Modeling the structure of bound peptide ligands to major histocompatibility complex

PANDORA v2.0: Benchmarking peptide-MHC II models and software improvements

T-cell specificity to differentiate between self and non-self relies on T-cell receptor (TCR) recognition of peptides presented by the Major Histocompatibility Complex (MHC). Investigations into the three-dimensional (3D) structures of peptide:MHC (pMHC) complexes have provided valuable insights of MHC functions. Given the limited availability of experimental pMHC structures and considerable diversity of peptides and MHC alleles, it calls for the development of efficient and reliable computational approaches for modeling pMHC structures. Here we present an update of PANDORA and the systematic evaluation of its performance in modelling 3D structures of pMHC class II complexes (pMHC-II), which play a key role in the cancer immune response. PANDORA is a modelling software that can build low-energy models in a few minutes by restraining peptide residues inside the MHC-II binding groove. We benchmarked PANDORA on 136 experimentally determined pMHC-II structures covering 44 unique αβ chain pairs. Our pipeline achieves a median backbone Ligand-Root Mean Squared Deviation (L-RMSD) of 0.42 Å on the binding core and 0.88 Å on the whole peptide for the benchmark dataset. We incorporated software improvements to make PANDORA a pan-allele framework and improved the user interface and software quality. Its computational efficiency allows enriching the wealth of pMHC binding affinity and mass spectrometry data with 3D models. These models can be used as a starting point for molecular dynamics simulations or structure-boosted deep learning algorithms to identify MHC-binding peptides. PANDORA is available as a Python package through Conda or as a source installation at https://github.com/X-lab-3D/PANDORA.

HLA3DB: comprehensive annotation of peptide/HLA complexes enables blind structure prediction of T cell epitopes

The class I proteins of the major histocompatibility complex (MHC-I) display epitopic peptides derived from endogenous proteins on the cell surface for immune surveillance. Accurate modeling of peptides bound to the human MHC, HLA, has been mired by conformational diversity of the central peptide residues, which are critical for recognition by T cell receptors. Here, analysis of X-ray crystal structures within our curated database (HLA3DB) shows that pHLA complexes encompassing multiple HLA allotypes present a discrete set of peptide backbone conformations. Leveraging these backbones, we employ a regression model trained on terms of a physically relevant energy function to develop a comparative modeling approach for nonamer pHLA structures named RepPred. Our method outperforms the top pHLA modeling approach by up to 19% in structural accuracy, and consistently predicts blind targets not included in our training set. Insights from our work may be applied towards predicting antigen immunogenicity, and receptor cross-reactivity.

HLA3DB: comprehensive annotation of peptide/HLA complexes enables blind structure prediction of T cell epitopes

The class I proteins of the major histocompatibility complex (MHC-I) display epitopic peptides derived from endogenous proteins on the cell surface for immune surveillance. Accurate modeling of peptide/HLA (pHLA, the human MHC) structures has been mired by conformational diversity of the central peptide residues, which are critical for recognition by T cell receptors. Here, analysis of X-ray crystal structures within a curated database (HLA3DB) shows that pHLA complexes encompassing multiple HLA allotypes present a discrete set of peptide backbone conformations. Leveraging these representative backbones, we employ a regression model trained on terms of a physically relevant energy function to develop a comparative modeling approach for nonamer peptide/HLA structures named RepPred. Our method outperforms the top pHLA modeling approach by up to 19% in terms of structural accuracy, and consistently predicts blind targets not included in our training set. Insights from our work provide a framework for linking conformational diversity with antigen immunogenicity and receptor cross-reactivity.

Physicochemical Heuristics for Identifying High Fidelity, Near-Native Structural Models of Peptide/MHC Complexes

There is long-standing interest in accurately modeling the structural features of peptides bound and presented by class I MHC proteins. This interest has grown with the advent of rapid genome sequencing and the prospect of personalized, peptide-based cancer vaccines, as well as the development of molecular and cellular therapeutics based on T cell receptor recognition of peptide-MHC. However, while the speed and accessibility of peptide-MHC modeling has improved substantially over the years, improvements in accuracy have been modest. Accuracy is crucial in peptide-MHC modeling, as T cell receptors are highly sensitive to peptide conformation and capturing fine details is therefore necessary for useful models. Studying nonameric peptides presented by the common class I MHC protein HLA-A*02:01, here we addressed a key question common to modern modeling efforts: from a set of models (or decoys) generated through conformational sampling, which is best? We found that the common strategy of decoy selection by lowest energy can lead to substantial errors in predicted structures. We therefore adopted a data-driven approach and trained functions capable of predicting near native decoys with exceptionally high accuracy. Although our implementation is limited to nonamer/HLA-A*02:01 complexes, our results serve as an important proof of concept from which improvements can be made and, given the significance of HLA-A*02:01 and its preference for nonameric peptides, should have immediate utility in select immunotherapeutic and other efforts for which structural information would be advantageous.

Deciphering the Structural Enigma of HLA Class-II Binding Peptides for Enhanced Immunoinformatics-based Prediction of Vaccine Epitopes

Vaccines remain the most efficacious means to avoid and eliminate morbid diseases associated with high morbidity and mortality. Clinical trials indicate the gaining impetus of peptide vaccines against diseases for which an effective treatment still remains obscure. CD4 T-cell-based peptide vaccines involve immunization with antigenic determinants from pathogens or neoplastic cells that possess the ability to elicit a robust T helper cell response, which subsequently activates other arms of the immune system. The available in silico predictors of human leukocyte antigen II (HLA-II) binding peptides are sequence-based techniques, which ostensibly have balanced sensitivity and specificity. Structural analysis and understanding of the cognate peptide and HLA-II interactions are essential to empirically derive a successful peptide vaccine. However, the availability of structure-based epitope prediction algorithms is inadequate compared with sequence-based prediction methods. The present study is an attempt to understand the structural aspects of HLA-II binders by analyzing the Protein Data Bank (PDB) complexes of pHLA-II. Furthermore, we mimic the peptide exchange mechanism and demonstrate the structural implication of an acidic environment on HLA-II binders. Finally, we discuss a structure-guided approach to decipher potential HLA-II binders within an antigenic protein. This strategy may accurately predict the peptide epitopes and thus aid in designing successful peptide vaccines.

pMHC Structural Comparisons as a Pivotal Element to Detect and Validate T-Cell Targets for Vaccine Development and Immunotherapy-A New Methodological Proposal

The search for epitopes that will effectively trigger an immune response remains the "El Dorado" for immunologists. The development of promising immunotherapeutic approaches requires the appropriate targets to elicit a proper immune response. Considering the high degree of HLA/TCR diversity, as well as the heterogeneity of viral and tumor proteins, this number will invariably be higher than ideal to test. It is known that the recognition of a peptide-MHC (pMHC) by the T-cell receptor is performed entirely in a structural fashion, where the atomic interactions of both structures, pMHC and TCR, dictate the fate of the process. However, epitopes with a similar composition of amino acids can produce dissimilar surfaces. Conversely, sequences with no conspicuous similarities can exhibit similar TCR interaction surfaces. In the last decade, our group developed a database and in silico structural methods to extract molecular fingerprints that trigger T-cell immune responses, mainly referring to physicochemical similarities, which could explain the immunogenic differences presented by different pMHC-I complexes. Here, we propose an immunoinformatic approach that considers a structural level of information, combined with an experimental technology that simulates the presentation of epitopes for a T cell, to improve vaccine production and immunotherapy efficacy.

Structure Based Prediction of Neoantigen Immunogenicity

The development of immunological therapies that incorporate peptide antigens presented to T cells by MHC proteins is a long sought-after goal, particularly for cancer, where mutated neoantigens are being explored as personalized cancer vaccines. Although neoantigens can be identified through sequencing, bioinformatics and mass spectrometry, identifying those which are immunogenic and able to promote tumor rejection remains a significant challenge. Here we examined the potential of high-resolution structural modeling followed by energetic scoring of structural features for predicting neoantigen immunogenicity. After developing a strategy to rapidly and accurately model nonameric peptides bound to the common class I MHC protein HLA-A2, we trained a neural network on structural features that influence T cell receptor (TCR) and peptide binding energies. The resulting structurally-parameterized neural network outperformed methods that do not incorporate explicit structural or energetic properties in predicting CD8+ T cell responses of HLA-A2 presented nonameric peptides, while also providing insight into the underlying structural and biophysical mechanisms governing immunogenicity. Our proof-of-concept study demonstrates the potential for structure-based immunogenicity predictions in the development of personalized peptide-based vaccines.

A stochastic individual-based model to explore the role of spatial interactions and antigen recognition in the immune response against solid tumours

Spatial interactions between cancer and immune cells, as well as the recognition of tumour antigens by cells of the immune system, play a key role in the immune response against solid tumours. The existing mathematical models generally focus only on one of these key aspects. We present here a spatial stochastic individual-based model that explicitly captures antigen expression and recognition. In our model, each cancer cell is characterised by an antigen profile which can change over time due to either epimutations or mutations. The immune response against the cancer cells is initiated by the dendritic cells that recognise the tumour antigens and present them to the cytotoxic T cells. Consequently, T cells become activated against the tumour cells expressing such antigens. Moreover, the differences in movement between inactive and active immune cells are explicitly taken into account by the model. Computational simulations of our model clarify the conditions for the emergence of tumour clearance, dormancy or escape, and allow us to assess the impact of antigenic heterogeneity of cancer cells on the efficacy of immune action. Ultimately, our results highlight the complex interplay between spatial interactions and adaptive mechanisms that underpins the immune response against solid tumours, and suggest how this may be exploited to further develop cancer immunotherapies.

APE-Gen: A Fast Method for Generating Ensembles of Bound Peptide-MHC Conformations

The Class I Major Histocompatibility Complex (MHC) is a central protein in immunology as it binds to intracellular peptides and displays them at the cell surface for recognition by T-cells. The structural analysis of bound peptide-MHC complexes (pMHCs) holds the promise of interpretable and general binding prediction (i.e., testing whether a given peptide binds to a given MHC). However, structural analysis is limited in part by the difficulty in modelling pMHCs given the size and flexibility of the peptides that can be presented by MHCs. This article describes APE-Gen (Anchored Peptide-MHC Ensemble Generator), a fast method for generating ensembles of bound pMHC conformations. APE-Gen generates an ensemble of bound conformations by iterated rounds of (i) anchoring the ends of a given peptide near known pockets in the binding site of the MHC, (ii) sampling peptide backbone conformations with loop modelling, and then (iii) performing energy minimization to fix steric clashes, accumulating conformations at each round. APE-Gen takes only minutes on a standard desktop to generate tens of bound conformations, and we show the ability of APE-Gen to sample conformations found in X-ray crystallography even when only sequence information is used as input. APE-Gen has the potential to be useful for its scalability (i.e., modelling thousands of pMHCs or even non-canonical longer peptides) and for its use as a flexible search tool. We demonstrate an example for studying cross-reactivity.

General Prediction of Peptide-MHC Binding Modes Using Incremental Docking: A Proof of Concept

The class I major histocompatibility complex (MHC) is capable of binding peptides derived from intracellular proteins and displaying them at the cell surface. The recognition of these peptide-MHC (pMHC) complexes by T-cells is the cornerstone of cellular immunity, enabling the elimination of infected or tumoral cells. T-cell-based immunotherapies against cancer, which leverage this mechanism, can greatly benefit from structural analyses of pMHC complexes. Several attempts have been made to use molecular docking for such analyses, but pMHC structure remains too challenging for even state-of-the-art docking tools. To overcome these limitations, we describe the use of an incremental meta-docking approach for structural prediction of pMHC complexes. Previous methods applied in this context used specific constraints to reduce the complexity of this prediction problem, at the expense of generality. Our strategy makes no assumption and can potentially be used to predict binding modes for any pMHC complex. Our method has been tested in a re-docking experiment, reproducing the binding modes of 25 pMHC complexes whose crystal structures are available. This study is a proof of concept that incremental docking strategies can lead to general geometry prediction of pMHC complexes, with potential applications for immunotherapy against cancer or infectious diseases.

Signatures of historical selection on MHC reveal different selection patterns in the moor frog (Rana arvalis)

MHC genes are key components in disease resistance and an excellent system for studying selection acting on genetic variation in natural populations. Current patterns of variation in MHC genes are likely to be influenced by past and ongoing selection as well as demographic fluctuations in population size such as those imposed by post-glacial recolonization processes. Here, we investigated signatures of historical selection and demography on an MHC class II gene in 12 moor frog populations along a 1700-km latitudinal gradient. Sequences were obtained from 207 individuals and consecutively assigned into two different clusters (northern and southern clusters, respectively) in concordance with a previously described dual post-glacial colonization route. Selection analyses comparing the relative rates of non-synonymous to synonymous substitutions (dN/dS) suggested evidence of different selection patterns in the northern and the southern clusters, with divergent selection prevailing in the south but uniform positive selection predominating in the north. Also, models of codon evolution revealed considerable differences in the strength of selection: The southern cluster appeared to be under strong selection while the northern cluster showed moderate signs of selection. Our results indicate that the MHC alleles in the north diverged from southern MHC alleles as a result of differential selection patterns.

Structure-based Methods for Binding Mode and Binding Affinity Prediction for Peptide-MHC Complexes

Understanding the mechanisms involved in the activation of an immune response is essential to many fields in human health, including vaccine development and personalized cancer immunotherapy. A central step in the activation of the adaptive immune response is the recognition, by T-cell lymphocytes, of peptides displayed by a special type of receptor known as Major Histocompatibility Complex (MHC). Considering the key role of MHC receptors in T-cell activation, the computational prediction of peptide binding to MHC has been an important goal for many immunological applications. Sequence- based methods have become the gold standard for peptide-MHC binding affinity prediction, but structure-based methods are expected to provide more general predictions (i.e., predictions applicable to all types of MHC receptors). In addition, structural modeling of peptide-MHC complexes has the potential to uncover yet unknown drivers of T-cell activation, thus allowing for the development of better and safer therapies. In this review, we discuss the use of computational methods for the structural modeling of peptide-MHC complexes (i.e., binding mode prediction) and for the structure-based prediction of binding affinity.

Emerging Concepts in TCR Specificity: Rationalizing and (Maybe) Predicting Outcomes

T cell specificity emerges from a myriad of processes, ranging from the biological pathways that control T cell signaling to the structural and physical mechanisms that influence how TCRs bind peptides and MHC proteins. Of these processes, the binding specificity of the TCR is a key component. However, TCR specificity is enigmatic: TCRs are at once specific but also cross-reactive. Although long appreciated, this duality continues to puzzle immunologists and has implications for the development of TCR-based therapeutics. In this review, we discuss TCR specificity, emphasizing results that have emerged from structural and physical studies of TCR binding. We show how the TCR specificity/cross-reactivity duality can be rationalized from structural and biophysical principles. There is excellent agreement between predictions from these principles and classic predictions about the scope of TCR cross-reactivity. We demonstrate how these same principles can also explain amino acid preferences in immunogenic epitopes and highlight opportunities for structural considerations in predictive immunology.

AnchorDock for Blind Flexible Docking of Peptides to Proteins

Due to increasing interest in peptides as signaling modulators and drug candidates, several methods for peptide docking to their target proteins are under active development. The "blind" docking problem, where the peptide-binding site on the protein surface is unknown, presents one of the current challenges in the field. AnchorDock protocol was developed by Ben-Shimon and Niv to address this challenge.This protocol narrows the docking search to the most relevant parts of the conformational space. This is achieved by pre-folding the free peptide and by computationally detecting anchoring spots on the surface of the unbound protein. Multiple flexible simulated annealing molecular dynamics (SAMD) simulations are subsequently carried out, starting from pre-folded peptide conformations, constrained to the various precomputed anchoring spots.Here, AnchorDock is demonstrated using two known protein-peptide complexes. A PDZ-peptide complex provides a relatively easy case due to the relatively small size of the protein, and a typical peptide conformation and binding region; a more challenging example is a complex between USP7_N-term and a p53-derived peptide, where the protein is larger, and the peptide conformation and a binding site are generally assumed to be unknown. AnchorDock returned native-like solutions ranked first and third for the PDZ and USP7 complexes, respectively. We describe the procedure step by step and discuss possible modifications where applicable.

Prediction of peptide binding to a major histocompatibility complex class I molecule based on docking simulation

Binding between major histocompatibility complex (MHC) class I molecules and immunogenic epitopes is one of the most important processes for cell-mediated immunity. Consequently, computational prediction of amino acid sequences of MHC class I binding peptides from a given sequence may lead to important biomedical advances. In this study, an efficient structure-based method for predicting peptide binding to MHC class I molecules was developed, in which the binding free energy of the peptide was evaluated by two individual docking simulations. An original penalty function and restriction of degrees of freedom were determined by analysis of 361 published X-ray structures of the complex and were then introduced into the docking simulations. To validate the method, calculations using a 50-amino acid sequence as a prediction target were performed. In 27 calculations, the binding free energy of the known peptide was within the top 5 of 166 peptides generated from the 50-amino acid sequence. Finally, demonstrative calculations using a whole sequence of a protein as a prediction target were performed. These data clearly demonstrate high potential of this method for predicting peptide binding to MHC class I molecules.

DockTope: a Web-based tool for automated pMHC-I modelling

The immune system is constantly challenged, being required to protect the organism against a wide variety of infectious pathogens and, at the same time, to avoid autoimmune disorders. One of the most important molecules involved in these events is the Major Histocompatibility Complex class I (MHC-I), responsible for binding and presenting small peptides from the intracellular environment to CD8⁺ T cells. The study of peptide:MHC-I (pMHC-I) molecules at a structural level is crucial to understand the molecular mechanisms underlying immunologic responses. Unfortunately, there are few pMHC-I structures in the Protein Data Bank (PDB) (especially considering the total number of complexes that could be formed combining different peptides), and pMHC-I modelling tools are scarce. Here, we present DockTope, a free and reliable web-based tool for pMHC-I modelling, based on crystal structures from the PDB. DockTope is fully automated and allows any researcher to construct a pMHC-I complex in an efficient way. We have reproduced a dataset of 135 non-redundant pMHC-I structures from the PDB (Cα RMSD below 1 Å). Modelling of pMHC-I complexes is remarkably important, contributing to the knowledge of important events such as cross-reactivity, autoimmunity, cancer therapy, transplantation and rational vaccine design.

Major histocompatibility complex linked databases and prediction tools for designing vaccines

Presently, the major histocompatibility complex (MHC) is receiving considerable interest owing to its remarkable role in antigen presentation and vaccine design. The specific databases and prediction approaches related to MHC sequences, structures and binding/nonbinding peptides have been aggressively developed in the past two decades with their own benchmarks and standards. Before using these databases and prediction tools, it is important to analyze why and how the tools are constructed along with their strengths and limitations. The current review presents insights into web-based immunological bioinformatics resources that include searchable databases of MHC sequences, epitopes and prediction tools that are linked to MHC based vaccine design, including population coverage analysis. In T cell epitope forecasts, MHC class I binding predictions are very accurate for most of the identified MHC alleles. However, these predictions could be further improved by integrating proteasome cleavage (in conjugation with transporter associated with antigen processing (TAP) binding) prediction, as well as T cell receptor binding prediction. On the other hand, MHC class II restricted epitope predictions display relatively low accuracy compared to MHC class I. To date, pan-specific tools have been developed, which not only deliver significantly improved predictions in terms of accuracy, but also in terms of the coverage of MHC alleles and supertypes. In addition, structural modeling and simulation systems for peptide-MHC complexes enable the molecular-level investigation of immune processes. Finally, epitope prediction tools, and their assessments and guidelines, have been presented to immunologist for the design of novel vaccine and diagnostics.

DINC: a new AutoDock-based protocol for docking large ligands

Background

Using the popular program AutoDock, computer-aided docking of small ligands with 6 or fewer rotatable bonds, is reasonably fast and accurate. However, docking large ligands using AutoDock's recommended standard docking protocol is less accurate and computationally slow.

Results

In our earlier work, we presented a novel AutoDock-based incremental protocol (DINC) that addresses the limitations of AutoDock's standard protocol by enabling improved docking of large ligands. Instead of docking a large ligand to a target protein in one single step as done in the standard protocol, our protocol docks the large ligand in increments. In this paper, we present three detailed examples of docking using DINC and compare the docking results with those obtained using AutoDock's standard protocol. We summarize the docking results from an extended docking study that was done on 73 protein-ligand complexes comprised of large ligands. We demonstrate not only that DINC is up to 2 orders of magnitude faster than AutoDock's standard protocol, but that it also achieves the speed-up without sacrificing docking accuracy. We also show that positional restraints can be applied to the large ligand using DINC: this is useful when computing a docked conformation of the ligand. Finally, we introduce a webserver for docking large ligands using DINC.

Conclusions

Docking large ligands using DINC is significantly faster than AutoDock's standard protocol without any loss of accuracy. Therefore, DINC could be used as an alternative protocol for docking large ligands. DINC has been implemented as a webserver and is available at http://dinc.kavrakilab.org. Applications such as therapeutic drug design, rational vaccine design, and others involving large ligands could benefit from DINC and its webserver implementation.

Exploring Protein-Peptide Binding Specificity through Computational Peptide Screening

The binding of short disordered peptide stretches to globular protein domains is important for a wide range of cellular processes, including signal transduction, protein transport, and immune response. The often promiscuous nature of these interactions and the conformational flexibility of the peptide chain, sometimes even when bound, make the binding specificity of this type of protein interaction a challenge to understand. Here we develop and test a Monte Carlo-based procedure for calculating protein-peptide binding thermodynamics for many sequences in a single run. The method explores both peptide sequence and conformational space simultaneously by simulating a joint probability distribution which, in particular, makes searching through peptide sequence space computationally efficient. To test our method, we apply it to 3 different peptide-binding protein domains and test its ability to capture the experimentally determined specificity profiles. Insight into the molecular underpinnings of the observed specificities is obtained by analyzing the peptide conformational ensembles of a large number of binding-competent sequences. We also explore the possibility of using our method to discover new peptide-binding pockets on protein structures.

The interactions between proteins play a crucial role for almost every undertaking of a cell. Many of these interactions are mediated by the binding of relatively short unstructured polypeptide segments, or peptides, in one protein to well-folded domains in other proteins. Such protein-peptide interactions have some interesting and special properties, e.g., promiscuity, which means many different peptide sequences are able to bind the same protein domain. Peptides also often exhibit structural flexibility even after binding a protein. These special properties make it desirable, but also challenging, to simulate protein-peptide binding in atomistic detail for many different peptide sequences. To this end, we have developed a computational algorithm that simultaneously explores the structure of protein-peptide complexes and the amino acid sequences of the peptide. In particular, our algorithm allows binding-competent peptide sequences to be generated in direct relation to their binding strengths. We also explored the possibility of using our method to locate new peptide-binding pockets on protein structures. Computational algorithms such as the one developed here may pave the way to reveal the full complexity of protein-protein interaction networks used in cells.

Using databases and data mining in vaccinology

Throughout time functional immunology has accumulated vast amounts of quantitative and qualitative data relevant to the design and discovery of vaccines. Such data includes, but is not limited to, components of the host and pathogen genome (including antigens and virulence factors), T- and B-cell epitopes and other components of the antigen presentation pathway and allergens. In this review the authors discuss a range of databases that archive such data. Built on such information, increasingly sophisticated data mining techniques have developed that create predictive models of utilitarian value. With special reference to epitope data, the authors discuss the strengths and weaknesses of the available techniques and how they can aid computer-aided vaccine design deliver added value for vaccinology.

T-cell epitope vaccine design by immunoinformatics

Vaccination is generally considered to be the most effective method of preventing infectious diseases. All vaccinations work by presenting a foreign antigen to the immune system in order to evoke an immune response. The active agent of a vaccine may be intact but inactivated ('attenuated') forms of the causative pathogens (bacteria or viruses), or purified components of the pathogen that have been found to be highly immunogenic. The increased understanding of antigen recognition at molecular level has resulted in the development of rationally designed peptide vaccines. The concept of peptide vaccines is based on identification and chemical synthesis of B-cell and T-cell epitopes which are immunodominant and can induce specific immune responses. The accelerating growth of bioinformatics techniques and applications along with the substantial amount of experimental data has given rise to a new field, called immunoinformatics. Immunoinformatics is a branch of bioinformatics dealing with in silico analysis and modelling of immunological data and problems. Different sequence- and structure-based immunoinformatics methods are reviewed in the paper.

HLA-B*27:05-specific cytotoxic T lymphocyte epitopes in Indian HIV type 1C

HLA-B*27:05 is one of the widely reported alleles associated with resistance to HIV, while HLA-A24, HLA-B7, HLA-B*07:02, HLA-B*35:01, HLA-B*53:01, and HLA-B40 are reported to be associated with susceptibility to HIV. Using a bioinformatics approach we attempted to predict potential HLA-B*27:05-specific HIV-1C epitopes that do not bind to susceptibility-associated HLA alleles based on our hypothesis that such epitopes have a greater probability of eliciting a protective immune response in the host. A consensus sequence was built for all proteins of Indian clade C virus. Epitopes specific to HLA-B*27:05 were predicted from the consensus sequence using two different bioinformatics methods to enhance the accuracy of the prediction. Epitopes that were also predicted to bind to any of the susceptibility-associated HLA alleles were excluded from the list. The short-listed epitopes were modeled using MODPROPEP to refine the prediction. Fourteen peptides were identified as epitopes by both sequence-based methods and were found to interact strongly with HLA-B*27:05 by molecular modeling studies. Five of the 14 epitopes were previously reported as immunogenic by other researchers, while the remaining nine are novel. The 14 epitopes have been repeatedly identified by three different methods indicating their potential as useful candidates for an effective HIV vaccine.

Predicting HLA class I non-permissive amino acid residues substitutions

Prediction of peptide binding to human leukocyte antigen (HLA) molecules is essential to a wide range of clinical entities from vaccine design to stem cell transplant compatibility. Here we present a new structure-based methodology that applies robust computational tools to model peptide-HLA (p-HLA) binding interactions. The method leverages the structural conservation observed in p-HLA complexes to significantly reduce the search space and calculate the system’s binding free energy. This approach is benchmarked against existing p-HLA complexes and the prediction performance is measured against a library of experimentally validated peptides. The effect on binding activity across a large set of high-affinity peptides is used to investigate amino acid mismatches reported as high-risk factors in hematopoietic stem cell transplantation.

Binding free energy landscape of domain-peptide interactions

Peptide recognition domains (PRDs) are ubiquitous protein domains which mediate large numbers of protein interactions in the cell. How these PRDs are able to recognize peptide sequences in a rapid and specific manner is incompletely understood. We explore the peptide binding process of PDZ domains, a large PRD family, from an equilibrium perspective using an all-atom Monte Carlo (MC) approach. Our focus is two different PDZ domains representing two major PDZ classes, I and II. For both domains, a binding free energy surface with a strong bias toward the native bound state is found. Moreover, both domains exhibit a binding process in which the peptides are mostly either bound at the PDZ binding pocket or else interact little with the domain surface. Consistent with this, various binding observables show a temperature dependence well described by a simple two-state model. We also find important differences in the details between the two domains. While both domains exhibit well-defined binding free energy barriers, the class I barrier is significantly weaker than the one for class II. To probe this issue further, we apply our method to a PDZ domain with dual specificity for class I and II peptides, and find an analogous difference in their binding free energy barriers. Lastly, we perform a large number of fixed-temperature MC kinetics trajectories under binding conditions. These trajectories reveal significantly slower binding dynamics for the class II domain relative to class I. Our combined results are consistent with a binding mechanism in which the peptide C terminal residue binds in an initial, rate-limiting step.

The complex biological processes occurring in living organisms are enabled by numerous networks of interacting proteins. It is therefore of great interest to understand the physical interplay between proteins and, in particular, how this process gives rise to highly specific network connectivities. For a long time, the dominant molecular view of protein-protein interactions was the docking of more or less static folded structures, with specificity obtained from a complementarity in shape and charge distributions. Lately it has been realized that many of the links in protein networks are mediated by interactions between folded domains, on the one hand, and disordered polypeptide segments, on the other. We use an all-atom Monte Carlo based approach which attempts to capture this domain-peptide binding process in full and apply it to representative members of a common domain family. This allows us to examine and compare detailed aspects of the binding free energy landscapes which underlie specificity and affinity. Being able to model domain-peptide binding in a physically sound, yet computationally tractable way is essential for identifying molecular binding mechanisms and opens up possibilities for modifying interaction networks in a controlled way.

Large-scale characterization of peptide-MHC binding landscapes with structural simulations

Class I major histocompatibility complex proteins play a critical role in the adaptive immune system by binding to peptides derived from cytosolic proteins and presenting them on the cell surface for surveillance by T cells. The varied peptide binding specificity of these highly polymorphic molecules has important consequences for vaccine design, transplantation, autoimmunity, and cancer development. Here, we describe a molecular modeling study of MHC-peptide interactions that integrates sampling techniques from protein-protein docking, loop modeling, de novo structure prediction, and protein design in order to construct atomically detailed peptide binding landscapes for a diverse set of MHC proteins. Specificity profiles derived from these landscapes recover key features of experimental binding profiles and can be used to predict peptide binding with reasonable accuracy. Family wide comparison of the predicted binding landscapes recapitulates previously reported patterns of specificity divergence and peptide-repertoire diversity while providing a structural basis for observed specificity patterns. The size and sequence diversity of these structure-based binding landscapes enable us to identify subtle patterns of covariation between peptide sequence positions; analysis of the associated structural models suggests physical interactions that may mediate these sequence correlations.

MHC Class II Binding Prediction by Molecular Docking

Proteins of the Major Histocompatibility Complex (MHC) bind self and nonself peptide antigens or epitopes within the cell and present them at the cell surface for recognition by T cells. All T-cell epitopes are MHC binders but not all MCH binders are T-cell epitopes. The MHC class II proteins are extremely polymorphic. Polymorphic residues cluster in the peptide-binding region and largely determine the MHC's peptide selectivity. The peptide binding site on MHC class II proteins consist of five binding pockets. Using molecular docking, we have modelled the interactions between peptide and MHC class II proteins from locus DRB1. A combinatorial peptide library was generated by mutation of residues at peptide positions which correspond to binding pockets (so called anchor positions). The binding affinities were assessed using different scoring functions. The normalized scoring functions for each amino acid at each anchor position were used to construct quantitative matrices (QM) for MHC class II binding prediction. Models were validated by external test sets comprising 4540 known binders. Eighty percent of the known binders are identified in the best predicted 15 % of all overlapping peptides, originating from one protein.

Design of glycopeptides used to investigate class II MHC binding and T-cell responses associated with autoimmune arthritis

The glycopeptide fragment CII259–273 from type II collagen (CII) binds to the murine A^q and human DR4 class II Major Histocompatibility Complex (MHC II) proteins, which are associated with development of murine collagen-induced arthritis (CIA) and rheumatoid arthritis (RA), respectively. It has been shown that CII259–273 can be used in therapeutic vaccination of CIA. This glycopeptide also elicits responses from T-cells obtained from RA patients, which indicates that it has an important role in RA as well. We now present a methodology for studies of (glyco)peptide-receptor interactions based on a combination of structure-based virtual screening, ligand-based statistical molecular design and biological evaluations. This methodology included the design of a CII259–273 glycopeptide library in which two anchor positions crucial for binding in pockets of A^q and DR4 were varied. Synthesis and biological evaluation of the designed glycopeptides provided novel structure-activity relationship (SAR) understanding of binding to A^q and DR4. Glycopeptides that retained high affinities for these MHC II proteins and induced strong responses in panels of T-cell hybridomas were also identified. An analysis of all the responses revealed groups of glycopeptides with different response patterns that are of high interest for vaccination studies in CIA. Moreover, the SAR understanding obtained in this study provides a platform for the design of second-generation glycopeptides with tuned MHC affinities and T-cell responses.

Modeling peptide-protein interactions

Peptide-protein interactions are prevalent in the living cell and form a key component of the overall protein-protein interaction network. These interactions are drawing increasing interest due to their part in signaling and regulation, and are thus attractive targets for computational structural modeling. Here we report an overview of current techniques for the high resolution modeling of peptide-protein complexes. We dissect this complicated challenge into several smaller subproblems, namely: modeling the receptor protein, predicting the peptide binding site, sampling an initial peptide backbone conformation and the final refinement of the peptide within the receptor binding site. For each of these conceptual stages, we present available tools, approaches, and their reported performance. We summarize with an illustrative example of this process, highlighting the success and current challenges still facing the automated blind modeling of peptide-protein interactions. We believe that the upcoming years will see considerable progress in our ability to create accurate models of peptide-protein interactions, with applications in binding-specificity prediction, rational design of peptide-mediated interactions and the usage of peptides as therapeutic agents.

Sub-angstrom modeling of complexes between flexible peptides and globular proteins

A wide range of regulatory processes in the cell are mediated by flexible peptides that fold upon binding to globular proteins. Computational efforts to model these interactions are hindered by the large number of rotatable bonds in flexible peptides relative to typical ligand molecules, and the fact that different peptides assume different backbone conformations within the same binding site. In this study, we present Rosetta FlexPepDock, a novel tool for refining coarse peptide-protein models that allows significant changes in both peptide backbone and side chains. We obtain high resolution models, often of sub-angstrom backbone quality, over an extensive and general benchmark that is based on a large nonredundant dataset of 89 peptide-protein interactions. Importantly, side chains of known binding motifs are modeled particularly well, typically with atomic accuracy. In addition, our protocol has improved modeling quality for the important application of cross docking to PDZ domains. We anticipate that the ability to create high resolution models for a wide range of peptide-protein complexes will have significant impact on structure-based functional characterization, controlled manipulation of peptide interactions, and on peptide-based drug design.

Structure-based identification of MHC binding peptides: Benchmarking of prediction accuracy

Identification of MHC binding peptides is essential for understanding the molecular mechanism of immune response. However, most of the prediction methods use motifs/profiles derived from experimental peptide binding data for specific MHC alleles, thus limiting their applicability only to those alleles for which such data is available. In this work we have developed a structure-based method which does not require experimental peptide binding data for training. Our method models MHC-peptide complexes using crystal structures of 170 MHC-peptide complexes and evaluates the binding energies using two well known residue based statistical pair potentials, namely Betancourt-Thirumalai (BT) and Miyazawa-Jernigan (MJ) matrices. Extensive benchmarking of prediction accuracy on a data set of 1654 epitopes from class I and class II alleles available in the SYFPEITHI database indicate that BT pair-potential can predict more than 60% of the known binders in case of 14 MHC alleles with AUC values for ROC curves ranging from 0.6 to 0.9. Similar benchmarking on 29,522 class I and class II MHC binding peptides with known IC(50) values in the IEDB database showed AUC values higher than 0.6 for 10 class I alleles and 9 class II alleles in predictions involving classification of a peptide to be binder or non-binder. Comparison with recently available benchmarking studies indicated that, the prediction accuracy of our method for many of the class I and class II MHC alleles was comparable to the sequence based methods, even if it does not use any experimental data for training. It is also encouraging to note that the ranks of true binding peptides could further be improved, when high scoring peptides obtained from pair potential were re-ranked using all atom forcefield and MM/PBSA method.

pDOCK: a new technique for rapid and accurate docking of peptide ligands to Major Histocompatibility Complexes

Background

Identification of antigenic peptide epitopes is an essential prerequisite in T cell-based molecular vaccine design. Computational (sequence-based and structure-based) methods are inexpensive and efficient compared to experimental approaches in screening numerous peptides against their cognate MHC alleles. In structure-based protocols, suited to alleles with limited epitope data, the first step is to identify high-binding peptides using docking techniques, which need improvement in speed and efficiency to be useful in large-scale screening studies. We present pDOCK: a new computational technique for rapid and accurate docking of flexible peptides to MHC receptors and primarily apply it on a non-redundant dataset of 186 pMHC (MHC-I and MHC-II) complexes with X-ray crystal structures.

Results

We have compared our docked structures with experimental crystallographic structures for the immunologically relevant nonameric core of the bound peptide for MHC-I and MHC-II complexes. Primary testing for re-docking of peptides into their respective MHC grooves generated 159 out of 186 peptides with Cα RMSD of less than 1.00 Å, with a mean of 0.56 Å. Amongst the 25 peptides used for single and variant template docking, the Cα RMSD values were below 1.00 Å for 23 peptides. Compared to our earlier docking methodology, pDOCK shows upto 2.5 fold improvement in the accuracy and is ~60% faster. Results of validation against previously published studies represent a seven-fold increase in pDOCK accuracy.

Conclusions

The limitations of our previous methodology have been addressed in the new docking protocol making it a rapid and accurate method to evaluate pMHC binding. pDOCK is a generic method and although benchmarks against experimental structures, it can be applied to alleles with no structural data using sequence information. Our outcomes establish the efficacy of our procedure to predict highly accurate peptide structures permitting conformational sampling of the peptide in MHC binding groove. Our results also support the applicability of pDOCK for in silico identification of promiscuous peptide epitopes that are relevant to higher proportions of human population with greater propensity to activate T cells making them key targets for the design of vaccines and immunotherapies.

DynaDock: A new molecular dynamics-based algorithm for protein-peptide docking including receptor flexibility

Molecular docking programs play an important role in drug development and many well-established methods exist. However, there are two situations for which the performance of most approaches is still not satisfactory, namely inclusion of receptor flexibility and docking of large, flexible ligands like peptides. In this publication a new approach is presented for docking peptides into flexible receptors. For this purpose a two step procedure was developed: first, the protein-peptide conformational space is scanned and approximate ligand poses are identified and second, the identified ligand poses are refined by a new molecular dynamics-based method, optimized potential molecular dynamics (OPMD). The OPMD approach uses soft-core potentials for the protein-peptide interactions and applies a new optimization scheme to the soft-core potential. Comparison with refinement results obtained by conventional molecular dynamics and a soft-core scaling approach shows significant improvements in the sampling capability for the OPMD method. Thus, the number of starting poses needed for successful refinement is much lower than for the other methods. The algorithm was evaluated on 15 protein-peptide complexes with 2-16mer peptides. Docking poses with peptide RMSD values <2.10 A from the equilibrated experimental structures were obtained in all cases. For four systems docking into the unbound receptor structures was performed, leading to peptide RMSD values <2.12 A. Using a specifically fitted scoring function in 11 of 15 cases the best scoring poses featured a peptide RMSD < or = 2.10 A.

Peptidic modulators of protein-protein interactions: progress and challenges in computational design

With the decline in productivity of drug-development efforts, novel approaches to rational drug design are being introduced and developed. Naturally occurring and synthetic peptides are emerging as novel promising compounds that can specifically and efficiently modulate signaling pathways in vitro and in vivo. We describe sequence-based approaches that use peptides to mimic proteins in order to inhibit the interaction of the mimicked protein with its partners. We then discuss a structure-based approach, in which protein-peptide complex structures are used to rationally design and optimize peptidic inhibitors. We survey flexible peptide docking techniques and discuss current challenges and future directions in the rational design of peptidic inhibitors.

An introduction to epitope prediction methods and software

In this paper, current prediction methods and algorithms for both T- and B cell epitopes are reviewed, and a comprehensive summary of epitope prediction software and databases currently available online is also provided. This review can offer researchers in this field a sense of direction and insights for future work. However, our main purpose is to introduce clinical and basic biomedical researchers who are not familiar with these biological analysis tools and databases to these online resources and to provide guidance on how to use them effectively.

Do rotamer libraries reproduce the side-chain conformations of peptidic ligands from the PDB?

Rotamer libraries are collections of side-chain conformations compiled for each type of amino acid. All the libraries developed so far were constructed based on different sets of proteins, none of which includes small peptidic molecules. Are the existing rotamer libraries suitable for modeling the side chains of peptidic ligands in complex with their receptors? To answer this question, we have tested 10 different, publicly available and commonly used rotamer libraries for their capability of reproducing the side-chain conformations (and therefore the receptor-ligand interactions) of hundreds of short peptidic ligands found in the Protein Data Bank, including large sets of class I and class II T-cell epitopes. Only the libraries developed by Xiang and Honig were able to correctly reproduce the experimental geometries for most of the analyzed residues, and the atomic interactions between the peptidic ligands and their receptors. Surprisingly, all the libraries showed a lower performance in reproducing the side chains conformations from structures solved at very high resolution (R<1.25A).

Mass spectral data for 64 eluted peptides and structural modeling define peptide binding preferences for class I alleles in two chicken MHC-B haplotypes associated with opposite responses to Marek's disease

In the chicken, resistance to lymphomas that form following infection with oncogenic strains of Marek's herpesvirus is strongly linked to the major histocompatibility complex (MHC)-B complex. MHC-B21 haplotype is associated with lower tumor-related mortality compared to other haplotypes including MHC-B13. The single, dominantly expressed class I gene (BF2) is postulated as responsible for the MHC-B haplotype association. We used mass spectrometry to identify peptides and structural modeling to define the peptide binding preferences of BF2 2101 and BF2 1301 proteins. Endogenous peptides (8-12 residues long) were eluted from affinity-purified BF2 2101 and BF2 1301 proteins obtained from transduced cDNA expressed in RP9 cells, hence expressed in the presence of heterologous TAP. Sequences of individual peptides were identified by mass spectrometry. BF2 2101 peptides appear to be tethered at the binding groove margins with longer peptides arching out but selected by preferred residues at positions P3, P5, and P8: X-X-[AVILFP]-X((1-5))-[AVLFWP]-X((2-3))-[VILFM]. BF2 1301 peptides appear selected for residues at P2, P3, P5, and P8: X-[DE]-[AVILFW]-X((1-2))-[DE]-X-X-[ED]-X((0-4)). Some longer BF2 1301 peptides likely also arch out, but others are apparently accommodated by repositioning of Arg83 so that peptides extend beyond the last preferred residue at P8. Comparisons of these peptides with earlier peptides derived in the presence of homologous TAP transport revealed the same side chain preferences. Scanning of Marek's and other viral proteins with the BF2 2101 motif identified many matches, as did the control human leukocyte antigen A 0201 motif. The BF2 1301 motif is more restricting suggesting that this allele may confer a selective advantage only in infections with a subset of viral pathogens.

Immunological hotspots analyzed by docking simulations: evidence for a general mechanism in pemphigus vulgaris pathology and transformation

Background

Pemphigus vulgaris (PV) is an acquired autoimmune blistering disorder in which greater than 80% of active patients produce autoantibodies to the desmosomal protein desmogelin 3 (Dsg3). As the disease progresses, 40–50% of patients may also develop reactivity to a second component of the desmosomal complex, desmogelin 1 (Dsg1). T cells are clearly required for the production of autoantibodies in PV. However, few T-cell specificities within Dsg3 or Dsg1 have been reported to date, and the precise role of T-cells in disease pathogenesis and evolution remains poorly understood. In particular, no studies have addressed the immunological mechanisms that underlie the observed clinical heterogeneity in pemphigus. We report here a structure-based technique for the screening of DRB1*0402-specific immunological (T-cell epitope) hotspots in both Dsg3 and Dsg1 glycoproteins.

Results

High predictivity was obtained for DRB1*0402 (r² = 0.90, s = 1.20 kJ/mol, q² = 0.82, s_press = 1.61 kJ/mol) predictive model, compared to experimental data. In silico mapping of the T-cell epitope repertoires in Dsg3 and Dsg1 glycoproteins revealed that the potential immunological hotspots of both target autoantigens are highly conserved, despite limited sequence identity (54% identical, 72% similar). A similar number of well-conserved (18%) high-affinity binders were predicted to exist within both Dsg3 and Dsg1, with analogous distribution of binding registers.

Conclusion

This study provides interesting new insights into the possible mechanism for PV disease progression. Our data suggests that the potential T-cell epitope repertoires encoded in Dsg1 and Dsg3 is substantially overlapping, and it may be possible to apply a common, antigen-specific therapeutic strategy with efficacy across distinct clinical phases of disease.

A practical guide to structure-based prediction of MHC-binding peptides

The binding of bound peptide ligands to major histocompatibility complex (MHC) molecules plays a key role in the activation of normal immune responses and is an intricate theoretical problem that remains unsolved. Geometric and energetic complementarities between an MHC molecule and its corresponding bound peptide ligand are critical in determining the stability of the complex. In this context, the introduction of structural information can greatly facilitate our understanding of how well a peptide ligand can associate with a particular MHC molecule. This chapter introduces the use of structural models as a predictive method to determine whether a peptide sequence can bind to a specific MHC allele.

Modeling Escherichia coli signal peptidase complex with bound substrate: determinants in the mature peptide influencing signal peptide cleavage

Background

Type I signal peptidases (SPases) are essential membrane-bound serine proteases responsible for the cleavage of signal peptides from proteins that are translocated across biological membranes. The crystal structure of SPase in complex with signal peptide has not been solved and their substrate-binding site and binding specificities remain poorly understood. We report here a structure-based model for Escherichia coli DsbA 13–25 in complex with its endogenous type I SPase.

Results

The bound structure of DsbA 13–25 in complex with its endogenous type I SPase reported here reveals the existence of an extended conformation of the precursor protein with a pronounced backbone twist between positions P3 and P1'. Residues 13–25 of DsbA occupy, and thereby define 13 subsites, S7 to S6', within the SPase substrate-binding site. The newly defined subsites, S1' to S6' play critical roles in the substrate specificities of E. coli SPase. Our results are in accord with available experimental data.

Conclusion

Collectively, the results of this study provide interesting new insights into the binding conformation of signal peptides and the substrate-binding site of E. coli SPase. This is the first report on the modeling of a precursor protein into the entire SPase binding site. Together with the conserved precursor protein binding conformation, the existing and newly identified substrate binding sites readily explain SPase cleavage fidelity, consistent with existing biochemical results and solution structures of inhibitors in complex with E. coli SPase. Our data suggests that both signal and mature moiety sequences play important roles and should be considered in the development of predictive tools.

Computer-aided biotechnology: from immuno-informatics to reverse vaccinology

Genome sequences from many organisms, including humans, have been completed, and high-throughput analyses have produced burgeoning volumes of 'omics' data. Bioinformatics is crucial for the management and analysis of such data and is increasingly used to accelerate progress in a wide variety of large-scale and object-specific functional analyses. Refined algorithms enable biotechnologists to follow 'computer-aided strategies' based on experiments driven by high-confidence predictions. In order to address compound problems, current efforts in immuno-informatics and reverse vaccinology are aimed at developing and tuning integrative approaches and user-friendly, automated bioinformatics environments. This will herald a move to 'computer-aided biotechnology': smart projects in which time-consuming and expensive large-scale experimental approaches are progressively replaced by prediction-driven investigations.

Amino acid biophysical properties in the statistical prediction of peptide-MHC class I binding

Background

A key step in the development of an adaptive immune response to pathogens or vaccines is the binding of short peptides to molecules of the Major Histocompatibility Complex (MHC) for presentation to T lymphocytes, which are thereby activated and differentiate into effector and memory cells. The rational design of vaccines consists in part in the identification of appropriate peptides to effect this process. There are several algorithms currently in use for making such predictions, but these are limited to a small number of MHC molecules and have good but imperfect prediction power.

Results

We have undertaken an exploration of the power gained by taking advantage of a natural representation of the amino acids in terms of their biophysical properties. We used several well-known statistical classifiers using either a naive encoding of amino acids by name or an encoding by biophysical properties. In all cases, the encoding by biophysical properties leads to substantially lower misclassification error.

Conclusion

Representation of amino acids using a few important bio-physio-chemical property provide a natural basis for representing peptides and greatly improves peptide-MHC class I binding prediction.

In silico characterization of immunogenic epitopes presented by HLA-Cw*0401

Background

HLA-C locus products are poorly understood in part due to their low expression at the cell surface. Recent data indicate that these molecules serve as major restriction elements for human immunodeficiency virus type 1 (HIV-1) cytotoxic T lymphocyte (CTL) epitopes. We report here a structure-based technique for the prediction of peptides binding to Cw*0401. The models were rigorously trained, tested and validated using experimentally verified Cw*0401 binding and non-binding peptides obtained from biochemical studies. A new scoring scheme facilitates the identification of immunological hot spots within antigens, based on the sum of predicted binding energies of the top four binders within a window of 30 amino acids.

Results

High predictivity is achieved when tested on the training (r² = 0.88, s = 3.56 kJ/mol, q² = 0.84, s_press = 5.18 kJ/mol) and test (A_ROC = 0.93) datasets. Characterization of the predicted Cw*0401 binding sequences indicate that amino acids at key anchor positions share common physico-chemical properties which correlate well with existing experimental studies.

Conclusion

The analysis of predicted Cw*0401-binding peptides showed that anchor residues may not be restrictive and the Cw*0401 binding pockets may possibly accommodate a wide variety of peptides with common physico-chemical properties. The potential Cw*0401-specific T-cell epitope repertoires for HIV-1 p24^gag and gp160^gag glycoproteins are well distributed throughout both glycoproteins, with thirteen and nine immunological hot spots for HIV-1 p24^gag and gp160^gag glycoproteins respectively. These findings provide new insights into HLA-C peptide selectivity, indicating that pre-selection of candidate HLA-C peptides may occur at the TAP level, prior to peptide loading in the endoplasmic reticulum.

MODPROPEP: a program for knowledge-based modeling of protein-peptide complexes

MODPROPEP is a web server for knowledge-based modeling of protein–peptide complexes, specifically peptides in complex with major histocompatibility complex (MHC) proteins and kinases. The available crystal structures of protein–peptide complexes in PDB are used as templates for modeling peptides of desired sequence in the substrate-binding pocket of MHCs or protein kinases. The substrate peptides are modeled using the same backbone conformation as in the template and the side-chain conformations are obtained by the program SCWRL. MODPROPEP provides a number of user-friendly interfaces for visualizing the structure of the modeled protein–peptide complexes and analyzing the contacts made by the modeled peptide ligand in the substrate-binding pocket of the MHC or protein kinase. Analysis of these specific inter-molecular contacts is crucial for understanding structural basis of the substrate specificity of these two protein families. This software also provides appropriate interfaces for identifying, putative MHC-binding peptides in the sequence of an antigen or phosphorylation sites on the substrate protein of a kinase, by scoring these inter-molecular contacts using residue-based statistical pair potentials. MODPROPEP would complement various available sequence-based programs (SYFPEITHI, SCANSITE, etc.) for predicting substrates of MHCs and protein kinases. The program is available at http://www.nii.res.in/modpropep.html

Prediction of desmoglein-3 peptides reveals multiple shared T-cell epitopes in HLA DR4- and DR6-associated pemphigus vulgaris

Background

Pemphigus vulgaris (PV) is a severe autoimmune blistering skin disorder that is strongly associated with major histocompatibility complex class II alleles DRB1*0402 and DQB1*0503. The target antigen of PV, desmoglein 3 (Dsg3), is crucial for initiating T-cell response in early disease. Although a number of T-cell specificities within Dsg3 have been reported, the number is limited and the role of T-cells in the pathogenesis of PV remains poorly understood. We report here a structure-based model for the prediction of peptide binding to DRB1*0402 and DQB1*0503. The scoring functions were rigorously trained, tested and validated using experimentally verified peptide sequences.

Results

High predictivity is obtained for both DRB1*0402 (r² = 0.90, s = 1.20 kJ/mol, q² = 0.82, s_press = 1.61 kJ/mol) and DQB1*0503 (r² = 0.95, s = 1.20 kJ/mol, q² = 0.75, s_press = 2.15 kJ/mol) models, compared to experimental data. We investigated the binding patterns of Dsg3 peptides and illustrate the existence of multiple immunodominant epitopes that may be responsible for both disease initiation and propagation in PV. Further analysis reveals that DRB1*0402 and DQB1*0503 may share similar specificities by binding peptides at different binding registers, thus providing a molecular mechanism for the dual HLA association observed in PV.

Conclusion

Collectively, the results of this study provide interesting new insights into the pathology of PV. This is the first report illustrating high-level of cross-reactivity between both PV-implicated alleles, DRB1*0402 and DQB1*0503, as well as the existence of a potentially large number of T-cell epitopes throughout the entire Dsg3 extracellular domain (ECD) and transmembrane region. Our results reveal that DR4 and DR6 PV may initiate in the ECD and transmembrane region respectively, with implications for immunotherapeutic strategies for the treatment of this autoimmune disease.

In silico grouping of peptide/HLA class I complexes using structural interaction characteristics

MOTIVATION

Classification of human leukocyte antigen (HLA) proteins into supertypes underpins the development of epitope-based vaccines with wide population coverage. Current methods for HLA supertype definition, based on common structural features of HLA proteins and/or their functional binding specificities, leave structural interaction characteristics among different HLA supertypes with antigenic peptides unexplored.

METHODS

We describe the use of structural interaction descriptors for the analysis of 68 peptide/HLA class I crystallographic structures. Interaction parameters computed include the number of intermolecular hydrogen bonds between each HLA protein and its corresponding bound peptide, solvent accessibility, gap volume and gap index.

RESULTS

The structural interactions patterns of peptide/HLA class I complexes investigated herein vary among individual alleles and may be grouped in a supertype dependent manner. Using the proposed methodology, eight HLA class I supertypes were defined based on existing experimental crystallographic structures which largely overlaps (77% consensus) with the definitions by binding motifs. This mode of classification, which considers conformational information of both peptide and HLA proteins, provides an alternative to the characterization of supertypes using either peptide or HLA protein information alone.