Download Physicochemical Analysis of the Interaction between Epstein

Physicochemical Analysis of the Interaction between Epstein-Barr Virus
Glycoprotein gp350 and Complement Receptor 2 using AESOP (Analysis
of Electrostatic Similarities Of Proteins)
Aaron Nichols, Dimitrios Morikis, Ronald D. Gorham Jr.
Department of Bioengineering
University of California, Riverside
A bstract
Epstein-Barr Virus (EBV) infects a large percentage of the world’s population and is responsible
for infectious mononucleosis and, in rare cases, Burkitt’s lymphoma and nasopharyngeal
carcinoma. EBV’s primary means of infection is the association of the viral surface glycoprotein
gp350 with Complement Receptor 2 (CR2) of the immune system. Various mutagenesis studies
have identified key residues on both gp350 and CR2 necessary for binding. These mutagenesis
studies have recently been used to derive constraints for a computational docking study in
order to generate a putative three-dimensional structure for the gp350-CR2 complex, using
the soft-docking program HADDOCK (High-Ambiguity Driven biomolecular DOCKing). We
have applied our own AESOP (Analysis of Electrostatic Similarities Of Proteins) protocol to
analyze the electrostatic contributions to complex formation, using the HADDOCK-derived
structure of the gp350-CR2 complex. Our atomic-detail studies using AESOP suggest that the
original HADDOCK structure may not be optimized and warrant a re-evaluation of the docking
process.
A U T H O R
Aaron Nichols
Bioengineering
Aaron Alan Nichols is a graduating
senior majoring in Bioengineering.
He is a member of the Medical
Scholars Program, Tau Beta Pi
Honors Engineering Society and
participated in the Amgen Scholars
Program at UCSF this past summer.
Aaron joined the BioMoDel laboratory,
led by Dr. Dimitrios Morikis, the summer
prior to his junior year after discovering
an avid fascination with computer
M e n t o rs
science and biology. His research
focuses on the electrostatic interactions
Faculty Mentor: Dimitrios Morikis
Graduate Student Mentor: Ronald D. Gorham Jr.
of proteins involved in our immune
Department of Bioengineering
system. He studied the infection by
Aaron has worked in the Biomolecular Modeling and Design Laboratory
(BioMoDeL) for nearly two years, gaining experience through a number
of different research projects. His initial research involved evaluation of
parameter selection in Poisson-Boltzmann electrostatic calculations through
comparison of computed and experimentally-determined free energy values
for association of protein complexes. Subsequently, Aaron has worked
on examining the interaction between Epstein-Barr Virus glycoprotein 350 (gp350) and
immune protein complement receptor 2 (CR2), aiming to better understand the molecular
mechanisms underlying viral immune system evasion. The first project is now published
in the major research journal Biopolymers, as part of a larger study led by graduate student
Ronald Gorham. The work of the second project is reported here, and is also co-authored by
Ronald Gorham who contributed by providing research guidance. Aaron has proved himself
as an independent researcher, taking on a challenging project involving docking of the
gp350-CR2 protein structures in light of his previous parametrization results. Aaron has
presented his work at the UCR Symposium for Undergraduate Research, Scholarship, and
Creative Activity in spring 2010, and at the Southern California Conference for Undergraduate
Research at Pepperdine University in fall 2010. In addition to his research at UCR, Aaron
participated in the Amgen Scholars program at UCSF during summer 2010.
system
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through
the
systematic
computational mutation of various
amino acids constituting the infection
mediating proteins. He plans on using
the skills he developed in research
towards the pursuit of a medical degree.
Aaron will be attending UC Riverside
next quarter as a Masters student in
the 5-year BS/MS Bioengineering
program.
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Physicochemical Analysis of the Interaction between Epstein-Barr Virus Glycoprotein gp350 and
Complement Receptor 2 using AESOP (Analysis of Electrostatic Similarities Of Proteins)
Aaron Nichols
Introduction
The Epstein-Barr Virus (EBV) is a herpes-virus that
occurs worldwide, and affects 95% of adults between the
ages 35-40. EBV infections are known to cause infectious
mononucleosis in 35-50% of adolescents leading to
symptoms such as fever, sore throat, swollen lymph
glands, and even spleen or liver complications. The virus
also establishes a lifelong infection of the body’s immune
system, and in rare cases, causes Burkitt’s lymphoma and
nasopharyngeal carcinoma1.
Infection by EBV is achieved by the association of the viral
surface glycoprotein, gp350, with Complement Receptor
2 (CR2), located on the surface of T-lymphocytes. Viral
gp350 is a large (907-residue) protein, with a large
percentage of its surface being glycosylated, or covered by
covalently attached sugars. The three-dimensional structure
of a truncated form of gp350 (440 residues) has been
experimentally determined and consists of three domains
(D1, D2, and D3) dominated by beta-sheets that are all
linked by short polypeptides, arranged into an L-shape
(see Figure 1). A distinct patch on the surface of one of
N-terminal domains is not glycosylated and coincides
with a negative “hotspot,” or aggregation of negatively
charged residues. Experimental deglycosylation of gp350
has been shown to have negligible effects on its ability to
bind ligand, suggesting that this “naked” patch is a possible
binding site for CR22.
CR2 is a cell receptor involved in the complement portion
of the immune system. It is a regulator of complement
activation and is characterized by the presence of repeating
modules known as short consensus repeats, or SCRs.
CR2 comprises between 15-16 modules that span the cell
membrane. Flow cytometry experiments suggest that gp350
interacts with the first two modules of CR2, SCR1 and
SCR2. The three dimensional structures of these modules
have been solved (see Figure 1); when crystallized, the
modules form a tight V-conformation, however X-ray
scattering suggests that the functional CR2 may open up1.
In a study conducted by Hannan and coworkers (2008)1,
an extensive amount of mutagenesis data targeting
the putative binding site on both gp350 and CR2 was
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collected and utilized in the determination of a potential
three dimensional structure of the bound complex. To
accomplish this, the HADDOCK, or High Ambiguity
Driven biomolecular DOCKing, program was used to dock
the two proteins. HADDOCK is a unique docking program
since it uses a wide array of experimental data as restraints
to find an optimal structure for the bound complex, and
thus provides a crucial link between experimental and
computational biology.
Figure 1. (a) The three-dimensional structure of gp350 in a
truncated, glycosylated state. Domains are labeled and colored
according to electrostatic potential (red for negative, blue for
positive). Region of concentrated negative amino acids (circled)
coincides with unglycosylated region2. (b) SCR1-SCR2 of CR2
colored by coulombic potential. (c) HADDOCK generated
structure of complex with domains and SCRs labeled.
Methods and Materials
AESOP - Since the interaction between gp350 and CR2
involves charged residues on each protein, an extensive
study of their electrostatic nature is warranted. To this
effect we have applied our own AESOP protocol, or the
Analysis of Electrostatic Similarities Of Proteins, which
provides the framework to rapidly analyze and quantify
the electrostatic make-up of a protein or protein complex.
AESOP was used to evaluate the HADDOCK structure
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Physicochemical Analysis of the Interaction between Epstein-Barr Virus Glycoprotein gp350 and
Complement Receptor 2 using AESOP (Analysis of Electrostatic Similarities Of Proteins)
Aaron Nichols
generated by Hannan and coworkers (2008)1. AESOP
allows the user to conduct computational alanine scans,
apply Poisson-Boltzmann electrostatic calculations to
determine electrostatic potentials, determine computational
free energies of binding, as well as cluster and analyze the
results using a variable metric of the user’s choosing. The
collection of these tools into a centralized location allows
the user to efficiently study the contribution of electrostatics
in the function of the protein3,4,5.
The application of AESOP to the gp350-CR2 complex
began by first retrieving the HADDOCK structure generated
by the Hannan and coworkers (2008)1. The coordinate file
was then cleaned by removing the header, leaving only
the ATOM lines necessary to fully describe the complex’s
three-dimensional structure. Once cleaned, a PQR file for
the parent complex was generated using the webserver,
PDB2PQR7. The PQR is similar to the coordinate file except
that the temperature and occupancy columns are replaced
by a per-atom charge term and radius term respectively.
Next, the parent complex PQR was used to generate
computational mutants with in an in-house R script. The
script locates Aspartates, Glutamates, Arginines, Histidines,
and Lysines (all of which may hold a charge at physiological
pH), and truncates their respective side chains to only the
beta carbon. The gamma carbon is replaced with hydrogen
and the bond length is shortened. This process converts each
of these ionizable amino acids into Alanine, and effectively
perturbs the electrostatic potential of the protein without
directly affecting global structure. There were 95 mutant
PDBs generated with 72 single residue mutants belonging to
gp350 and 23 to CR2.
for electrostatic potential φ(r). Here, ε refers to the distance
dependent dielectric, κ captures the implicit effect of
solvation by water and ions on the proteins, and Qi is the
fixed protein ith charge at some atom position r. Calculations
were conducted for each mutant at both 0mM and 150mM
ionic strength with a protein dielectric of 20.00 and solvent
dielectric of 78.50.
Visualizing the calculated electrostatic potential around
a mutant protein of interest is useful in qualitatively
understanding the contributions particular ionizable
residues have on the protein. This can be accomplished
simply by loading the data points into a common
visualization software package like Chimera or VMD.
A quantitative analysis, however, can be achieved by
calculating the free energy associated with the mutation as
it affects the complex, and can be described by,
(2)
Here, qi refers to charge and φi is the electrostatic potential
calculated by APBS. We use a theoretical thermodynamic
cycle (Figure 2) to account for both the free energy changes
during association as well as the energy of solvating both
the proteins individually as well as in complex. The
following free energies are used to derive the solvation
free energy of association, which is used to quantitatively
compare the effects each computational mutant rendered
on the complex.
(3)
After the computational mutants were generated, their
respective electrostatic potentials were calculated. A local
version of the Adaptive Poisson Boltzmann Solver, or
APBS6, was used. APBS numerically solves the PoissonBoltzmann equation (shown below in its simplified
linear form),
(1)
Each ΔG term represents moving either vertically or
horizontally in the thermodynamic cycle shown in Figure
2. ΔΔGsolvation, or the association free energy of solvation, is
the difference in free energy of the horizontal and vertical
processes.
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(4)
(5)
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Physicochemical Analysis of the Interaction between Epstein-Barr Virus Glycoprotein gp350 and
Complement Receptor 2 using AESOP (Analysis of Electrostatic Similarities Of Proteins)
Aaron Nichols
Results
Figure 2. Thermodynamic cycle used to calculate free
energies. The top process represents protein association in
a low dielectric reference state, with a free energy change of
ΔGref. The bottom process is identical, except for occurring in
a more realistic solvated state, with a low dielectric protein
interior and high dielectric solvent which is captured by ΔGsolu.
Finally, the vertical processes represent the free energy change
in moving each of the proteins from the ideal environment to a
solvated one, and are stored in the ΔGsolvation term.
To simultaneously account for the energy change
associated with solvation and association we use the
ΔΔGsolvation measure. ΔΔGsolvation can then be compared
across mutants. Furthermore, the electrostatic potentials
surrounding each mutant can be compared using a
comparative metric of our choosing. For this study
we used the Average Normalized Distance measure as
described by4,
(6)
Using AESOP, clustered dendrograms as well as free
energy plots of association were generated (Only the
dendrogram and free energy plot for CR2 at 0mM is
shown). Each line in the dendrogram represents a single
mutant of the parent protein and is colored according
to the physiological charge associated with the amino
acid: blue for positively charged residues and red for
negatively charged residues. The lines are terminated
with circles that indicate the distance the particular
residue is from the interaction interface. The dendrogram
indicates that AESOP was able to successfully “cluster”
mutants of similar charge, e.g. basic residues cluster
separately from acidic residues. Additionally, the mutants
that are located closer to the interaction interface also
cluster together, suggesting that these mutations have
a similar effect on the global electrostatic potential of
the parent protein. Although clustering dendrograms
indicate the similarity between the various mutants
they do not provide any information about the effect
the mutation had on the ability of the proteins to bind.
The free energy diagrams generated by AESOP indicate
the effect each mutation had on gp350-CR2 complex.
Basic mutants are located energetically below the parent
protein and indicate an unfavorable mutation while all
the acidic mutants are located energetically above the
parent protein and indicate a favorable mutation. Also
included are the experimental “crosses” that indicate
the deleterious effect the particular mutation had on the
ability of gp350 to bind CR2.
Discussion
This comparison can be presented in a hierarchical
dendrogram, in which mutants that have similar
electrostatic potentials “cluster,” or group up together. It
is our hypothesis that mutants that cluster together will
behave similarly in their physiological function. The
ultimate goal of AESOP then is to provide researchers
a computational tool for screening mutants that ideally
correlates well with experimental data.
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It is readily noticeable that the mutation computationally
predicted to have the greatest deleterious effect on
binding, Lysine 67 to Alanine (K67A), is reported,
experimentally, to reduce the binding affinity of the
proteins by a mere 30%. This discrepancy was further
studied by inspecting the three-dimensional structure
of the HADDOCK complex, which places K67 at the
interface of gp350 and CR2. In this conformation,
K67 will be in a position to potentially form three very
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Physicochemical Analysis of the Interaction between Epstein-Barr Virus Glycoprotein gp350 and
Complement Receptor 2 using AESOP (Analysis of Electrostatic Similarities Of Proteins)
Aaron Nichols
Figure 3. AESOP results for calculations
at 0mM ionic strength. The top panel shows
the dendrogram generated for the CR2
mutants. Each line represents a mutant
form of the parent protein. Each line is
colored according to the charge that amino
acid holds, e.g. blue for basic residues and
red for acidic residues. Additionally each
line is terminated in a color that indicates
how far the residue is located from the
interaction interface. Bottom panel is
the corresponding free energy diagram
for CR2 at 0mM. Each point on graph is
located directly below its corresponding
line in the dendrogram to ease the analysis
of the energetics of each cluster. Crosses
indicate experimental data from Hannan
and coworkers (2008)1. In percentage of
activity when compared to parent: +++,
89.9 – 70%; ++, 69.9 – 40%; +, 39.9 –
20%; –, 19 – 0.0%.
strong interfacial salt-bridges with D18, D19, and E152
of gp350. The presence of these potential interactions
suggests that the mutation of K67 to Alanine should render
a much greater deleterious effect on binding than has been
experimentally shown, thus supporting our hypothesis that
the HADDOCK-generated structure may not be optimal.
The original HADDOCK structure was driven using
mutants that had a relatively minor effect on binding. In
the study by Hannan and coworkers (2008)1 the K67A
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mutation was rated +++, indicating a mere 30% decrease
in binding in the ELISA experiments. This mutation data
was used to perform gp350-CR2 docking in conjunction
with other residues that had been shown to nearly abolish
binding. During the docking procedure, it is likely that
K67 became “trapped” by the sheer number of favorable
Coulombic interactions, and thus did not explore its full
range of conformational space. Additionally, most of the
restraints used to dock gp350 and CR2 were ionizable
residues. Ideally, non-polar or hydrophobic residues should
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Physicochemical Analysis of the Interaction between Epstein-Barr Virus Glycoprotein gp350 and
Complement Receptor 2 using AESOP (Analysis of Electrostatic Similarities Of Proteins)
Aaron Nichols
Figure 4. On left, the HADDOCK structure with experimental mutants highlighted on the structure of the CR2. Mutants are colored
according to their experimentally determined deleterious effect on binding upon mutation, red residues having the highest effect and
green residues the least. K67 is circled. AESOP calculations suggest K67 participates in a multitude of strong interfacial Coulombic
interactions, shown on the right.
be used as restraints for HADDOCK since it is well
understood that these residues act at very small distances
when compared to residues that interact electrostatically.
The mutation of a hydrophobic residue which abolishes
binding is much more likely to reside at the proteinprotein interface than a charged residue with a similar
deleterious effect on binding. The interactions of polar
and charged residues can be both short and long range,
and thus their mutation can significantly affect binding
despite being located away from the interface. The
inclusion of polar and charged residues as restraints in
the HADDOCK, then, is likely to introduce error in the
generation of a putative structure. Additionally, it must
be considered that mutations of hydrophobic residues
are likely to introduce perturbations in the global
structure of the protein. These perturbations can inhibit
the proteins from binding despite not being located at
the complex interface. This can be addressed with the
use of spectroscopic methods as a standardized control.
In the absence of a structure for the protein complex,
it is arguable whether ionizable residues should be
considered “active” restraints in the HADDOCK
docking process. Additionally, no reasonable cutoff for
inhibition of binding exists when choosing the active
residues for each docking process, therefore the use of
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mutagenesis data that does not abolish binding may lead
to the generation of non-optimal complex structures.
Summary
A computationally derived structure for the gp350-CR2
complex was determined by Hannan and coworkers
(2008)1 with the use of the HADDOCK program.
HADDOCK utilizes experimental mutagenesis data to
drive the docking process and thus provides a crucial link
between experimental and computational biology. We
studied the electrostatic characteristics of the suggested
complex structure using our own in-house protocol
AESOP. Using AESOP we conducted computational
single mutant Alanine scans of the complex and
generated free energies of association for each mutation.
The free energy data generated by AESOP suggests that
the mutation of K67 of CR2 will cause a significant
decrease in the binding ability of gp350 and CR2,
however experimental mutagenesis data does not agree.
We will construct a new HADDOCK structure of the
gp350-CR2 complex with an alternative set of restraints,
and specifically omit K67. We will then analyze the
newly generated structures with our AESOP protocol in
order to optimize the potential structure of the gp350CR2 complex.
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Physicochemical Analysis of the Interaction between Epstein-Barr Virus Glycoprotein gp350 and
Complement Receptor 2 using AESOP (Analysis of Electrostatic Similarities Of Proteins)
Aaron Nichols
5. Kieslich, C. A., R. D. Gorham Jr., and D. Morikis, Is
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the effects of dynamics on the electrostatic analysis
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