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B RIEFINGS IN BIOINF ORMATICS . VOL 13. NO 3. 329^336
Advance Access published on 19 September 2011
doi:10.1093/bib/bbr052
A practical guide for the
computational selection of residues to
be experimentally characterized in
protein families
Alfonso Ben|¤ tez-Pa¤ez, Sonia Ca¤rdenas-Brito and Andre¤s J. Gutie¤rrez
Submitted: 23rd May 2011; Received (in revised form) : 2nd August 2011
Abstract
In recent years, numerous biocomputational tools have been designed to extract functional and evolutionary information from multiple sequence alignments (MSAs) of proteins and genes. Most biologists working actively on the
characterization of proteins from a single or family perspective use the MSA analysis to retrieve valuable information about amino acid conservation and the functional role of residues in query protein(s). In MSAs, adjustment of
alignment parameters is a key point to improve the quality of MSA output. However, this issue is frequently underestimated and/or misunderstood by scientists and there is no in-depth knowledge available in this field. This brief
review focuses on biocomputational approaches complementary to MSA to help distinguish functional residues in
protein families. These additional analyses involve issues ranging from phylogenetic to statistical, which address the
detection of amino acids pivotal for protein function at any level. In recent years, a large number of tools has been
designed for this very purpose. Using some of these relevant, useful tools, we have designed a practical pipeline to
perform in silico studies with a view to improving the characterization of family proteins and their functional residues. This review-guide aims to present biologists a set of specially designed tools to study proteins. These tools
are user-friendly as they use web servers or easy-to-handle applications. Such criteria are essential for this review
as most of the biologists (experimentalists) working in this field are unfamiliar with these biocomputational analysis
approaches.
Keywords: functional divergence; residue selection; multiple sequence alignment; mutagenesis; protein families; evolutionary
information
INTRODUCTION
Combining computational and experimental
approaches are the current trend in scientific research
to solve central cell biology questions. This synergic
effort permits a more rational design of scientific
studies in favour of bench routines. The best biocomputational contributions to experimentalists are
based on the development of tools for the selection
and prioritization of the genes, proteins and residues
to be examined. Consequently, a selection of candidates (i.e. genes, proteins, residues, etc.) based on
rigorous analyses supported by biological and statistical evidence helps save the time and funds invested
by wet laboratories. Protein characterization
Corresponding author. Alfonso Benı́tez-Páez, Bioinformatic Analysis Group – GABi, Centro de Investigación y Desarrollo en
Biotecnologı́a, Bogotá DC, Colombia. Tel/fax: þ34 63355672, E-mail: [email protected]
Alfonso Ben|¤ tez-Pa¤ez, PhD in Molecular Biology. Head of the Bioinformatic Analysis Group in Bogotá DC, Colombia; Associate
Researcher in the Molecular Genetics Laboratory (CIPF) in Valencia, Spain; research line: evolution and characterization of
tRNA-modifying enzymes.
Sonia Ca¤rdenas-Brito, MSc in Biology Science. Researcher in the Bioinformatic Analysis Group in Bogotá DC, Colombia; research
line: protein sequence.
Andre¤s J. Gutie¤rrez, MSc in Biology Science. Researcher in the Bioinformatic Analysis Group in Bogotá DC, Colombia; research
line: molecular evolution of proteins, genome structure.
ß The Author 2011. Published by Oxford University Press. For Permissions, please email: [email protected]
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Ben|¤ tez-Pa¤ez et al.
frequently involves dissection of residue(s) accomplishing a specific molecular task. In recent years,
this field has been supported by computational studies addressing the identification of pivotal residues
for a given protein function. Expert scientists in the
study and characterization of a specific family of proteins normally obtain in-depth knowledge (after
years of experimental research) of the particular and
constrained set of residues, which helps classify a protein into a functional family (i.e. catalytic triads, dinucleotide binding motif, G-motifs of GTPases,
DEAD-boxes, etc.). Thus, it is quite normal to
find studies that examine the function of homologue
residues in the different and/or novel members of
the given family being examined (i.e. NTPases, oxidoreductases, methyltransferases, helicases, proteases,
etc.). Consequently, many studies contain redundant
data about equivalent and conserved residues, which
carry out the same task in homologue proteins.
Moreover, there are several studies that consist in
testing the residues that play new molecular roles,
such as protein–protein interaction, molecular
switches, substrate specificity, etc. In all these studies,
the main approach used by experimentalists for residue selection is multiple sequence alignment (MSA).
However, the limited knowledge that scientists have
about MSA parameter adjustment (i.e. substitution
matrix, word, window, algorithm, etc.) can produce
poor quality alignments, which do not prove very
useful for residue selection purposes. Besides, the use
of a low number of sequences in MSAs is also a
frequent error, especially when a large family is
being studied. Accordingly, residue conservation in
a specific position of MSA will probably be true for
that small set of analysed proteins, but not for a larger
set. In order to overcome such failures and to present
further useful computational tools for the selection
of undisclosed residues for functional characterizations in any family of proteins, some important considerations in MSA analyses are explained, while
other alternative MSA-based methods for the selection of functional residues in a protein are described
below.
conservation in a protein family, at sequence level,
and to reflect the most important residues, as well as
the probably indispensable amino acids for the correct function of that family of proteins. However,
MSA can change according to the number of sequences, the number of organisms (and variability),
the amino acid substitution matrix, and the algorithm
being used. Thus, it is important to take into account
certain considerations to use an appropriate MSA
method.
CHOOSING THE RIGHT MSA
ALGORITHM
Among the different algorithms designed for MSA,
accuracy, scalability and computational cost must also
be considered. Nowadays, the best method or algorithm to build an MSA must be capable of: (i) processing alignments based on dozens or hundreds of
protein sequences; and (ii) being flexible in comparing non-homologue proteins sharing a functional
domain. Since ClustalW was published in 1994 [1],
it has rapidly become the most widely used MSA
algorithm. However, some improvements have
been made since then, and more accurate and
faster methods have been developed. Some of the
current, improved methods include MAFFT [2],
PROBCONS [3], T-COFFEE [4] and MUSCLE
[5, 6]. In particular, we should use highly accurate
MSA methods (i.e. PROBCONS or T-COFFEE)
given their implications for subsequent analyses.
However, if computational cost is a disadvantage,
MUSCLE offers good performance and global balance in accuracy, scalability and computational cost
terms [7]. Like most MSA tools, it can be executed
locally by downloading a simple file (for the Linux or
Windows systems), and by calling the MSA algorithm using a basic command line; alternatively, we
can upload an input text file of sequences on the
MUSCLE web server at EBI (http://www.ebi.ac.
uk/Tools/msa/muscle/), where we can use other
reliable tools such as MAFFT, T-COFFEE or
KALIGN to perform MSA. Both options retrieve
an MSA output file in the FASTA format, which is
readable by any MSA viewer.
MSA AS CORNERSTONE
Most scientific knowledge on the biological understanding of living organisms is based on the comparison of sequences, structures, pathways, reactions,
metabolites, etc., to infer functionality by homology.
MSA is the simplest tool to detect residue
SEQUENCE SELECTION FOR MSA
The characterization of the functional residues of a
protein is frequently based on a poor MSA analysis
with 3–5 homologue sequences (maximum 10) to
Residue selection in proteins
detect residue constraints. To further exploit MSA
and the data acquired from it, we should build an
MSA from a representative sample of the entire set
of organisms in which the query protein is present.
Thus, if we are interested in studying a bacterial
family of proteins, we should have at least one
representative protein sequence of each bacterial
group in accordance with conventional taxonomy
(i.e. Alfaproteobacteria, Betaproteobacteria, Delta/
Epsilon,
Gammaproteobactaria,
Firmicutes,
Cyanobacteria, Chloroflexi, etc.). To compile a
useful file of sequences for MSA, non-redundant
searching in major protein databases must be performed: UNIPROT [8] or GENBANK [9]; both
are accessible using the BLAST tool [10] from the
NCBI site [http://blast.ncbi.nlm.nih.gov/Blast.cgi].
In addition, we have to select a large number of
sequences, preferably more than twenty-five
(whenever possible), with the highest possible variability. For this object, the BLAST interface offers
an advanced option to limit searches by taxonomy,
which includes or excludes groups, families, genera
or species. In this way, we should select a representative sequence per genus, or even per family,
when organisms are highly related. If there is a
low number of sequences to be analysed in the
MSA, we could add related sequences by detecting
distant homologues following the PSI-BLAST approach [11], construct MSAs with the PROMALS
algorithm (http://prodata.swmed.edu/promals/promals.php) [12, 13], or modify the BLAST parameters by employing more permissive BLOSUM
matrices [14], such as BLOSUM-45 instead of
BLOSUM-62; the latter is used by default in
BLAST searches. Alternatively, we can find additional members of the query family by searching
in the Pfam database [15], which contains the
largest collection of protein families, domains and
motifs clustered by sequence conservation and
amino acid profiles based on Hidden Markov
Model (HMM). If we are interested in studying
the residues of a functional domain in the query
protein, we can include non-homologue proteins,
but those that contain equivalent functional
domains in their architecture. Thus, we can
apply further phylogenetic and statistical analyses
to the MSA, such as those described in the
following sections. A quantitative evaluation of
sequence selection and variability in MSA can
be obtained from the DIVERGE analysis (see
below).
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DETECTING FUNCTIONAL
CONSTRAINTS IN MSA
By employing user-friendly MSA viewers such as
JALVIEW [16, 17] or SEAVIEW [18, 19], we can
explore the MSA text file to search the highly conserved positions along the alignment. These conserved positions can be highlighted by colouring
according to different criteria, such as percentage of
identity. The residues showing whole conservation
in the expanded set of sequences used for MSA will
provide more information than those from an MSA
based on a low number of sequences. In addition to
residue conservation, the biochemical nature of residues can also be constrained. Consequently, different
positively- (K, R) or negatively (D, E) charged residues will be present in a specific position within the
MSA. Conservation is not so easy to observe in a
polar or hydrophobic position. If we wish to draw
a residue map for each position easy for our eyes and
brains to understand, an amino acid profile based on
the HMM can be built from MSA [20], interpreted
by the HMM-Logo server [21] on its web site or
locally by downloading the HMMVE viewer [22].
Good views of amino acid preferential usage per
position in MSA can help us understand and recognize most of the relevant residues and those selected
by evolution forces.
Complementary studies must be carried out if one
of the query proteins has a three-dimensional X-ray
or NMR structure; otherwise, we could follow several strategies to obtain a protein structure model in
which we can localize the selected residues [23].
Thus, each conserved residue/position in the MSA
can be localized on the respective protein structure,
and some predictions of their respective roles may be
stated for subsequent experimental corroboration.
These predictions can focus on the residues adjacent
to the active site of the query proteins, or even on
the protein surface where they are possibly involved
in protein–protein interaction (PPI) or in nucleic
acid binding.
BEYOND THE MSA ANALYSIS
Further analyses can address the detection of those
residues predicted to be involved in a specific task.
One of these methods is the In Silico Two Hybrid
method (I2H), which predicts PPIs based on the
amino acid-correlated changes/mutations between
interacting proteins [24]. Moreover, other methods
have been developed to detect altered functional
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Ben|¤ tez-Pa¤ez et al.
Figure 1: General pipeline for the selection of residue candidates for experimentally functional characterization.
The steps and paths in the pipeline are outlined. There are steps that are connected by grey arrows in which, in
some cases, the required computational tools are cited. The blue arrows in the boxes show three major computational methods to select residues (Functional Constraints, Functional Divergence and PCA), and show the localization of the hypothetical candidate residues to be analysed in each case.
constraints, which directly compare different clusters
(or subfamilies) of a protein family (or superfamily).
In this brief guide, we focus on the last methodology
because its permits MSAs to be explored in depth
and to extract the evolutionary information deriving
from speciation/specialization after gene duplication.
The following methods have been illustrated in
Figure 1 to provide a better understanding.
WHAT DOES THE FUNCTIONAL
DIVERGENCE ANALYSIS CONFER?
The residue constraints extracted from an MSA can
be altered if we add other functional-related proteins
to the original MSA. These new proteins incorporated into this MSA must essentially be the paralogues that emerge after gene duplication during
evolution. They are probably specialized in other
molecular functions, but retain both high sequence
identity and similarity to the original query protein(s)
because of their closely related function. At this
point, we have two different clusters belonging to
a common evolution-related family, or superfamily,
of proteins; a direct comparison made among all the
sequences present in both clusters will provide valuable ways to detect those residues of functional relevance which are difficult to disclose by direct MSA
observation in viewers, but are easier to detect
through phylogenetic and statistical analyses
[25–27]. The DIVERGE tool [28] (web site:
http://www.xungulab.com) offers a complete interface to manage phylogeny, MSA and protein structure data. Its aim is to study type-I (or the
site-specific rate shift of amino acids) and type-II
Residue selection in proteins
(or the radical shift of the amino acid property) functional divergence [25, 27]. This methodology has
provided good results in the different protein families
to which it has been applied [29–31]. Although
DIVERGE does not define a specific function for
the residues selected in the query protein, it finds
the residues that most probably act as functional determinants, which are ranked according to statistical
evaluation. We can modulate such statistical significance by increasing the cut-off value (posterior probability score 0.85) to rescue residues under a strong
functional divergence, together with a function assignment, in accordance with our expertise and
structural data. The results obtained with this approach will largely depend on the MSA analysis
where the selection of sequences and its variability
are critical. For the quantitative proposes of this step,
DIVERGE retrieves the coefficient of functional
divergence (y) after each pair-wise cluster comparison. As a result, a y value higher than zero suggests
that a significantly altered functional constraint has
occurred between a pair of clusters. Then if y does
not differ from zero, this means that the divergence
in MSA and in each cluster of proteins does not
suffice to detect functional constraints. Therefore,
we should attempt to make comparisons between
different clusters or acquire new sequences to be
studied.
DIVERGE results are also influenced by phylogenetic tree construction methods. In that way, a
reliable and accurate phylogenetic tree have to be
constructed prior to this analysis. Given that tree
construction is essential for any DIVERGE analysis,
we should follow a PROTTEST approach [32]
to determine the best evolutionary model for the
set of query proteins. This web server (http://
darwin.uvigo.es/software/prottest.html) only requires an MSA file to be used to test more than 60
different evolutionary models in the protein family.
The selection of a specific model explaining
molecular variation in this set of proteins will be
based on ‘goodness-and-fit’ measures such as
Akaike Information Criterion (AIC) or Bayesian
Information Criterion (BIC), which will represent
the uncertainty of all the models tested and the relevance of different model parameters (invariable sites:
þI; amino acid categories: þG; amino acid frequencies: þF) in the evolution of query proteins. After
testing all the different evolutionary models,
PROTTEST retrieves a rank of models according
to the AIC or BIC values. Consequently, the
333
evolutionary model with the lowest AIC or BIC
value will be selected to draw the evolutionary tree
of proteins.
Despite having obtained a reliable tree from
the PROTTEST for DIVERGE analysis, the
DIVERGE suite offers a recommended option to
enhance protein clustering when users are not familiar with tree presentation or if users are using an
input tree that contains unsolved relationships. In
such cases, the re-rooting of an input tree is a convenient way to select gene clusters before proceeding
to the functional divergence analysis. By using the
Neighbor-Joining option by selecting the ‘NJ
Tree-Making’ button, the software will employ the
Neighbor-Joining algorithm to quickly generate a
tree based on the desired distance measure
(i.e. p-Distance, Poisson or Kimura). Among the different distance calculation options, we should preferably employ those using corrected distances for
multiple substitutions per site, such as the Poisson
or Kimura algorithms. Once the tree has been
re-rooted by DIVERGE, we can cluster all the proteins in well-defined groups, preferably with more
than four sequences per cluster/group (i.e. bacteria
versus mammals; or g-proteobacteria versus
a-proteobacteria for the proteins restricted to bacteria). At least two clusters are required to perform
this analysis. However, if multiple clusters are
selected, a pair-wise comparison should always
be performed. Then we can compute types I and
II functional divergence to extract the residues
that are more likely to be under functional
divergence.
In addition to the cluster comparison, a more specific intra-cluster analysis to detect functional divergence can be achieved according to phylogenetic
distribution (see Supplementary Figure S1; where
functional aspects of the characterised MnmC
family of proteins [see ref 47–52] are outlined).
Thus, a residue showing gain-of-function in some
species within a particular analysed cluster can be
easily observed by residue fixation in these sequences, but not in others belonging to the same
protein cluster. This additional approach requires
the extensive usage of highly related sequences instead of the diversity required for the
above-mentioned cluster comparison. Furthermore,
a cut-off value (or posterior probability) to select
residues under functional divergence must be relaxed
because the estimated variability of an amino acid per
site is lower at this level of comparison.
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Ben|¤ tez-Pa¤ez et al.
COMPLEMENTARY ANALYSES
Despite having focused on a well-known method to
detect the cluster-dependent amino acid conservation based on phylogenetic and statistical approaches,
other alternative methods have been widely studied
and successfully tested to functionally predict important residues. These methods include multivariate
and related approaches that help predict pivotal residues when the functional and phylogenetic relationship is ambiguous [33–37]. Some methods explore
the sequence information content of MSA to create a
vector transformation where residues are evaluated
according to their two- or three-dimensional clustering [33, 35, 37], while others use robust information from three-dimensional structures to predict the
functional interfaces and residues involved in
ligand-binding sites, protein–protein or protein–nucleic acid interactions, or ligand specificity [34, 36].
Notwithstanding, the gap between the available
number of sequences and the three-dimensional
structures of proteins can limit the use of the latter
method type.
For the purpose of performing an additional analysis that is complementary to the functional divergence method extensively described as the scope of
this guide, we can use the TreeDet web server
(http://treedetv2.bioinfo.cnio.es/treedet/index.
html) to predict the functional sites in a family of
proteins [38]. TreeDet integrates related methods
using a principal component analysis (PCA) to
study the ‘Sequence Space’ of an input MSA. It
also predicts functional sites after their representation
in a multidimensional plot and statistical evaluation.
The TreeDet authors encourage us to submit MSA
from MUSCLE, T-COFFEE and PROBCONS
algorithms, all of which are briefly described in earlier sections of this guide.
EXPERIMENTAL TESTING OF
SELECTED RESIDUES
After completing the various steps stated throughout
this guide for residue selection as regards functional
characterization in a family of proteins, it is important to filter the set of selected residues for experimental testing. Accordingly, we should reduce the
number of possible protein mutants to be studied by
the directed mutagenesis approach. Then, we should
focus on those residues with greater statistical and
biological significance, otherwise the design and
management of several mutants can result in a
tedious and expensive experimental task that is
time-consuming, and can even lead to ambiguous
results, which are difficult to explain by the multiple
mutants and functionalities, explored.
It is also necessary to consider appropriate amino
acid changes. Traditionally, alanine scanning has
been the initial approach followed to study the relevance of a specific residue; nonetheless, the function
of a mutant protein will notably fluctuate in accordance with the amino acid selected to replace the
targeted one. Consequently, electrostatic charge
changes will help better elucidate the role of a positively- or negatively charged residue [39]. Likewise,
polarity changes shed light on the role played by
hydrophobic residues [40]. PPIs, or pocket binding
sites, could also be studied by applying the aforementioned criteria. Nevertheless, steric hindrance by
replacing small side-chain amino acids with those
that have bulky side-chains can impair PPIs with
single amino acid substitutions [41, 42]. For this
case, we may consider that the protein interactions
involving other larger molecules such as DNA or
RNA could be supported by several interaction
sites. Therefore, multiple mutants will be necessary
to notably impair interaction [43], even when
considering steric hindrance in all the mutated
positions.
EVALUATION OF SELECTED
MUTATIONS
At this point, we refine a list of residues that are
probably involved in functions such as catalysis, cofactor binding, proton donation and in the mediation
of the PPIs obtained by applying MSA, functional
divergence, phylogenetic and/or PCA-related methods. Moreover, another analysis can be done to describe the thermodynamic implications of the desired
mutational changes on protein structure stability
before proceeding to mutagenesis and experimental
studies.
The CUPSAT server [44] (http://cupsat.tu-bs.de)
is a tool that predicts changes in protein stability
upon point mutations. The CUPSAT evaluation of
protein mutants requires the input files from PDB, as
well as custom-developed protein structures. Its prediction model uses: (i) protein–environment potentials to predict protein stability; (ii) atom potentials;
using the 40 amino acid atom classes from the
Melo-Feytmans model [45, 46]; (iii) the torsion
angles ’ and c; and (iv) the Gaussian apodization
Residue selection in proteins
function to adjust torsion angle perturbation in mutants. As a result, we can obtain a friendship list of the
most probable and favourable amino acid changes
given in ‘overall stability’, ‘torsion’, and ‘protein
thermodynamics’ terms. Consequently, at the end
stage of this analysis, we will have obtained strong
computational evidence for both residues and probable mutations to support an experimental and practical approach to study the functional relevance of
the different residues in any family of proteins.
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SUPPLEMENTARY DATA
4.
Supplementary data are available online at http://
bib.oxfordjournals.org/.
Key points
The considerations, issues, tools and suggestions reviewed in this
guide have been addressed to improve the functional characterization of residues in any family of proteins under study by
experimentalists.
The general and central considerations of the MSA analysis have
been reviewed for the purpose of constructing better and
more informative alignments of protein families; simultaneously,
different tools have been exposed according to user
requirements.
The different approaches presented herein help to shed light on
the extraction of the evolutionary and functional information
derived from sequence comparisons. A methodical usage of
these approaches will permit a more rational design of experimental studies, which will be supported by stronger biological
and statistical evidence.
The aim of this review is to also promote the development of reliable, useful and combined computational bench routines given
that the design of studies with more biological and statistical evidence prior to experimental testing could save time and funds,
thus enabling more efficient research.
Finally, the aim of the step-by-step design of this workflow is to
encourage experimental biologists to learn and to acquire
in-depth knowledge on biocomputacional analyses, tools and
methods which could permit flexible analyses according to user
demands and the complexity of the query proteins without potential black box results assumed by the users.
Acknowledgements
The authors thank the Centro de Investigación y Desarrollo en
Biotecnologı́a for its support in the implementation, edition and
publication of this guide; they also thank M.E. Armengod, I.
Moukadiri and M.J. Garzón for inviting us to collaborate in their
MnmC project. Finally, they particularly thank the editors and
peer reviewers of this guide for their constructive criticisms that
have helped improve the quality of this review.
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DC, Colombia.
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