A novel MHCp binding prediction model

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.

Computational investigation of peptide binding stabilities of HLA-B*27 and HLA-B*44 alleles

Major Histocompatibility Complex (MHC) is a cell surface glycoprotein that binds to foreign antigens and presents them to T lymphocyte cells on the surface of Antigen Presenting Cells (APCs) for appropriate immune recognition. Recently, studies focusing on peptide-based vaccine design have allowed a better understanding of peptide immunogenicity mechanisms, which is defined as the ability of a peptide to stimulate CTL-mediated immune response. Peptide immunogenicity is also known to be related to the stability of peptide-loaded MHC (pMHC) complex. In this study, ENCoM server was used for structure-based estimation of the impact of single point mutations on pMHC complex stabilities. For this purpose, two human MHC molecules from the HLA-B*27 group (HLA-B*27:05 and HLA-B*27:09) in complex with four different peptides (GRFAAAIAK, RRKWRRWHL, RRRWRRLTV and IRAAPPPLF) and three HLA-B*44 molecules (HLA-B*44:02, HLA-B*44:03 and HLA-B*44:05) in complex with two different peptides (EEYLQAFTY and EEYLKAWTF) were analyzed. We found that the stability of pMHC complexes is dependent on both peptide sequence and MHC allele. Furthermore, we demonstrate that allele-specific peptide-binding preferences can be accurately revealed using structure-based computational methods predicting the effect of mutations on protein stability.

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.

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.

Computer-aided design of T-cell epitope-based vaccines: addressing population coverage

Epitope-based vaccines (EVs) make use of short antigen-derived peptides corresponding to immune epitopes, which are administered to trigger a protective humoral and/or cellular immune response. EVs potentially allow for precise control over the immune response activation by focusing on the most relevant - immunogenic and conserved - antigen regions. Experimental screening of large sets of peptides is time-consuming and costly; therefore, in silico methods that facilitate T-cell epitope mapping of protein antigens are paramount for EV development. The prediction of T-cell epitopes focuses on the peptide presentation process by proteins encoded by the major histocompatibility complex (MHC). Because different MHCs have different specificities and T-cell epitope repertoires, individuals are likely to respond to a different set of peptides from a given pathogen in genetically heterogeneous human populations. In addition, protective immune responses are only expected if T-cell epitopes are restricted by MHC proteins expressed at high frequencies in the target population. Therefore, without careful consideration of the specificity and prevalence of the MHC proteins, EVs could fail to adequately cover the target population. This article reviews state-of-the-art algorithms and computational tools to guide EV design through all the stages of the process: epitope prediction, epitope selection and vaccine assembly, while optimizing vaccine immunogenicity and coping with genetic variation in humans and pathogens.

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.

Immunoinformatics: a brief review

A large volume of data relevant to immunology research has accumulated due to sequencing of genomes of the human and other model organisms. At the same time, huge amounts of clinical and epidemiologic data are being deposited in various scientific literature and clinical records. This accumulation of the information is like a goldmine for researchers looking for mechanisms of immune function and disease pathogenesis. Thus the need to handle this rapidly growing immunological resource has given rise to the field known as immunoinformatics. Immunoinformatics, otherwise known as computational immunology, is the interface between computer science and experimental immunology. It represents the use of computational methods and resources for the understanding of immunological information. It not only helps in dealing with huge amount of data but also plays a great role in defining new hypotheses related to immune responses. This chapter reviews classical immunology, different databases, and prediction tool. Further, it briefly describes applications of immunoinformatics in reverse vaccinology, immune system modeling, and cancer diagnosis and therapy. It also explores the idea of integrating immunoinformatics with systems biology for the development of personalized medicine. All these efforts save time and cost to a great extent.

Immunogenicity of two FMDV nonameric peptides encapsulated in liposomes in mice and the protective efficacy in guinea pigs

It has been predicted that nonameric peptides I (VP1_26–34, RRQHTDVSF), II (VP1_157–165, RTLPTSFNY) and III (VP1_45–53, KEQVNVLDL) from the VP1 capsid protein of the foot-and-mouth disease virus (FMDV) are T cell epitopes. To investigate whether these peptides have immunological activity, BALB/c mice were immunized with peptide I, II or III conjugated with immunostimulating complexes (ISCOMs). A cytotoxic T lymphocyte assay was used to evaluate the cytotoxic activity induced by peptides along with by measuring peptide-specific T-cell proliferation and CD8⁺ T lymphocyte numbers in whole blood and interferon (IFN)-γ production in peripheral blood mononuclear cells induced by peptides. To further identify the protective efficacy of peptides, an FMDV challenge assay was done in guinea pigs. Peptides I and II stimulated significant increases in T-cell proliferation, CD8⁺ T lymphocytes, and IFN-γ secretion and cytotoxic activity compared to controls. The FMDV challenge assay indicated peptides I and II can protect over 60% of animals from virus attack. The results demonstrate that peptides I and II encapsulated in liposomes should be CTL epitopes of FMDV and can protect animals from virus attack to some extent.

Specific conformational epitope features of pathogenesis-related proteins mediating cross-reactivity between pollen and food allergens

Selected members of plant pathogenesis-related and seed storage proteins represent specific groups of proteins with potential characteristics of allergens. Efforts to understand the mechanism by which pathogenesis-related proteins mediate a broad cross-reactivity in pollen-plant food allergens are still limited. In this study, computational biology approach was used to reveal specific structural implications and conservation of different epitopes from members of Bet v 1 and nsLTP protein families mediating cross-reactivity between pollen and food (fruits, vegetables, legumes, and nut/seeds) allergens. A commonly shared epitope conservation was found among all pollen and food Bet v 1 and nsLTP protein families, respectively. However, other allergenic epitopes were also specifically detected in each family. The implication of these conserved epitopes in a broad cross-reactivity for allergy clinical trials is here discussed.

Immunoinformatics: an integrated scenario

Genome sequencing of humans and other organisms has led to the accumulation of huge amounts of data, which include immunologically relevant data. A large volume of clinical data has been deposited in several immunological databases and as a result immunoinformatics has emerged as an important field which acts as an intersection between experimental immunology and computational approaches. It not only helps in dealing with the huge amount of data but also plays a role in defining new hypotheses related to immune responses. This article reviews classical immunology, different databases and prediction tools. It also describes applications of immunoinformatics in designing in silico vaccination and immune system modelling. All these efforts save time and reduce cost.

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.

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.

HLA-peptide binding prediction using structural and modeling principles

Short peptides binding to specific human leukocyte antigen (HLA) alleles elicit immune response. These candidate peptides have potential utility in peptide vaccine design and development. The binding of peptides to allele-specific HLA molecule is estimated using competitive binding assay and biochemical binding constants. Application of this method for proteome-wide screening in parasites, viruses, and virulent bacterial strains is laborious and expensive. However, short listing of candidate peptides using prediction approaches have been realized lately. Prediction of peptide binding to HLA alleles using structural and modeling principles has gained momentum in recent years. Here, we discuss the current status of such prediction.

Reconstruction of a swine SLA-I protein complex and determination of binding nonameric peptides derived from the foot-and-mouth disease virus

No experimental system to date is available to identify viral T-cell epitopes in swine. In order to reconstruct the system for identification of short antigenic peptides, the swine SLA-2 gene was linked to the beta(2)m gene via (G4S)3, a linker encoding a 15-amino acid glycine-rich sequence (G4S)3, using splicing overlap extension-PCR (SOE-PCR). The maltose binding protein (MBP)-SLA-2-(G4S)3-beta(2)m fusion protein was expressed and purified in a pMAL-p2X/Escherichia coli TB1 system. The purified MBP-SLA-2-(G4S)3-beta(2)m protein was cleaved by factor Xa protease, and further purified by DEAE-Sepharose chromatography. The conformation of the SLA-2-(G4S)3-beta(2)m protein was determined by circular dichroism (CD) spectrum. In addition, the refolded SLA-2-(G4S)3-beta(2)m protein was used to bind three nonameric peptides derived from the foot-and-mouth disease virus (FMDV) O subtype VP1. The SLA-2-(G4S)3-beta(2)m-associated peptides were detected by mass spectrometry. The molecular weights and amino acid sequences of the peptides were confirmed by primary and secondary spectra, respectively. The results indicate that the SLA-2-(G4S)3-beta(2)m was 41.6kDa, and its alpha-helix, beta-sheet, turn, and random coil by CD estimation were 78 aa, 149 aa, 67 aa, and 93 aa, respectively. SLA-2-(G4S)3-beta(2)m protein was able to bind the nonameric peptides derived from the FMDV VP1 region: 26-34 (RRQHTDVSF) and 157-165 (RTLPTSFNY). The experimental system demonstrated that the reconstructed SLA-2-(G4S)3-beta(2)m protein complex can be used to identify nonameric peptides, including T-cell epitopes in swine.

Structures and homology modeling of chicken major histocompatibility complex protein class I (BF2 and β2m)

In order to elucidate the two-dimensional (2D) and three-dimensional (3D) structures of chicken major histocompatibility complex (MHC) class I protein (BF2 and beta2m) and further reconstruct their complex identifying the virus-derived antigenic peptides, the mature protein of BF2 and beta2m genes were expressed solubility in pMAL-p2X/Escherichia coli. TB1 system. The expressed MBP-BF2- and MBP-beta2m-fusion proteins were purified, and cleaved by the factor Xa protease. Subsequently, the monomers were further separated, and the purified MBP-BF2, -beta2m, and MBP were analyzed by circular dichroism (CD) spectrum. The contents of alpha-helix, beta-sheet, turn, and random coil in BF2 protein were 72, 102, 70, and 90 amino acids (aa), respectively. The beta2m proteins displayed a typical beta-sheet and the contents of alpha-helix, beta-sheet, turn, and random coil were 0, 46, 30, and 22 aa, respectively. Homology modeling of BF2 and beta2m proteins were similar as the 3D structure of human MHC class I (HLA-A2). The results showed that pMAL-p2X expression and purification system could be used to obtain the right conformational BF2 and beta2m proteins, and the 2D and 3D structures of BF2 and beta2m were revealed to be similar to human's. The recombinant BF2 and beta2m-based proteins might be a powerful tool for further detecting antigenic peptides.

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/