge number of small
molecules of likely therapeutic value remain to be exploited.
Drug discovery and development is an expensive and time-consuming
process. The average total cost per drug development varies between around
US$ 1–2 billion, the typical development time is 10–15 years [5] and 98% of
initially promising projects fail at later stages of development. Integration of
novel computational approaches at every stage of drug discovery and development, such as the lead discovery, lead optimisation and other pre-clinical
stages, might reduce the failure rate, costs and time for drug discovery and
development. In recent years, many different computational methods have
been developed [6-9], the most common of which are briefly introduced
separately below.
1.2.1 Virtual High-throughput Screening (V-HTS)
Conventional high-throughput screening (HTS) is the process of assaying
large libraries of compounds against drug-targets for their biological and
potential therapeutic activity.
Although, conventional HTS is a common method at early stages of the
drug discovery process, its application in large projects is costly and inefficient. Many of the hits identified from HTS fail in later stages, mainly due to
undesired effects in vivo [10].
Virtual High-throughput Screening (V-HTS) is the process in which large
libraries of diverse compounds are created virtually in a computer and then
sorted by using suitable algorithms in order to find potential hits, which may
then be obtained physically and tested in vitro to confirm their activity
against the target [11]. V-HTS reduces costly syntheses and testing of a large
number of compounds and thus provides the best way to cover a large
chemical space without the commitment in time, materials and infrastructure
that conventional HTS demands.
One example of V-HTS is high-throughput docking (HTD) which is applied in cases where the 3D structural information of the target is available
(see Structure-based drug design).
1.2.2 Structure-Based Drug Design (SBD)
When the 3D structure of the target is available, various computational
methods can be applied to predict the interaction of ligands with the target.
One commonly used approach is molecular docking, where the 3D structure
of the target macromolecule and the ligand are used to model the targetligand binding [5,8][paper IV].
Structure-based design is a highly useful approach during early phases of
modern drug discovery [12]. For example, high-throughput docking is applied to screen a large number of compounds (V-HTS) on targets with
known 3D structures. However, the main problem restricting its application
is that reliable 3D information (i.e. crystal structure or homology model) of
target macromolecules is often unknown [13,14].
1.2.3 Quantitative Structure-Activity Relationship (QSAR)
Quantitative structure-activity relationship (QSAR) is a computational
modelling technique that correlates the chemical structure (represented by
multiple physicochemical property descriptors) of a series of compounds
with their activities, especially biologic activities, and the model thus created
can be used to analyse the properties of compounds and to predict new
structures with improved properties [15,16].
QSAR modelling is of fundamental importance in early and preclinical
drug development stages. Initially, global QSAR models are generated on
earlier collected data (from in-house experimentation, databases and/or literature). At a later stage, when a large number of compounds are selected
and synthesised in specific series, local/specific and more accurate QSAR
models are created based on specific compound series [15].
In the pharmaceutical industry, QSAR is generally used to screen for
ADMET (absorption, distribution, metabolism, excretion and toxicity) studies, which are crucial to the success of candidate drugs.
Many of the mathematical techniques used in QSAR are common to those
used in proteochemometrics (PCM), and are summarised under the PCMsection below.
1.2.4 Statistical Molecular Design (SMD)
Congeneric series of compounds are often assembled from two or more
chemical building blocks. SMD provides an approach for selecting chemical
building blocks to efficiently span the chemical space and increase the information content in libraries of compounds [15][paper V]. However, SMD
can also be used to optimally select compounds from large collections, even
if they do comprise series constructed from distinct building blocks.
Statistical experimental design (SED), or design of experiments (DoE) as
it is also called, is the approach used to design a set of experiments to investigate the effects of a number of experimental variables (factors) on response
SMD is DoE applied to design of molecules, i.e. the factors, experiments,
and responses representing the chemical features of compounds to be synthesised, and their biological actions [19].
The advantage of SMD is that it produces more representative and accurate
data for experimental assessment at less expense, compared with random
testing of chemicals. Therefore, SMD has a great potential in modern drug
1.2.5 Proteochemometrics (PCM)
Proteochemometrics (PCM), which was developed by Wikberg and coworkers in 1999 [20], is a novel bioactivity modelling technique that describes the interaction space (the space spanned by the non-covalent interactions between all molecules) between a series of multiple ligands and targets
PCM is similar to QSAR, but superior in the sense that PCM not only
utilises the chemical information confined in a series of chemical compounds, as does QSAR, but that it also utilises the information confined in a
series of biological targets (Figure 2). PCM models are therefore more descriptive and generally have higher predictability, compared with QSAR
A simplified schema for PCM modelling is given below1:
Derivation of descriptors of biological macromolecules (targets), and
chemical compounds (ligands) - The descriptor matrix of the targets can be
viewed as a set of points in an n-dimensional space representing the chemical space of macromolecules. Similarly, the descriptors matrix of ligands can
be viewed as a set of points in an m-dimensional space representing the
chemical space of ligands. To improve the quality of the PCM model, preprocessing of descriptor matrices, prior the modelling, is performed by different methods such as data centring and scaling to unit variance or blockscaling of the groups of related descriptors, etc. Thus two matrices (target
and ligand) are obtained in this step.
Generation of interaction-term (cross-term) descriptors – These descriptors are obtained by multiplying any two descriptors in the target and ligand
matrices generated in the previous step. Usually three types of interaction
Note that many of the steps in PCM modelling apply also to QSAR modelling.
(cross-term) descriptor matrices are generated, namely, target-target, ligandligand and target-ligand.
Correlation of Independent and Response variables – The descriptors of
targets, ligands and the cross-terms are used as independent variables and the
values of measured biological activity are considered to be the dependent
(response) variables in PCM modelling. The independent variables matrix is
correlated with the response variables matrix, using various machine learning techniques such as PLS, SVM, RS etc., and PCM models are created.
Model Validation – Validation (the process of determining the degree to
which a model is an accurate representation of the real process from the perspective of the intended uses of the model) is a very important step in modelling because it allows us to evaluate, compare and eventually choose the best
model in terms of predictability.
The most common validation techniques include cross-validation, external validation, permutation techniques, and interpretation validation [21,22].
Here the internal cross-validation and external cross-validation – used in this
thesis [paper I] – are introduced below.
Cross-validation (CV) - CV is the most popular approach for the estimation
of the predictive power and potential over-fitting of models [21,23,24]. In gfold CV, the data is divided into G equal-sized groups, each gth group is excluded one at a time, the model is then developed on the G-1 groups (training set) of the data and a prediction error is calculated on the excluded set
(test set). This procedure is repeated for g = 1, 2…G and prediction errors
for all test sets are combined. In general, the number of groups (G) is 5–10.
When G is equal to the number of observations in the data set, the CV is
known as leave-one-out-validation (LOOCV). The fraction of the predicted
y-variation (Q2 or cross-validated R2) - the measure of the predictability of
the regression model – is calculated as:
Q2 = 1−
¦( y
i =1
i =1
i . ob
i . ob
− yi . pr ) 2
− y ob.av ) 2
Here yi.ob, and yob.av represent the observed, predicted, and mean values,
respectively, of response variables.
External cross-validation (ECV) – A trained model is used to predict the
output variable for a set of data points for which an observed activity value
is available, and the points are not included in the model training and hence
completely unknown (i.e. external) to the model [25]. After model training,
the performance of the model on the test set is estimated using the explained
variance for prediction of the external test set (Q2ext)
Q 2 ext = 1 −
i =1
i =1
i . ob
− yi . pr ) 2
i . ob
− yob.av )
Here, yi.ob, and yob.av represent the observed, predicted, and mean values, respectively, of response variables.
ECV provides more reliable performance estimate than internal validation
to assess the quality of model predictions on a data set of unknown compounds.
Model Interpretation – PCM model can be used for interpretation of factors that determine target-ligand interaction activity and for prediction of the
activity of novel ligands and novel targets. The simple way of a validated
PCM model interpretation is to observe the coefficients of the PLS regression equation, in the case that PLS is used for creation of the PCM model,
i.e. a large value of a coefficient for a ligand or protein descriptor would
indicate that the property represented by this descriptor has a large influence
on the interaction activity and vice versa. In the same way, a large absolute
value of a regression coefficient for a cross-term would indicate high influence of the represented combination of features for selective interactions,
and vice versa. The ± sign of a coefficient value indicates the direction (positive/negative) of the influence of the descriptor on the response. Further, the
contribution of a group of descriptors (e.g., amino acid, sequence segment,
part of a ligand, etc.) to the activity (e.g. drug-resistance, binding affinity,
inhibition, etc.) can be computed from the sum of values for all the descriptors involved.
All such information presents a detailed picture of the nature of molecular
interactions and provide basis for the application of PCM modelling in rational drug design and therapy. In drug design, the information would give
directions on how to design/modify a ligand in order to achieve a desired
property for its interaction with the target. Moreover, in target validation,
PCM can be used to identify and quantify not only the role of the active/binding site residues but also the influence of other residues distant to
the active site in a target [paper I & VI], [26,27]
The major advantage of PCM, in contrast to QSAR models which are
built on single targets, is that PCM incorporates data from multiple targets in
a single general model which can be used to extrapolate the activities not
only of novel ligands on known targets or mutants but also of known compounds on new targets.
Another advantage of PCM is that, unlike structure-based drug design, it
can also be applied in situations where reliable 3D information of the target
macromolecules is unavailable.
Overall, PCM is a robust and general method and therefore has a wide
scope and applications in modern drug discovery, design and therapy. Since
its introduction in 1999, PCM has been successfully applied in several studies, including analysis of drug interaction with different classes of G-protein
coupled receptors, [20,28-33], antibody-antigen interactions [34], cleavability of protease substrates [35], substrate interactions with dengue virus
NS3 proteases [36] HIV resistance to protease inhibitors [37] and HIV resistance to NRTIs [papers I & VI].
In conclusion, PCM will have a pivotal role for the future, not only in the
discovery of novel drugs and targets but also in optimising drug therapy.
Figure 2. General Schema of PCM Modelling
1.3 Enzyme as Drug Target in Modern Medicine
Modern medicine is a molecular science, mainly directed towards diagnosis
and therapy. For the latter the main approach is the use of medicines, which
to the largest extent encompasses the small molecules that we term drugs
and which are directed against macromolecular drug targets whose perturbation has a beneficial effect on disease states. A majority of the drug targets
are enzymes, which have vital role in the life processes. Enzymes are ideal
drug targets because of their: 1) essentiality in biochemical processes, 2)
specificity for specific physiological and pathological processes and substrates, 3) simplicity, which allow them to, in fairly simple ways, be characterised structurally and mechanistically, 4) general availability (i.e. to generally be obtainable on a large scale and with high purity for high throughput
studies), and 5) the propensity to find small molecules capable of inhibiting
Today, almost half of the small molecule drugs on the market are inhibitors of enzymes [38]. Current human genome surveys suggest that druggable
targets are dominated by enzymes. In 2001 the worldwide sales of small
molecule drug-inhibitors of enzymes were about US$ 65 billion, and the
market was growing with an average annual increase of 8% [38]. Another
survey suggests that 50–75% of all drug discovery research at the major US
pharmaceutical and biotechnological companies is focused on enzymes as
their primary targets [39]. Therefore it is certain that specific enzyme inhibition will remain a major focus of pharmaceutical research also for long time
to come.
1.4 The Virus in Modern Medicine
“It’s time to close the book on infectious diseases and pay more attention to
chronic ailments such as cancer and heart disease…the war against infectious diseases has been won…”
This was a remark made by the eminent epidemiologist, paediatrician
and American Surgeon General, Willium H. Stewart, in 1967, who, like
many others at that time, was buoyed up by the pace of scientific
advancement in the mid of the 20th century and believed that the battle
against bacteria and viruses was over [40,41]. Unfortunately, as we know he
has been proved wrong, and since then more than 20 new infectious
diseases, including the HIV/AIDS, have appeared on the world stage. Today,
more than 25% of the deaths worldwide are caused by (mainly) viral and
bacterial infectious diseases. Twenty-six years later Donald A. Henderson at
the U.S. Office of Science and Technology Policy had to say:
“The recent emergence of AIDS and Dengue haemorrhagic infections,
among others, is serving usefully to disturb our ill-founded complacency
about infectious diseases. Such complacency has prevailed in this country
(USA) throughout much of my career ... It is evident now, as it should have
been then, that mutation and change are facts of nature, that the world is
increasingly interdependent, and that human health and survival will be challenged, ad infinitum, by new mutant microbes, with unpredictable pathophysiological manifestations...” [42].
A virus (Latin - toxin or poison [43]) is a sub-microscopic particle and the
simplest infectious microorganism found in nature. It has neither a cellular
organisation, nor the replication ability outside living host cells, or the genetic information encoding the molecular machinery required for the generation of genetic information and for protein synthesis. It possesses only one
type of nucleic acid, namely either DNA or RNA [44,45]. When the virus
enters the cell and hijacks its internal machinery, things change and this nonliving miniature capsule for genetic information emerges as a dominant living and replicating organism, which may cause cancers, or acute or chronic
The simple and most useful classification system of viruses is considered to
be the Baltimore Classification (Figure 3) [46] which places the viruses into
one of seven groups based on their genetic contents (DNA or RNA) and
replication strategies (mRNA synthesis).
+ ssRNA
+ ssDNA
+ ssRNA
- ssRNA
Figure 3. The Baltimore Classification System. The unique pathways from various
viral genomes to messenger RNA define specific virus based on the nature and the
polarity of viral genome [46].
1.5 Retroviruses
Retroviruses (Class VI in the Baltimore Classification) belong to the genus
Lentivirus of the family Retroviridae, and are the viruses that contain two
copies of positive-sense ssRNA as genetic material, and the reverse transcriptase enzyme needed to transcribe ssRNA into dsDNA.
Retroviruses are unique in their reproduction: they reproduce by transcribing themselves into DNA from RNA by using the retroviral reverse
transcriptase enzyme. All retroviruses have similar genomic organisation and
express three main polypeptides, namely 1) the gag which contains the matrix, the capsid and the nucleocapsid proteins that make the internal structure
of the virion; 2) the gag-pol which contains the above gag structural proteins, and three enzymes, namely reverse transcriptase, protease, and integrase; and 3) the env that contains two viral envelope proteins located at the
exterior of the mature virion and which take an important part in the infection process, by their involvement in the cell receptor recognitions [47].
Retroviruses cause a broad spectrum of diseases, which include certain
cancers in humans and animals, immune-deficiencies such as AIDS, myelopathies, and chronic infections such as arthritis, infective dermatitis, and
The first retrovirus to be identified was the feline leukemia virus (FeLV)
in 1973 [48]. The first human retrovirus that was isolated was the T-cell
leukemia virus (HTLV-1) by Poiesz and co-workers in 1980 [49].
1.5.1 HIV/AIDS
HIV-1 was discovered in 1983 in AIDS patients in France and United states
[50,51]. HIV-2 was subsequently isolated from patients in West Africa in
1986 [52]. Both HIV-1 and HIV-2 are transmitted by body fluids, and they
have indistinguishable clinical manifestations, including acquired immunodeficiency syndrome AIDS (see also under progression and symptoms).
Prevalence and Transmission
Molecular phylogenetic studies suggest that HIV evolved from the closely
related simian immunodeficiency virus (SIV), a lentivirus which infects
chimpanzees and gorillas in Africa [53,54]. It is believed that SIV transmitted in the late 19th or early 20th century to humans involved in bushmeat activities.
AIDS was first recognised among homosexual men in the United States in
1981 [55]. Since then, HIV/AIDS has killed more than 25 million people. In
2010, around 34 million people were living with HIV (which constitutes an
increase by 17% since 2001), 1.8 million people died, and 2.7 million people
became infected with the virus (Figure 4). From 2001 to 2010, the number
of people living with HIV increased by 34% in North America and Western
and Central Europe, reaching 2.2 million cases (Figure 5). In Eastern Europe
and Central Asia it increased about 250%, reaching 1.5 million cases, and
about 5% in South and South-east Asia, reaching 4.0 million cases. However, sub-Saharan Africa is the worst affected region, with around 68% (23
million) of the total global HIV-positive people living there [56].
North America
1.3 million
Western &
Central Europe
0.84 million
0.2 million
Eastern Europe &
Central Asia
1.5 million
Middle East &
N. Africa
0.47 million
East Asia
0.79 million
South &
South-East Asia
4.0 million
0.054 million
Latin America
1.5 million
Sub-Saharan Africa
22.9 million
Figure 4. Estimated HIV Prevalence among young adults (age 15–49 years) worldwide, as of 2010 [56].
Figure 5. Number of people living with HIV worldwide from 1990 to 2010. (Figure
reproduced from [56], with permission).
HIV can be transmitted with body fluids from infected individuals, such as
during unprotected sex (semen, and vaginal secretions), via blood transfusions (blood and its products), pregnancy, breast-feeding, and child birth
(mother to child with blood, milk), injection, surgery, tattooing and alike
(sharing of contaminated injection and surgical equipment and skin piercing
CD4+ Cells Count
Chronic (Latency)
HIV RNA Plasma Level: Log[Copies/ml]
CD4+ T-Lymphocyte Count [Cells/l]
Progression and Symptoms
There are three stages of HIV progression: 1) acute or primary infection, 2)
chronic infection or latency, and 3) AIDS (Figure 6).
During the first stage, the virus replicates rapidly, which leads to acute
viremia, with millions of virus particles per millilitre of blood [57]. This
stage is associated with a marked drop in the level of CD4+ T cells, activation of CD8+ T cells, and production of antibodies. The acute stage may last
for several (usually 2–4) weeks, and may be associated with flu-like symptoms (fever, swollen lymph nodes, sore throat, muscle pain, rash and weakness). Sometimes headache, nausea and vomiting, enlarged liver/
spleen, weight loss, and neurological symptoms are also seen [58]. This is
the most infectious stage.
Figure 6. Schematic representation of the typical course of HIV progression. During
the primary infection (acute phase), the viremia (red) rises to a maximum, CD4+ Tcell count (blue) drops rapidly and then rises again to a subnormal level, and the
immune system is rapidly activated. During the latency phase, the CD4+ T-cell count
decreases slowly, the viremia and the immune response rise slowly. In the AIDS
phase, the virus counts increases rapidly and opportunistic infections appear. (Modified with permission from the journal [59]).
During the latency stage, the number of viral particles is decreased by the
immune system, which causes the acute infection to turn into a chronic infection. This stage can last from two weeks up to 20 years. Different tissues
such as lymph nodes and other tissues rich in CD4+ T cells become infected.
There may be few or no symptoms, but the individuals are still infectious in
this stage [60].
AIDS is the final stage of the HIV infection, in which the CD4+ T cell
count drops below a critical level (< 200 l-1), and various opportunistic
bacterial, viral and fungal infections (respiratory tract, skin and throat infec-
tions) cancers and other conditions appear due to loss of cell-mediated immunity. The viral load also increases during this stage.
Despite decades of research in the field, no effective HIV vaccine has been
developed so far. The major challenge to HIV vaccine development is the
hyper-variability of the virus [61].
There is one clinical trial, however, that demonstrates that development of
an effective HIV vaccine might be feasible. It was conducted in Thailand in
2009. In this community-based trial, the test subjects were given a combination of two previously unsuccessful recombinant vaccines (canarypox vector
vaccine, ALVAC-HIV, and glycoprotein 120 subunit vaccine, AIDSVAX
B/E). The trial showed that there was a 31.2% reduction in the probability
over a 42 months period to become infected compared to controls that did
not receive the vaccine [62]. Further investigation is underway to assess the
effects of additional boosting doses of the vaccine and to determine its efficacy against HIV subtypes found in other parts of the world, such as subSaharan Africa [56].
1.5.2 Life Cycle of HIV
All retroviruses share a similar replication pathway and it is here exemplified
by the HIV-1 replication cycle (Figure 7). The cycle starts when the virus
attaches to the surface of a cell hosting a receptor to which the virus can
attach, and then passes through a series of crucial steps including the manyfolding of the virus, and finishes with the release of immature virus particles
from the host cell.
The main target cell for HIV-1 is the CD4+ cell, where the virus in the
first step binds to the CD4 receptor, and one of two co-receptors, CCR5 and
CXCR4. The virus’ envelope then fuses with the CD4+ cell’s cell membrane
and the HIV matrix, capsid protein, two RNA molecules, reverse transcriptase, protease, and integrase enter into the cytoplasm of the CD4+ cell. During this process of so-called uncoating, the viral RNA is thus released into
the CD4+ cell. Reverse transcription of the viral ssRNA into dsDNA then
occurs, with the help of the HIV-1 reverse transcriptase (HIV-1 RT). The
HIV-1 reverse transcriptase, unlike many other DNA polymerases, lacks
proof-reading activity, and therefore the reverse transcription is highly errorprone, and multiple mutations may occur during this stage in the HIV-1 genome.
The transcribed DNA must then be imported into the nucleus of the
CD4+ cell, where it becomes integrated into a host chromosome by the HIV1 integrase enzyme. In the next step, viral DNA is transcribed into mRNA to
produce several regulatory proteins, such as tat (trans-activator of transcription), and rev (regulator of virion expression). Next, the gag, gag-pol and
env polyproteins are expressed from viral mRNA. In the final step, the viral
genome and polyproteins are assembled into immature virions that bud out
of the cell. The virions mature and become infectious after the cleavage of
the gag and gag-pol polyproteins into different structural and functional
proteins, by the proteolytic action of the HIV-1 protease.
Glycoprotein (gp120)
CD4 Receptor
Host Cell
HIV Proteins
Figure 7. Schematic representation of HIV-1 replication cycle. 1. Binding, 2. Fusion
and infection, 3. Reverse transcription, 4. Integration, 5. Transcription, 6. Assembly,
7. Budding, 8. Maturation.
1.5.3 HIV/AIDS Drug Targets and Drugs
Access to antiviral therapy has transformed infection by HIV from an almost
certain death to a chronic condition. In 2002 only 2.7% of HIV-positives in
low- and middle-income countries had access to anti-HIV therapy, while by
the end of 2010 this number raised up to 47%. The major factor for this increased access is the falling of prices of antiretroviral therapy, mainly due to
generic competition. In the year 2000, the cost for a three-drug HAART
combination lay in the range US$ 10 000–15 000 per person, per year. The
current cost for the same regimen is less than US$ 120 per person, per year.
More than 25 anti-HIV drugs have been approved, and all, except three,
are targeting one of three HIV-1 enzymes, namely the reverse transcriptase,
protease or integrase [64,65]. The main classes of anti-HIV drugs are described below.
HIV-1RT Inhibitors
HIV-1RT is the sole enzyme that produces dsDNA from the ssRNA genome,
which is an essential step in retroviral replication [66-68]. The enzyme’s
substrate binding site is the drug target for more than half of the anti-HIV
drugs. There are two types of drugs that target HIV-1RT, namely
nucleoside/nucleotide analogue reverse transcriptase inhibitors (NRTIs) and
non-nucleoside analogue reverse transcriptase inhibitors (NNRTIs).
NRTIs are analogues of nucleotide substrates that lack the 3´-OH group
present in the ribose ring of the four natural deoxyribonucleotides (Figure
Figure 8. Chemical structures of clinical NRTIs.
NRTIs are phosphorylated by cellular kinases to form 5-triphosphates,
which are then used by HIV-RT as substrates and become incorporated in
the extending DNA chain [66,68]. Due to the absence of a 3-OH group in
their ribose ring, NRTIs act as chain terminators, blocking further elongation
of the DNA [69]. Eight NRTIs have been approved for clinical use, namely:
zidovudine (AZT), didanosine (ddI), zalcitabine (ddC), tenofovir (TDF),
lamivudine (3TC), emtricitabine (FTC), abacavir (ABC), and stavudine
(d4T). Of these zalcitabine (ddC) was discontinued in 2006 due to the availability of better alternatives, and due to its severe adverse effects, which
include peripheral neuropathies [70].
NNRTIs represent a group of HIV-1 RT inhibitors that bind in the socalled NNRTI binding pocket, where they induce conformational changes in
the RT that terminate the reverse transcription of ssRNA. The approved
NNRTIs are nivirapine (NVP), delaviridine (DLV), rilpivirine (RPV),
etravirine (ETR) and efavirenz (EFV).
HIV-1Protease Inhibitors (PIs)
Protease inhibitors represent the second largest group of HIV-1 inhibitors in
use. PIs are analogues and competitors of the natural peptide substrates for
the enzyme active site. Most of the clinically approved PIs, such as
amprenavir (APV), darunavir (DRV), atazanavir (ATV), lopinavir (LPV),
indinavir (IDV), nelfinavir (NFV), ritonavir (RTV) and saquinavir (SQV),
are non-cleavable peptidomimetic substrate analogues that contain a similar
structural determinant as the normal substrates, namely a non-cleavable
hydroxyethylene core rather than the natural peptidic linkage [71]. A second
generation PI named tipranavir (TPV) has also been approved. TPV has been
found to be effective against a majority of drug-resistant strains of HIV-1
and HIV-2. However, new strains resistant to TPV have also been identified
HIV-1 Integrase Inhibitors
There is only one approved inhibitor belonging to this class, namely
raltegravir (RGV). RGV inhibits the HIV-1 integrase, which is the enzyme
responsible for integration of viral DNA into the host cell DNA [74].
Other inhibitors of HIV-1
There are two inhibitors that target the viral entry into the cell. One of them
binds to the viral trans-membrane protein gp41, and thereby prevents the
viral fusion with the host cell. This one is classified as a ‘fusion inhibitor’,
and is called enfuviritide (ENF) [65]. The other recently developed inhibitor,
maraviroc (MVC), binds to the CCR5 chemokine co-receptors and is classified as ‘CCR5 co-receptor antagonist entry inhibitor’ [75]. Another class of
potential HIV inhibitors are called ‘maturation inhibitors’ and inhibit the last
step of the gag polyprotein processing, which results in the formation of
defective and non-infective viral particles [76].
1.5.4 Highly Active Antiretroviral Therapy (HAART)
None of the available antiretrovirals administered as monotherapy are able to
suppress HIV replication for any extended period of time due to rapid emergence of resistant strains of HIV-1. Serial use of individual drugs leads to
multi-drug resistance. Moreover, resistance to one drug may cause resistance
to other similar drugs (cross-resistance) of the same class of antiretrovirals.
Therefore, highly active antiretroviral therapy (HAART), which is the combination of three or more drugs from at least two different classes of antiretrovirals, was introduced. The first line treatments include two NRTIs in
combination with one NNRTI or a PI [77].
HAART offers several advantages in anti-HIV therapy: 1) multiple drugs
suppress viral replication more effectively than single drugs [78,79], 2) the
generation of new HIV variants (including the resistant variants) are arrested
due to suppression of viral replication, and 3) the emergence of resistance
becomes unlikely, as many different mutations need to appear concomitantly
that render all drugs in the regimen resistant for resistance to occur. In patients who receive HAART as their first line therapy, the emergence of HIV
resistance to the multiple drugs in the HAART regimen is possible only if
the levels of drugs are insufficient to block the viral replication, but sufficient to exert a positive selective pressure on variants with decreased drug
susceptibility [52].
Although HAART has increased the life expectancy of people with
HIV/AIDS dramatically since its introduction in 1996 [80,81], the virus is
not eradicated from the patient. Therefore the therapy is to be continued
throughout the patient’s life [65].
1.5.5 HIV Drug Resistance: The Bottle Neck
HIV drug resistance is the ability of the virus to withstand the inhibitory
effects of a given anti-HIV drug and thus continue to reproduce in its presence [82].
Although the access to highly active antiretroviral therapy (HAART) has
reduced the mortality, the high replication rate combined with the high mutability of HIV leads to emergence of drug-resistant strains, which undermines our efforts to stop the AIDS pandemic [83]. About 109 to 1010 virions
are produced in an untreated HIV infected individual every day, and it has
been estimated that each possible single-point mutation arises 104 to 105
times per day in this population [84]. While some mutations result in function-impaired viruses, others lead to drug-resistant forms. Drug resistance
can be considered as a natural evolutionary response to escape from the drug
pressure. In a general case, the evolutionary responses can, for example, be
decreased drug influx or increased drug efflux, development of alternate
pathways to bypass the inhibition reaction, increased production of drug29
sensitive enzymes, increased amount of competing substrates, or decreased
requirement of the product of inhibited reaction. However, the most common
mechanism of resistance mediated by mutations in the genes of infectious
organisms alters the respective drug target macromolecule and consequently
its interaction with the drug [85-89].
HIV-1 develops resistance to NRTIs by two distinct mechanisms. The
first is by mutations that primarily cause steric hindrance, decreasing the rate
of incorporation of the NRTI into the elongating DNA chain. The other is by
mutations that increase phosphorolysis leading to removal of already incorporated chain-terminating inhibitors from the DNA, thereby allowing reverse
transcription to continue [83].
HIV-1 resistance to NNRTIs involves prevention of the drug binding to
its binding pocket near the HIV-RT active site, thus allowing DNA polymerization to proceed normally [83].
HIV-1 Resistance to PIs involves mutations in HIV-1 protease which reduce binding affinity of PIs to the HIV-1 protease, e.g. by modifying the
points of interaction of the drug with the enzyme [90].
When HIV develops resistance to the first line therapy, a new regimen has
to be selected from multiple alternative possible drug combinations. Since
anti-HIV drugs that act at the same target (and binding site) are rather similar
in their molecular properties, cross-resistance is frequent, and a new regimen
cannot be based on the assumption that the virus will be susceptible to the
drugs remaining in the therapeutic arsenal [83]. Therefore resistance testing
has become an important tool in the management of HIV/AIDS. There are
three types of such resistance tests. Two of these are phenotypic resistance
testing (PRT) and genotypic resistance testing (GRT). In PRT, the viral activity is measured in the absence and presence of varying concentrations of
ARV medication. In GRT, the viral drug target coding genes are sequenced
and examined for resistance-related mutations. PRT is comparatively easy to
interpret but difficult to perform. GRT is much faster and cost-effective than
the PRT. However, sequence data provide only indirect evidence of resistance and interpretation of the results is difficult for complex mutational
In an attempt to overcome GRT interpretation complexity, a third newly
introduced approach is the so-called virtual phenotypic testing (VPT). VPT
is the combination of GRT and PRT. First a GRT is performed and then the
data obtained is subjected to computational/statistical models that correlate
the GRT data to phenotype, using large datasets containing genotype and
phenotype information from individual viral isolates [91].
Several types of statistical and machine learning modelling techniques
have been used for finding relationships between mutations in the HIV genome and phenotypic drug susceptibility. These include artificial neural
networks [92,93], support vector regression [92,94], least-squares regression,
decision trees [92], and non-linear regression methods [95]. However, mod30
els created by these methods were hitherto developed for a single drug at a
time, and do not allow the models to draw conclusions for any other drug
beside the drug for which the model was created.
Similarities and differences in the interaction of drugs with susceptible or
resistant drug targets are determined by the similarities and differences in
structural and physicochemical properties of the drugs as well as the targets.
Stated that a model of a drug is based on only the mutation pattern in the
targets, the model will not be able to generalise towards another drug. It will
be valid only for the drug for which the model is trained on. Such a model is
thus not able to explain cross-resistance, and is unable to extrapolate to new
anti-HIV agents. Further, in some previous modelling approaches, mutations
were represented by the amino acid letter codes as binary indicator variables,
rather than quantitative molecular property descriptors of the sequence residues that are relevant for drug-protein interactions (e.g. amino acid size and
shape, charge, hydrophobicity, solubility, etc.). Therefore, the models have
limited ability to generalise and predict the effect of less common mutations
on interactions between the target and the drug.
One of the aims of this thesis is to present the application of an advanced
and more generalised modelling approach, termed proteochemometrics
(PCM), for the analysis of HIV-1 resistance to NRTIs (see 1.2.5 & papers I
& VI).
1.6 Flaviviruses
The Flavivirus genus (family Flaviviridae) comprises over 70 viruses, including dengue virus (DEN), Japanese encephalitis virus (JEV), West Nile
virus (WNV), tick-borne encephalitis virus (TBEV), and yellow fever virus
(YFV), which are all primarily transmitted by arthropods. These flaviviruses
share a similar genomic organisation and replication strategy, yet cause a
broad spectrum of distinct clinical diseases, ranging from mild fever and
malaise to fatal hemorrhagic fever, encephalitis and severe inflammation of
the liver. They can be broadly grouped into viruses that have the capacity to
cause vascular leakage and haemorrhage, such as DEN, and those that can
cause encephalitis, including JEV, WNV and TBEV.
The flaviviruses (class IV in Baltimore Classification [46]), as defined in
the eighth report of the International Committee on Taxonomy of Viruses
(ICTV), are the viruses which contain a positive polarity single stranded
RNA genome, icosahedral nucleocapsid, size (diameter) of about 40–65 nM,
surrounded by a lipoprotein envelope [96]
1.6.1 Dengue
Dengue is considered the most important arthropod-borne viral infection of
humans [97]. The origin of word ‘dengue’ is unknown but some people believe that it is derived from the Swahili phrase ‘Ka-Dinga Pepo’, meaning
‘cramp-like seizure caused by an evil spirit’, which was the word used for a
dengue-like illness during 1823 in Zanzibar and on the East African coast
[98]. Dengue is caused by the dengue virus (DEN), a mosquito-borne positive single-stranded RNA virus of genus Flavivirus, family Flaviviridae.
There are four serotypes, DEN1–4, characterised by genetic and immunological differences [99]. Infection induces life-long protection against the
infecting serotype, but it gives only a short time (2–3 months) cross protective immunity against the other types.
Prevalence and Transmission
Dengue is the most rapidly spreading mosquito-borne disease in the world
today, with a 30-fold increase over the last 50 years [97]. WHO estimates
that globally, 2.5 billion people (two-fifth of the world’s population) are at
risk, and annually, 50-100 million people become infected and 0.5 million
people with severe dengue require hospitalization [100].
The disease is endemic in more than 100 countries of the tropical and subtropical regions (Figure 9), and the dengue hemorrhagic fever (DHF) has
occurred in more than 60 of these countries.
January Isotherm
July Isotherm
Areas at risk of dengue transmission
Figure 9. Areas where dengue has been reported, as of 2008. The contour lines of
January and July indicate the potential limits of the northern and southern hemispheres for year-round survival of the dengue vector Aedes aegypti [97]. (Adapted
with permission from WHO).
The factors contributing to the increasing incidence of dengue include increasing urban populations, increased movement of infected persons, spread
of mosquitoes, resistance of mosquitoes to insecticides, and global warming.
The dengue viruses are maintained in a cycle that involves humans as the
primary amplifying hosts and the mosquitoes of genus Aedes, commonly
Aedes aegypti, as the primary vectors (Figure 10). The virus enters into the
female mosquito when the mosquito feeds on the blood of an infected individual during viremia, the later which develops after about 4 days of infection, and lasts for about 5–12 days [101].
Aedes aegypti
Figure 10. Dengue virus transmission cycles - urban epidemic, and enzootic (modified from [102] with permission from the journal).
Once in the mosquito, the virus replicates there during an extrinsic incubation period of 8–12 days. The mosquito thereby gets the life-long capability
to infect susceptible individuals, which amounts to a time of around 3–4
weeks. The mosquitoes may also transmit the virus through their offspring
by trans-ovarian transmission; i.e. via its eggs. The mosquito eggs have the
ability to survive without water for several months, which poses a challenge
to vector control efforts. Some dengue ecology studies in sylvatic habitats in
West Africa [103] and Malaysia [104] have identified the enzootic cycle
involving non-human primates as reservoir hosts, and tree-hole dwelling
Aedes spp. as vector (Figure 10) [102].
When the mosquito (female Aedes aegypti) ingests the blood containing the
virus from a dengue infected viremic person, the virus enters and replicates
in the mosquitoes mid gut, ovaries, nerve tissues, fat body, and finally in the
salivary glands. When the mosquito then bites a non-infected individual, the
virus enters into his/her body from the mosquito’s saliva, and the cycle continues (Figure 11).
Figure 11. Dengue transmission phases [105]. (Adapted with permission from
Progression and Symptoms
After the virus enters into the human body, it localises and replicates in various target organs such as the liver and lymph nodes. It then starts spreading
through the blood and infects white blood cells and other lymphatic tissues,
and circulates in the blood for about five days. During this viremic phase, the
virus can be transmitted to feeding mosquitoes.
The dengue infection causes a broad spectrum of symptoms, ranging from
mild undifferentiated fever, dengue fever (DF) to the life-threatening dengue
haemorrhagic fever (DHF) with plasma leakage that may lead to hypovolaemic shock, called dengue shock syndrome (DSS). The symptoms appear
after an incubation period of 3–14 days (most commonly 4–7 days), at a time
point shortly after the onset of viremia, and they may last for 3–10 days
(Figure 11). Dengue fever in its classic form is a non-fatal illness, characterised by fever, severe headache, muscle and joint pain, rash, nausea and vomiting, that lasts for about a week. The potentially deadly severe form dengue
hemorrhagic fever, is characterised by high continuous fever that usually
lasts for 2-7 days, and which can rise as high as 40–41 °C; possibly with
febrile convulsions, and a haemorrhagic condition, with liver enlargement
and circulatory failure due to massive plasma leakage from vascular capillaries. Many patients recover spontaneously after a short period of fluid and
electrolyte therapy. In severe cases, the patient’s condition suddenly deteriorates. After having had fever for 2–7 days, the body temperature drops, followed by signs of circulatory failure with rapid and weak pulse, hypotension,
cold and clammy skin, and the patient may reach a critical state of shock
(DSS). Death may occur within 12–24 hours if appropriate supportive treatment is not provided [106,107].
The pathogenesis of DHF is not well defined, though most cases of DHF
are attributed to immune-pathologic mechanisms [108]. Further, it has been
shown that secondary infection with different dengue serotype can lead to
severe disease (DHF and DSS), and that the anti-DEN antibodies passively
transferred from mother to infants increase the risk of DHF and DSS in infants for a certain period of time. The proposed mechanism for this phenomenon is antibody-dependent enhancement (ADE) [109,110].
ADE can be defined as the process where non-neutralising cross-reactive
antibodies (raised during a primary infection, or acquired passively at birth)
bind to epitopes on the surface of a heterologous infecting virus and facilitate virus entry into Fc-receptor-bearing cells such as monocytes or macrophages, and thus increase virus replication and produce massive infection
[97]. The rapid increase in the levels of inflammatory cytokines and chemical mediators (caused by activation of CD4+ and CD8+ T-lymphocytes and
lysis of infected monocytes by cytotoxic T-cells) leads to induced plasma
leakage from vascular endothelial cells, and cause DHF/DSS [108].
Dengue Vaccine Development
Four types of dengue vaccines are in various stages of development (Phase I
and II trials). These are based on live attenuated viruses, chimeric live attenuated viruses, inactivated viruses, and nucleic acid-based vaccines.
There are several challenges to dengue vaccine development. First, the
vaccine must be tetravalent, because there are four serotypes responsible for
dengue pathogenesis. Second, the mechanism for protective immunity has
not been fully elucidated. Third, there is a lack of a suitable animal model
that recapitulates human disease and which can be used to evaluate candidate
vaccines. Finally, the potential for immune-pathogenesis, including
antibody-dependent enhancement (ADE) which pose a danger that the
vaccine can potentially cause severe disease (DHF or DSS) in vaccine
recipients for the case that solid immunity is not established against all four
serotypes [97].
Despite the significant progress in dengue vaccine development research,
there is still a long way to go and several hurdles to cross. Therefore, along
with vaccine development, there is a dire need to search for dengue antivirals.
1.6.2 Japanese encephalitis (JE)
Japanese encephalitis (JE) constitutes acute encephalitis that can progress to
paralysis, seizures, coma and death. JE is caused by Japanese encephalitis
virus (JEV); a mosquito-borne single-stranded positive sense RNA virus of
genus Flavivirus of the family Flaviviridae. There are four major genotypes
of JEV, specifically, G-I, G-II, G-III and G-IV, classified based on nucleotide differences which amount to more than 12%. All genotypes can cause
encephalitis and paralysis, but have different virulence based on the genetic
variation. For unknown reasons the G-III is the most virulent genotype.
Prevalence and Transmission
The first cases of this disease were recorded in the 1870s in Japan, and hence
its name, Japanese encephalitis. The prototype Nakayama strain of JEV was
first isolated from the brain of a fatal case in 1934 [111] and from the Culex
tritaeniorhynchus mosquito in 1938 [112].
JE is endemic in many countries in Asia, Africa and South America and it
appears sporadically in Australia and the western Pacific [113] (Figure 12).
Figure 12. Distribution of Japanese encephalitis in Asia. The dark areas in the map
indicate the countries of a large portion of Asia and the western Pacific region in
which Japanese encephalitis is prevalent [113].
The worldwide annual number of JE cases is about 68 000, out of which
approximately 20–30% of cases are fatal and 30–50% of survivors develop
significant neurologic sequelae [114,115]. However, the true annual incidence of encephalitis cases is estimated to be closer to 175 000, if they had
been reported properly [116].
Pigs and wild birds are the amplifying hosts, and Culex tritaeniorhynchus is the primary vector [117]. Pigs are necessary for preepizootic amplification of the virus [118]. Humans and horses can develop
fatal encephalitis, but they are only incidentally infected and are dead-end
hosts for the virus, as mosquitoes do not get the infection from JE patients,
essentially due to the low and short-lived viremia (Figure 13).
The female Culex tritaeniorhynchus mosquitoes become infected by feeding on viremic pigs and after an extrinsic incubation period (7–14 days)
transmit the virus to other susceptible animals and humans.
Figure 13. The cyclical pattern of JEV transmission between vertebrate reservoir
(pigs, birds), vectors (mosquitoes) and dead-end hosts (humans, horses). (Modified
from [102] with permission from the journal).
Progression and Symptoms
After the virus enters the human body from an infected mosquito, it replicates peripherally and causes a transient viremia, before the virus invades the
central nervous system. The most likely mechanism of crossing the blood
brain barrier and neuro-invasion for JEV is considered to be passive transfer
The symptoms of infection, which appear after an incubation period of 6–
14 days, are non-specific and include fever of about 38 °C, chills, muscle
pain, and meningitis-type headaches with vomiting. In children, initially
gastrointestinal symptoms, such as nausea, vomiting and abdominal pain
appear. The symptoms last for 2–4 days and then the condition of the patient
deteriorates rapidly with a progressive decline in alertness eventually resulting in coma [120]. The majority of patients have painful neck stiffness and
convulsions. Motor paralysis, tremor, rigidity, and abdominal movements
are also present in some patients [121]. Respiratory distress and opisthotonos
(i.e. tetanic spasm of back muscles with forward arching of the spine) indicate infection of the brain. Fatality is associated with acute cerebral oedema
or severe respiratory distress from pulmonary oedema.
Currently there is no antiviral therapy for JEV or any other flaviviral infection, and so far the main strategy to control the disease is by preventive
methods such as vaccination and preventing mosquito bites [122-124]. Since
the zoonosis is endemic in large parts of Asia, it is not likely to ever be extinguished. For prevention, human vaccination is considered the most reliable method. There are two vaccines licensed by FDA, as follows:
1. Mouse brain-derived, formalin-inactivated vaccine (JE-MB): This was
developed using the prototype Nakayama strain, by Biken Institute in Japan
[125] and it has caused substantial reduction in JE incidence in several countries [126,127]. This vaccine was approved in 1992, but due to certain allergic and neurologic reactions [128] its manufacture was terminated by the
Japanese government in 2006, and is therefore no longer available.
2. Inactivated vero cell culture-derived vaccine (JE-VC): This vaccine
was approved by the FDA in 2009 for use in persons 17 years of age. The
safety of JE-VC in children is under investigation and hence the vaccine has
not been approved for use in children less than 17 years of age [129].
Other JE vaccines also exist, which include live-attenuated SA14-14-2 JE
vaccine, which are manufactured and used in Asia, but they are not approved
in the United States and Europe [130-132].
Although the improvements in JEV vaccination coverage has reduced the
JE incidence, still about 55 000 (more than 80% of the total) annual cases
occur in areas with well established or developing JE vaccination programs[133]. Therefore, therapeutic agents for JE would be of great importance to develop.
1.6.3 Life Cycle of Flavivirus
For most flaviviruses, the natural reservoirs are mostly birds, rodents and
other non-human wild primates. Humans and other domestic mammals are
considered to be mostly dead-end hosts, because the viral load in the blood
remains low, and the transmission from a human host to another human is
ineffective. One exception to this is in case of DEN, in which humans can
become a reservoir host because of high viral load in the blood and further
transmission of virus can occur [134].
The arthropod vector (mosquito or tick) is infected when taking blood
from the animal reservoir and then introduces the virus into a host during its
blood meal. The viruses enter the target cells (e.g. dendritic cells) by receptor-mediated endocytosis and reach the endosomes. In endosomes, the acidic
environment triggers conformational changes in the viral envelope glycoprotein (E) that induce fusion of the viral and the host cell membranes
[135,136] (Figure 14).
Figure 14. Schematic representation of flavivirus replication cycle. 1. Virus-host cell
binding; 2. Receptor- mediated endocytosis; 3. Membrane fusion and uncoating; 4.
Translation; 5. Polyprotein processing (5A. non-structural proteins, e.g. viral helicase and RdRp; 5B. structural proteins, e.g. prM and capsid); 6. RNA synthesis; 7.
Encapsidation and budding of virions in the ER; 8. Transportation through transgolgi network. 9. Maturation; 10. Release of mature virions by exocytosis [137].
Like a cellular mRNA, the released genome encodes a single open reading
frame (orf) which is translated into a 3400 amino acids long single precursor
polyprotein that is cleaved by viral NS2B-NS3 proteases and host signalases
to produce three structural proteins (C, prM, and E) which constitute the
viral particle and seven non-structural (NS) proteins (NS1, NS2A, NS2B,
NS3, NS4A, NS4B, NS5) (Figure 15), which are involved in viral RNA
replication, virus assembly, and modulation of the host cell responses [137].
Replication occurs in modified membrane structures derived from the endoplasmic reticulum (ER). After translation of input genomic RNA, a noncapped negative-stand RNA is copied from genomic RNA by the NS5 RNAdependent RNA polymerase (NS5 RdRp). This negative-stand RNA serves
as a template for the synthesis of new positive strand viral RNA [138].
Capped genomic RNAs are generated that provide a template for translation
into precursor polypeptide and for packaging into virions. An immature noninfectious flavivirus virion is formed by the assembly of a single copy of
viral positive-strand genomic RNA and the structural proteins C, prM and E
components in the ER, where viral RNA-protein C complex is packaged into
an ER-derived lipid bilayer containing hetero-dimers of the prM and E proteins [139,140]. Mature infectious viral particles are produced by structural
rearrangements, such as cleavage of the prM into M by host cell protease,
furin, in the low pH of the trans-golgi network, and released into the ex-
tracellular medium by exocytosis, which often results in the cell lysis and
cell death [141-143].
1.6.4 Flaviviral Drug Targets: NS2B-NS3 Protease
In principle, every step in a viral life cycle can host a potential drug target.
However, the molecular events in the morphogenesis of flaviviruses, like
DEN and JEV, are poorly characterised which makes design of inhibitors a
daunting task. Conversely, viral proteases have been the focus for drug discovery and development for a long time for other viruses, with several success stories, such as the development of clinically useful HIV protease inhibitors. However, as yet no effective antivirals are available for the treatment of flavivirus infection in humans, and the identification and characterisation of novel drug targets is critical to developing new types of antiflaviviral drugs.
The Flaviviral NS3 Protein: The flaviviral NS3 protein is a multifunctional enzyme that contains protease, helicase and RNA triphosphatase
(RTPase) activities. The N-terminal domain of ~180 amino acids of NS3
conveys the protease activity and is called NS3pro. The two C-terminal domains consist of 450 amino acids that convey the RNA helicase and RTPase
activities [144-146].
NS3pro: NS3pro is a trypsin-like serine protease domain, which exists in
a complex with an essential cofactor, NS2B, the latter which is also encoded
by the viral genome. The complex is crucial for the cleavage of the viral
precursor polyprotein (Figure 15) and consequently for viral replication
C prM
Host protease
2A 2B
NS3 Cofactor
IFN Resistance?
IFN Resistance?
Protease, Helicase
Guanyl Transferase
Figure 15. Schematic diagram of flavivirus polyprotein, cleaved by virus-encoded
NS2B-NS3pro complex (black down-pointing triangle - ) and the host-encoded
proteases (grey up-pointing triangle - ).
This makes the NS2B-NS3pro an attractive target for anti-flaviviral inhibitor
development. The NS2B-NS3 endopeptidases of the Flavivirus genus are
now collectively termed flavivirin (EC [149].
Deletion analysis of DEN NS2B indicates that the central hydrophilic domain – NS2B(H) – of NS2B is sufficient as a co-factor for activation of
NS3pro [150-152]. In addition, proteolytic activity of WNV NS3pro in complex with the NS2B hydrophilic region of WNV (residues 50-97) has been
demonstrated using recombinant enzyme expressed in Escherichia coli (E.
coli) [153].
It is important to establish structural and functional determinants in
flaviviral NS2B-NS3pro in order to understand their roles and to facilitate
design of inhibitors. Site-directed mutagenesis studies of NS2B and NS3pro
have provided useful insights [154,155]. The high level of conservation and
similarities of sequences within the flaviviral proteases suggest that specificity characteristics found for one flavivirus protease could also be of relevance for other closely related flaviviral NS3 protease. For example, NS2BNS3pro requires a dibasic recognition sequence (P1-Arg, P2-Lys) for substrate cleavage which is conserved throughout the flaviviruses [156]. However, the NS3 proteases from different flaviviruses, such as DEN and WNV,
exhibit different substrate specificities, suggesting that their respective active
site are distinctly conformationally organised [157]. Active site mutagenesis
and SAR studies are useful to gain insights.
Some early studies dedicated to site directed mutagenesis for ultraconserved residues among the flaviviral proteases identified His51, Asp75
and Ser135 as the catalytic triad residues which are putatively involved in
catalysis or substrate binding [158,159]. However, the protease variants were
in these studies assayed by polyacrylamide gel electrophoresis (PAGE) to
reveal auto-proteolytic cleavages of the NS2B-NS3 precursor in vivo. Although this approach yielded semi-quantitative data for activity of the mutant enzymes, it did not report precise numerical kinetic parameters of the
enzyme mutants with substrates supplied for trans cleavage reactions. Furthermore, a number of important residues such as L115, S163 and I165 were
not included in the study; residues in which possible roles for enzyme activity were later determined by structural experiments [160,161]. Therefore,
one of the studies of the present thesis is directed to site-directed mutagenesis of selected positions in the active site of dengue NS2B(H)-NS3pro to
determine their roles for the catalytic efficiency of the dengue virus NS3
protease [paper II].
The functional profiles and substrate specificity patterns of NS2B-NS3 of
some flaviviruses, such as DEN2, and some other related flaviviruses like
WNV have been studied recently [162-166] [paper II], and efforts were focused on design strategies for inhibitors [papers IV & V].
The JEV NS2B-NS3pro is a hitherto neglected potential drug target, and
needs to be characterised structurally and mechanistically. The cloning, ex41
pression, purification, biochemical characterisation, substrate specificity and
inhibition studies [paper III] of JEV NS2B(H)-NS3pro was therefore one of
the objectives of this thesis.
1.6.5 Drug Development for Flaviviral NS2B-NS3 Proteases
Despite the tremendous success in development of inhibitors for HIV
(AIDS) and hepatitis C virus proteases, the potential for dengue and related
flaviviral proteases as drug targets remains still to be exploited [167].
One of the strategies applied in the discovery of anti-flavivirals has been
HTS of non-peptidic small molecule libraries. Several such studies were
reported but very few small molecule inhibitors have been identified so far
using this technique [168-174].
A more popular strategy has been the design of peptide-based substrate
analogue inhibitors. In one such study, synthetic peptides mimicking the
uncleavable transition state isosteres of the P6-P2 residues of the native
polyprotein cleavage sites were tested. A peptide with -keto amide group in
place of the cleavable amide bond inhibited the DEN NS3pro competitively
in the micromolar range, and another NS3/NS4A-derived peptide with a Cterminal aldehyde group in place of P1-P2 (Ac-FAAGRR-CHO) demonstrated competitive inhibition of NS3pro at 16 μM (Ki) [152]. In another
study, hexapeptides derived from the cleavage sites of DEN NS3 protease
showed low micromolar inhibitory activity; the best one, Ac-RTSKKRCHO, was based on the NS2A/NS2B cleavage site and showed a Ki of 12
μM [175].
In another similar study, small peptide chains resembling the non-prime
amino acids sequences of the natural substrates with different electrophilic,
aldehydic, ketonic or acidic warheads added, were designed and screened.
Boronic acid and trifluromethyl ketone derivatives were found to be potent
competitive inhibitors of DEN2 NS2B-NS3 protease (Bz-nKRR-B(OH)2, Ki:
0.04 M; Bz-nKRR-CF3, Ki: 0.9 M) [176]. Recently, a series of N-terminalcapped tripeptide aldehyde inhibitors were reported, showing inhibitory activity against DEN2 and WNV protease in micromolar and submicromolar
IC50 ranges, respectively [177].
However, due to the basic nature of the non-prime residues of the natural
substrate (arginine, lysine) and the large size of the peptides, these highly
charged peptide based substrate-analogue inhibitors are presumably not cell
permeable and therefore of little value as leads for further drug discovery.
The X-ray crystal structures of the target protein and the target-ligand
complex provide basis for structure-based inhibitor discovery and helps accelerate the discovery process. Although the previous crystal structures of
DEN NS3pro alone and its complex with Bowman-Birk inhibitor did not
represent the active form of the enzyme, they were successfully used in
structure-based discovery of small molecule inhibitors [168,169]. Recently,
the active NS2B-NS3 protease was crystallized [161,178] and it was found
that although the protease catalytic triad (His51, Asp75, Ser131) was located
similarly in the NS3 and NS2B-NS3 crystal structures, large conformational
differences between NS3pro and NS2B-NS3pro structures were evident. For
example, the S1 site in the substrate-binding region formed a deep pocket in
NS3pro, while in NS2B-NS3pro it formed a shallow depression. These differences would surely affect the active site-ligand binding and must be accounted for in structure-based drug discovery [179].
The increasing availability of molecular structural and process information and the existence of powerful computer modelling approaches accelerate rational drug discovery. Several computational approaches are in use,
which include computer-aided structure based design and virtual screening,
with notable success stories in antiviral drug discovery [180-182]. As already reviewed above proteochemometrics modelling (PCM) is a novel tool
that can be used to correlate structural and physicochemical properties of
targets, like viral enzymes (HIV, DEN), and ligands (substrates and inhibitors) with functional responses to create predictive PCM models. The results
already achieved by this approach suggest that the PCM models are valid
and useful and that they can be used for accelerated design of novel antivirals [36,183], as well as in analysis of viral drug resistance to viral enzyme
inhibitors [27,35,184,185][paper I].
Intensive efforts and sustained multidisciplinary research is required for
the future to explore the scope and exploit the potential of viral enzymes as
drug targets for inhibitor design and thus cope with growing threat to human
health posed by HIV, DEN, JEV and other viruses.
2 Aims
The overall aim of this thesis was to study retroviral reverse transcriptase
(HIV-1RT) and flaviviral protease (NS2B-NS3pro) enzymes as drug targets,
with main focus on the major challenges faced by enzyme inhibitors in combating retro- and flavi-viruses. In vitro (kinetic) and in silico (computational)
approaches were applied to characterise the inhibition of HIV-1RT and the
DEN/JEV NS2B-NS3pro enzymes.
The specific aims were:
1. To analyse the contribution of different mutations in the active site of
HIV-1RT for the susceptibility of HIV to NRTIs, aiming for a computer
model with potential use in informed anti-HIV combination therapy.
2. To investigate the effect of specific amino acid substitutions in the DEN
NS2B(H)-NS3pro active site on the activity of the enzyme, in order to
achieve better understanding of structural determinants of enzymatic activity of the dengue virus NS2B(H)-NS3 protease.
3. To present a simple and efficient procedure to clone, express, purify JEV
NS2B(H)-NS3pro, characterise its substrate specificity and inhibition
4. To analyse new NS2B(H)-NS3 protease peptide inhibitors, providing
insights into how such compounds can be designed and optimised.
3 Results Discussion
3.1 PCM analysis of HIV mutants susceptibility to
NRTIs (paper I)
HIV-RT is the sole enzyme producing double-stranded DNA from the HIV’s
single-stranded RNA genome. It hence participates in an essential step in
retroviral replication [66-68] and it is a major target for approved antiretrovirals, namely the NRTIs and NNRTIs.
Although combination therapy (HAART) greatly increases the life expectancy of people with HIV, the virus still mutates its genes encoding the drug
targets, which thus creates resistant strains and which leads to HIV escaping
the antiretroviral therapy.
When first-line therapy fails, the complex mutational patterns make it difficult to select a new regimen from multiple alternative drug combinations.
Proper computational models that can predict HIV drug susceptibilities from
virus genotype and drug molecular properties might be a useful remedy to
this. Therefore, the main aim of paper I was to model the susceptibility of
mutated HIV1-RT variants to the clinically used NRTIs. The approach presented might find use in genome-based optimisation of HIV therapy.
The data set used comprised 728 HIV variants with unique RT sequences
and their corresponding phenotypic assays for eight NRTIs. The molecular
property descriptors of NRTIs, the physicochemical properties (z-scales) of
RT, and their cross-terms were correlated with the log-fold-decrease in susceptibility (logFDS) using PLS regression, which resulted in a highly valid
predictive PCM model for HIV-1RT drug susceptibility. Validation of the
model demonstrated its robustness, accuracy and predictability in modelling
the susceptibility of the excluded RT variants and inhibitors.
There are previous models that address HIV-RT drug resistance; however
the resolution of the PCM model was superior, as estimated in terms of Q2ext
and RMSEP, compared with the other hitherto best-performing approaches,
which analysed the susceptibilities for each NRTI in separate models
The model of paper I was used to analyse the role of individual mutations
and mutation combinations in drug resistance, and pinpoint the mutations
that have specific effects to either single or multiple inhibitors (Figure 16).
A web server ( was developed based on the PCM model to
facilitate predictions of the drug susceptibility of HIV-RT variants bearing
clinically known or hypothetical mutations. The server has the potential to
guide the selection of inhibitors for combination therapy against particular
RT variants, thus giving a direction how genome-based HIV therapy can be
optimised [paper VI].
74 65
67 228
Figure 16. Locations of major Drug resistance mutations in the HIV-1 RT, identified
by the PCM model.
3.2 Kinetics of DEN NS2B(H)-NS3pro mutants
(paper II)
Being indispensable for the flaviviral replication, the NS2B-NS3 proteases
of human-pathogenic flaviviruses, such as DEN and WNV, have received
substantial scientific attention as potential targets for the development of
anti-flaviral therapeutics.
Due to the unusually high specificity of the NS2B-NS3 protease for substrate processing [147], an understanding of the interaction of the enzyme
active site residues with the substrates is fundamental to protease inhibitor
The aim of paper II was to achieve a better understanding of structural determinants of substrate specificity of the dengue virus NS2B(H)-NS3 protease. To this end, alanine substitutions were introduced at selected positions
within the active site by site-directed mutagenesis. The recombinant enzymes mutants were over-expressed, purified and assayed enzymatically,
using the model substrate Boc-GRR-amc [154].
Figure 17. Presentation of kinetic parameters for samples of NS2B(H)-NS3pro wildtype and mutant derivatives. Samples were assayed by using fluorescence emission
from cleavage of the peptide substrate GRR-amc at 37°C as described under Methods. The bar graph shows a comparison of numerical constants obtained for Km
(panel A), kcat (panel B) and catalytic efficiency kcat/Km (panel C) for the wild-type
protein NS2B(H)-NS3pro and active site mutant proteins L115A, D129A, G133A,
T134A, N152A, S163A and I165A. Samples of Y150A and G151A were inactive in
the enzyme assay and a 23-fold increase in enzyme concentration did not result in
detectable activity. Data represent the mean of triplicate measurements and error
bars are indicated.
All mutations, except L115A, decreased the activity of the protease enzyme
at different degrees compared to the wild type enzyme. The changes in catalytic efficiency observed for the mutant NS3pro enzymes can be summarised
to fall in the order: L115A>wild-type>T134A>S163A>G133A>
D129A>N152A>I165A. The Y150A and G151A mutants displayed negligible activity under the conditions of the assay (Figure 17). Even a 23-fold
increase in enzyme concentration over the amount used in the normal assays
for the wild-type protein did not result in detectable activity, thus suggesting
that these mutations inactivate the enzyme completely.
Previously, certain ultra-conserved residues were found among flaviviral
proteases to be involved in substrate binding and catalysis of the dengue
NS3 protease [158]. However, the activities of the mutant proteases were
assayed by SDS-PAGE analysis of auto-proteolytic cleavage of the NS2BNS3 precursor in vivo, and no precise numerical values for the kinetic activity of the mutant protease were reported. Moreover, a number of residues,
such as L115, S163 and I165, were not included in the previous investigation. To the best of my knowledge, paper II describes for the first time a
kinetic analysis of mutations in the dengue virus NS3 protease by a transcleavage assay with a synthetic peptide model substrate. These structural
requirements provide a basis in informed discovery and optimisation of selective inhibitors against the flaviviral NS3proteases. The high level of sequence conservation within flaviviral proteases indicates that the findings
may also be useful in designing protease inhibitors for other flaviviruses
such as WNV and JEV.
3.3 JEV NS2B(H)-NS3pro substrate specificity
(paper III)
Contrary to related flaviviral proteases, JEV NS2B-NS3 protease is structurally and mechanistically much less characterised. Here we aimed at establishing a straightforward procedure for cloning, expression, purification and
first time biochemical characterisation of JEV NS2B(H)-NS3 protease.
The full-length NS2B-NS3 polyprotein region of JEV was obtained by
time- and cost-efficient gene synthesis, and the synthetic gene was used as
template for the generation of NS2B(H)-NS3pro by SOE-PCR. The recombinant protease produced by E. coli as a soluble protein was purified by
metal chelate affinity chromatography to >95% purity. The kinetic parameters (Table 1, paper III) were obtained by fluorescence release from several
small synthetic peptide substrates resembling the dibasic cleavage site sequences of the JEV polyprotein precursor.
The highest binding affinity and cleavage efficiency was observed for the
PyrRTKR-amc substrate, which represents the native JEV NS2B/NS3
cleavage site at the P1 to P3 positions. The binding affinity for a substrate
with leucine at P3 (Boc-LRR-amc) is 3-fold higher when compared to Gly
at P3 (Boc-GRR-amc). However, catalytic efficiency for the latter was about
10-fold higher than for Boc-LRR-amc, thereby suggesting a significant
contribution of the P3 position to the catalytic mechanism, as was also seen
in an earlier study on the DEN NS3 protease [163]. The prominent
contribution to catalytic efficiency of residues at the P3 and P4 position (and
possibly also prime-side residues) is also reflected in the comparatively
weak activity of the internally quenched peptide Abz-(R)4SAGnY-NH2, a
potent NS3pro substrate originally designed against the capsid protein
sequence RRRRSAG of DEN2 [162]. Whereas the tetrabasic non-prime
side sequence is strongly favoured by DEN NS3pro, the presence of Q-N at
P4-P3 of JEV capsid cleavage site apparently confers a high difference in
specificity between the two enzymes. Moreover, it cannot be ruled out that
the prime-side sequences (SAG in DEN and GGN in JEV) contribute
substantially to the decrease in efficiency as seen for this substrate peptide.
It can be concluded that the enzyme from JEV favours substrates with KR at the P2-P1 positions, whereas the NS3 protease from DEN prefers arginine at these positions, thereby suggesting a greater similarity of the JEV protease to WNV than to DEN [166]. This view is supported by amino acid
sequence alignments, crystal structures for the enzymes from DEN and
WNV, and structure-guided mutagenesis studies which show that functional
determinants of activation and substrate recognition for JEV and WNV are
more closely related than they are with other flaviviral proteases. It is noteworthy that the sequence of the NS2B cofactor from JEV shows greater
identity to NS2B from WNV (67.2%) than with other mosquito-borne
flaviviruses (28-34%) as well as with tick-borne flaviviruses (18–22%)
[155]. The serine protease inhibitor aprotinin was tested on JEV NS3 protease and it was found that the protein inhibitor aprotinin inhibit JEV
NS2B/NS3 protease with surprisingly low potency (IC50: 4.13 ± 0.167 M).
Using multiple sequence alignment of the NS2B-NS3 proteases of WNV,
JEV, DEN and YFV, we identified four amino acid residues (positions 84
and 86 in NS2B and 132 and 155 in NS3pro), which were different for all
four proteases. In order to confirm the role of these residues in substrate
binding and cleavage preferences of NS2B-NS3 proteases, a structural 3D
homology modelling was made of the JEV protease. The model revealed that
in spite of the high degree of overall similarity of the 3D conformation of the
JEV protease with that of the WNV NS2B-NS3pro complex [155,161,187],
the change of N84, Q86, T132 and I155 in WNV NS2B-NS3 protease to
D84, H86, R132 and E155 in the JEV NS2B-NS3 protease leads to
rearrangement of the possible H-bonds between the ligand and the S1, S3,
and S4 pockets of the enzyme (paper III, Figure 9, panel B, C). Moreover,
the presence of a basic amino acid in S1, and an acidic amino acid in S4,
may influence both the binding pocket geometry and ligand-protease
interactions. Similarly, an earlier study employing hybrid NS2B-NS3
constructs of DEN and JEV sequences showed that only a (DEN)NS2B(JEV)NS3 protein could efficiently process the JEV polyprotein, whereas the
(JEV)NS2B-(DEN(NS3) construct was inactive, supporting the notion that
NS2B proteins of different origins modulate the structure and substrate
affinity of the protease [155,188].
To sum up, in paper III we present for the first time an enzyme-kinetic
analysis of a recombinant JEV protease by employing model substrate peptides. Our data demonstrate that the JEV protease bears marked differences
to other known flaviviral proteases. Our study may serve as an entry point to
the development of efficient JEV inhibitors, e.g. by employment of highthroughput screening.
3.4 DEN NS2B(H)-NS3pro inhibitors design &
evaluation (papers IV & V)
In response to the global problem of flaviviral diseases, considerable efforts
are being devoted to exploit the potential of drug targets in flaviviruses, using different strategies such as:
1. HTS studies of non-peptidic small molecule inhibitors, including
charged [168,173,174] and uncharged [169-172], molecules. Although
several such studies have been conducted for the DEN protease, very
few active molecules have been found.
2. Substrate-analogue peptide derivatives. Several such peptides are effective, with Ki in the lower micromolar or even nanomolar range
[176,189]. However, due to the basic nature of the non-prime residues of
the natural substrate (arginine, lysine) and the large size of the peptides,
these substrate-analogue inhibitors are not cell permeable.
The aim of papers IV and V was to design and screen series of peptides with
varying sizes and residues to find out their roles to afford NS3 protease inhibition.
In the first step (paper IV), a series of 45 peptides with varying number
(four to nine), type and relative position of their residues, were designed, by
substitutions, deletions, extensions, and insertions, using an internally
quenched dengue protease substrate Abz-(R)4SAGnY-NH2 as starting point
(see paper IV, table 1). The general structures of the peptides were Abz(R)0/2/4-XXXX-nY-NH2, (R)0/1/2/4-XXXX-NH2, and Ac-(R)0/1/2/4-XXXX-NH2.
The peptides were assayed for their inhibition of DEN1–4 NS2B(H)-NS3
proteases using the fluorogenic internally quenched peptide Abz(R)4SAGnY-NH2 as substrate. Several designed octapeptides showed inhibitory activities towards all the four proteases in the low micromolar to sub50
micromolar range. Systematic removal of cationic substrate non-prime side
residues and variations in the prime side sequence resulted finally in an uncharged tetrapeptide, WYCW-NH2, with Ki values of 4.2, 4.8, 24.4, and 11.2
μM against the proteases from DEN1–4, respectively. While the tetrapeptides HWCW-NH2, HLCW-NH2, and WYCG-NH2 were found inactive, they
became active when the N-terminal was extended by adding one or several
arginine residues, which is the most common residue at P1 and P2 positions
of NS2B-NS3 protease natural substrates. While the tetrapeptide SRP22
(WYCG-NH2) was inactive, the peptide SRP12 (Abz-WYCG-nY-NH2)
showed high inhibitory activity against three of the four proteases. It is possible that this is due to the fact that no steric hindrance from neighbouring
glycine (G) residue allows 3-nitrotyrosine (nY) to adopt a conformation
which is favourable for interactions with the protease.
The peptides SRP25-SRP36 were designed and screened to investigate
the influence of N-terminal acetylation on the peptide activity. These peptides were synthesised in parallel with non-acetylated analogues (SRP13SRP24) and in most cases they showed similar or up to two fold lower activity. However, in one case the activity of acetylated peptide SRP-27 was
higher than the activity of its analogue SRP-15 (6.3 μM versus 142.6 μM
against DEN-2 protease). Adding an N-terminal acetyl decreased the activity
of the most active WYCW-NH2 peptide (Ac-WYCW-NH2, Ki: 18 μM for
DEN2 versus 4.8 μM for the non-acetylated variant).
Paper V aimed to find improved peptides using the best peptide from paper
IV (WYCW-NH2) as starting point. To this end a targeted library of 25
tetrapeptides with the general structure XXXX-NH2 was designed with aid
of statistical molecular design (SMD). To overcome the cell permeability
issues posed by basic residues (R, K) used in classical substrate-analogues,
neutral residues (W, F, Y, C, L, S, T, A and N) were used instead. The peptide library was custom-synthesised by Sigma (Singapore).
The active target DEN2 NS2B(H)-NS3 protease was purified and renatured from inclusion bodies expressed in E. coli. The kinetic parameters
Km, kcat, and kcat/Km were 100 ± 8.6 uM, 0.112 ± 0.003 s-1, and 1125 ± 66.78
M-1s-1 respectively for Ac-nKRR-amc.
The library was screened biochemically for its inhibition of the DEN2
NS2B(H)-NS3 protease. Out of the 25 peptides, four peptides showed significant enzyme inhibition with the inhibition constants (Ki) amounting to
10.84 ± 0.98μM, 11.2 ± 0.86μM, 11.3 ± 1.05μM, and 21.5 ± 1.97μM for
FFCW-NH2, WFCW-NH2, WWCW-NH2, and WYCF-NH2, respectively.
The results show that both the type and the relative position of all the four
residues in the peptide are important for inhibition activity. The only amino
acid tolerated in the third position of the tetrapeptides is cysteine. Its replacement with residues such as alanine (A) and asparagine (N) resulted in
complete loss of inhibitory activity. Further, even physico-chemically seem51
ingly small modifications such as replacement of cysteine (C) with serine or
threonine (T) resulted in loss of activity.
This study illustrates and encourages the development of improved peptide inhibitors for dengue protease based on the selection of residues not
restricted to the classical residues in the natural cleavage sites such as arginine and lysine. The findings may serve as starting point for designing more
potent and lower molecular weight drug-like inhibitors, using natural amino
acids and their synthetic derivatives.
4 Conclusions and Perspectives
The work in this thesis covered different in silico and in vitro methods to
investigate the influence of structural variations in viral targets (RT,
NS3pro), viral enzyme substrates (fluorogenic peptides) and viral enzyme
inhibitors (peptides, organic compounds) on their functions, with the aim to
promote drug discovery and drug therapy. This is summarised as follows:
1. Role of HIV1-RT mutations in drug susceptibility: The generalised PCM
model created here is capable to predict HIV susceptibility to NRTIs for
existing and to some extent for novel never-before seen mutations in RT.
The web server ( developed based on the model facilitates
predictions of the drug susceptibility of HIV-RT variant, and has the potential to guide the selection of inhibitors for combination therapy
against particular RT variants, thus giving a direction how genomebased HIV therapy can be optimised.
2. Effects of DEN-NS3pro mutations on enzyme activity: The study encompassed mutations in DEN NS3 protease of importance for the enzyme’s substrate cleavage activity. The structural requirements of DEN
NS3pro presented may aid informed discovery and optimisation of selective inhibitors against the flaviviral NS3proteases. The high level of sequence conservation among flaviviral proteases indicates that design of
inhibitors for the DEN flaviviral proteases will aid in the design of inhibitors for other flaviviral proteases, such as those of WNV and JEV.
3. Role of substrate residues in JEV-NS3pro activity: This was the rst
study characterising substrate preferences and inhibitor proles for the
JEV-NS3pro. The study can serve as an entry point to the design of efficient screening assays aimed at antiviral inhibitors discovery.
4. Role of basic peptide residues in DEN-NS3pro inhibition: Peptides with
basic residues (Arg, Lys) at P1–P4 positions yield the most efficiently
cleaved substrates and the most potent inhibitors. However, due to their
basic nature they show weak membrane permeability, which disqualify
them as antiviral drug candidates. The study applied a successful strategy to the stepwise removal of basic residues and shortening of the peptide, which resulted in a tetrapeptide with low micromolar inhibitory activity against the DEN proteases. The compound is promising for further
optimisation to achieve high affinity inhibitors of DEN proteases.
5. Role of non-basic tetrapeptides for DEN-NS3pro inhibition: Tetrapeptides containing Trp, Cys, Tyr, Phe, Leu, Thr, Ser, Ala and Asp residues,
were designed based on the DEN inhibitory tetrapeptide from the previ53
ous study [paper IV]. The residues were varied systematically using statistical molecular design approach. Four novel peptides with micromolar
inhibitory activities were found. The data showed that both the type and
the position of the residues are important for inhibition activity. The results indicate that further improved inhibitors may be developed based
on the found tetrapeptides as leads, which might lead to potent inhibitors
with lower molecular weight and with drug-like properties, for potential
use in treatment of flaviviral infections.
5 Acknowledgements
I would express my sincere gratitude to all the nice people who motivated
and supported me over the years and who made this long journey enjoyable
and rewarding.
I am especially grateful to:
Prof. Dr. Jarl Wikberg, my supervisor, for giving me the opportunity to be
part of your group, for providing a creative, pleasant and collaborative work
environment, for patient support, continuous encouragement and for sharing
your exceptional knowledge, enthusiasm, and experience in science. Thank
you also for giving me free choice to steer the project in my own direction.
Dr. Maris Lapins, my co-supervisor, for introduction to PCM, valuable suggestions, critical comments, expert guidance and for always being ready to
help. It would not be possible to complete this thesis without your collaboration, contribution, guidance and help. Thank you for your patient support I
could wish.
Prof. Dr. Gerd Katzenmeier, Mahidol University, for the nice working environment, never-ending support, patience, understanding, concern and exceptional hospitality. Thank you for always taking time to discuss, guide and
help. This work would have never been completed without your collaboration and support.
Prof. Eva Brittebo, (IPBS, Uppsala University), Prof. Prasert Auewarakul,
Prof. Chanan Angsuthanasombat (IMBS, Mahidol University), and Prof.
Staffan Uhlen, (University of Bergen, Norway), for teaching, understanding,
support, concern and hospitality.
My wonderful co-authors – Maris Lapins, Gerd Katzenmeier, Sviatlana Yahorava, Aleh Yahorav, Martin Eklund, Ola Spjuth, Ramona Petrovska, Iryna
Shutava, Prof. Chanan Angsuthanasombat, Peteris Prusis, Anna S. Torrejon,
Dr. Taian Cui, Wanisa Salemae and Ckakard Chalayut for your input, support, collaboration and friendship.
Prof. Margareta Hammarlund-Udenaes, Prof. Lena Bergstrom, Prof. Ingrid
Nylander, Dr. Anne-Lie Svensson, Dr. Erika Roman, Prof. Mats Karlsson,
Prof. Georgy Bakalkin, Prof. Fred Nyberg, Prof. Ernst Oliw, Dr. Tatiana I,
Dr. Hiroyuki W, Prof. Albert Ketterman, Prof. Varaporn A, Dr. Sarin C and
Prof. Duncan R. Smith for your valuable time, suggestions and guidance.
The honourable members of the graduate studies committee and the examination committee for volunteering your expertise, energy and time in assessing and ensuring the overall quality of this work.
My nice present and former colleagues and friends at IPBS – Iryna shutava
(again), Stephy Prakash, Aleksejs Kontijevskis, Anton Shulukh, Ilona
Mandrika, Sadia Oreland, Jonathan Alvrtsan, Berg Arvid, Klas Jonsson,
Valentin Georgiev, Polina Georgiev, Loudin Daoura, Apilak W, MW Sadiq,
M. Mumtaz, M. Zubair for your company, friendship and support.
My wonderful friends at IMBS – esp. Sarbast Algubare, Thanate Juntadech
(Bot), Dr. Somphob L, Anchalee N, M. Yousof, M. Ijaz, Surian, Shumpo,
Jum, Somsri, Opas, Pan, Ae, Niramon, Tunmai, and all for your support,
friendship and hospitality.
Magnus Jansson, Erica Johnsson (the late), Ulrica Bergstrom, Agneta
Bergstrom, Marina, Marianne Danersund, Agneta Hortlund, Kjel Akerlund,
Pernille Husberg, Umnaj, Pepo, Jesper Andersson & colleagues (thesis production team) and all other administrative and technical staff for administrative and technical support. Thank you for always being ready to help.
My ULLA Summer School fellows and friends from all over the world for
sharing their knowledge, wonderful experiences and for all the fun.
My fellows and friends in the ‘Training of Teaching in Higher Education’,
for sharing knowledge and experiences.
All my teachers for sharing knowledge and experience, for guidance, support
and encouragement throughout my studies, from pre-school to university.
All my other fellows and friends, no one mentioned, no one forgotten.
My all family members and relatives for encouragement and support, optimism and love, prayers and cares. Special thanks to my parents and grandparents (Abai au Baba) for the immortal love, prayers and memories.
Higher Education Commission (HEC) and University of Malakand (UOM),
Government of Pakistan, for financial support.
April 12, 2012
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