Towards the MHC-peptide combinatorics

From Anna University to America and to Agriculture

Anna University (AU) is an awesome alma mater for attracting the attention of the invincible through awareness from education. It is a place with a plan for preparing a palace in a person's life. It is an avenue for America through adequate cGPA and Advanced GRE (AGRE) with good TOEFL score. The views,visions, modes and models of several faculty members shaped many technocrats, teachers, entrepreneurs, journalists, editors and even farmers. Technology is engineering with science. The foundation and facilities at AU is priceless. AU created the framework for Industrial Biotechnology, a truly inter disciplinary curriculum with an optimal blend of Engineering and Science (Biology especially Agriculture and Healthcare through Organic chemistry) in 1992 almost 28 years back. The place was positioned just perfect in the world for wonders to come true. The Raman auditorium (in reverence to the Nobel Laureate Sir CV Raman) reassured rational research with reasonable respect in many minds at the ACTECH (Alagappa College of Technology) under the administration of AU. The admiration, acknowledgement and accountability for the alma mater, the AU will always remain precious.

HLA class I molecular variation and peptide-binding properties suggest a model of joint divergent asymmetric selection

The main function of HLA class I molecules is to present pathogen-derived peptides to cytotoxic T lymphocytes. This function is assumed to drive the maintenance of an extraordinary amount of polymorphism at each HLA locus, providing an immune advantage to heterozygote individuals capable to present larger repertories of peptides than homozygotes. This seems contradictory, however, with a reduced diversity at individual HLA loci exhibited by some isolated populations. This study shows that the level of functional diversity predicted for the two HLA-A and HLA-B genes considered simultaneously is similar (almost invariant) between 46 human populations, even when a reduced diversity exists at each locus. We thus propose that HLA-A and HLA-B evolved through a model of joint divergent asymmetric selection conferring all populations an equivalent immune potential. The distinct pattern observed for HLA-C is explained by its functional evolution towards killer cell immunoglobulin-like receptor (KIR) activity regulation rather than peptide presentation.

Short Peptide Vaccine Design and Development: Promises and Challenges

Vaccine development for viral diseases is a challenge where subunit vaccines are often ineffective. Therefore, the need for alternative solutions is crucial. Thus, short peptide vaccine candidates promise effective answers under such circumstances. Short peptide vaccine candidates are linear T-cell epitopes (antigenic determinants that are recognized by the immune system) that specifically function by binding human leukocyte antigen (HLA) alleles of different ethnicities (including Black, Caucasian, Oriental, Hispanic, Pacific Islander, American Indian, Australian aboriginal, and mixed ethnicities). The population-specific allele-level HLA sequence data in the public IMGT/HLA database contains approximately 12542 nomenclature defined class I (9437) and class II (3105) HLA alleles as of March 2015 present in several ethnic populations.

The bottleneck in short peptide vaccine design and development is HLA polymorphism on the one hand and viral diversity on the other hand. Hence, a crucial step in its design and development is HLA allele-specific binding of short antigen peptides. This is usually combinatorial and computationally labor intensive. Mathematical models utilizing structure-defined pockets are currently available for class I and class II HLA-peptide-binding peptides. Frameworks have been developed to design protocols to identify the most feasible short peptide cocktails as vaccine candidates with superantigen properties among known HLA supertypes. This approach is a promising solution to develop new viral vaccines given the current advancement in T-cell immuno-informatics, yet challenging in terms of prediction efficiency and protocol development.

Class II HLA-peptide binding prediction using structural principles

The precise prediction of class II human leukocyte antigen (HLA) peptide binding finds application in epitope design for the development of vaccines and diagnostics of diseases associated with CD4+ T-cellular immunity. HLA II binding peptides have an extended conformation at the binding groove unlike class I. This increases peptide binding combinations of varying length at the groove, having an eventual effect in the host immune response to infectious agents. Here we describe the development of a prediction model using information gleaned from HLA II-peptide (HLA II-p) structural data. We created a manually curated dataset of 15 HLA II-p structural complexes from Protein databank (PDB). The dataset was used to develop virtual binding pockets for accommodating HLA-II-specific short peptides. The binding of peptides to the virtual pockets is estimated using the Q matrix (a quantitative matrix based on amino acid residue properties). Internal cross-validation of the model using the 15 HLA II-p structural complexes produced an accuracy of 53% with a sensitivity of 53%. The model was further evaluated using a dataset of 3676 class II-specific peptides consisting of 1188 binders and 2488 nonbinders derived from MHCBN (a database of HLA binders and nonbinders). The model produced an accuracy of 53% with 70.8% specificity and 27.6% sensitivity. The positive predictive value (PPV) was 62% and the negative predictive value (NPV) 58%. A 62% PPV suggests that the model fairly predicts a good number of binders among predicted binders and thus that the success rate among predicted binder for further verification is good. The described model is simple and rapid, with large HLA allele coverage representing the sampled global population, despite weak prediction accuracy. The ability of the model to predict a wide array of defined class II alleles is found to be applicable for proteome-wide scanning of parasitic genomes.

Implications from predictions of HLA-DRB1 binding peptides in the membrane proteins of Corynebacterium diphtheriae

The aerobic gram positive bacterium Corynebacterium diphtheriae causes diphtheria, a respiratory tract illness characterized by symptoms such as sore throat, low fever, and an adherent membrane on the tonsils, pharynx, and/or nasal cavity. Therefore, it is important to develop preventive vaccines for diphtheria. The availability of the 2,488,635 bp long complete sequence for the C. diphtheriae genome provides an opportunity to understand cell mediated immune response using Computational Biology tools from the bacterial proteome sequence data. We selected 355 membrane proteins from the C. diphtheriae proteome using annotation data to identify potential HLA-DRB1 binding short peptide using modeling, simulations and predictions. This exercise identified 30 short peptides in membrane proteins showing binding capability to HLA-DRB1 alleles. These peptides serve as outline for the understanding of cell mediated immune response to C. diphtheriae. It should be noted that the predicted data to be verified using binding assays for further consideration.

Grouping of class I HLA alleles using electrostatic distribution maps of the peptide binding grooves

Human leukocyte antigen (HLA) molecules involved in immune function by binding to short peptides (8-20 residues) have different sequences in different individuals belonging to distinct ethnic population. Hence, the peptide-binding function of HLA alleles is specific. Class I HLA alleles (alternative forms of a gene) are associated with CD8+ T cells, and their allele-specific sequence information is available at the IMGT/HLA database. The available sequences are one-dimensional (ID), and the peptide-binding functional inference often requires 3-dimensional (3D) structural models of respective alleles. Hence, 3D structures were constructed for 1,000 class I HLA alleles (310 A, 570 B, and 120 C) using MODELLER (a comparative protein modeling program for modeling protein structures). The electrostatic distribution maps were generated for each modeled structure using Deep View (Swiss PDB Viewer Version 3.7). The 1,000 models were then grouped into different categories by visual inspection of their electrostatic distribution maps in the peptide binding grooves. The distribution of the models based on electrostatic distribution was 30% negative (300), 1% positive (12), 8% neutral (84), and 60% (604) mixed (random mixture of negative, positive, and neutral). This grouping provides insight toward the inference for functional overlap among HLA alleles.

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.

MPID-T

Normal adaptive immune responses operate under major histocompatibility complex (MHC) restriction by binding to specific, short antigenic peptides and presenting them to appropriate T-cell receptors (TcRs). Sequence-structure-function information is critical in understanding the principles governing peptide/MHC (pMHC) and TcR/pMHC recognition and binding. A new database for sequence-structure-function information on TcR/pMHC interactions, MHC-Peptide Interaction Database version T (MPID-T), is now available with the latest available Protein Data Bank (PDB) data and interaction parameters on TcR/pMHC complexes. MPID-T is a manually curated MySQL database containing experimentally determined structures of 187 pMHC complexes and 16 TcR/pMHC complexes available in the PDB. Each structure is manually verified, classified, and analysed for intermolecular interactions (i) between the MHC and its corresponding bound peptide and (ii) between TcR and its bound pMHC complex where TcR structural information is available. The MPID-T database retrieval system has precomputed interaction parameters that include solvent accessibility, hydrogen bonds, gap volume and gap index. Structural visualisation of the TcR/pMHC complex, pMHC complex, MHC or the bound peptide can be performed using freely available graphics applications such as MDL Chime or RasMol, while structural alignment (based on MHC class and peptide length) can be viewed using the Jmol molecular viewer or an MDL Chime-compatible web browser client. MPID-T contains structural descriptors for in-depth characterisation of TcR/pMHC and pMHC interactions. The ultimate purpose of MPID-T is to enhance the understanding of the binding mechanism underlying TcR/pMHC and pMHC interactions by mapping the TcR footprint on the MHC and its bound peptide, as this eventually determines T-cell recognition and binding.

AVAILABILITY

The MPID-T database retrieval system is available at http://surya.bic.nus.edu.sg/mpidt

CONTACT

Joo Chuan Tong (jctong@i2r.a-star.edu.sg).

T-Epitope Designer: A HLA-peptide binding prediction server

The current challenge in synthetic vaccine design is the development of a methodology to identify and test short antigen peptides as potential T-cell epitopes. Recently, we described a HLA-peptide binding model (using structural properties) capable of predicting peptides binding to any HLA allele. Consequently, we have developed a web server named T-EPITOPE DESIGNER to facilitate HLA-peptide binding prediction. The prediction server is based on a model that defines peptide binding pockets using information gleaned from X-ray crystal structures of HLA-peptide complexes, followed by the estimation of peptide binding to binding pockets. Thus, the prediction server enables the calculation of peptide binding to HLA alleles. This model is superior to many existing methods because of its potential application to any given HLA allele whose sequence is clearly defined. The web server finds potential application in T cell epitope vaccine design.

AVAILABILITY

http://www.bioinformation.net/ted/

A novel MHCp binding prediction model

Many statistical and molecular mechanics models have been developed and tested for major histocompatibility complex peptide (MHCp) binding predictions during the last decade. The statistical model prediction using pooled peptide sequence data and three-dimensional modeling prediction by molecular mechanics calculations have been assessed for efficiency and human leukocyte antigen diversity coverage. We describe a novel predictive model using information gleaned from 29 human MHCp crystal structures. The validation for the new model is performed using four different sets of data: (1) MHCp crystal structures, (2) peptides with known IC(50) binding values, (3) peptides tested positive by tetramer staining, (4) peptides with known binding information at the MHCBN database. The model produces high prediction efficiencies (average 60 %) with good sensitivity (approximately 50%-73%) and specificity (52%-58%) values. The average positive predictive value of the model is 89%, while the average negative predictive value is only 18%. The efficiency is very high in predicting binders and very low in predicting nonbinders. This model is superior to many existing methods because of its potential application to any given MHC allele whose sequence is clearly defined.

Compression of functional space in HLA-A sequence diversity

The major histocompatibility complex (MHC) is highly polymorphic and more than 1500 human MHC alleles are known to date. These alleles do not bind to a given peptide with identical affinity. Although MHC alleles are functionally related, it is difficult to quantify the functional variation between them. Three-dimensional structures of known MHC-peptide (MHCp) complexes suggest that specific peptide residues bind selectively to functional pockets in the binding groove. From a set of known MHCp structures we identified 21 critical polymorphic functional residue positions (CPFRP) that significantly reduced functional pocket variability to just 189 among 212 HLA-A alleles. Interestingly 101 HLA-A alleles clustered into 29 clusters such that the six functional pockets formed by the CPFRPs are identical within the cluster.

Types of inter-atomic interactions at the MHC-peptide interface: identifying commonality from accumulated data

BACKGROUND

Quantitative information on the types of inter-atomic interactions at the MHC-peptide interface will provide insights to backbone/sidechain atom preference during binding. Qualitative descriptions of such interactions in each complex have been documented by protein crystallographers. However, no comprehensive report is available to account for the common types of inter-atomic interactions in a set of MHC-peptide complexes characterized by variation in MHC allele and peptide sequence. The available x-ray crystallography data for these complexes in the Protein Databank (PDB) provides an opportunity to identify the prevalent types of such interactions at the binding interface.

RESULTS

We calculated the percentage distributions of four types of interactions at varying inter-atomic distances. The mean percentage distribution for these interactions and their standard deviation about the mean distribution is presented. The prevalence of SS and SB interactions at the MHC-peptide interface is shown in this study. SB is clearly dominant at an inter-atomic distance of 3A.

CONCLUSION

The prevalently dominant SB interactions at the interface suggest the importance of peptide backbone conformation during MHC-peptide binding. Currently, available algorithms are developed for protein sidechain prediction upon fixed backbone template. This study shows the preference of backbone atoms in MHC-peptide binding and hence emphasizes the need for accurate peptide backbone prediction in quantitative MHC-peptide binding calculations.