Structure-based prediction of MHC-peptide association: algorithm comparison and application to cancer vaccine design

Mining the Proteome of Leishmania donovani for the Development of Novel MHC Class I Restricted Epitope for the Control of Visceral Leishmaniasis

Although, the precise host defence mechanism(s) is not completely understood, T cell-mediated immune responses is believed to play a pivotal role in controlling parasite infection. Here we target the stage dependent over expressed gene. Here, the consensus based computational approach was adopted for the screening of potential major histocompatibility complex class I restricted epitopes. Based on the computational analysis and previously published report, a set 19 antigenic proteins derived from Leishmania donovani were screened for further characterization as vaccine candidates. A total of 49 epitopes were predicted, which revealed a comprehensive binding affinity to the 40 different MHC class I supertypes. Based on the population coverage and HLA cross presentation, nine highly promiscuous epitopes such as LTYDDVWTV (P1), FLFPQRTAL(P2), FLFSNGAVV (P3), YIYNFGIRV (P4), YMTAAFAAL (P5), KLLRPFAPL (P6), FMLGWIVTI (P7), SLFERNKRV (P8), and SVWNRIFTL (P9) which have either a high or an intermediate TAP binding affinity were selected for further analysis. Theoretical population coverage analysis of polytope vaccine (P1-P9) revealed more than 92% population. Stimulation with the cocktail of peptide revealed a proliferative CD8⁺ T cell response and increased IFN-γ production. An upregulated NF-κB activity is thought to be play a pivotal role in T cell proliferation against the selected peptide. The Th1-type cytokine profile (presence of IFN-γ and absence of IL-10) suggests the potentiality of the cocktail of epitope as a subunit vaccine against leishmaniasis. However, the efficiency of these epitopes to trigger other Th1 cytokines and chemokines in a humanized mice model could explore its plausibility as a vaccine candidate. J. Cell. Biochem. 119: 378-391, 2018. © 2017 Wiley Periodicals, Inc.

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.

Computational elucidation of potential antigenic CTL epitopes in Ebola virus

Cell-mediated immunity is important for the control of Ebola virus infection. We hypothesized that those HLA A0201 and HLA B40 restricted epitopes derived from Ebola virus proteins, would mount a good antigenic response. Here we employed an immunoinformatics approach to identify specific 9mer amino acid which may be capable of inducing a robust cell-mediated immune response in humans. We identified a set of 28 epitopes that had no homologs in humans. Specifically, the epitopes derived from NP, RdRp, GP and VP40 share population coverage of 93.40%, 84.15%, 74.94% and 77.12%, respectively. Based on the other HLA binding specificity and population coverage, seven novel promiscuous epitopes were identified. These 7 promiscuous epitopes from NP, RdRp and GP were found to have world-wide population coverage of more than 95% indicating their potential significance as useful candidates for vaccine design. Epitope conservancy analysis also suggested that most of the peptides are highly conserved (100%) in other virulent Ebola strain (Mayinga-76, Kikwit-95 and Makona-G3816- 2014) and can therefore be further investigated for their immunological relevance and usefulness as vaccine candidates.

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.

Predicting peptide binding affinities to MHC molecules using a modified semi-empirical scoring function

The Major Histocompatibility Complex (MHC) plays an important role in the human immune system. The MHC is involved in the antigen presentation system assisting T cells to identify foreign or pathogenic proteins. However, an MHC molecule binding a self-peptide may incorrectly trigger an immune response and cause an autoimmune disease, such as multiple sclerosis. Understanding the molecular mechanism of this process will greatly assist in determining the aetiology of various diseases and in the design of effective drugs. In the present study, we have used the Fresno semi-empirical scoring function and modify the approach to the prediction of peptide-MHC binding by using open-source and public domain software. We apply the method to HLA class II alleles DR15, DR1, and DR4, and the HLA class I allele HLA A2. Our analysis shows that using a large set of binding data and multiple crystal structures improves the predictive capability of the method. The performance of the method is also shown to be correlated to the structural similarity of the crystal structures used. We have exposed some of the obstacles faced by structure-based prediction methods and proposed possible solutions to those obstacles. It is envisaged that these obstacles need to be addressed before the performance of structure-based methods can be on par with the sequence-based methods.

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.

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.

Identification of immunogenic peptides of the self-tumor antigen: our experience with telomerase reverse transcriptase

The general approach, termed reverse immunology, to predict and identify immunogenic peptides from the sequence of a gene product of interest has been postulated to be a particularly efficient, high-throughput approach to discover tumor antigens. This laboratory has successfully identified immunogenic peptides of the human telomerase reverse transcriptase (hTERT), a self-tumor antigen, by using a multi-step approach. These steps include the following: the use of predictive bioinformatics algorithms, molecular methods to identify tumor-specific transcripts, prediction of proteasomal cleavage sites, peptide-binding prediction to HLA molecules and experimental validation, assessment of the in vitro and in vivo immunogenic potential of selected peptide antigens, isolation of specific cytolytic T lymphocyte clones, and final validation in functional assays of tumor cell recognition. This laboratory, and others have used similar methods to identify immunogenic peptides of self-tumor antigens, and many of those peptides are included in vaccines currently tested in clinical trials.

Peptide-based immunotherapy for multiple myeloma: current approaches

Multiple myeloma (MM) is a clonal B-cell malignancy with many fatal clinical sequelae. Despite extensive therapeutic approaches, cures remain rare exceptions. A recent promising area of investigation is the development of immunotherapeutic approaches that target and eliminate myeloma cells more selectively. Because of its potential to promote the destruction of cancerous cells via cytotoxic T-cell responses, peptide-based immunotherapy is one of these strategies to have attracted considerable attention. Furthermore, many studies were carried out to identify the best epitope peptides, the optimal vaccine formulation and schedule, and the preferable clinical situation for vaccination. Based on these results, various epitope peptides have been identified that may be selectively targeted by host immunity, and various approaches have been used to enhance the immune responses of peptides. This chapter focuses on reviewing previous immunotherapy trials, describing the current strategies for peptide-based immunotherapy, and discussing the achievable prospects in MM.

Immunoinformatics and modeling perspective of T cell epitope-based cancer immunotherapy: a holistic picture

Cancer immunotherapy is fast gaining global attention with its unique position as a potential therapy showing promise in cancer prevention and cure. It utilizes the natural system of immunity as opposed to chemotherapy and radiotherapy that utilize chemical drugs and radiation, respectively. Cancer immunotherapy essentially involves treatment and/or prevention with vaccines in the form of peptide vaccines (T and B cell epitopes), DNA vaccines and vaccination using whole tumor cells, dendritic cells, viral vectors, antibodies and adoptive transfer of T cells to harness the body's own immune system towards the targeting of cancer cells for destruction. Given the time, cost and labor involved in the vaccine discovery and development, researchers have evinced interest in the novel field of immunoinformatics to cut down the escalation of these critical resources. Immunoinformatics is a relatively new buzzword in the scientific circuit that is showing its potential and delivering on its promise in expediting the development of effective cancer immunotherapeutic agents. This review attempts to present a holistic picture of our race against cancer and time using the science and technology of immunoinformatics and molecular modeling in T cell epitope-based cancer immunotherapy. It also attempts to showcase some problem areas as well as novel ones waiting to be explored where development of novel immunoinformatics tools and simulations in the context of cancer immunotherapy would be highly welcome.

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.