1
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Zhou W, Yu J, Li Y, Wang K. Neoantigen-specific TCR-T cell-based immunotherapy for acute myeloid leukemia. Exp Hematol Oncol 2022; 11:100. [PMID: 36384590 PMCID: PMC9667632 DOI: 10.1186/s40164-022-00353-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 10/26/2022] [Indexed: 11/17/2022] Open
Abstract
Neoantigens derived from non-synonymous somatic mutations are restricted to malignant cells and are thus considered ideal targets for T cell receptor (TCR)-based immunotherapy. Adoptive transfer of T cells bearing neoantigen-specific TCRs exhibits the ability to preferentially target tumor cells while remaining harmless to normal cells. High-avidity TCRs specific for neoantigens expressed on AML cells have been identified in vitro and verified using xenograft mouse models. Preclinical studies of these neoantigen-specific TCR-T cells are underway and offer great promise as safe and effective therapies. Additionally, TCR-based immunotherapies targeting tumor-associated antigens are used in early-phase clinical trials for the treatment of AML and show encouraging anti-leukemic effects. These clinical experiences support the application of TCR-T cells that are specifically designed to recognize neoantigens. In this review, we will provide a detailed profile of verified neoantigens in AML, describe the strategies to identify neoantigen-specific TCRs, and discuss the potential of neoantigen-specific T-cell-based immunotherapy in AML.
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2
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Ochoa R, Lunardelli VAS, Rosa DS, Laio A, Cossio P. Multiple-Allele MHC Class II Epitope Engineering by a Molecular Dynamics-Based Evolution Protocol. Front Immunol 2022; 13:862851. [PMID: 35572587 PMCID: PMC9094701 DOI: 10.3389/fimmu.2022.862851] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 03/28/2022] [Indexed: 11/13/2022] Open
Abstract
Epitopes that bind simultaneously to all human alleles of Major Histocompatibility Complex class II (MHC II) are considered one of the key factors for the development of improved vaccines and cancer immunotherapies. To engineer MHC II multiple-allele binders, we developed a protocol called PanMHC-PARCE, based on the unsupervised optimization of the epitope sequence by single-point mutations, parallel explicit-solvent molecular dynamics simulations and scoring of the MHC II-epitope complexes. The key idea is accepting mutations that not only improve the affinity but also reduce the affinity gap between the alleles. We applied this methodology to enhance a Plasmodium vivax epitope for multiple-allele binding. In vitro rate-binding assays showed that four engineered peptides were able to bind with improved affinity toward multiple human MHC II alleles. Moreover, we demonstrated that mice immunized with the peptides exhibited interferon-gamma cellular immune response. Overall, the method enables the engineering of peptides with improved binding properties that can be used for the generation of new immunotherapies.
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Affiliation(s)
- Rodrigo Ochoa
- Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia UdeA, Medellin, Colombia
| | | | - Daniela Santoro Rosa
- Department of Microbiology, Immunology and Parasitology, Federal University of Sao Paulo, Sao Paulo, Brazil.,Institute for Investigation in Immunology (iii), Instituto Nacional de Ciência e Tecnologia (INCT), Sao Paulo, Brazil
| | - Alessandro Laio
- Physics Area, International School for Advanced Studies (SISSA), Trieste, Italy.,Condensed Matter and Statistical Physics Section, International Centre for Theoretical Physics (ICTP), Trieste, Italy
| | - Pilar Cossio
- Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia UdeA, Medellin, Colombia.,Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Frankfurt am Main, Germany.,Center for Computational Mathematics, Flatiron Institute, New York, NY, United States.,Center for Computational Biology, Flatiron Institute, New York, NY, United States
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3
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Bell DR, Chen SH. Toward Guided Mutagenesis: Gaussian Process Regression Predicts MHC Class II Antigen Mutant Binding. J Chem Inf Model 2021; 61:4857-4867. [PMID: 34375111 DOI: 10.1021/acs.jcim.1c00458] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Antigen-specific immunotherapies (ASI) require successful loading and presentation of antigen peptides into the major histocompatibility complex (MHC) binding cleft. One route of ASI design is to mutate native antigens for either stronger or weaker binding interaction to MHC. Exploring all possible mutations is costly both experimentally and computationally. To reduce experimental and computational expense, here we investigate the minimal amount of prior data required to accurately predict the relative binding affinity of point mutations for peptide-MHC class II (pMHCII) binding. Using data from different residue subsets, we interpolate pMHCII mutant binding affinities by Gaussian process (GP) regression of residue volume and hydrophobicity. We apply GP regression to an experimental data set from the Immune Epitope Database, and theoretical data sets from NetMHCIIpan and Free Energy Perturbation calculations. We find that GP regression can predict binding affinities of nine neutral residues from a six-residue subset with an average R2 coefficient of determination value of 0.62 ± 0.04 (±95% CI), average error of 0.09 ± 0.01 kcal/mol (±95% CI), and with an receiver operating characteristic (ROC) AUC value of 0.92 for binary classification of enhanced or diminished binding affinity. Similarly, metrics increase to an R2 value of 0.69 ± 0.04, average error of 0.07 ± 0.01 kcal/mol, and an ROC AUC value of 0.94 for predicting seven neutral residues from an eight-residue subset. Our work finds that prediction is most accurate for neutral residues at anchor residue sites without register shift. This work holds relevance to predicting pMHCII binding and accelerating ASI design.
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Affiliation(s)
- David R Bell
- Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
| | - Serena H Chen
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
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4
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Flórez MM, Rodríguez R, Cabrera JA, Robledo SM, Delgado G. Leishmania spp Epitopes in Humans Naturally Resistant to the Disease: Working Toward a Synthetic Vaccine. Front Cell Infect Microbiol 2021; 11:631019. [PMID: 34164345 PMCID: PMC8216392 DOI: 10.3389/fcimb.2021.631019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 04/20/2021] [Indexed: 11/29/2022] Open
Abstract
Vaccines are one of the most effective strategies to fight infectious diseases. Reverse vaccinology strategies provide tools to perform in silico screening and a rational selection of potential candidates on a large scale before reaching in vitro and in vivo evaluations. Leishmania infection in humans produces clinical symptoms in some individuals, while another part of the population is naturally resistant (asymptomatic course) to the disease, and therefore their immune response controls parasite replication. By the identification of epitopes directly in humans, especially in those resistant to the disease, the probabilities of designing an effective vaccine are higher. The aim of this work was the identification of Leishmania epitopes in resistant humans. To achieve that, 11 peptide sequences (from Leishmania antigenic proteins) were selected using epitope prediction tools, and then, peripheral blood mononuclear cells (PBMCs) were isolated from human volunteers who were previously divided into four clinical groups: susceptible, resistant, exposed and not exposed to the parasite. The induction of inflammatory cytokines and lymphoproliferation was assessed using monocyte-derived dendritic cells (moDCs) as antigen-presenting cells (APCs). The response was evaluated after exposing volunteers’ cells to each peptide. As a result, we learned that STI41 and STI46 peptides induced IL-8 and IL-12 in moDCs and lymphoproliferation and low levels of IL-10 in lymphocytes differentially in resistant volunteers, similar behavior to that observed in those individuals to L. panamensis lysate antigens. We conclude that, in silico analysis allowed for the identification of natural Leishmania epitopes in humans, and also STI41 and STI46 peptides could be epitopes that lead to a cellular immune response directed at parasite control.
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Affiliation(s)
- Magda Melissa Flórez
- Grupo de Investigación en Inmunotoxicología, Departamento de Farmacia, Facultad de Ciencias, Universidad Nacional de Colombia, Bogotá D.C., Colombia
| | - Rocío Rodríguez
- Secretaría Municipal de Salud, Municipio de Rovira, Tolima, Colombia
| | | | - Sara M Robledo
- Programa de Estudio y Control de Enfermedades Tropicales (PECET)-Facultad de Medicina, Universidad de Antioquia, Medellín, Colombia
| | - Gabriela Delgado
- Grupo de Investigación en Inmunotoxicología, Departamento de Farmacia, Facultad de Ciencias, Universidad Nacional de Colombia, Bogotá D.C., Colombia
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5
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Zinsli LV, Stierlin N, Loessner MJ, Schmelcher M. Deimmunization of protein therapeutics - Recent advances in experimental and computational epitope prediction and deletion. Comput Struct Biotechnol J 2020; 19:315-329. [PMID: 33425259 PMCID: PMC7779837 DOI: 10.1016/j.csbj.2020.12.024] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 12/15/2020] [Accepted: 12/16/2020] [Indexed: 12/11/2022] Open
Abstract
Biotherapeutics, and antimicrobial proteins in particular, are of increasing interest for human medicine. An important challenge in the development of such therapeutics is their potential immunogenicity, which can induce production of anti-drug-antibodies, resulting in altered pharmacokinetics, reduced efficacy, and potentially severe anaphylactic or hypersensitivity reactions. For this reason, the development and application of effective deimmunization methods for protein drugs is of utmost importance. Deimmunization may be achieved by unspecific shielding approaches, which include PEGylation, fusion to polypeptides (e.g., XTEN or PAS), reductive methylation, glycosylation, and polysialylation. Alternatively, the identification of epitopes for T cells or B cells and their subsequent deletion through site-directed mutagenesis represent promising deimmunization strategies and can be accomplished through either experimental or computational approaches. This review highlights the most recent advances and current challenges in the deimmunization of protein therapeutics, with a special focus on computational epitope prediction and deletion tools.
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Key Words
- ABR, Antigen-binding region
- ADA, Anti-drug antibody
- ANN, Artificial neural network
- APC, Antigen-presenting cell
- Anti-drug-antibody
- B cell epitope
- BCR, B cell receptor
- Bab, Binding antibody
- CDR, Complementarity determining region
- CRISPR, Clustered regularly interspaced short palindromic repeats
- DC, Dendritic cell
- ELP, Elastin-like polypeptide
- EPO, Erythropoietin
- ER, Endoplasmatic reticulum
- GLK, Gelatin-like protein
- HAP, Homo-amino-acid polymer
- HLA, Human leukocyte antigen
- HMM, Hidden Markov model
- IL, Interleukin
- Ig, Immunoglobulin
- Immunogenicity
- LPS, Lipopolysaccharide
- MHC, Major histocompatibility complex
- NMR, Nuclear magnetic resonance
- Nab, Neutralizing antibody
- PAMP, Pathogen-associated molecular pattern
- PAS, Polypeptide composed of proline, alanine, and/or serine
- PBMC, Peripheral blood mononuclear cell
- PD, Pharmacodynamics
- PEG, Polyethylene glycol
- PK, Pharmacokinetics
- PRR, Pattern recognition receptor
- PSA, Sialic acid polymers
- Protein therapeutic
- RNN, Recurrent artificial neural network
- SVM, Support vector machine
- T cell epitope
- TAP, Transporter associated with antigen processing
- TCR, T cell receptor
- TLR, Toll-like receptor
- XTEN, “Xtended” recombinant polypeptide
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Affiliation(s)
- Léa V. Zinsli
- Institute of Food, Nutrition and Health, ETH Zurich, Zurich, Switzerland
| | - Noël Stierlin
- Institute of Food, Nutrition and Health, ETH Zurich, Zurich, Switzerland
| | - Martin J. Loessner
- Institute of Food, Nutrition and Health, ETH Zurich, Zurich, Switzerland
| | - Mathias Schmelcher
- Institute of Food, Nutrition and Health, ETH Zurich, Zurich, Switzerland
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6
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Abstract
Immunoinformatics is a discipline that applies methods of computer science to study and model the immune system. A fundamental question addressed by immunoinformatics is how to understand the rules of antigen presentation by MHC molecules to T cells, a process that is central to adaptive immune responses to infections and cancer. In the modern era of personalized medicine, the ability to model and predict which antigens can be presented by MHC is key to manipulating the immune system and designing strategies for therapeutic intervention. Since the MHC is both polygenic and extremely polymorphic, each individual possesses a personalized set of MHC molecules with different peptide-binding specificities, and collectively they present a unique individualized peptide imprint of the ongoing protein metabolism. Mapping all MHC allotypes is an enormous undertaking that cannot be achieved without a strong bioinformatics component. Computational tools for the prediction of peptide-MHC binding have thus become essential in most pipelines for T cell epitope discovery and an inescapable component of vaccine and cancer research. Here, we describe the development of several such tools, from pioneering efforts to the current state-of-the-art methods, that have allowed for accurate predictions of peptide binding of all MHC molecules, even including those that have not yet been characterized experimentally.
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Affiliation(s)
- Morten Nielsen
- Department of Health Technology, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, CP 1650 San Martin, Buenos Aires, Argentina
| | - Massimo Andreatta
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, CP 1650 San Martin, Buenos Aires, Argentina
| | - Bjoern Peters
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, California 92037, USA
- Department of Medicine, University of California, San Diego, La Jolla, California 92093, USA
| | - Søren Buus
- Department of Immunology and Microbiology, Faculty of Health Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark
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7
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Abstract
Throughout the body, T cells monitor MHC-bound ligands expressed on the surface of essentially all cell types. MHC ligands that trigger a T cell immune response are referred to as T cell epitopes. Identifying such epitopes enables tracking, phenotyping, and stimulating T cells involved in immune responses in infectious disease, allergy, autoimmunity, transplantation, and cancer. The specific T cell epitopes recognized in an individual are determined by genetic factors such as the MHC molecules the individual expresses, in parallel to the individual's environmental exposure history. The complexity and importance of T cell epitope mapping have motivated the development of computational approaches that predict what T cell epitopes are likely to be recognized in a given individual or in a broader population. Such predictions guide experimental epitope mapping studies and enable computational analysis of the immunogenic potential of a given protein sequence region.
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Affiliation(s)
- Bjoern Peters
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, California 92037, USA; ,
- Department of Medicine, University of California San Diego, La Jolla, California 92093, USA
| | - Morten Nielsen
- Department of Health Technology, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark;
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, B1650 Buenos Aires, Argentina
| | - Alessandro Sette
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, California 92037, USA; ,
- Department of Medicine, University of California San Diego, La Jolla, California 92093, USA
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8
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Lazarou G, Chelliah V, Small BG, Walker M, van der Graaf PH, Kierzek AM. Integration of Omics Data Sources to Inform Mechanistic Modeling of Immune-Oncology Therapies: A Tutorial for Clinical Pharmacologists. Clin Pharmacol Ther 2020; 107:858-870. [PMID: 31955413 PMCID: PMC7158209 DOI: 10.1002/cpt.1786] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Accepted: 01/03/2020] [Indexed: 12/15/2022]
Abstract
Application of contemporary molecular biology techniques to clinical samples in oncology resulted in the accumulation of unprecedented experimental data. These "omics" data are mined for discovery of therapeutic target combinations and diagnostic biomarkers. It is less appreciated that omics resources could also revolutionize development of the mechanistic models informing clinical pharmacology quantitative decisions about dose amount, timing, and sequence. We discuss the integration of omics data to inform mechanistic models supporting drug development in immuno-oncology. To illustrate our arguments, we present a minimal clinical model of the Cancer Immunity Cycle (CIC), calibrated for non-small cell lung carcinoma using tumor microenvironment composition inferred from transcriptomics of clinical samples. We review omics data resources, which can be integrated to parameterize mechanistic models of the CIC. We propose that virtual trial simulations with clinical Quantitative Systems Pharmacology platforms informed by omics data will be making increasing impact in the development of cancer immunotherapies.
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9
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Richters MM, Xia H, Campbell KM, Gillanders WE, Griffith OL, Griffith M. Best practices for bioinformatic characterization of neoantigens for clinical utility. Genome Med 2019; 11:56. [PMID: 31462330 PMCID: PMC6714459 DOI: 10.1186/s13073-019-0666-2] [Citation(s) in RCA: 129] [Impact Index Per Article: 25.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Accepted: 08/16/2019] [Indexed: 12/13/2022] Open
Abstract
Neoantigens are newly formed peptides created from somatic mutations that are capable of inducing tumor-specific T cell recognition. Recently, researchers and clinicians have leveraged next generation sequencing technologies to identify neoantigens and to create personalized immunotherapies for cancer treatment. To create a personalized cancer vaccine, neoantigens must be computationally predicted from matched tumor-normal sequencing data, and then ranked according to their predicted capability in stimulating a T cell response. This candidate neoantigen prediction process involves multiple steps, including somatic mutation identification, HLA typing, peptide processing, and peptide-MHC binding prediction. The general workflow has been utilized for many preclinical and clinical trials, but there is no current consensus approach and few established best practices. In this article, we review recent discoveries, summarize the available computational tools, and provide analysis considerations for each step, including neoantigen prediction, prioritization, delivery, and validation methods. In addition to reviewing the current state of neoantigen analysis, we provide practical guidance, specific recommendations, and extensive discussion of critical concepts and points of confusion in the practice of neoantigen characterization for clinical use. Finally, we outline necessary areas of development, including the need to improve HLA class II typing accuracy, to expand software support for diverse neoantigen sources, and to incorporate clinical response data to improve neoantigen prediction algorithms. The ultimate goal of neoantigen characterization workflows is to create personalized vaccines that improve patient outcomes in diverse cancer types.
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Affiliation(s)
- Megan M Richters
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO, 63110, USA
- McDonnell Genome Institute, Forest Park Avenue, Washington University School of Medicine, St. Louis, MO, 63108, USA
| | - Huiming Xia
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO, 63110, USA
- McDonnell Genome Institute, Forest Park Avenue, Washington University School of Medicine, St. Louis, MO, 63108, USA
| | - Katie M Campbell
- Division of Hematology and Oncology, Medical Plaza Driveway, Department of Medicine, University of California, Los Angeles, Los Angeles, CA, 90024, USA
| | - William E Gillanders
- Department of Surgery, South Euclid Avenue, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Siteman Cancer Center, Parkview Place, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Obi L Griffith
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO, 63110, USA.
- McDonnell Genome Institute, Forest Park Avenue, Washington University School of Medicine, St. Louis, MO, 63108, USA.
- Siteman Cancer Center, Parkview Place, Washington University School of Medicine, St. Louis, MO, 63110, USA.
- Department of Genetics, South Euclid Avenue, Washington University School of Medicine, St. Louis, MO, 63110, USA.
| | - Malachi Griffith
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO, 63110, USA.
- McDonnell Genome Institute, Forest Park Avenue, Washington University School of Medicine, St. Louis, MO, 63108, USA.
- Siteman Cancer Center, Parkview Place, Washington University School of Medicine, St. Louis, MO, 63110, USA.
- Department of Genetics, South Euclid Avenue, Washington University School of Medicine, St. Louis, MO, 63110, USA.
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10
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Degoot AM, Adabor ES, Chirove F, Ndifon W. Predicting Antigenicity of Influenza A Viruses Using biophysical ideas. Sci Rep 2019; 9:10218. [PMID: 31308446 PMCID: PMC6629677 DOI: 10.1038/s41598-019-46740-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Accepted: 07/01/2019] [Indexed: 11/18/2022] Open
Abstract
Antigenic variations of influenza A viruses are induced by genomic mutation in their trans-membrane protein HA1, eliciting viral escape from neutralization by antibodies generated in prior infections or vaccinations. Prediction of antigenic relationships among influenza viruses is useful for designing (or updating the existing) influenza vaccines, provides important insights into the evolutionary mechanisms underpinning viral antigenic variations, and helps to understand viral epidemiology. In this study, we present a simple and physically interpretable model that can predict antigenic relationships among influenza A viruses, based on biophysical ideas, using both genomic amino acid sequences and experimental antigenic data. We demonstrate the applicability of the model using a benchmark dataset of four subtypes of influenza A (H1N1, H3N2, H5N1, and H9N2) viruses and report on its performance profiles. Additionally, analysis of the model’s parameters confirms several observations that are consistent with the findings of other previous studies, for which we provide plausible explanations.
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Affiliation(s)
- Abdoelnaser M Degoot
- Research Department, African Institute for Mathematical Sciences, Next Einstein Initiative, Kigali, Rwanda. .,University of KwaZulu-Natal, School of Mathematics, Statistics and Computer Science, Pietermaritzburg, 3209, South Africa. .,DST-NRF Centre of Excellence in Mathematical and Statistical Sciences (CoE-MaSS), Gauteng, Wits, 2050, South Africa.
| | - Emmanuel S Adabor
- Research Centre, African Institute for Mathematical Sciences, Cape Town, 7945, South Africa
| | - Faraimunashe Chirove
- University of KwaZulu-Natal, School of Mathematics, Statistics and Computer Science, Pietermaritzburg, 3209, South Africa
| | - Wilfred Ndifon
- Research Department, African Institute for Mathematical Sciences, Next Einstein Initiative, Kigali, Rwanda.
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11
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Degoot AM, Chirove F, Ndifon W. Trans-Allelic Model for Prediction of Peptide:MHC-II Interactions. Front Immunol 2018; 9:1410. [PMID: 29988560 PMCID: PMC6026802 DOI: 10.3389/fimmu.2018.01410] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Accepted: 06/06/2018] [Indexed: 12/30/2022] Open
Abstract
Major histocompatibility complex class two (MHC-II) molecules are trans-membrane proteins and key components of the cellular immune system. Upon recognition of foreign peptides expressed on the MHC-II binding groove, CD4+ T cells mount an immune response against invading pathogens. Therefore, mechanistic identification and knowledge of physicochemical features that govern interactions between peptides and MHC-II molecules is useful for the design of effective epitope-based vaccines, as well as for understanding of immune responses. In this article, we present a comprehensive trans-allelic prediction model, a generalized version of our previous biophysical model, that can predict peptide interactions for all three human MHC-II loci (HLA-DR, HLA-DP, and HLA-DQ), using both peptide sequence data and structural information of MHC-II molecules. The advantage of this approach over other machine learning models is that it offers a simple and plausible physical explanation for peptide–MHC-II interactions. We train the model using a benchmark experimental dataset and measure its predictive performance using novel data. Despite its relative simplicity, we find that the model has comparable performance to the state-of-the-art method, the NetMHCIIpan method. Focusing on the physical basis of peptide–MHC binding, we find support for previous theoretical predictions about the contributions of certain binding pockets to the binding energy. In addition, we find that binding pocket P5 of HLA-DP, which was not previously considered as a primary anchor, does make strong contribution to the binding energy. Together, the results indicate that our model can serve as a useful complement to alternative approaches to predicting peptide–MHC interactions.
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Affiliation(s)
- Abdoelnaser M Degoot
- African Institute of Mathematical Sciences (AIMS), Muizenberg, South Africa.,School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, South Africa.,DST-NRF Centre of Excellence in Mathematical and Statistical Sciences (CoE-MaSS), Gauteng, South Africa
| | - Faraimunashe Chirove
- School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, South Africa
| | - Wilfred Ndifon
- African Institute of Mathematical Sciences (AIMS), Muizenberg, South Africa
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12
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Aray Y, Aguilera-García R, Izquierdo DR. Exploring the nature of the H-bonds between the human class II MHC protein, HLA-DR1 (DRB*0101) and the influenza virus hemagglutinin peptide, HA306-318, using the quantum theory of atoms in molecules. J Biomol Struct Dyn 2017; 37:48-64. [PMID: 29246090 DOI: 10.1080/07391102.2017.1418432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
The nature of the H-bonds between the human protein HLA-DR1 (DRB*0101) and the hemagglutinin peptide HA306-318 has been studied using the Quantum Theory of Atoms in Molecules for the first time. We have found four H-bond groups: one conventional CO··HN bond group and three nonconventional CO··HC, π··HC involving aromatic rings and HN··HCaliphatic groups. The calculated electron density at the determined H-bond critical points suggests the follow protein pocket binding trend: P1 (2,311) >> P9 (1.109) > P4 (0.950) > P6 (0.553) > P7 (0.213) which agrees and reveal the nature of experimental findings, showing that P1 produces by a long way the strongest binding of the HLA-DR1 human protein molecule with the peptide backbone as consequence of the vast number of H-bonds in the P1 area and at the same time the largest specific binding of the peptide Tyr308 residue with aromatic residues located at the binding groove floor. The present results suggest the topological analysis of the electronic density as a valuable tool that allows a non-arbitrary partition of the pockets binding energy via the calculated electron density at the determined critical points.
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Affiliation(s)
- Yosslen Aray
- a Facultad de Ciencias , Universidad de Ciencias Aplicadas y Ambientales, UDCA , Bogotá , Colombia
| | - Ricardo Aguilera-García
- a Facultad de Ciencias , Universidad de Ciencias Aplicadas y Ambientales, UDCA , Bogotá , Colombia
| | - Daniel R Izquierdo
- a Facultad de Ciencias , Universidad de Ciencias Aplicadas y Ambientales, UDCA , Bogotá , Colombia
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13
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Cortés A, Coral J, McLachlan C, Benítez R, Pinilla L. Planar molecular arrangements aid the design of MHC class II binding peptides. Mol Biol 2017. [DOI: 10.1134/s002689331702008x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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14
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A Novel Peptide Binding Prediction Approach for HLA-DR Molecule Based on Sequence and Structural Information. BIOMED RESEARCH INTERNATIONAL 2016; 2016:3832176. [PMID: 27340658 PMCID: PMC4906198 DOI: 10.1155/2016/3832176] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2016] [Accepted: 05/04/2016] [Indexed: 11/18/2022]
Abstract
MHC molecule plays a key role in immunology, and the molecule binding reaction with peptide is an important prerequisite for T cell immunity induced. MHC II molecules do not have conserved residues, so they appear as open grooves. As a consequence, this will increase the difficulty in predicting MHC II molecules binding peptides. In this paper, we aim to propose a novel prediction method for MHC II molecules binding peptides. First, we calculate sequence similarity and structural similarity between different MHC II molecules. Then, we reorder pseudosequences according to descending similarity values and use a weight calculation formula to calculate new pocket profiles. Finally, we use three scoring functions to predict binding cores and evaluate the accuracy of prediction to judge performance of each scoring function. In the experiment, we set a parameter α in the weight formula. By changing α value, we can observe different performances of each scoring function. We compare our method with the best function to some popular prediction methods and ultimately find that our method outperforms them in identifying binding cores of HLA-DR molecules.
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15
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Gutiérrez AH, Martin WD, Bailey-Kellogg C, Terry F, Moise L, De Groot AS. Development and validation of an epitope prediction tool for swine (PigMatrix) based on the pocket profile method. BMC Bioinformatics 2015; 16:290. [PMID: 26370412 PMCID: PMC4570239 DOI: 10.1186/s12859-015-0724-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2015] [Accepted: 08/26/2015] [Indexed: 12/14/2022] Open
Abstract
Background T cell epitope prediction tools and associated vaccine design algorithms have accelerated the development of vaccines for humans. Predictive tools for swine and other food animals are not as well developed, primarily because the data required to develop the tools are lacking. Here, we overcome a lack of T cell epitope data to construct swine epitope predictors by systematically leveraging available human information. Applying the “pocket profile method”, we use sequence and structural similarities in the binding pockets of human and swine major histocompatibility complex proteins to infer Swine Leukocyte Antigen (SLA) peptide binding preferences. We developed epitope-prediction matrices (PigMatrices), for three SLA class I alleles (SLA-1*0401, 2*0401 and 3*0401) and one class II allele (SLA-DRB1*0201), based on the binding preferences of the best-matched Human Leukocyte Antigen (HLA) pocket for each SLA pocket. The contact residues involved in the binding pockets were defined for class I based on crystal structures of either SLA (SLA-specific contacts, Ssc) or HLA supertype alleles (HLA contacts, Hc); for class II, only Hc was possible. Different substitution matrices were evaluated (PAM and BLOSUM) for scoring pocket similarity and identifying the best human match. The accuracy of the PigMatrices was compared to available online swine epitope prediction tools such as PickPocket and NetMHCpan. Results PigMatrices that used Ssc to define the pocket sequences and PAM30 to score pocket similarity demonstrated the best predictive performance and were able to accurately separate binders from random peptides. For SLA-1*0401 and 2*0401, PigMatrix achieved area under the receiver operating characteristic curves (AUC) of 0.78 and 0.73, respectively, which were equivalent or better than PickPocket (0.76 and 0.54) and NetMHCpan version 2.4 (0.41 and 0.51) and version 2.8 (0.72 and 0.71). In addition, we developed the first predictive SLA class II matrix, obtaining an AUC of 0.73 for existing SLA-DRB1*0201 epitopes. Notably, PigMatrix achieved this level of predictive power without training on SLA binding data. Conclusion Overall, the pocket profile method combined with binding preferences from HLA binding data shows significant promise for developing T cell epitope prediction tools for pigs. When combined with existing vaccine design algorithms, PigMatrix will be useful for developing genome-derived vaccines for a range of pig pathogens for which no effective vaccines currently exist (e.g. porcine reproductive and respiratory syndrome, influenza and porcine epidemic diarrhea). Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0724-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Andres H Gutiérrez
- Institute for Immunology and Informatics, CMB/CELS, University of Rhode Island, Providence, RI, 02903, USA.
| | | | | | | | - Leonard Moise
- Institute for Immunology and Informatics, CMB/CELS, University of Rhode Island, Providence, RI, 02903, USA. .,EpiVax, Inc., Providence, RI, 02860, USA.
| | - Anne S De Groot
- Institute for Immunology and Informatics, CMB/CELS, University of Rhode Island, Providence, RI, 02903, USA. .,EpiVax, Inc., Providence, RI, 02860, USA.
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Oyarzun P, Kobe B. Computer-aided design of T-cell epitope-based vaccines: addressing population coverage. Int J Immunogenet 2015. [PMID: 26211755 DOI: 10.1111/iji.12214] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
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.
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Affiliation(s)
- P Oyarzun
- Biotechnology Centre, Facultad de Ingeniería y Tecnología, Universidad San Sebastián, Concepción, Chile
| | - B Kobe
- School of Chemistry and Molecular Biosciences, Institute for Molecular Bioscience and Australian Infectious Diseases Research Centre, University of Queensland, Brisbane, QLD, Australia
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Kamisetty H, Ghosh B, Langmead CJ, Bailey-Kellogg C. Learning sequence determinants of protein:protein interaction specificity with sparse graphical models. J Comput Biol 2015; 22:474-86. [PMID: 25973864 DOI: 10.1089/cmb.2014.0289] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
In studying the strength and specificity of interaction between members of two protein families, key questions center on which pairs of possible partners actually interact, how well they interact, and why they interact while others do not. The advent of large-scale experimental studies of interactions between members of a target family and a diverse set of possible interaction partners offers the opportunity to address these questions. We develop here a method, DgSpi (data-driven graphical models of specificity in protein:protein interactions), for learning and using graphical models that explicitly represent the amino acid basis for interaction specificity (why) and extend earlier classification-oriented approaches (which) to predict the ΔG of binding (how well). We demonstrate the effectiveness of our approach in analyzing and predicting interactions between a set of 82 PDZ recognition modules against a panel of 217 possible peptide partners, based on data from MacBeath and colleagues. Our predicted ΔG values are highly predictive of the experimentally measured ones, reaching correlation coefficients of 0.69 in 10-fold cross-validation and 0.63 in leave-one-PDZ-out cross-validation. Furthermore, the model serves as a compact representation of amino acid constraints underlying the interactions, enabling protein-level ΔG predictions to be naturally understood in terms of residue-level constraints. Finally, the model DgSpi readily enables the design of new interacting partners, and we demonstrate that designed ligands are novel and diverse.
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Affiliation(s)
| | - Bornika Ghosh
- 3Department of Computer Science, Dartmouth, Hanover, New Hampshire
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Oyarzun P, Ellis JJ, Gonzalez-Galarza FF, Jones AR, Middleton D, Boden M, Kobe B. A bioinformatics tool for epitope-based vaccine design that accounts for human ethnic diversity: application to emerging infectious diseases. Vaccine 2015; 33:1267-73. [PMID: 25629524 DOI: 10.1016/j.vaccine.2015.01.040] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2014] [Revised: 12/11/2014] [Accepted: 01/14/2015] [Indexed: 11/17/2022]
Abstract
BACKGROUND Peptide vaccination based on multiple T-cell epitopes can be used to target well-defined ethnic populations. Because the response to T-cell epitopes is restricted by HLA proteins, the HLA specificity of T-cell epitopes becomes a major consideration for epitope-based vaccine design. We have previously shown that CD4+ T-cell epitopes restricted by 95% of human MHC class II proteins can be predicted with high-specificity. METHODS We describe here the integration of epitope prediction with population coverage and epitope selection algorithms. The population coverage assessment makes use of the Allele Frequency Net Database. We present the computational platform Predivac-2.0 for HLA class II-restricted epitope-based vaccine design, which accounts comprehensively for human genetic diversity. RESULTS We validated the performance of the tool on the identification of promiscuous and immunodominant CD4+ T-cell epitopes from the human immunodeficiency virus (HIV) protein Gag. We further describe an application for epitope-based vaccine design in the context of emerging infectious diseases associated with Lassa, Nipah and Hendra viruses. Putative CD4+ T-cell epitopes were mapped on the surface glycoproteins of these pathogens and are good candidates to be experimentally tested, as they hold potential to provide cognate help in vaccination settings in their respective target populations. CONCLUSION Predivac-2.0 is a novel approach in epitope-based vaccine design, particularly suited to be applied to virus-related emerging infectious diseases, because the geographic distributions of the viruses are well defined and ethnic populations in need of vaccination can be determined ("ethnicity-oriented approach"). Predivac-2.0 is accessible through the website http://predivac.biosci.uq.edu.au/.
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Affiliation(s)
- Patricio Oyarzun
- School of Chemistry and Molecular Biosciences, Institute for Molecular Bioscience and Australian Infectious Diseases Research Centre, University of Queensland, Australia; Biotechnology Centre, Universidad San Sebastián, Concepción, Chile.
| | - Jonathan J Ellis
- School of Chemistry and Molecular Biosciences, Institute for Molecular Bioscience and Australian Infectious Diseases Research Centre, University of Queensland, Australia
| | | | - Andrew R Jones
- Institute of Integrative Biology, University of Liverpool, United Kingdom
| | - Derek Middleton
- Transplant Immunology Laboratory, Royal Liverpool University Hospital & School of Infection and Host Defence University of Liverpool, United Kingdom
| | - Mikael Boden
- School of Chemistry and Molecular Biosciences, Institute for Molecular Bioscience and Australian Infectious Diseases Research Centre, University of Queensland, Australia; School of Information Technology and Electrical Engineering, University of Queensland, Queensland 4072, Australia
| | - Bostjan Kobe
- School of Chemistry and Molecular Biosciences, Institute for Molecular Bioscience and Australian Infectious Diseases Research Centre, University of Queensland, Australia.
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Snyder A, Chan TA. Immunogenic peptide discovery in cancer genomes. Curr Opin Genet Dev 2015; 30:7-16. [PMID: 25588790 DOI: 10.1016/j.gde.2014.12.003] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2014] [Revised: 12/15/2014] [Accepted: 12/16/2014] [Indexed: 12/12/2022]
Abstract
As immunotherapies to treat malignancy continue to diversify along with the tumor types amenable to treatment, it will become very important to predict which treatment is most likely to benefit a given patient. Tumor neoantigens, novel peptides resulting from somatic tumor mutations and recognized by the immune system as foreign, are likely to contribute significantly to the efficacy of immunotherapy. Multiple in silico methods have been developed to predict whether peptides, including tumor neoantigens, will be presented by the major histocompatibility complex (MHC) Class I or Class II, and interact with the T cell receptor (TCR). The methods for neoantigen prediction will be reviewed here, along with the most important examples of their use in the field of oncology.
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Affiliation(s)
- Alexandra Snyder
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, United States; Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Timothy A Chan
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, United States; Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States.
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20
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Xu Y, Luo C, Qian M, Huang X, Zhu S. MHC2MIL: a novel multiple instance learning based method for MHC-II peptide binding prediction by considering peptide flanking region and residue positions. BMC Genomics 2014; 15 Suppl 9:S9. [PMID: 25521198 PMCID: PMC4290625 DOI: 10.1186/1471-2164-15-s9-s9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Background Computational prediction of major histocompatibility complex class II (MHC-II) binding peptides can assist researchers in understanding the mechanism of immune systems and developing peptide based vaccines. Although many computational methods have been proposed, the performance of these methods are far from satisfactory. The difficulty of MHC-II peptide binding prediction comes mainly from the large length variation of binding peptides. Methods We develop a novel multiple instance learning based method called MHC2MIL, in order to predict MHC-II binding peptides. We deem each peptide in MHC2MIL as a bag, and some substrings of the peptide as the instances in the bag. Unlike previous multiple instance learning based methods that consider only instances of fixed length 9 (9 amino acids), MHC2MIL is able to deal with instances of both lengths of 9 and 11 (11 amino acids), simultaneously. As such, MHC2MIL incorporates important information in the peptide flanking region. For measuring the distances between different instances, furthermore, MHC2MIL explicitly highlights the amino acids in some important positions. Results Experimental results on a benchmark dataset have shown that, the performance of MHC2MIL is significantly improved by considering the instances of both 9 and 11 amino acids, as well as by emphasizing amino acids at key positions in the instance. The results are consistent with those reported in the literature on MHC-II peptide binding. In addition to five important positions (1, 4, 6, 7 and 9) for HLA(human leukocyte antigen, the name of MHC in Humans) DR peptide binding, we also find that position 2 may play some roles in the binding process. By using 5-fold cross validation on the benchmark dataset, MHC2MIL outperforms two state-of-the-art methods of MHC2SK and NN-align with being statistically significant, on 12 HLA DP and DQ molecules. In addition, it achieves comparable performance with MHC2SK and NN-align on 14 HLA DR molecules. MHC2MIL is freely available at http://datamining-iip.fudan.edu.cn/service/MHC2MIL/index.html.
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Kamisetty H, Ghosh B, Langmead CJ, Bailey-Kellogg C. Learning Sequence Determinants of Protein:protein Interaction Specificity with Sparse Graphical Models. RESEARCH IN COMPUTATIONAL MOLECULAR BIOLOGY : ... ANNUAL INTERNATIONAL CONFERENCE, RECOMB ... : PROCEEDINGS. RECOMB (CONFERENCE : 2005- ) 2014; 8394:129-143. [PMID: 25414914 DOI: 10.1007/978-3-319-05269-4_10] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
In studying the strength and specificity of interaction between members of two protein families, key questions center on which pairs of possible partners actually interact, how well they interact, and why they interact while others do not. The advent of large-scale experimental studies of interactions between members of a target family and a diverse set of possible interaction partners offers the opportunity to address these questions. We develop here a method, DgSpi (Data-driven Graphical models of Specificity in Protein:protein Interactions), for learning and using graphical models that explicitly represent the amino acid basis for interaction specificity (why) and extend earlier classification-oriented approaches (which) to predict the ΔG of binding (how well). We demonstrate the effectiveness of our approach in analyzing and predicting interactions between a set of 82 PDZ recognition modules, against a panel of 217 possible peptide partners, based on data from MacBeath and colleagues. Our predicted ΔG values are highly predictive of the experimentally measured ones, reaching correlation coefficients of 0.69 in 10-fold cross-validation and 0.63 in leave-one-PDZ-out cross-validation. Furthermore, the model serves as a compact representation of amino acid constraints underlying the interactions, enabling protein-level ΔG predictions to be naturally understood in terms of residue-level constraints. Finally, as a generative model, DgSpi readily enables the design of new interacting partners, and we demonstrate that designed ligands are novel and diverse.
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22
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Shen WJ, Zhang S, Wong HS. An effective and effecient peptide binding prediction approach for a broad set of HLA-DR molecules based on ordered weighted averaging of binding pocket profiles. Proteome Sci 2013; 11:S15. [PMID: 24565049 PMCID: PMC3908610 DOI: 10.1186/1477-5956-11-s1-s15] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Background The immune system must detect a wide variety of microbial pathogens, such as viruses, bacteria, fungi and parasitic worms, to protect the host against disease. Antigenic peptides displayed by MHC II (class II Major Histocompatibility Complex) molecules is a pivotal process to activate CD4+ TH cells (Helper T cells). The activated TH cells can differentiate into effector cells which assist various cells in activating against pathogen invasion. Each MHC locus encodes a great number of allele variants. Yet this limited number of MHC molecules are required to display enormous number of antigenic peptides. Since the peptide binding measurements of MHC molecules by biochemical experiments are expensive, only a few of the MHC molecules have suffecient measured peptides. To perform accurate binding prediction for those MHC alleles without suffecient measured peptides, a number of computational algorithms were proposed in the last decades. Results Here, we propose a new MHC II binding prediction approach, OWA-PSSM, which is a significantly extended version of a well known method called TEPITOPE. The TEPITOPE method is able to perform prediction for only 50 MHC alleles, while OWA-PSSM is able to perform prediction for much more, up to 879 HLA-DR molecules. We evaluate the method on five benchmark datasets. The method is demonstrated to be the best one in identifying binding cores compared with several other popular state-of-the-art approaches. Meanwhile, the method performs comparably to the TEPITOPE and NetMHCIIpan2.0 approaches in identifying HLA-DR epitopes and ligands, and it performs significantly better than TEPITOPEpan in the identification of HLA-DR ligands and MultiRTA in identifying HLA-DR T cell epitopes. Conclusions The proposed approach OWA-PSSM is fast and robust in identifying ligands, epitopes and binding cores for up to 879 MHC II molecules.
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Guo L, Luo C, Zhu S. MHC2SKpan: a novel kernel based approach for pan-specific MHC class II peptide binding prediction. BMC Genomics 2013; 14 Suppl 5:S11. [PMID: 24564280 PMCID: PMC3852073 DOI: 10.1186/1471-2164-14-s5-s11] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Computational methods for the prediction of Major Histocompatibility Complex (MHC) class II binding peptides play an important role in facilitating the understanding of immune recognition and the process of epitope discovery. To develop an effective computational method, we need to consider two important characteristics of the problem: (1) the length of binding peptides is highly flexible; and (2) MHC molecules are extremely polymorphic and for the vast majority of them there are no sufficient training data. METHODS We develop a novel string kernel MHC2SK (MHC-II String Kernel) method to measure the similarities among peptides with variable lengths. By considering the distinct features of MHC-II peptide binding prediction problem, MHC2SK differs significantly from the recently developed kernel based method, GS (Generic String) kernel, in the way of computing similarities. Furthermore, we extend MHC2SK to MHC2SKpan for pan-specific MHC-II peptide binding prediction by leveraging the binding data of various MHC molecules. RESULTS MHC2SK outperformed GS in allele specific prediction using a benchmark dataset, which demonstrates the effectiveness of MHC2SK. Furthermore, we evaluated the performance of MHC2SKpan using various benckmark data sets from several different perspectives: Leave-one-allele-out (LOO), 5-fold cross validation as well as independent data testing. MHC2SKpan has achieved comparable performance with NetMHCIIpan-2.0 and outperformed NetMHCIIpan-1.0, TEPITOPEpan and MultiRTA, being statistically significant. MHC2SKpan can be freely accessed at http://datamining-iip.fudan.edu.cn/service/MHC2SKpan/index.html.
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Learning a peptide-protein binding affinity predictor with kernel ridge regression. BMC Bioinformatics 2013; 14:82. [PMID: 23497081 PMCID: PMC3651388 DOI: 10.1186/1471-2105-14-82] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2012] [Accepted: 02/21/2013] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND The cellular function of a vast majority of proteins is performed through physical interactions with other biomolecules, which, most of the time, are other proteins. Peptides represent templates of choice for mimicking a secondary structure in order to modulate protein-protein interaction. They are thus an interesting class of therapeutics since they also display strong activity, high selectivity, low toxicity and few drug-drug interactions. Furthermore, predicting peptides that would bind to a specific MHC alleles would be of tremendous benefit to improve vaccine based therapy and possibly generate antibodies with greater affinity. Modern computational methods have the potential to accelerate and lower the cost of drug and vaccine discovery by selecting potential compounds for testing in silico prior to biological validation. RESULTS We propose a specialized string kernel for small bio-molecules, peptides and pseudo-sequences of binding interfaces. The kernel incorporates physico-chemical properties of amino acids and elegantly generalizes eight kernels, comprised of the Oligo, the Weighted Degree, the Blended Spectrum, and the Radial Basis Function. We provide a low complexity dynamic programming algorithm for the exact computation of the kernel and a linear time algorithm for it's approximation. Combined with kernel ridge regression and SupCK, a novel binding pocket kernel, the proposed kernel yields biologically relevant and good prediction accuracy on the PepX database. For the first time, a machine learning predictor is capable of predicting the binding affinity of any peptide to any protein with reasonable accuracy. The method was also applied to both single-target and pan-specific Major Histocompatibility Complex class II benchmark datasets and three Quantitative Structure Affinity Model benchmark datasets. CONCLUSION On all benchmarks, our method significantly (p-value ≤ 0.057) outperforms the current state-of-the-art methods at predicting peptide-protein binding affinities. The proposed approach is flexible and can be applied to predict any quantitative biological activity. Moreover, generating reliable peptide-protein binding affinities will also improve system biology modelling of interaction pathways. Lastly, the method should be of value to a large segment of the research community with the potential to accelerate the discovery of peptide-based drugs and facilitate vaccine development. The proposed kernel is freely available at http://graal.ift.ulaval.ca/downloads/gs-kernel/.
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Oyarzún P, Ellis JJ, Bodén M, Kobe B. PREDIVAC: CD4+ T-cell epitope prediction for vaccine design that covers 95% of HLA class II DR protein diversity. BMC Bioinformatics 2013; 14:52. [PMID: 23409948 PMCID: PMC3598884 DOI: 10.1186/1471-2105-14-52] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2012] [Accepted: 01/31/2013] [Indexed: 12/18/2022] Open
Abstract
Background CD4+ T-cell epitopes play a crucial role in eliciting vigorous protective immune responses during peptide (epitope)-based vaccination. The prediction of these epitopes focuses on the peptide binding process by MHC class II proteins. The ability to account for MHC class II polymorphism is critical for epitope-based vaccine design tools, as different allelic variants can have different peptide repertoires. In addition, the specificity of CD4+ T-cells is often directed to a very limited set of immunodominant peptides in pathogen proteins. The ability to predict what epitopes are most likely to dominate an immune response remains a challenge. Results We developed the computational tool Predivac to predict CD4+ T-cell epitopes. Predivac can make predictions for 95% of all MHC class II protein variants (allotypes), a substantial advance over other available methods. Predivac bases its prediction on the concept of specificity-determining residues. The performance of the method was assessed both for high-affinity HLA class II peptide binding and CD4+ T-cell epitope prediction. In terms of epitope prediction, Predivac outperformed three available pan-specific approaches (delivering the highest specificity). A central finding was the high accuracy delivered by the method in the identification of immunodominant and promiscuous CD4+ T-cell epitopes, which play an essential role in epitope-based vaccine design. Conclusions The comprehensive HLA class II allele coverage along with the high specificity in identifying immunodominant CD4+ T-cell epitopes makes Predivac a valuable tool to aid epitope-based vaccine design in the context of a genetically heterogeneous human population.The tool is available at: http://predivac.biosci.uq.edu.au/.
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Affiliation(s)
- Patricio Oyarzún
- School of Chemistry and Molecular Biosciences, Institute for Molecular Bioscience and Australian Infectious Diseases Research Centre, University of Queensland, Brisbane, QLD 4072, Australia.
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Abstract
The varied landscape of the adaptive immune response is determined by the peptides presented by immune cells, derived from viral or microbial pathogens or cancerous cells. The study of immune biomarkers or antigens is not new and classical methods such as agglutination, enzyme-linked immunosorbent assay, or Western blotting have been used for many years to study the immune response to vaccination or disease. However, in many of these traditional techniques, protein or peptide identification has often been the bottleneck. Recent advances in genomics and proteomics, has led to many of the rapid advances in proteomics approaches. Immunoproteomics describes a rapidly growing collection of approaches that have the common goal of identifying and measuring antigenic peptides or proteins. This includes gel based, array based, mass spectrometry, DNA based, or in silico approaches. Immunoproteomics is yielding an understanding of disease and disease progression, vaccine candidates, and biomarkers. This review gives an overview of immunoproteomics and closely related technologies that are used to define the full set of antigens targeted by the immune system during disease.
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Affiliation(s)
- Kelly M Fulton
- Human Health Therapeutics, National Research Council Canada, Ottawa, ON, Canada
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Zhang L, Chen Y, Wong HS, Zhou S, Mamitsuka H, Zhu S. TEPITOPEpan: extending TEPITOPE for peptide binding prediction covering over 700 HLA-DR molecules. PLoS One 2012; 7:e30483. [PMID: 22383964 PMCID: PMC3285624 DOI: 10.1371/journal.pone.0030483] [Citation(s) in RCA: 77] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2011] [Accepted: 12/16/2011] [Indexed: 12/28/2022] Open
Abstract
Motivation Accurate identification of peptides binding to specific Major Histocompatibility Complex Class II (MHC-II) molecules is of great importance for elucidating the underlying mechanism of immune recognition, as well as for developing effective epitope-based vaccines and promising immunotherapies for many severe diseases. Due to extreme polymorphism of MHC-II alleles and the high cost of biochemical experiments, the development of computational methods for accurate prediction of binding peptides of MHC-II molecules, particularly for the ones with few or no experimental data, has become a topic of increasing interest. TEPITOPE is a well-used computational approach because of its good interpretability and relatively high performance. However, TEPITOPE can be applied to only 51 out of over 700 known HLA DR molecules. Method We have developed a new method, called TEPITOPEpan, by extrapolating from the binding specificities of HLA DR molecules characterized by TEPITOPE to those uncharacterized. First, each HLA-DR binding pocket is represented by amino acid residues that have close contact with the corresponding peptide binding core residues. Then the pocket similarity between two HLA-DR molecules is calculated as the sequence similarity of the residues. Finally, for an uncharacterized HLA-DR molecule, the binding specificity of each pocket is computed as a weighted average in pocket binding specificities over HLA-DR molecules characterized by TEPITOPE. Result The performance of TEPITOPEpan has been extensively evaluated using various data sets from different viewpoints: predicting MHC binding peptides, identifying HLA ligands and T-cell epitopes and recognizing binding cores. Among the four state-of-the-art competing pan-specific methods, for predicting binding specificities of unknown HLA-DR molecules, TEPITOPEpan was roughly the second best method next to NETMHCIIpan-2.0. Additionally, TEPITOPEpan achieved the best performance in recognizing binding cores. We further analyzed the motifs detected by TEPITOPEpan, examining the corresponding literature of immunology. Its online server and PSSMs therein are available at http://www.biokdd.fudan.edu.cn/Service/TEPITOPEpan/.
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Affiliation(s)
- Lianming Zhang
- School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China
| | - Yiqing Chen
- School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China
| | - Hau-San Wong
- Department of Computer Science, City University of Hong Kong, Hong Kong, China
| | - Shuigeng Zhou
- School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China
| | - Hiroshi Mamitsuka
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Japan
| | - Shanfeng Zhu
- School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China
- * E-mail:
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Zhang L, Udaka K, Mamitsuka H, Zhu S. Toward more accurate pan-specific MHC-peptide binding prediction: a review of current methods and tools. Brief Bioinform 2011; 13:350-64. [PMID: 21949215 DOI: 10.1093/bib/bbr060] [Citation(s) in RCA: 100] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Binding of short antigenic peptides to major histocompatibility complex (MHC) molecules is a core step in adaptive immune response. Precise identification of MHC-restricted peptides is of great significance for understanding the mechanism of immune response and promoting the discovery of immunogenic epitopes. However, due to the extremely high MHC polymorphism and huge cost of biochemical experiments, there is no experimentally measured binding data for most MHC molecules. To address the problem of predicting peptides binding to these MHC molecules, recently computational approaches, called pan-specific methods, have received keen interest. Pan-specific methods make use of experimentally obtained binding data of multiple alleles, by which binding peptides (binders) of not only these alleles but also those alleles with no known binders can be predicted. To investigate the possibility of further improvement in performance and usability of pan-specific methods, this article extensively reviews existing pan-specific methods and their web servers. We first present a general framework of pan-specific methods. Then, the strategies and performance as well as utilities of web servers are compared. Finally, we discuss the future direction to improve pan-specific methods for MHC-peptide binding prediction.
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Affiliation(s)
- Lianming Zhang
- School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai 200433, China
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Zhang GL, Lin HH, Keskin DB, Reinherz EL, Brusic V. Dana-Farber repository for machine learning in immunology. J Immunol Methods 2011; 374:18-25. [PMID: 21782820 DOI: 10.1016/j.jim.2011.07.007] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2011] [Accepted: 07/06/2011] [Indexed: 11/27/2022]
Abstract
The immune system is characterized by high combinatorial complexity that necessitates the use of specialized computational tools for analysis of immunological data. Machine learning (ML) algorithms are used in combination with classical experimentation for the selection of vaccine targets and in computational simulations that reduce the number of necessary experiments. The development of ML algorithms requires standardized data sets, consistent measurement methods, and uniform scales. To bridge the gap between the immunology community and the ML community, we designed a repository for machine learning in immunology named Dana-Farber Repository for Machine Learning in Immunology (DFRMLI). This repository provides standardized data sets of HLA-binding peptides with all binding affinities mapped onto a common scale. It also provides a list of experimentally validated naturally processed T cell epitopes derived from tumor or virus antigens. The DFRMLI data were preprocessed and ensure consistency, comparability, detailed descriptions, and statistically meaningful sample sizes for peptides that bind to various HLA molecules. The repository is accessible at http://bio.dfci.harvard.edu/DFRMLI/.
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Affiliation(s)
- Guang Lan Zhang
- Cancer Vaccine Center, Dana-Farber Cancer Institute, Boston, MA 02115, USA
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30
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Patronov A, Dimitrov I, Flower DR, Doytchinova I. Peptide binding prediction for the human class II MHC allele HLA-DP2: a molecular docking approach. BMC STRUCTURAL BIOLOGY 2011; 11:32. [PMID: 21752305 PMCID: PMC3146810 DOI: 10.1186/1472-6807-11-32] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2011] [Accepted: 07/14/2011] [Indexed: 12/04/2022]
Abstract
Background MHC class II proteins bind oligopeptide fragments derived from proteolysis of pathogen antigens, presenting them at the cell surface for recognition by CD4+ T cells. Human MHC class II alleles are grouped into three loci: HLA-DP, HLA-DQ and HLA-DR. In contrast to HLA-DR and HLA-DQ, HLA-DP proteins have not been studied extensively, as they have been viewed as less important in immune responses than DRs and DQs. However, it is now known that HLA-DP alleles are associated with many autoimmune diseases. Quite recently, the X-ray structure of the HLA-DP2 molecule (DPA*0103, DPB1*0201) in complex with a self-peptide derived from the HLA-DR α-chain has been determined. In the present study, we applied a validated molecular docking protocol to a library of 247 modelled peptide-DP2 complexes, seeking to assess the contribution made by each of the 20 naturally occurred amino acids at each of the nine binding core peptide positions and the four flanking residues (two on both sides). Results The free binding energies (FBEs) derived from the docking experiments were normalized on a position-dependent (npp) and on an overall basis (nap), and two docking score-based quantitative matrices (DS-QMs) were derived: QMnpp and QMnap. They reveal the amino acid preferences at each of the 13 positions considered in the study. Apart from the leading role of anchor positions p1 and p6, the binding to HLA-DP2 depends on the preferences at p2. No effect of the flanking residues was found on the peptide binding predictions to DP2, although all four of them show strong preferences for particular amino acids. The predictive ability of the DS-QMs was tested using a set of 457 known binders to HLA-DP2, originating from 24 proteins. The sensitivities of the predictions at five different thresholds (5%, 10%, 15%, 20% and 25%) were calculated and compared to the predictions made by the NetMHCII and IEDB servers. Analysis of the DS-QMs indicated an improvement in performance. Additionally, DS-QMs identified the binding cores of several known DP2 binders. Conclusions The molecular docking protocol, as applied to a combinatorial library of peptides, models the peptide-HLA-DP2 protein interaction effectively, generating reliable predictions in a quantitative assessment. The method is structure-based and does not require extensive experimental sequence-based data. Thus, it is universal and can be applied to model any peptide - protein interaction.
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Affiliation(s)
- Atanas Patronov
- Rebirth, Hannover Biomedical Research School, Carl-Neuberg strasse 1, Hannover, Germany
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31
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Bordner AJ. Towards universal structure-based prediction of class II MHC epitopes for diverse allotypes. PLoS One 2010; 5:e14383. [PMID: 21187956 PMCID: PMC3004863 DOI: 10.1371/journal.pone.0014383] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2010] [Accepted: 11/30/2010] [Indexed: 01/10/2023] Open
Abstract
The binding of peptide fragments of antigens to class II MHC proteins is a crucial step in initiating a helper T cell immune response. The discovery of these peptide epitopes is important for understanding the normal immune response and its misregulation in autoimmunity and allergies and also for vaccine design. In spite of their biomedical importance, the high diversity of class II MHC proteins combined with the large number of possible peptide sequences make comprehensive experimental determination of epitopes for all MHC allotypes infeasible. Computational methods can address this need by predicting epitopes for a particular MHC allotype. We present a structure-based method for predicting class II epitopes that combines molecular mechanics docking of a fully flexible peptide into the MHC binding cleft followed by binding affinity prediction using a machine learning classifier trained on interaction energy components calculated from the docking solution. Although the primary advantage of structure-based prediction methods over the commonly employed sequence-based methods is their applicability to essentially any MHC allotype, this has not yet been convincingly demonstrated. In order to test the transferability of the prediction method to different MHC proteins, we trained the scoring method on binding data for DRB1*0101 and used it to make predictions for multiple MHC allotypes with distinct peptide binding specificities including representatives from the other human class II MHC loci, HLA-DP and HLA-DQ, as well as for two murine allotypes. The results showed that the prediction method was able to achieve significant discrimination between epitope and non-epitope peptides for all MHC allotypes examined, based on AUC values in the range 0.632-0.821. We also discuss how accounting for peptide binding in multiple registers to class II MHC largely explains the systematically worse performance of prediction methods for class II MHC compared with those for class I MHC based on quantitative prediction performance estimates for peptide binding to class II MHC in a fixed register.
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Affiliation(s)
- Andrew J Bordner
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Scottsdale, Arizona, United States of America.
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