Major Histocompatibility Complex Binding Research Articles

A Chemical Approach to Assess the Impact of Post-translational Modification on MHC Peptide Binding and Effector Cell Engagement.

The human major histocompatibility complex (MHC) plays a pivotal role in the presentation of peptidic fragments from proteins, which can originate from self-proteins or from nonhuman antigens, such as those produced by viruses or bacteria. To prevent cytotoxicity against healthy cells, thymocytes expressing T cell receptors (TCRs) that recognize self-peptides are removed from circulation (negative selection), thus leaving T cells that recognize nonself-peptides. Current understanding suggests that post-translationally modified (PTM) proteins and the resulting peptide fragments they generate following proteolysis are largely excluded from negative selection; this feature means that PTMs can generate nonself-peptides that potentially contribute to the development of autoreactive T cells and subsequent autoimmune diseases. Although it is well-established that PTMs are prevalent in peptides present on MHCs, the precise mechanisms by which PTMs influence the antigen presentation machinery remain poorly understood. In the present work, we introduce chemical modifications mimicking PTMs on synthetic peptides. This is the first systematic study isolating the impact of PTMs on MHC binding and also their impact on TCR recognition. Our findings reveal various ways PTMs alter antigen presentation, which could have implications for tumor neoantigen presentation.

Gene network-based and ensemble modeling-based selection of tumor-associated antigens with a predicted low risk of tissue damage for targeted immunotherapy

BackgroundTumor-associated antigens and their derived peptides constitute an opportunity to design off-the-shelf mainline or adjuvant anti-cancer immunotherapies for a broad array of patients. A performant and rational antigen selection pipeline...

Targeted acidosis mediated delivery of antigenic MHC-binding peptides.

Cytotoxic T lymphocytes are the primary effector immune cells responsible for protection against cancer, as they target peptide neoantigens presented through the major histocompatibility complex (MHC) on cancer cells, leading to cell death. Targeting peptide-MHC (pMHC) complex offers a promising strategy for immunotherapy due to their specificity and effectiveness against cancer. In this work, we exploit the acidic tumor micro-environment to selectively deliver antigenic peptides to cancer using pH(low) insertion peptides (pHLIP). We demonstrated the delivery of MHC binding peptides directly to the cytoplasm of melanoma cells resulted in the presentation of antigenic peptides on MHC, and activation of T cells. This work highlights the potential of pHLIP as a vehicle for the targeted delivery of antigenic peptides and its presentation via MHC-bound complexes on cancer cell surface for activation of T cells with implications for enhancing anti-cancer immunotherapy.

A large-scale study of peptide features defining immunogenicity of cancer neo-epitopes.

Accurate prediction of immunogenicity for neo-epitopes arising from a cancer associated mutation is a crucial step in many bioinformatics pipelines that predict outcome of checkpoint blockade treatments or that aim to design personalised cancer immunotherapies and vaccines. In this study, we performed a comprehensive analysis of peptide features relevant for prediction of immunogenicity using the Cancer Epitope Database and Analysis Resource (CEDAR), a curated database of cancer epitopes with experimentally validated immunogenicity annotations from peer-reviewed publications. The developed model, ICERFIRE (ICore-based Ensemble Random Forest for neo-epitope Immunogenicity pREdiction), extracts the predicted ICORE from the full neo-epitope as input, i.e. the nested peptide with the highest predicted major histocompatibility complex (MHC) binding potential combined with its predicted likelihood of antigen presentation (%Rank). Key additional features integrated into the model include assessment of the BLOSUM mutation score of the neo-epitope, and antigen expression levels of the wild-type counterpart which is often reflecting a neo-epitope's abundance. We demonstrate improved and robust performance of ICERFIRE over existing immunogenicity and epitope prediction models, both in cross-validation and on external validation datasets.

MHCSeqNet2-improved peptide-class I MHC binding prediction for alleles with low data.

The binding of a peptide antigen to a Class I major histocompatibility complex (MHC) protein is part of a key process that lets the immune system recognize an infected cell or a cancer cell. This mechanism enabled the development of peptide-based vaccines that can activate the patient's immune response to treat cancers. Hence, the ability of accurately predict peptide-MHC binding is an essential component for prioritizing the best peptides for each patient. However, peptide-MHC binding experimental data for many MHC alleles are still lacking, which limited the accuracy of existing prediction models. In this study, we presented an improved version of MHCSeqNet that utilized sub-word-level peptide features, a 3D structure embedding for MHC alleles, and an expanded training dataset to achieve better generalizability on MHC alleles with small amounts of data. Visualization of MHC allele embeddings confirms that the model was able to group alleles with similar binding specificity, including those with no peptide ligand in the training dataset. Furthermore, an external evaluation suggests that MHCSeqNet2 can improve the prioritization of T cell epitopes for MHC alleles with small amount of training data. The source code and installation instruction for MHCSeqNet2 are available at https://github.com/cmb-chula/MHCSeqNet2.

Unsupervised and supervised AI on molecular dynamics simulations reveals complex characteristics of HLA-A2-peptide immunogenicity.

Immunologic recognition of peptide antigens bound to class I major histocompatibility complex (MHC) molecules is essential to both novel immunotherapeutic development and human health at large. Current methods for predicting antigen peptide immunogenicity rely primarily on simple sequence representations, which allow for some understanding of immunogenic features but provide inadequate consideration of the full scale of molecular mechanisms tied to peptide recognition. We here characterize contributions that unsupervised and supervised artificial intelligence (AI) methods can make toward understanding and predicting MHC(HLA-A2)-peptide complex immunogenicity when applied to large ensembles of molecular dynamics simulations. We first show that an unsupervised AI method allows us to identify subtle features that drive immunogenicity differences between a cancer neoantigen and its wild-type peptide counterpart. Next, we demonstrate that a supervised AI method for class I MHC(HLA-A2)-peptide complex classification significantly outperforms a sequence model on small datasets corrected for trivial sequence correlations. Furthermore, we show that both unsupervised and supervised approaches reveal determinants of immunogenicity based on time-dependent molecular fluctuations and anchor position dynamics outside the MHC binding groove. We discuss implications of these structural and dynamic immunogenicity correlates for the induction of T cell responses and therapeutic T cell receptor design.

O02 Neoantigens from actinic keratosis are predicted to be more immunogenic than those from cutaneous squamous cell carcinoma – a strategy for immune escape?

Abstract Actinic keratosis (AK) is a precursor of cutaneous squamous cell carcinoma (cSCC). More than 70% of cSCC arise from AKs, but fewer than 0.6% of AKs progress to cSCC. We have shown that the molecular landscapes of AK and cSCC are similar in terms of tumour mutational burden (TMB), copy number burden, mutational signatures and driver gene mutations but differ in levels of intrasample heterogeneity. We have also demonstrated that alterations in signalling pathways, including transforming growth factor-β, may be important for AK-to-SCC progression. However, the pathomechanisms underlying progression are not well characterized and it remains unclear why the majority of AKs do not progress, and most will regress. There is some evidence that differences in the neoantigen landscapes of AK and cSCC may play a role, and a previous study examining neoantigens in AK and cSCC from nine patients was the first to suggest that there is downregulation of the expression of highly immunogenic neoantigens in the immune escape of cSCC. In this study we further explore, in a larger data set, the hypothesis that the neoantigen recognition potential of AK vs. cSCC is a key determinant of AK-to-cSCC progression. We generated exome and transcriptome data from AK (n = 5) and cSCC (n = 15) with matched normal exome data (from blood). Human leucocyte antigen typing was performed using the OptiType algorithm and neoantigens restricted to major histocompatibility complex (MHC) class I were predicted using the NeoPredPipe workflow. Fitness of neoantigens, defined as the overall effectiveness of a neoantigen at stimulating an immune response, was determined by combining measures of MHC binding affinity and T-cell receptor recognition potential for the predicted neoantigen, and an overall neoantigen recognition potential score was derived for each neoantigen. The difference in median total neoantigen burden for AK and cSCC was not statistically significant [11 224 (range 30–17 330) and 1680 (range 3159–67 357), respectively (P = 0.07)]. Neoantigen burden was strongly correlated with TMB in both AK and cSCC. We found that, compared with cSCC, AK had predicted neoantigens with significantly higher mean MHC-binding affinity (P = 0.049), mean T-cell receptor recognition potential (P = 0.0093) and overall neoantigen recognition potential (P = 0.02). Taken together, our results confirm that neoantigens in AKs are predicted to be more effective at eliciting an immune response than those in cSCC. This suggests there may be a selection pressure in AKs for mutations that result in fewer immunogenic neoantigens resulting in immune escape and progression to cSCC. Finally, our results underscore the critical requirement for an intact immune system in prevention of AK-to-cSCC progression.

Discordant results among major histocompatibility complex binding affinity prediction tools

Background: Human leukocyte antigen (HLA) alleles are critical components of the immune system’s ability to recognize and eliminate tumors and infections. A large number of machine learning-based major histocompatibility complex (MHC) binding affinity (BA) prediction tools have been developed and are widely used for both investigational and therapeutic applications, so it is important to explore differences in tool outputs. Methods: We examined predictions of four popular tools (netMHCpan, HLAthena, MHCflurry, and MHCnuggets) across a range of possible peptide sources (human, viral, and randomly generated) and MHC class I alleles. Results: We uncovered inconsistencies in predictions of BA, allele promiscuity and the relationship between physical properties of peptides by source and BA predictions, as well as quality of training data. We found amount of training data does not explain inconsistencies between tools and yet for all tools, predicted binding quantities are similar between human and viral proteomes. Lastly, we find peptide physical properties are associated with allele-specific binding predictions. Conclusions: Our work raises fundamental questions about the fidelity of peptide-MHC binding prediction tools and their real-world implications. The real-world use of these prediction tools for theoretical binding of peptides to alleles is worrying, as the range of allele promiscuity is substantial yet does not differentiate between potential foreign versus self-antigens. Evaluating more viruses – as well as bacteria, fungi, and other pathogens – and linking these analyses with metrics such as evolutionary distance may give greater insight into the relationship between HLA evolution and disease.

Design and validation of a novel multi-epitopes vaccine against hantavirus

Hantavirus is a member of the order Bunyavirales and an emerging global pathogen. Hantavirus infections have affected millions of people globally based on available epidemiological data and research studies. Hemorrhagic fever with renal syndrome (HFRS) and hantavirus pulmonary syndrome (HPS) are the two main human diseases associated with hantavirus infections. Hence, efforts are required to develop a potent vaccine against the pathogen. The only vaccine that is in use for hantavirus is an inactivated virus vaccine, “Hantavax”, but it failed to produce neutralizing antibodies. Vaccine development is of much importance in dealing with the surge of hantavirus globally. In this study, hantavirus five proteins (N protein, G1 and G2, L protein, and non-structural proteins) were used in NetCTL 1.2 program to predict T-cell epitopes. To predict major histocompatibility complex (MHC) binding alleles, an immune epitope database (IEDB) was used. All predicted epitopes were then investigated for different immunoinformatics analyses such as antigenicity and toxicity analyses. The good water-soluble, non-toxic, probable antigenic, and DRB*0101 binder was selected. A multi-epitopes-based vaccine designing was then done where linkers were used to connect the shortlisted epitopes. In addition, an adjuvant molecule was supplementary to the multi-epitopes peptide to improve the vaccine’s immunogenic potential. The final vaccine construct’s three-dimensional structure was modeled by ab initio method. The vaccine molecule was then evaluated for its binding potential with TLR-3 immune receptor, which is key for its recognition and processing by the host immune system. Docking studies were performed using HADDOCK software. The best-docked complex was selected and visualized for intermolecular binding and interactions using UCSF Chimera 1.16 software. The findings revealed that the designed vaccine might be a potential vaccine against hantavirus and can be used in experimental animal model testings. Communicated by Ramaswamy H. Sarma

Beyond MHC binding: immunogenicity prediction tools to refine neoantigen selection in cancer patients

In the last years, multiple efforts have been made to accurately predict neoantigens derived from somatic mutations in cancer patients, either to develop personalized therapeutic vaccines or to study immune responses after cancer immunotherapy. In this context, the increasing accessibility of paired whole-exome sequencing (WES) of tumor biopsies and matched normal tissue as well as RNA sequencing (RNA-Seq) has provided a basis for the development of bioinformatics tools that predict and prioritize neoantigen candidates. Most pipelines rely on the binding prediction of candidate peptides to the patient’s major histocompatibility complex (MHC), but these methods return a high number of false positives since they lack information related to other features that influence T cell responses to neoantigens. This review explores available computational methods that incorporate information on T cell preferences to predict their activation after encountering a peptide-MHC complex. Specifically, methods that predict i) biological features that may increase the availability of a neopeptide to be exposed to the immune system, ii) metrics of self-similarity representing the chances of a neoantigen to break immune tolerance, iii) pathogen immunogenicity, and iv) tumor immunogenicity. Also, this review describes the characteristics of these tools and addresses their performance in the context of a novel benchmark dataset of experimentally validated neoantigens from patients treated with a melanoma vaccine (VACCIMEL) in a phase II clinical study. The overall results of the evaluation indicate that current tools have a limited ability to predict the activation of a cytotoxic response against neoantigens. Based on this result, the limitations that make this problem an unsolved challenge in immunoinformatics are discussed.

Dapsone hypersensitivity syndrome.

Dapsone hypersensitivity syndrome.

Characterization of immunomodulatory B-cells and T-cells to generate extremely targeted immunoassays and vaccines

Introduction: The Rubella virus has worldwide occurrence and congenital Rubella syndrome are widely recognized as emerging infection in several parts of the world. Methodology: We investigated for peptide vaccine with specific T and B-cell epitopes was identified through bioinformatics-based approaches. These were identified utilizing available Rubella virus E1 glycoprotein sequence databases. The outer-membrane glycoprotein, E1 is a target protein for the prediction of best antigens. Results: The bioinformatics online software Bepipred 2 was used for prediction of potential B-cell epitope (pfcntphgqlevqvppdpgd) was identified and it has shown high conversation against E1 glycoprotein and few other bioinformatics online software NetMHCpan, IEDB and NetCTL was used to identify maximum surface-exposed residues. T-Cell epitope (rpvalpral) was identified and shown to be conserved to E1 Glycoprotein. Predicted epitopes were found to had promiscuous class-I major histocompatibility complex binding affinity to major histocompatibility complex super types, antigenicity scores and high proteasomal cleavage. The three-dimensional modelled structures was created using I-TASSER online server for highlighting the predicted T- and B- cell epitopes. Conclusion: The predicted T and B cell epitope could be used for development of immunoglobulin assay and vaccines.

A Novel Information-Theory-Based Genetic Distance That Approximates Phenotypic Differences.

Application of genetic distances to measure phenotypic relatedness is a challenging task, reflecting the complex relationship between genotype and phenotype. Accurate assessment of proximity among sequences with different phenotypic traits depends on how strongly the chosen distance is associated with structural and functional properties. In this study, we present a new distance measure Mutual Information and Entropy H (MIH) for categorical data such as nucleotide or amino acid sequences. MIH applies an information matrix (IM), which is calculated from the data and captures heterogeneity of individual positions as measured by Shannon entropy and coordinated substitutions among positions as measured by mutual information. In general, MIH assigns low weights to differences occurring at high entropy positions or at dependent positions. MIH distance was compared with other common distances on two experimental and two simulated data sets. MIH showed the best ability to distinguish cross-immunoreactive sequence pairs from non-cross-immunoreactive pairs of variants of the hepatitis C virus hypervariable region 1 (26,883 pairwise comparisons), and Major Histocompatibility Complex (MHC) binding peptides (n = 181) from non-binding peptides (n = 129). Analysis of 74 simulated RNA secondary structures also showed that the ratio between MIH distance of sequences from the same RNA structure and MIH of sequences from different structures is three orders of magnitude greater than for Hamming distances. These findings indicate that lower MIH between two sequences is associated with greater probability of the sequences to belong to the same phenotype. Examination of rule-based phenotypes generated in silico showed that (1) MIH is strongly associated with phenotypic differences, (2) IM of sequences under selection is very different from IM generated under random scenarios, and (3) IM is robust to sampling. In conclusion, MIH strongly approximates structural/functional distances and should have important applications to a wide range of biological problems, including evolution, artificial selection of biological functions and structures, and measuring phenotypic similarity.

The MHC Motif Atlas: a database of MHC binding specificities and ligands.

The highly polymorphic Major Histocompatibility Complex (MHC) genes are responsible for the binding and cell surface presentation of pathogen or cancer specific T-cell epitopes. This process is fundamental for eliciting T-cell recognition of infected or malignant cells. Epitopes displayed on MHC molecules further provide therapeutic targets for personalized cancer vaccines or adoptive T-cell therapy. To help visualizing, analyzing and comparing the different binding specificities of MHC molecules, we developed the MHC Motif Atlas (http://mhcmotifatlas.org/). This database contains information about thousands of class I and class II MHC molecules, including binding motifs, peptide length distributions, motifs of phosphorylated ligands, multiple specificities or links to X-ray crystallography structures. The database further enables users to download curated datasets of MHC ligands. By combining intuitive visualization of the main binding properties of MHC molecules together with access to more than a million ligands, the MHC Motif Atlas provides a central resource to analyze and interpret the binding specificities of MHC molecules.

Identification of Immunogenic T and B-Cell Epitope Peptides of Rubella Virus E1 Glycoprotein towards the Development of Highly Specific Immunoassays and Vaccine

Introduction: The Rubella virus has a worldwide occurrence and congenital Rubella syndromes are widely recognized as an emerging infection in several parts of the world. Miscarriage, perinatal mortality, and stillbirth can develop in pregnancy during the first trimester. The most frequent techniques for laboratory diagnosis of Rubella virus infection are IgM and IgG-based serological detection methods. Such emerging viral and bacterial pathogen emphasizes the development of fast diagnostic devices; there is a need for enhanced and quicker methods.
 Materials and Methods: Search for peptide vaccine with specific T and B-cell epitopes was identified through bioinformatics-based approaches. These were identified utilizing available Rubella virus E1 glycoprotein sequence databases. The outer-membrane glycoprotein, E1 is a target protein for the prediction of best antigens.
 Results: Using BepiPred2 program, the potential B-cell epitope PFCNTPHGQLEVQVPPDPGD with high conservation among E1 glycoprotein of rubella virus and the maximum surface exposed residues was identified. Using IEDB, NetMHCpan, and NetCTL programs, T-cell epitope RPVALPRAL was identified. Predicted epitopes were found to have promiscuous class-I major histocompatibility complex binding affinity to major histocompatibility complex super types, antigenicity scores, and high proteasomal cleavage. The three-dimensional modeled structures were created using I-TASSER online server for highlighting the predicted T- and B- cell epitopes.
 Conclusion: The predicted T and B cell epitope could be used for the development of immunoglobulin assay and vaccine candidate peptide.

Variation of Proteolytic Cleavage Sites towards the N-Terminal End of the S2 Subunit of the Novel SARS-CoV-2 Omicron Sublineage BA.2.12.1.

The prevalence of novel SARS-CoV-2 variants is also accompanied by an increased turnover rate and additional cleavage sites at the positions necessary for priming the Spike (S) protein. Of these priming sites, the proteolytically sensitive polybasic sequence of the activation loop at the S1/S2 interface and the S2′ location within the S2 subunit of the S protein are cleaved by furin and TMPRSS2, which are important for the infection of the target cell. Neutrophils, migrating to the site of infection, secrete serine proteases to fight against pathogens. The serine proteases encompass neutrophil elastase (NE), proteinase 3 (PR3), and cathepsin G (CatG), which can hydrolyze the peptide bond adjacent to the S1/S2 interface. SARS-CoV-2 might take the opportunity to hijack proteases from an immune response to support viral entry to the cell. The region near S704L within the S2 subunit, a novel amino acid substitution of SARS-CoV-2 Omicron sublineage BA.2.12.1, is located close to the S1/S2 interface. We found that NE, PR3, and CatG digested the peptide within this region; however, the S704L amino acid substitution altered cleavage sites for PR3. In conclusion, such an amino acid substitution modifies S2 antigen processing and might further impact the major histocompatibility complex (MHC) binding and T cell activation.

A high-throughput yeast display approach to profile pathogen proteomes for MHC-II binding.

T cells play a critical role in the adaptive immune response, recognizing peptide antigens presented on the cell surface by major histocompatibility complex (MHC) proteins. While assessing peptides for MHC binding is an important component of probing these interactions, traditional assays for testing peptides of interest for MHC binding are limited in throughput. Here, we present a yeast display-based platform for assessing the binding of tens of thousands of user-defined peptides in a high-throughput manner. We apply this approach to assess a tiled library covering the SARS-CoV-2 proteome and four dengue virus serotypes for binding to human class II MHCs, including HLA-DR401, -DR402, and -DR404. While the peptide datasets show broad agreement with previously described MHC-binding motifs, they additionally reveal experimentally validated computational false positives and false negatives. We therefore present this approach as able to complement current experimental datasets and computational predictions. Further, our yeast display approach underlines design considerations for epitope identification experiments and serves as a framework for examining relationships between viral conservation and MHC binding, which can be used to identify potentially high-interest peptide binders from viral proteins. These results demonstrate the utility of our approach to determine peptide-MHC binding interactions in a manner that can supplement and potentially enhance current algorithm-based approaches.

Prediction of Major Histocompatibility Complex Binding with Bilateral and Variable Long Short Term Memory Networks.

Simple SummaryMajor histocompatibility complex molecules are of significant biological and clinical importance due to their utility in immunotherapy. The prediction of potential MHC binding peptides can estimate a T-cell immune response. The variable length of existing MHC binding peptides creates difficulty for MHC binding prediction algorithms. Thus, we utilized a bilateral and variable long-short term memory neural network to address this specific problem and developed a novel MHC binding prediction tool.As an important part of immune surveillance, major histocompatibility complex (MHC) is a set of proteins that recognize foreign molecules. Computational prediction methods for MHC binding peptides have been developed. However, existing methods share the limitation of fixed peptide sequence length, which necessitates the training of models by peptide length or prediction with a length reduction technique. Using a bidirectional long short-term memory neural network, we constructed BVMHC, an MHC class I and II binding prediction tool that is independent of peptide length. The performance of BVMHC was compared to seven MHC class I prediction tools and three MHC class II prediction tools using eight performance criteria independently. BVMHC attained the best performance in three of the eight criteria for MHC class I, and the best performance in four of the eight criteria for MHC class II, including accuracy and AUC. Furthermore, models for non-human species were also trained using the same strategy and made available for applications in mice, chimpanzees, macaques, and rats. BVMHC is composed of a series of peptide length independent MHC class I and II binding predictors. Models from this study have been implemented in an online web portal for easy access and use.

ProGeo-Neo v2.0: A One-Stop Software for Neoantigen Prediction and Filtering Based on the Proteogenomics Strategy.

A proteogenomics-based neoantigen prediction pipeline, namely ProGeo-neo, was previously developed by our team to predict neoantigens, allowing the identification of class-I major histocompatibility complex (MHC) binding peptides based on single-nucleotide variation (SNV) mutations. To improve it, we here present an updated pipeline, i.e., ProGeo-neo v2.0, in which a one-stop software solution was proposed to identify neoantigens based on the paired tumor-normal whole genome sequencing (WGS)/whole exome sequencing (WES) data in FASTQ format. Preferably, in ProGeo-neo v2.0, several new features are provided. In addition to the identification of MHC-I neoantigens, the new version supports the prediction of MHC class II-restricted neoantigens, i.e., peptides up to 30-mer in length. Moreover, the source of neoantigens has been expanded, allowing more candidate neoantigens to be identified, such as in-frame insertion-deletion (indels) mutations, frameshift mutations, and gene fusion analysis. In addition, we propose two more efficient screening approaches, including an in-group authentic neoantigen peptides database and two more stringent thresholds. The range of candidate peptides was effectively narrowed down to those that are more likely to elicit an immune response, providing a more meaningful reference for subsequent experimental validation. Compared to ProGeo-neo, the ProGeo-neo v2.0 performed well based on the same dataset, including updated functionality and improved accuracy.

Harnessing Fuzzy Rule Based System for Screening Major Histocompatibility Complex Class I Peptide Epitopes from the Whole Proteome: An Implementation on the Proteome of Leishmania donovani.

The development of peptide-based vaccines is enhanced by immunoinformatics, which predicts the patterns that B cells and T cells recognize. Although several tools are available for predicting the Major histocompatibility complex (MHC-I) binding peptides, the wide variants of human leucocyte antigen allele make it challenging to choose a peptide that will induce an immune response in a majority of people. In addition, for a peptide to be considered a potential vaccine candidate, factors such as T cell affinity, proteasome cleavage, and similarity to human proteins also play a major role. Identifying peptides that satisfy the earlier cited measures across the entire proteome is, therefore, challenging. Hence, the fuzzy inference system (FIS) is proposed to detect each peptide's potential as a vaccine candidate and assign it either a very high, high, moderate, or low ranking. The FIS includes input features from 6 modules (binding of 27 major alleles, T cell propensity, pro-inflammatory response, proteasome cleavage, transporter associated with antigen processing, and similarity with human peptide) and rules derived from an observation of features on positive samples. On validation of experimentally verified peptides, a balanced accuracy of ∼80% was achieved, with a Mathew's correlation coefficient score of 0.67 and an F-1 score of 0.74. In addition, the method was implemented on complete proteome of Leishmania donovani, which contains ∼4,800,000 peptides. Lastly, a searchable database of the ranked results of the L. donovani proteome was made and is available online (MHC-FIS-LdDB). It is hoped that this method will simplify the identification of potential MHC-I binding candidates from a large proteome.