1
|
Le HN, de Freitas MV, Antunes DA. Strengths and limitations of web servers for the modeling of TCRpMHC complexes. Comput Struct Biotechnol J 2024; 23:2938-2948. [PMID: 39104710 PMCID: PMC11298609 DOI: 10.1016/j.csbj.2024.06.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Revised: 06/22/2024] [Accepted: 06/23/2024] [Indexed: 08/07/2024] Open
Abstract
Cellular immunity relies on the ability of a T-cell receptor (TCR) to recognize a peptide (p) presented by a class I major histocompatibility complex (MHC) receptor on the surface of a cell. The TCR-peptide-MHC (TCRpMHC) interaction is a crucial step in activating T-cells, and the structural characteristics of these molecules play a significant role in determining the specificity and affinity of this interaction. Hence, obtaining 3D structures of TCRpMHC complexes offers valuable insights into various aspects of cellular immunity and can facilitate the development of T-cell-based immunotherapies. Here, we aimed to compare three popular web servers for modeling the structures of TCRpMHC complexes, namely ImmuneScape (IS), TCRpMHCmodels, and TCRmodel2, to examine their strengths and limitations. Each method employs a different modeling strategy, including docking, homology modeling, and deep learning. The accuracy of each method was evaluated by reproducing the 3D structures of a dataset of 87 TCRpMHC complexes with experimentally determined crystal structures available on the Protein Data Bank (PDB). All selected structures were limited to human MHC alleles, presenting a diverse set of peptide ligands. A detailed analysis of produced models was conducted using multiple metrics, including Root Mean Square Deviation (RMSD) and standardized assessments from CAPRI and DockQ. Special attention was given to the complementarity-determining region (CDR) loops of the TCRs and to the peptide ligands, which define most of the unique features and specificity of a given TCRpMHC interaction. Our study provides an optimistic view of the current state-of-the-art for TCRpMHC modeling but highlights some remaining challenges that must be addressed in order to support the future application of these tools for TCR engineering and computer-aided design of TCR-based immunotherapies.
Collapse
Affiliation(s)
- Hoa Nhu Le
- University of Houston, Departments of Biology and Biochemistry, Houston, 77204, TX, USA
| | | | - Dinler Amaral Antunes
- University of Houston, Departments of Biology and Biochemistry, Houston, 77204, TX, USA
| |
Collapse
|
2
|
Tommeurd W, Thueng-in K, Theerawatanasirikul S, Tuyapala N, Poonsuk S, Petcharat N, Thangthamniyom N, Lekcharoensuk P. Identification of Conserved Linear Epitopes on Viral Protein 2 of Foot-and-Mouth Disease Virus Serotype O by Monoclonal Antibodies 6F4.D11.B6 and 8D6.B9.C3. Antibodies (Basel) 2024; 13:67. [PMID: 39189238 PMCID: PMC11348169 DOI: 10.3390/antib13030067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2024] [Revised: 06/25/2024] [Accepted: 07/18/2024] [Indexed: 08/28/2024] Open
Abstract
Foot-and-mouth disease (FMD) is a highly infectious disease of cloven-hoofed animals with a significant economic impact. Early diagnosis and effective prevention and control could reduce the spread of the disease which could possibly minimize economic losses. Epitope characterization based on monoclonal antibodies provide essential information for developing diagnostic assays and vaccine designs. In this study, monoclonal antibodies raised against FMD virus (FMDV) were produced. Sixty-six monoclonal antibodies demonstrated strong reactivity and specificity to FMDV. The purified monoclonal antibodies were further used for bio-panning to select phage expressing specific epitopes from phage-displayed 12 mer-peptide library. The phage peptide sequences were analyzed using multiple sequence alignment and evaluated by peptide ELISA. Two hybridoma clones secreted monoclonal antibodies recognizing linear epitopes on VP2 of FMDV serotype O. The non-neutralizing monoclonal antibody 6F4.D11.B6 recognized the residues 67-78 on antigenic site 2 resinding in VP2, while the neutralizing monoclonal antibody 8D6.B9.C3 recognized a novel linear epitope encompassing residues 115-126 on VP2. This information and the FMDV-specific monoclonal antibodies provide valuable sources for further study and application in diagnosis, therapeutics and vaccine designs to strengthen the disease prevention and control measures.
Collapse
Affiliation(s)
- Wantanee Tommeurd
- Interdisciplinary Graduate Program in Genetic Engineering, The Graduate School, Kasetsart University, Bangkok 10900, Thailand; (W.T.); (S.T.)
| | - Kanyarat Thueng-in
- School of Pathology, Translational Medicine Program, Institute of Medicine, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand;
| | - Sirin Theerawatanasirikul
- Interdisciplinary Graduate Program in Genetic Engineering, The Graduate School, Kasetsart University, Bangkok 10900, Thailand; (W.T.); (S.T.)
- Department of Anatomy, Faculty of Veterinary Medicine, Kasetsart University, Bangkok 10900, Thailand
| | - Nongnaput Tuyapala
- Protein-Ligand Engineering and Molecular Biology Research Team, National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), Pathum Thani 12120, Thailand;
| | - Sukontip Poonsuk
- Department of Pathology, Faculty of Veterinary Medicine, Kasetsart University, Bangkok 10900, Thailand;
| | - Nantawan Petcharat
- Department of Microbiology and Immunology, Faculty of Veterinary Medicine, Kasetsart University, Bangkok 10900, Thailand;
| | - Nattarat Thangthamniyom
- Research and Development Department, Animal Health and Diagnostic Center, CPF (Thailand) Public Company Limited, Bangkok 10530, Thailand;
| | - Porntippa Lekcharoensuk
- Interdisciplinary Graduate Program in Genetic Engineering, The Graduate School, Kasetsart University, Bangkok 10900, Thailand; (W.T.); (S.T.)
- Department of Microbiology and Immunology, Faculty of Veterinary Medicine, Kasetsart University, Bangkok 10900, Thailand;
| |
Collapse
|
3
|
Yan Z, Kim K, Kim H, Ha B, Gambiez A, Bennett J, de Almeida Mendes M, Trevizani R, Mahita J, Richardson E, Marrama D, Blazeska N, Koşaloğlu-Yalçın Z, Nielsen M, Sette A, Peters B, Greenbaum J. Next-generation IEDB tools: a platform for epitope prediction and analysis. Nucleic Acids Res 2024; 52:W526-W532. [PMID: 38783079 PMCID: PMC11223806 DOI: 10.1093/nar/gkae407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 04/24/2024] [Accepted: 05/01/2024] [Indexed: 05/25/2024] Open
Abstract
The Next-Generation (NG) IEDB Tools website (https://nextgen-tools.iedb.org) provides users with a redesigned interface to many of the algorithms for epitope prediction and analysis that were originally released on the legacy IEDB Tools website. The initial release focuses on consolidation of all tools related to HLA class I epitopes (MHC binding, elution, immunogenicity, and processing), making all of these predictions accessible from a single application and allowing for their simultaneous execution with minimal user inputs. Additionally, the PEPMatch tool for identifying highly similar epitopes in a set of curated proteomes, as well as a tool for epitope clustering, are available on the site. The NG Tools site allows users to build data pipelines by sending the output of one tool as input for the next. Over the next several years, all pre-existing IEDB Tools, and any newly developed tools, will be integrated into this new site. Here we describe the philosophy behind the redesign and demonstrate the utility and productivity enhancements that are enabled by the new interface.
Collapse
Affiliation(s)
- Zhen Yan
- Bioinformatics Core, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| | - Kevin Kim
- Information Technology, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| | - Haeuk Kim
- Bioinformatics Core, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| | - Brendan Ha
- Bioinformatics Core, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| | - Anaïs Gambiez
- Bioinformatics Core, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| | - Jason Bennett
- Center for Vaccine Innovation, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| | | | - Raphael Trevizani
- Center for Vaccine Innovation, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
- Fiocruz Ceará, Fundação Oswaldo Cruz, Rua São José s/n, Precabura, Eusébio/CE, Brazil
| | - Jarjapu Mahita
- Center for Vaccine Innovation, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| | - Eve Richardson
- Center for Vaccine Innovation, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| | - Daniel Marrama
- Center for Vaccine Innovation, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| | - Nina Blazeska
- Center for Vaccine Innovation, La Jolla Institute for Immunology, La Jolla, CA 92037, 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
- Center for Vaccine Innovation, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Bjoern Peters
- Center for Vaccine Innovation, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Jason A Greenbaum
- Bioinformatics Core, La Jolla Institute for Immunology, La Jolla, CA 92037, USA
| |
Collapse
|
4
|
Rollins ZA, Curtis MB, George SC, Faller R. A Computational Strategy for the Rapid Identification and Ranking of Patient-Specific T Cell Receptors Bound to Neoantigens. Macromol Rapid Commun 2024:e2400225. [PMID: 38839076 DOI: 10.1002/marc.202400225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 06/02/2024] [Indexed: 06/07/2024]
Abstract
T cell receptor (TCR) recognition of a peptide-major histocompatibility complex (pMHC) is crucial for adaptive immune response. The identification of therapeutically relevant TCR-pMHC protein pairs is a bottleneck in the implementation of TCR-based immunotherapies. The ability to computationally design TCRs to target a specific pMHC requires automated integration of next-generation sequencing, protein-protein structure prediction, molecular dynamics, and TCR ranking. A pipeline to evaluate patient-specific, sequence-based TCRs to a target pMHC is presented. Using the three most frequently expressed TCRs from 16 colorectal cancer patients, the protein-protein structure of the TCRs to the target CEA peptide-MHC is predicted using Modeller and ColabFold. TCR-pMHC structures are compared using automated equilibration and successive analysis. ColabFold generated configurations require an ≈2.5× reduction in equilibration time of TCR-pMHC structures compared to Modeller. The structural differences between Modeller and ColabFold are demonstrated by root mean square deviation (≈0.20 nm) between clusters of equilibrated configurations, which impact the number of hydrogen bonds and Lennard-Jones contacts between the TCR and pMHC. TCR ranking criteria that may prioritize TCRs for evaluation of in vitro immunogenicity are identified, and this ranking is validated by comparing to state-of-the-art machine learning-based methods trained to predict the probability of TCR-pMHC binding.
Collapse
Affiliation(s)
- Zachary A Rollins
- Department of Chemical Engineering, University of California, Davis, 1 Shields Ave, Bainer Hall, Davis, CA, 95616, USA
| | - Matthew B Curtis
- Department of Biomedical Engineering, University of California, Davis, 451 E. Health Sciences Dr., GBSF 2303, Davis, CA, 95616, USA
| | - Steven C George
- Department of Biomedical Engineering, University of California, Davis, 451 E. Health Sciences Dr., GBSF 2303, Davis, CA, 95616, USA
| | - Roland Faller
- Department of Chemical Engineering, University of California, Davis, 1 Shields Ave, Bainer Hall, Davis, CA, 95616, USA
- Department of Chemical Engineering, Texas Tech University, Lubbock, TX, 79409, USA
| |
Collapse
|
5
|
McMaster B, Thorpe C, Ogg G, Deane CM, Koohy H. Can AlphaFold's breakthrough in protein structure help decode the fundamental principles of adaptive cellular immunity? Nat Methods 2024; 21:766-776. [PMID: 38654083 DOI: 10.1038/s41592-024-02240-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 03/08/2024] [Indexed: 04/25/2024]
Abstract
T cells are essential immune cells responsible for identifying and eliminating pathogens. Through interactions between their T-cell antigen receptors (TCRs) and antigens presented by major histocompatibility complex molecules (MHCs) or MHC-like molecules, T cells discriminate foreign and self peptides. Determining the fundamental principles that govern these interactions has important implications in numerous medical contexts. However, reconstructing a map between T cells and their antagonist antigens remains an open challenge for the field of immunology, and success of in silico reconstructions of this relationship has remained incremental. In this Perspective, we discuss the role that new state-of-the-art deep-learning models for predicting protein structure may play in resolving some of the unanswered questions the field faces linking TCR and peptide-MHC properties to T-cell specificity. We provide a comprehensive overview of structural databases and the evolution of predictive models, and highlight the breakthrough AlphaFold provided the field.
Collapse
Affiliation(s)
- Benjamin McMaster
- MRC Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | - Christopher Thorpe
- Open Targets, Wellcome Genome Campus, Hinxton, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK
| | - Graham Ogg
- MRC Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Chinese Academy of Medical Sciences Oxford Institute, University of Oxford, Oxford, UK
| | | | - Hashem Koohy
- MRC Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
- Alan Turning Fellow in Health and Medicine, University of Oxford, Oxford, UK.
| |
Collapse
|
6
|
Shahjahan, Dey JK, Dey SK. Translational bioinformatics approach to combat cardiovascular disease and cancers. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2024; 139:221-261. [PMID: 38448136 DOI: 10.1016/bs.apcsb.2023.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
Bioinformatics is an interconnected subject of science dealing with diverse fields including biology, chemistry, physics, statistics, mathematics, and computer science as the key fields to answer complicated physiological problems. Key intention of bioinformatics is to store, analyze, organize, and retrieve essential information about genome, proteome, transcriptome, metabolome, as well as organisms to investigate the biological system along with its dynamics, if any. The outcome of bioinformatics depends on the type, quantity, and quality of the raw data provided and the algorithm employed to analyze the same. Despite several approved medicines available, cardiovascular disorders (CVDs) and cancers comprises of the two leading causes of human deaths. Understanding the unknown facts of both these non-communicable disorders is inevitable to discover new pathways, find new drug targets, and eventually newer drugs to combat them successfully. Since, all these goals involve complex investigation and handling of various types of macro- and small- molecules of the human body, bioinformatics plays a key role in such processes. Results from such investigation has direct human application and thus we call this filed as translational bioinformatics. Current book chapter thus deals with diverse scope and applications of this translational bioinformatics to find cure, diagnosis, and understanding the mechanisms of CVDs and cancers. Developing complex yet small or long algorithms to address such problems is very common in translational bioinformatics. Structure-based drug discovery or AI-guided invention of novel antibodies that too with super-high accuracy, speed, and involvement of considerably low amount of investment are some of the astonishing features of the translational bioinformatics and its applications in the fields of CVDs and cancers.
Collapse
Affiliation(s)
- Shahjahan
- Laboratory for Structural Biology of Membrane Proteins, Dr. B.R. Ambedkar Center for Biomedical Research, University of Delhi, Delhi, India
| | - Joy Kumar Dey
- Central Council for Research in Homoeopathy, Ministry of Ayush, Govt. of India, New Delhi, Delhi, India
| | - Sanjay Kumar Dey
- Laboratory for Structural Biology of Membrane Proteins, Dr. B.R. Ambedkar Center for Biomedical Research, University of Delhi, Delhi, India.
| |
Collapse
|
7
|
Israeli S, Louzoun Y. Single-residue linear and conformational B cell epitopes prediction using random and ESM-2 based projections. Brief Bioinform 2024; 25:bbae084. [PMID: 38487845 PMCID: PMC10940830 DOI: 10.1093/bib/bbae084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 01/24/2024] [Accepted: 02/07/2024] [Indexed: 03/18/2024] Open
Abstract
B cell epitope prediction methods are separated into linear sequence-based predictors and conformational epitope predictions that typically use the measured or predicted protein structure. Most linear predictions rely on the translation of the sequence to biologically based representations and the applications of machine learning on these representations. We here present CALIBER 'Conformational And LInear B cell Epitopes pRediction', and show that a bidirectional long short-term memory with random projection produces a more accurate prediction (test set AUC=0.789) than all current linear methods. The same predictor when combined with an Evolutionary Scale Modeling-2 projection also improves on the state of the art in conformational epitopes (AUC = 0.776). The inclusion of the graph of the 3D distances between residues did not increase the prediction accuracy. However, the long-range sequence information was essential for high accuracy. While the same model structure was applicable for linear and conformational epitopes, separate training was required for each. Combining the two slightly increased the linear accuracy (AUC 0.775 versus 0.768) and reduced the conformational accuracy (AUC = 0.769).
Collapse
Affiliation(s)
- Sapir Israeli
- Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel
| | - Yoram Louzoun
- Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel
| |
Collapse
|
8
|
Chu X, Shin S, Baek DS, Zhang L, Conard A, Shi M, Kim YJ, Adams C, Hines M, Liu X, Chen C, Sun Z, Jelev DV, Mellors JW, Dimitrov DS, Li W. Discovery of a novel highly specific, fully human PSCA antibody and its application as an antibody-drug conjugate in prostate cancer. MAbs 2024; 16:2387240. [PMID: 39113562 PMCID: PMC11312989 DOI: 10.1080/19420862.2024.2387240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 07/25/2024] [Accepted: 07/29/2024] [Indexed: 08/11/2024] Open
Abstract
Prostate stem cell antigen (PSCA) is expressed in all stages of prostate cancer, including in advanced androgen-independent tumors and bone metastasis. PSCA may associate with prostate carcinogenesis and lineage plasticity in prostate cancer. PSCA is also a promising theranostic marker for a variety of other solid tumors, including pancreatic adenocarcinoma and renal cell carcinoma. Here, we identified a novel fully human PSCA antibody using phage display methodology. The structure-based affinity maturation yielded a high-affinity binder, F12, which is highly specific and does not bind to 6,000 human membrane proteins based on a membrane proteome array assay. F12 targets PSCA amino acids 63-69 as tested by the peptide scanning microarray, and it cross-reacts with the murine PSCA. IgG1 F12 efficiently internalizes into PSCA-expressing tumor cells. The antimitotic reagent monomethyl auristatin E (MMAE)-conjugated IgG1 F12 (ADC, F12-MMAE) exhibits dose-dependent efficacy and specificity in a human prostate cancer PC-3-PSCA xenograft NSG mouse model. This is a first reported ADC based on a fully human PSCA antibody and MMAE that is characterized in a xenograft murine model, which warrants further optimizations and investigations in additional preclinical tumor models, including prostate and other solid tumors.
Collapse
Affiliation(s)
- Xiaojie Chu
- Center for Antibody Therapeutics, Division of Infectious Diseases, Department of Medicine, University of Pittsburgh Medical School, Pittsburgh, PA, USA
| | - Seungmin Shin
- Center for Antibody Therapeutics, Division of Infectious Diseases, Department of Medicine, University of Pittsburgh Medical School, Pittsburgh, PA, USA
| | | | - Liyong Zhang
- Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Megan Shi
- Computational and System Biology, School of Medicine, University of Pittsburgh, Pittsburgh, USA
| | | | | | - Maggie Hines
- Center for Antibody Therapeutics, Division of Infectious Diseases, Department of Medicine, University of Pittsburgh Medical School, Pittsburgh, PA, USA
| | - Xianglei Liu
- Center for Antibody Therapeutics, Division of Infectious Diseases, Department of Medicine, University of Pittsburgh Medical School, Pittsburgh, PA, USA
| | - Chuan Chen
- Center for Antibody Therapeutics, Division of Infectious Diseases, Department of Medicine, University of Pittsburgh Medical School, Pittsburgh, PA, USA
| | | | - Dontcho V. Jelev
- Center for Antibody Therapeutics, Division of Infectious Diseases, Department of Medicine, University of Pittsburgh Medical School, Pittsburgh, PA, USA
| | - John W. Mellors
- Center for Antibody Therapeutics, Division of Infectious Diseases, Department of Medicine, University of Pittsburgh Medical School, Pittsburgh, PA, USA
- GLPG, Pittsburgh, PA, USA
| | - Dimiter S. Dimitrov
- Center for Antibody Therapeutics, Division of Infectious Diseases, Department of Medicine, University of Pittsburgh Medical School, Pittsburgh, PA, USA
- GLPG, Pittsburgh, PA, USA
| | - Wei Li
- Center for Antibody Therapeutics, Division of Infectious Diseases, Department of Medicine, University of Pittsburgh Medical School, Pittsburgh, PA, USA
| |
Collapse
|
9
|
Kumar N, Bajiya N, Patiyal S, Raghava GPS. Multi-perspectives and challenges in identifying B-cell epitopes. Protein Sci 2023; 32:e4785. [PMID: 37733481 PMCID: PMC10578127 DOI: 10.1002/pro.4785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 09/11/2023] [Accepted: 09/16/2023] [Indexed: 09/23/2023]
Abstract
The identification of B-cell epitopes (BCEs) in antigens is a crucial step in developing recombinant vaccines or immunotherapies for various diseases. Over the past four decades, numerous in silico methods have been developed for predicting BCEs. However, existing reviews have only covered specific aspects, such as the progress in predicting conformational or linear BCEs. Therefore, in this paper, we have undertaken a systematic approach to provide a comprehensive review covering all aspects associated with the identification of BCEs. First, we have covered the experimental techniques developed over the years for identifying linear and conformational epitopes, including the limitations and challenges associated with these techniques. Second, we have briefly described the historical perspectives and resources that maintain experimentally validated information on BCEs. Third, we have extensively reviewed the computational methods developed for predicting conformational BCEs from the structure of the antigen, as well as the methods for predicting conformational epitopes from the sequence. Fourth, we have systematically reviewed the in silico methods developed in the last four decades for predicting linear or continuous BCEs. Finally, we have discussed the overall challenge of identifying continuous or conformational BCEs. In this review, we only listed major computational resources; a complete list with the URL is available from the BCinfo website (https://webs.iiitd.edu.in/raghava/bcinfo/).
Collapse
Affiliation(s)
- Nishant Kumar
- Department of Computational BiologyIndraprastha Institute of Information TechnologyNew DelhiIndia
| | - Nisha Bajiya
- Department of Computational BiologyIndraprastha Institute of Information TechnologyNew DelhiIndia
| | - Sumeet Patiyal
- Department of Computational BiologyIndraprastha Institute of Information TechnologyNew DelhiIndia
| | - Gajendra P. S. Raghava
- Department of Computational BiologyIndraprastha Institute of Information TechnologyNew DelhiIndia
| |
Collapse
|
10
|
Guarra F, Colombo G. Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens. J Chem Theory Comput 2023; 19:5315-5333. [PMID: 37527403 PMCID: PMC10448727 DOI: 10.1021/acs.jctc.3c00513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Indexed: 08/03/2023]
Abstract
The design of new biomolecules able to harness immune mechanisms for the treatment of diseases is a prime challenge for computational and simulative approaches. For instance, in recent years, antibodies have emerged as an important class of therapeutics against a spectrum of pathologies. In cancer, immune-inspired approaches are witnessing a surge thanks to a better understanding of tumor-associated antigens and the mechanisms of their engagement or evasion from the human immune system. Here, we provide a summary of the main state-of-the-art computational approaches that are used to design antibodies and antigens, and in parallel, we review key methodologies for epitope identification for both B- and T-cell mediated responses. A special focus is devoted to the description of structure- and physics-based models, privileged over purely sequence-based approaches. We discuss the implications of novel methods in engineering biomolecules with tailored immunological properties for possible therapeutic uses. Finally, we highlight the extraordinary challenges and opportunities presented by the possible integration of structure- and physics-based methods with emerging Artificial Intelligence technologies for the prediction and design of novel antigens, epitopes, and antibodies.
Collapse
Affiliation(s)
- Federica Guarra
- Department of Chemistry, University
of Pavia, Via Taramelli 12, 27100 Pavia, Italy
| | - Giorgio Colombo
- Department of Chemistry, University
of Pavia, Via Taramelli 12, 27100 Pavia, Italy
| |
Collapse
|
11
|
Shcherbinin DS, Karnaukhov VK, Zvyagin IV, Chudakov DM, Shugay M. Large-scale template-based structural modeling of T-cell receptors with known antigen specificity reveals complementarity features. Front Immunol 2023; 14:1224969. [PMID: 37649481 PMCID: PMC10464843 DOI: 10.3389/fimmu.2023.1224969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 07/27/2023] [Indexed: 09/01/2023] Open
Abstract
Introduction T-cell receptor (TCR) recognition of foreign peptides presented by the major histocompatibility complex (MHC) initiates the adaptive immune response against pathogens. While a large number of TCR sequences specific to different antigenic peptides are known to date, the structural data describing the conformation and contacting residues for TCR-peptide-MHC complexes is relatively limited. In the present study we aim to extend and analyze the set of available structures by performing highly accurate template-based modeling of these complexes using TCR sequences with known specificity. Methods Identification of CDR3 sequences and their further clustering, based on available spatial structures, V- and J-genes of corresponding T-cell receptors, and epitopes, was performed using the VDJdb database. Modeling of the selected CDR3 loops was conducted using a stepwise introduction of single amino acid substitutions to the template PDB structures, followed by optimization of the TCR-peptide-MHC contacting interface using the Rosetta package applications. Statistical analysis and recursive feature elimination procedures were carried out on computed energy values and properties of contacting amino acid residues between CDR3 loops and peptides, using R. Results Using the set of 29 complex templates (including a template with SARS-CoV-2 antigen) and 732 specificity records, we built a database of 1585 model structures carrying substitutions in either TCRα or TCRβ chains with some models representing the result of different mutation pathways for the same final structure. This database allowed us to analyze features of amino acid contacts in TCR - peptide interfaces that govern antigen recognition preferences and interpret these interactions in terms of physicochemical properties of interacting residues. Conclusion Our results provide a methodology for creating high-quality TCR-peptide-MHC models for antigens of interest that can be utilized to predict TCR specificity.
Collapse
Affiliation(s)
- Dmitrii S. Shcherbinin
- Institute of Translational Medicine, Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia
- Laboratory of Structural Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
| | - Vadim K. Karnaukhov
- Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Ivan V. Zvyagin
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
| | - Dmitriy M. Chudakov
- Institute of Translational Medicine, Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia
- Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
- Center of Molecular Medicine, Central European Institute of Technology (CEITEC), Masaryk University, Brno, Czechia
| | - Mikhail Shugay
- Institute of Translational Medicine, Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
| |
Collapse
|
12
|
Bauer J, Rajagopal N, Gupta P, Gupta P, Nixon AE, Kumar S. How can we discover developable antibody-based biotherapeutics? Front Mol Biosci 2023; 10:1221626. [PMID: 37609373 PMCID: PMC10441133 DOI: 10.3389/fmolb.2023.1221626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 07/10/2023] [Indexed: 08/24/2023] Open
Abstract
Antibody-based biotherapeutics have emerged as a successful class of pharmaceuticals despite significant challenges and risks to their discovery and development. This review discusses the most frequently encountered hurdles in the research and development (R&D) of antibody-based biotherapeutics and proposes a conceptual framework called biopharmaceutical informatics. Our vision advocates for the syncretic use of computation and experimentation at every stage of biologic drug discovery, considering developability (manufacturability, safety, efficacy, and pharmacology) of potential drug candidates from the earliest stages of the drug discovery phase. The computational advances in recent years allow for more precise formulation of disease concepts, rapid identification, and validation of targets suitable for therapeutic intervention and discovery of potential biotherapeutics that can agonize or antagonize them. Furthermore, computational methods for de novo and epitope-specific antibody design are increasingly being developed, opening novel computationally driven opportunities for biologic drug discovery. Here, we review the opportunities and limitations of emerging computational approaches for optimizing antigens to generate robust immune responses, in silico generation of antibody sequences, discovery of potential antibody binders through virtual screening, assessment of hits, identification of lead drug candidates and their affinity maturation, and optimization for developability. The adoption of biopharmaceutical informatics across all aspects of drug discovery and development cycles should help bring affordable and effective biotherapeutics to patients more quickly.
Collapse
Affiliation(s)
- Joschka Bauer
- Early Stage Pharmaceutical Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach/Riss, Germany
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
| | - Nandhini Rajagopal
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Priyanka Gupta
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Pankaj Gupta
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Andrew E. Nixon
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| | - Sandeep Kumar
- In Silico Team, Boehringer Ingelheim, Hannover, Germany
- Biotherapeutics Discovery, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, United States
| |
Collapse
|
13
|
Desta IT, Kotelnikov S, Jones G, Ghani U, Abyzov M, Kholodov Y, Standley DM, Beglov D, Vajda S, Kozakov D. The ClusPro AbEMap web server for the prediction of antibody epitopes. Nat Protoc 2023; 18:1814-1840. [PMID: 37188806 PMCID: PMC10898366 DOI: 10.1038/s41596-023-00826-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 01/19/2023] [Indexed: 05/17/2023]
Abstract
Antibodies play an important role in the immune system by binding to molecules called antigens at their respective epitopes. These interfaces or epitopes are structural entities determined by the interactions between an antibody and an antigen, making them ideal systems to analyze by using docking programs. Since the advent of high-throughput antibody sequencing, the ability to perform epitope mapping using only the sequence of the antibody has become a high priority. ClusPro, a leading protein-protein docking server, together with its template-based modeling version, ClusPro-TBM, have been re-purposed to map epitopes for specific antibody-antigen interactions by using the Antibody Epitope Mapping server (AbEMap). ClusPro-AbEMap offers three different modes for users depending on the information available on the antibody as follows: (i) X-ray structure, (ii) computational/predicted model of the structure or (iii) only the amino acid sequence. The AbEMap server presents a likelihood score for each antigen residue of being part of the epitope. We provide detailed information on the server's capabilities for the three options and discuss how to obtain the best results. In light of the recent introduction of AlphaFold2 (AF2), we also show how one of the modes allows users to use their AF2-generated antibody models as input. The protocol describes the relative advantages of the server compared to other epitope-mapping tools, its limitations and potential areas of improvement. The server may take 45-90 min depending on the size of the proteins.
Collapse
Affiliation(s)
- Israel T Desta
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Sergei Kotelnikov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - George Jones
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Usman Ghani
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | | | | | - Daron M Standley
- Department of Genome Informatics, Osaka University, Osaka, Japan
- Center for Infectious Disease Education and Research, Osaka University, Osaka, Japan
| | - Dmitri Beglov
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.
| |
Collapse
|
14
|
Yin R, Ribeiro-Filho HV, Lin V, Gowthaman R, Cheung M, Pierce BG. TCRmodel2: high-resolution modeling of T cell receptor recognition using deep learning. Nucleic Acids Res 2023:7151345. [PMID: 37140040 DOI: 10.1093/nar/gkad356] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 04/08/2023] [Accepted: 04/25/2023] [Indexed: 05/05/2023] Open
Abstract
The cellular immune system, which is a critical component of human immunity, uses T cell receptors (TCRs) to recognize antigenic proteins in the form of peptides presented by major histocompatibility complex (MHC) proteins. Accurate definition of the structural basis of TCRs and their engagement of peptide-MHCs can provide major insights into normal and aberrant immunity, and can help guide the design of vaccines and immunotherapeutics. Given the limited amount of experimentally determined TCR-peptide-MHC structures and the vast amount of TCRs within each individual as well as antigenic targets, accurate computational modeling approaches are needed. Here, we report a major update to our web server, TCRmodel, which was originally developed to model unbound TCRs from sequence, to now model TCR-peptide-MHC complexes from sequence, utilizing several adaptations of AlphaFold. This method, named TCRmodel2, allows users to submit sequences through an easy-to-use interface and shows similar or greater accuracy than AlphaFold and other methods to model TCR-peptide-MHC complexes based on benchmarking. It can generate models of complexes in 15 minutes, and output models are provided with confidence scores and an integrated molecular viewer. TCRmodel2 is available at https://tcrmodel.ibbr.umd.edu.
Collapse
Affiliation(s)
- Rui Yin
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA
| | - Helder V Ribeiro-Filho
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA
- Brazilian Biosciences National Laboratory, Brazilian Center for Research in Energy and Materials, Campinas 13083-100, Brazil
| | - Valerie Lin
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA
- Thomas S. Wootton High School, Rockville, MD 20850, USA
| | - Ragul Gowthaman
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA
| | - Melyssa Cheung
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA
- Department of Chemistry and Biochemistry, University of Maryland, College Park, MD 20742, USA
| | - Brian G Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA
- University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, Baltimore, MD 21201, USA
| |
Collapse
|
15
|
Sternke-Hoffmann R, Pauly T, Norrild RK, Hansen J, Tucholski F, Høie MH, Marcatili P, Dupré M, Duchateau M, Rey M, Malosse C, Metzger S, Boquoi A, Platten F, Egelhaaf SU, Chamot-Rooke J, Fenk R, Nagel-Steger L, Haas R, Buell AK. Widespread amyloidogenicity potential of multiple myeloma patient-derived immunoglobulin light chains. BMC Biol 2023; 21:21. [PMID: 36737754 PMCID: PMC9898917 DOI: 10.1186/s12915-022-01506-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 12/15/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND In a range of human disorders such as multiple myeloma (MM), immunoglobulin light chains (IgLCs) can be produced at very high concentrations. This can lead to pathological aggregation and deposition of IgLCs in different tissues, which in turn leads to severe and potentially fatal organ damage. However, IgLCs can also be highly soluble and non-toxic. It is generally thought that the cause for this differential solubility behaviour is solely found within the IgLC amino acid sequences, and a variety of individual sequence-related biophysical properties (e.g. thermal stability, dimerisation) have been proposed in different studies as major determinants of the aggregation in vivo. Here, we investigate biophysical properties underlying IgLC amyloidogenicity. RESULTS We introduce a novel and systematic workflow, Thermodynamic and Aggregation Fingerprinting (ThAgg-Fip), for detailed biophysical characterisation, and apply it to nine different MM patient-derived IgLCs. Our set of pathogenic IgLCs spans the entire range of values in those parameters previously proposed to define in vivo amyloidogenicity; however, none actually forms amyloid in patients. Even more surprisingly, we were able to show that all our IgLCs are able to form amyloid fibrils readily in vitro under the influence of proteolytic cleavage by co-purified cathepsins. CONCLUSIONS We show that (I) in vivo aggregation behaviour is unlikely to be mechanistically linked to any single biophysical or biochemical parameter and (II) amyloidogenic potential is widespread in IgLC sequences and is not confined to those sequences that form amyloid fibrils in patients. Our findings suggest that protein sequence, environmental conditions and presence and action of proteases all determine the ability of light chains to form amyloid fibrils in patients.
Collapse
Affiliation(s)
- Rebecca Sternke-Hoffmann
- grid.411327.20000 0001 2176 9917Institut für Physikalische Biologie, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany ,grid.5991.40000 0001 1090 7501Department of Biology and Chemistry, Paul Scherrer Institute, Villigen, Switzerland
| | - Thomas Pauly
- grid.411327.20000 0001 2176 9917Institut für Physikalische Biologie, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany ,grid.8385.60000 0001 2297 375XForschungszentrum Jülich GmbH, IBI-7, Jülich, Germany
| | - Rasmus K. Norrild
- grid.5170.30000 0001 2181 8870Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, Denmark
| | - Jan Hansen
- grid.411327.20000 0001 2176 9917Condensed Matter Physics Laboratory, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
| | - Florian Tucholski
- grid.411327.20000 0001 2176 9917Institut für Physikalische Biologie, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
| | - Magnus Haraldson Høie
- grid.5170.30000 0001 2181 8870Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Paolo Marcatili
- grid.5170.30000 0001 2181 8870Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Mathieu Dupré
- grid.428999.70000 0001 2353 6535Mass Spectrometry for Biology Unit, CNRS USR2000, Institut Pasteur, 75015 Paris, France
| | - Magalie Duchateau
- grid.428999.70000 0001 2353 6535Mass Spectrometry for Biology Unit, CNRS USR2000, Institut Pasteur, 75015 Paris, France
| | - Martial Rey
- grid.428999.70000 0001 2353 6535Mass Spectrometry for Biology Unit, CNRS USR2000, Institut Pasteur, 75015 Paris, France
| | - Christian Malosse
- grid.428999.70000 0001 2353 6535Mass Spectrometry for Biology Unit, CNRS USR2000, Institut Pasteur, 75015 Paris, France
| | - Sabine Metzger
- grid.6190.e0000 0000 8580 3777Cologne Biocenter, Cluster of Excellence on Plant Sciences, Mass Spectrometry Platform, University of Cologne, Cologne, Germany
| | - Amelie Boquoi
- grid.411327.20000 0001 2176 9917Department of Hematology, Oncology and Clinical Oncology, Heinrich-Heine Universität Düsseldorf, Düsseldorf, Germany
| | - Florian Platten
- grid.411327.20000 0001 2176 9917Condensed Matter Physics Laboratory, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany ,grid.8385.60000 0001 2297 375XForschungszentrum Jülich GmbH, IBI-4, Jülich, Germany
| | - Stefan U. Egelhaaf
- grid.411327.20000 0001 2176 9917Condensed Matter Physics Laboratory, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
| | - Julia Chamot-Rooke
- grid.428999.70000 0001 2353 6535Mass Spectrometry for Biology Unit, CNRS USR2000, Institut Pasteur, 75015 Paris, France
| | - Roland Fenk
- grid.411327.20000 0001 2176 9917Department of Hematology, Oncology and Clinical Oncology, Heinrich-Heine Universität Düsseldorf, Düsseldorf, Germany
| | - Luitgard Nagel-Steger
- grid.411327.20000 0001 2176 9917Institut für Physikalische Biologie, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany ,grid.8385.60000 0001 2297 375XForschungszentrum Jülich GmbH, IBI-7, Jülich, Germany
| | - Rainer Haas
- Department of Hematology, Oncology and Clinical Oncology, Heinrich-Heine Universität Düsseldorf, Düsseldorf, Germany.
| | - Alexander K. Buell
- grid.411327.20000 0001 2176 9917Institut für Physikalische Biologie, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany ,grid.5170.30000 0001 2181 8870Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, Denmark
| |
Collapse
|
16
|
Lusiany T, Xu Z, Saputri DS, Ismanto HS, Nazlica SA, Standley DM. Structural Modeling of Adaptive Immune Responses to Infection. Methods Mol Biol 2023; 2552:283-294. [PMID: 36346598 DOI: 10.1007/978-1-0716-2609-2_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Antibody and TCR modeling are becoming important as more and more sequence data becomes available to the public. One of the pressing questions now is how to use such data to understand adaptive immune responses to disease. Infectious disease is of particular interest because the antigens driving such responses are often known to some extent. Here, we describe tips for gathering data and cleaning it for use in downstream analysis. We present a method for high-throughput structural modeling of antibodies or TCRs using Repertoire Builder and its extensions. AbAdapt is an extension of Repertoire Builder for antibody-antigen docking from antibody and antigen sequences. ImmuneScape is a corresponding extension for TCR-pMHC 3D modeling. Together, these pipelines can help researchers to understand immune responses to infection from a structural point of view.
Collapse
Affiliation(s)
- Tina Lusiany
- Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - Zichang Xu
- Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - Dianita S Saputri
- Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | - Hendra S Ismanto
- Research Institute for Microbial Diseases, Osaka University, Suita, Japan
| | | | - Daron M Standley
- Research Institute for Microbial Diseases, Osaka University, Suita, Japan.
| |
Collapse
|
17
|
Marin FI, Marcatili P. Computational Modeling of Antibody and T-Cell Receptor (CDR3 Loops). Methods Mol Biol 2023; 2552:83-100. [PMID: 36346586 DOI: 10.1007/978-1-0716-2609-2_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Antibodies and T-cell receptors have been a subject of much interest due to their central role in the immune system and their potential applications in several biotechnological and medical applications from cancer therapy to vaccine development. A unique feature of these two lymphocyte receptors is their ability to bind a huge variety of different (pathogen) targets. This ability stems from six short loops in the binding domain that have hypervariable sequence due to genetic recombination mechanism. Particularly one of these loops, the third complementarity determining region (CDR3), has the highest sequence variability and a dominant role in binding the target. However, it has also been proven the most difficult to be modeled structurally, which is vitally important for downstream tasks such as binding prediction. This difficulty stems from its variability in sequence that both reduces the possibility of finding homologues and introduces unique structural features in the loop. We present here a general protocol for modeling such loops in antibodies and T-cell receptors. We also discuss the difficulties in loop modeling and the advantages and limitations of different modeling methods.
Collapse
Affiliation(s)
- Frederikke I Marin
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Paolo Marcatili
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark.
| |
Collapse
|
18
|
Katoh H, Komura D, Furuya G, Ishikawa S. Immune repertoire profiling for disease pathobiology. Pathol Int 2023; 73:1-11. [PMID: 36342353 PMCID: PMC10099665 DOI: 10.1111/pin.13284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 09/20/2022] [Accepted: 10/13/2022] [Indexed: 11/09/2022]
Abstract
Lymphocytes consist of highly heterogeneous populations, each expressing a specific cell surface receptor corresponding to a particular antigen. Lymphocytes are both the cause and regulator of various diseases, including autoimmune/allergic diseases, lifestyle diseases, neurodegenerative diseases, and cancers. Recently, immune repertoire sequencing has attracted much attention because it helps obtain global profiles of the immune receptor sequences of infiltrating T and B cells in specimens. Immune repertoire sequencing not only helps deepen our understanding of the molecular mechanisms of immune-related pathology but also assists in discovering novel therapeutic modalities for diseases, thereby shedding colorful light on otherwise tiny monotonous cells when observed under a microscope. In this review article, we introduce and detail the background and methodology of immune repertoire sequencing and summarize recent scientific achievements in association with human diseases. Future perspectives on this genetic technique in the field of histopathological research will also be discussed.
Collapse
Affiliation(s)
- Hiroto Katoh
- Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Daisuke Komura
- Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Genta Furuya
- Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Shumpei Ishikawa
- Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| |
Collapse
|
19
|
Montemurro A, Jessen LE, Nielsen M. NetTCR-2.1: Lessons and guidance on how to develop models for TCR specificity predictions. Front Immunol 2022; 13:1055151. [PMID: 36561755 PMCID: PMC9763291 DOI: 10.3389/fimmu.2022.1055151] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 11/22/2022] [Indexed: 12/12/2022] Open
Abstract
T cell receptors (TCR) define the specificity of T cells and are responsible for their interaction with peptide antigen targets presented in complex with major histocompatibility complex (MHC) molecules. Understanding the rules underlying this interaction hence forms the foundation for our understanding of basic adaptive immunology. Over the last decade, efforts have been dedicated to developing assays for high throughput identification of peptide-specific TCRs. Based on such data, several computational methods have been proposed for predicting the TCR-pMHC interaction. The general conclusion from these studies is that the prediction of TCR interactions with MHC-peptide complexes remains highly challenging. Several reasons form the basis for this including scarcity and quality of data, and ill-defined modeling objectives imposed by the high redundancy of the available data. In this work, we propose a framework for dealing with this redundancy, allowing us to address essential questions related to the modeling of TCR specificity including the use of peptide- versus pan-specific models, how to best define negative data, and the performance impact of integrating of CDR1 and 2 loops. Further, we illustrate how and why it is strongly recommended to include simple similarity-based modeling approaches when validating an improved predictive power of machine learning models, and that such validation should include a performance evaluation as a function of "distance" to the training data, to quantify the potential for generalization of the proposed model. The conclusion of the work is that, given current data, TCR specificity is best modeled using peptide-specific approaches, integrating information from all 6 CDR loops, and with negative data constructed from a combination of true and mislabeled negatives. Comparing such machine learning models to similarity-based approaches demonstrated an increased performance gain of the former as the "distance" to the training data was increased; thus demonstrating an improved generalization ability of the machine learning-based approaches. We believe these results demonstrate that the outlined modeling framework and proposed evaluation strategy form a solid basis for investigating the modeling of TCR specificities and that adhering to such a framework will allow for faster progress within the field. The final devolved model, NetTCR-2.1, is available at https://services.healthtech.dtu.dk/service.php?NetTCR-2.1.
Collapse
Affiliation(s)
- Alessandro Montemurro
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs., Lyngby, Denmark
| | - Leon Eyrich Jessen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs., Lyngby, Denmark
| | - Morten Nielsen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800 Kgs., Lyngby, Denmark,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina,*Correspondence: Morten Nielsen,
| |
Collapse
|
20
|
Vishwakarma P, Vattekatte AM, Shinada N, Diharce J, Martins C, Cadet F, Gardebien F, Etchebest C, Nadaradjane AA, de Brevern AG. V HH Structural Modelling Approaches: A Critical Review. Int J Mol Sci 2022; 23:3721. [PMID: 35409081 PMCID: PMC8998791 DOI: 10.3390/ijms23073721] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 03/23/2022] [Accepted: 03/23/2022] [Indexed: 12/20/2022] Open
Abstract
VHH, i.e., VH domains of camelid single-chain antibodies, are very promising therapeutic agents due to their significant physicochemical advantages compared to classical mammalian antibodies. The number of experimentally solved VHH structures has significantly improved recently, which is of great help, because it offers the ability to directly work on 3D structures to humanise or improve them. Unfortunately, most VHHs do not have 3D structures. Thus, it is essential to find alternative ways to get structural information. The methods of structure prediction from the primary amino acid sequence appear essential to bypass this limitation. This review presents the most extensive overview of structure prediction methods applied for the 3D modelling of a given VHH sequence (a total of 21). Besides the historical overview, it aims at showing how model software programs have been shaping the structural predictions of VHHs. A brief explanation of each methodology is supplied, and pertinent examples of their usage are provided. Finally, we present a structure prediction case study of a recently solved VHH structure. According to some recent studies and the present analysis, AlphaFold 2 and NanoNet appear to be the best tools to predict a structural model of VHH from its sequence.
Collapse
Affiliation(s)
- Poonam Vishwakarma
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-75015 Paris, France; (P.V.); (A.M.V.); (J.D.); (C.M.); (C.E.); (A.A.N.)
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-97715 Saint Denis Messag, France; (F.C.); (F.G.)
| | - Akhila Melarkode Vattekatte
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-75015 Paris, France; (P.V.); (A.M.V.); (J.D.); (C.M.); (C.E.); (A.A.N.)
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-97715 Saint Denis Messag, France; (F.C.); (F.G.)
| | | | - Julien Diharce
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-75015 Paris, France; (P.V.); (A.M.V.); (J.D.); (C.M.); (C.E.); (A.A.N.)
| | - Carla Martins
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-75015 Paris, France; (P.V.); (A.M.V.); (J.D.); (C.M.); (C.E.); (A.A.N.)
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-97715 Saint Denis Messag, France; (F.C.); (F.G.)
| | - Frédéric Cadet
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-97715 Saint Denis Messag, France; (F.C.); (F.G.)
- PEACCEL, Artificial Intelligence Department, Square Albin Cachot, F-75013 Paris, France
| | - Fabrice Gardebien
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-97715 Saint Denis Messag, France; (F.C.); (F.G.)
| | - Catherine Etchebest
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-75015 Paris, France; (P.V.); (A.M.V.); (J.D.); (C.M.); (C.E.); (A.A.N.)
| | - Aravindan Arun Nadaradjane
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-75015 Paris, France; (P.V.); (A.M.V.); (J.D.); (C.M.); (C.E.); (A.A.N.)
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-97715 Saint Denis Messag, France; (F.C.); (F.G.)
| | - Alexandre G. de Brevern
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-75015 Paris, France; (P.V.); (A.M.V.); (J.D.); (C.M.); (C.E.); (A.A.N.)
- INSERM UMR_S 1134, BIGR, DSIMB Team, Université de Paris and Université de la Réunion, F-97715 Saint Denis Messag, France; (F.C.); (F.G.)
| |
Collapse
|
21
|
Alghamdi W, Attique M, Alzahrani E, Ullah MZ, Khan YD. LBCEPred: a machine learning model to predict linear B-cell epitopes. Brief Bioinform 2022; 23:6543896. [PMID: 35262658 DOI: 10.1093/bib/bbac035] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 01/03/2022] [Accepted: 01/25/2022] [Indexed: 01/15/2023] Open
Abstract
B-cell epitopes have the capability to recognize and attach to the surface of antigen receptors to stimulate the immune system against pathogens. Identification of B-cell epitopes from antigens has a great significance in several biomedical and biotechnological applications, provides support in the development of therapeutics, design and development of an epitope-based vaccine and antibody production. However, the identification of epitopes with experimental mapping approaches is a challenging job and usually requires extensive laboratory efforts. However, considerable efforts have been placed for the identification of epitopes using computational methods in the recent past but deprived of considerable achievements. In this study, we present LBCEPred, a python-based web-tool (http://lbcepred.pythonanywhere.com/), build with random forest classifier and statistical moment-based descriptors to predict the B-cell epitopes from the protein sequences. LBECPred outperforms all sequence-based available models that are currently in use for the B-cell epitopes prediction, with 0.868 accuracy value and 0.934 area under the curve. Moreover, the prediction performance of proposed models compared to other state-of-the-art models is 56.3% higher on average for Mathews Correlation Coefficient. LBCEPred is easy to use tool even for novice users and has also shown the models stability and reliability, thus we believe in its significant contribution to the research community and the area of bioinformatics.
Collapse
Affiliation(s)
- Wajdi Alghamdi
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, P.O. Box 80221, Jeddah, Saudi Arabia
| | - Muhammad Attique
- Department of Computer Science, University of Management and Technology, Lahore, 54000, Pakistan.,Department of Information Technology, University of Gujrat, Gujrat, 50700, Pakistan
| | - Ebraheem Alzahrani
- Department of Mathematics, Faculty of Science, King Abdulaziz University, P.O. Box 80203, Jeddah 21589, Saudi Arabia
| | - Malik Zaka Ullah
- Department of Mathematics, Faculty of Science, King Abdulaziz University, P.O. Box 80203, Jeddah 21589, Saudi Arabia
| | - Yaser Daanial Khan
- Department of Computer Science, University of Management and Technology, Lahore, 54000, Pakistan
| |
Collapse
|
22
|
Dao T, Mun SS, Molvi Z, Korontsvit T, Klatt MG, Khan AG, Nyakatura EK, Pohl MA, White TE, Balderes PJ, Lorenz IC, O'Reilly RJ, Scheinberg DA. A TCR mimic monoclonal antibody reactive with the "public" phospho-neoantigen pIRS2/HLA-A*02:01 complex. JCI Insight 2022; 7:151624. [PMID: 35260532 PMCID: PMC8983142 DOI: 10.1172/jci.insight.151624] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 01/26/2022] [Indexed: 02/06/2023] Open
Abstract
Phosphopeptides derived from dysregulated protein phosphorylation in cancer cells can be processed and presented by MHC class I and class II molecules and, therefore, represent an untapped class of tumor-specific antigens that could be used as widely expressed “public” cancer neoantigens (NeoAgs). We generated a TCR mimic (TCRm) mAb, 6B1, specific for a phosphopeptide derived from insulin receptor substrate 2 (pIRS2) presented by HLA-A*02:01. The pIRS2 epitope’s presentation by HLA-A*02:01 was confirmed by mass spectrometry. The TCRm 6B1 specifically bound to pIRS2/HLA-A2 complex on tumor cell lines that expressed pIRS2 in the context of HLA-A*02:01. Bispecific mAbs engaging CD3 of T cells were able to kill tumor cell lines in a pIRS2- and HLA-A*02:01–restricted manner. Structure modeling shows a prerequisite for an arginine or lysine at the first position to bind mAb. Therefore, 6B1 could recognize phosphopeptides derived from various phosphorylated proteins with similar amino acid compositions. This raised the possibility that a TCRm specific for the pIRS2/HLA-A2 complex could target a range of phosphopeptides presented by HLA-A*02:01 in various tumor cells. This is the first TCRm mAb to our knowledge targeting a phosphopeptide/MHC class I complex; the potential of this class of agents for clinical applications warrants further investigation.
Collapse
Affiliation(s)
- Tao Dao
- Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center (MSKCC), New York, New York, USA
| | - Sung Soo Mun
- Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center (MSKCC), New York, New York, USA
| | - Zaki Molvi
- Immunology Program, Weill Cornell Medicine, New York, New York, USA
| | - Tatyana Korontsvit
- Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center (MSKCC), New York, New York, USA
| | - Martin G Klatt
- Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center (MSKCC), New York, New York, USA
| | - Abdul G Khan
- Tri-Institutional Therapeutics Discovery Institute, New York, New York, USA
| | | | - Mary Ann Pohl
- Tri-Institutional Therapeutics Discovery Institute, New York, New York, USA
| | - Thomas E White
- Tri-Institutional Therapeutics Discovery Institute, New York, New York, USA
| | - Paul J Balderes
- Tri-Institutional Therapeutics Discovery Institute, New York, New York, USA
| | - Ivo C Lorenz
- Tri-Institutional Therapeutics Discovery Institute, New York, New York, USA
| | - Richard J O'Reilly
- Immunology Program, Weill Cornell Medicine, New York, New York, USA.,Weill Cornell Medicine, New York, New York, USA
| | - David A Scheinberg
- Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center (MSKCC), New York, New York, USA.,Weill Cornell Medicine, New York, New York, USA
| |
Collapse
|
23
|
Davila A, Xu Z, Li S, Rozewicki J, Wilamowski J, Kotelnikov S, Kozakov D, Teraguchi S, Standley DM. AbAdapt: an adaptive approach to predicting antibody-antigen complex structures from sequence. BIOINFORMATICS ADVANCES 2022; 2:vbac015. [PMID: 36699363 PMCID: PMC9710585 DOI: 10.1093/bioadv/vbac015] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 02/15/2022] [Accepted: 03/03/2022] [Indexed: 01/28/2023]
Abstract
Motivation The scoring of antibody-antigen docked poses starting from unbound homology models has not been systematically optimized for a large and diverse set of input sequences. Results To address this need, we have developed AbAdapt, a webserver that accepts antibody and antigen sequences, models their 3D structures, predicts epitope and paratope, and then docks the modeled structures using two established docking engines (Piper and Hex). Each of the key steps has been optimized by developing and training new machine-learning models. The sequences from a diverse set of 622 antibody-antigen pairs with known structure were used as inputs for leave-one-out cross-validation. The final set of cluster representatives included at least one 'Adequate' pose for 550/622 (88.4%) of the queries. The median (interquartile range) ranks of these 'Adequate' poses were 22 (5-77). Similar results were obtained on a holdout set of 100 unrelated antibody-antigen pairs. When epitopes were repredicted using docking-derived features for specific antibodies, the median ROC AUC increased from 0.679 to 0.720 in cross-validation and from 0.694 to 0.730 in the holdout set. Availability and implementation AbAdapt and related data are available at https://sysimm.org/abadapt/. Supplementary information Supplementary data are available at Bioinformatics Advances online.
Collapse
Affiliation(s)
- Ana Davila
- Research Institute for Microbial Diseases, Department of Genome Informatics, Osaka University, Suita 565-0871, Japan
| | - Zichang Xu
- Research Institute for Microbial Diseases, Department of Genome Informatics, Osaka University, Suita 565-0871, Japan
| | - Songling Li
- Research Institute for Microbial Diseases, Department of Genome Informatics, Osaka University, Suita 565-0871, Japan
| | - John Rozewicki
- Research Institute for Microbial Diseases, Department of Genome Informatics, Osaka University, Suita 565-0871, Japan
| | - Jan Wilamowski
- Research Institute for Microbial Diseases, Department of Genome Informatics, Osaka University, Suita 565-0871, Japan
| | - Sergei Kotelnikov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794-5252, USA,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794-5252, USA
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794-5252, USA,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794-5252, USA
| | - Shunsuke Teraguchi
- Research Institute for Microbial Diseases, Department of Genome Informatics, Osaka University, Suita 565-0871, Japan,Faculty of Data Science, Shiga University, Hikone 522-8522, Japan
| | - Daron M Standley
- Research Institute for Microbial Diseases, Department of Genome Informatics, Osaka University, Suita 565-0871, Japan,Immunology Frontier Research Center, Department of Systems Immunology, Osaka University, Suita 565-0871, Japan,To whom correspondence should be addressed.
| |
Collapse
|
24
|
Gong W, Pan C, Cheng P, Wang J, Zhao G, Wu X. Peptide-Based Vaccines for Tuberculosis. Front Immunol 2022; 13:830497. [PMID: 35173740 PMCID: PMC8841753 DOI: 10.3389/fimmu.2022.830497] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 01/10/2022] [Indexed: 12/12/2022] Open
Abstract
Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis. As a result of the coronavirus disease 2019 (COVID-19) pandemic, the global TB mortality rate in 2020 is rising, making TB prevention and control more challenging. Vaccination has been considered the best approach to reduce the TB burden. Unfortunately, BCG, the only TB vaccine currently approved for use, offers some protection against childhood TB but is less effective in adults. Therefore, it is urgent to develop new TB vaccines that are more effective than BCG. Accumulating data indicated that peptides or epitopes play essential roles in bridging innate and adaptive immunity and triggering adaptive immunity. Furthermore, innovations in bioinformatics, immunoinformatics, synthetic technologies, new materials, and transgenic animal models have put wings on the research of peptide-based vaccines for TB. Hence, this review seeks to give an overview of current tools that can be used to design a peptide-based vaccine, the research status of peptide-based vaccines for TB, protein-based bacterial vaccine delivery systems, and animal models for the peptide-based vaccines. These explorations will provide approaches and strategies for developing safer and more effective peptide-based vaccines and contribute to achieving the WHO's End TB Strategy.
Collapse
Affiliation(s)
- Wenping Gong
- Tuberculosis Prevention and Control Key Laboratory/Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, The 8th Medical Center of PLA General Hospital, Beijing, China
| | - Chao Pan
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Biotechnology, Beijing, China
| | - Peng Cheng
- Tuberculosis Prevention and Control Key Laboratory/Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, The 8th Medical Center of PLA General Hospital, Beijing, China
- Hebei North University, Zhangjiakou City, China
| | - Jie Wang
- Tuberculosis Prevention and Control Key Laboratory/Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, The 8th Medical Center of PLA General Hospital, Beijing, China
| | - Guangyu Zhao
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Xueqiong Wu
- Tuberculosis Prevention and Control Key Laboratory/Beijing Key Laboratory of New Techniques of Tuberculosis Diagnosis and Treatment, Senior Department of Tuberculosis, The 8th Medical Center of PLA General Hospital, Beijing, China
| |
Collapse
|
25
|
Bonaventura P, Alcazer V, Mutez V, Tonon L, Martin J, Chuvin N, Michel E, Boulos RE, Estornes Y, Valladeau-Guilemond J, Viari A, Wang Q, Caux C, Depil S. Identification of shared tumor epitopes from endogenous retroviruses inducing high-avidity cytotoxic T cells for cancer immunotherapy. SCIENCE ADVANCES 2022; 8:eabj3671. [PMID: 35080970 PMCID: PMC8791462 DOI: 10.1126/sciadv.abj3671] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Human endogenous retroviruses (HERVs) represent 8% of the human genome. HERV products may represent tumor antigens relevant for cancer immunotherapy. We developed a bioinformatic approach to identify shared CD8+ T cell epitopes derived from cancer-associated HERVs in solid tumors. Six candidates among the most commonly shared HLA-A2 epitopes with evidence of translation were selected for immunological evaluation. In vitro priming assays confirmed the immunogenicity of these epitopes, which induced high-avidity CD8+ T cell clones. These T cells specifically recognize and kill HLA-A2+ tumor cells presenting HERV epitopes on HLA molecules, as demonstrated by mass spectrometry. Furthermore, epitope-specific CD8+ T cells were identified by dextramer staining among tumor-infiltrating lymphocytes from HLA-A2+ patients with breast cancer. Last, we showed that HERV-specific T cells lyse patient-derived organoids. These shared virus-like epitopes are of major interest for the development of cancer vaccines or T cell-based immunotherapies, especially in tumors with low/intermediate mutational burden.
Collapse
Affiliation(s)
- Paola Bonaventura
- Centre de Recherche en Cancérologie de Lyon (CRCL), UMR INSERM U1052 CNRS 5286, Lyon, France
- Centre Léon Bérard, Lyon, France
| | - Vincent Alcazer
- Centre de Recherche en Cancérologie de Lyon (CRCL), UMR INSERM U1052 CNRS 5286, Lyon, France
| | | | - Laurie Tonon
- Synergie Lyon Cancer, Plateforme de bioinformatique « Gilles Thomas », Lyon, France
| | - Juliette Martin
- CNRS-Institut de Biologie et Chimie des Protéines UMR 5086, Lyon, France
| | | | | | | | | | | | - Alain Viari
- Synergie Lyon Cancer, Plateforme de bioinformatique « Gilles Thomas », Lyon, France
| | | | - Christophe Caux
- Centre de Recherche en Cancérologie de Lyon (CRCL), UMR INSERM U1052 CNRS 5286, Lyon, France
- Centre Léon Bérard, Lyon, France
| | - Stéphane Depil
- Centre de Recherche en Cancérologie de Lyon (CRCL), UMR INSERM U1052 CNRS 5286, Lyon, France
- Centre Léon Bérard, Lyon, France
- ErVaccine Technologies, Lyon, France
- Université Claude Bernard Lyon 1, Lyon, France
- Corresponding author.
| |
Collapse
|
26
|
Milighetti M, Shawe-Taylor J, Chain B. Predicting T Cell Receptor Antigen Specificity From Structural Features Derived From Homology Models of Receptor-Peptide-Major Histocompatibility Complexes. Front Physiol 2021; 12:730908. [PMID: 34566692 PMCID: PMC8456106 DOI: 10.3389/fphys.2021.730908] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 08/02/2021] [Indexed: 11/13/2022] Open
Abstract
The physical interaction between the T cell receptor (TCR) and its cognate antigen causes T cells to activate and participate in the immune response. Understanding this physical interaction is important in predicting TCR binding to a target epitope, as well as potential cross-reactivity. Here, we propose a way of collecting informative features of the binding interface from homology models of T cell receptor-peptide-major histocompatibility complex (TCR-pMHC) complexes. The information collected from these structures is sufficient to discriminate binding from non-binding TCR-pMHC pairs in multiple independent datasets. The classifier is limited by the number of crystal structures available for the homology modelling and by the size of the training set. However, the classifier shows comparable performance to sequence-based classifiers requiring much larger training sets.
Collapse
Affiliation(s)
- Martina Milighetti
- Division of Infection and Immunity, University College London, London, United Kingdom
- Cancer Institute, University College London, London, United Kingdom
| | - John Shawe-Taylor
- Department of Computer Science, University College London, London, United Kingdom
| | - Benny Chain
- Division of Infection and Immunity, University College London, London, United Kingdom
- Department of Computer Science, University College London, London, United Kingdom
| |
Collapse
|
27
|
Shklovskaya E, Rizos H. MHC Class I Deficiency in Solid Tumors and Therapeutic Strategies to Overcome It. Int J Mol Sci 2021; 22:ijms22136741. [PMID: 34201655 PMCID: PMC8268865 DOI: 10.3390/ijms22136741] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 06/18/2021] [Accepted: 06/21/2021] [Indexed: 12/21/2022] Open
Abstract
It is now well accepted that the immune system can control cancer growth. However, tumors escape immune-mediated control through multiple mechanisms and the downregulation or loss of major histocompatibility class (MHC)-I molecules is a common immune escape mechanism in many cancers. MHC-I molecules present antigenic peptides to cytotoxic T cells, and MHC-I loss can render tumor cells invisible to the immune system. In this review, we examine the dysregulation of MHC-I expression in cancer, explore the nature of MHC-I-bound antigenic peptides recognized by immune cells, and discuss therapeutic strategies that can be used to overcome MHC-I deficiency in solid tumors, with a focus on the role of natural killer (NK) cells and CD4 T cells.
Collapse
|
28
|
Cortés GT, Beltran MMG, Gómez-Alegría CJ, Wiser MF. Identification of a protein unique to the genus Plasmodium that contains a WD40 repeat domain and extensive low-complexity sequence. Parasitol Res 2021; 120:2617-2629. [PMID: 34142223 DOI: 10.1007/s00436-021-07190-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 05/11/2021] [Indexed: 11/27/2022]
Abstract
Proteins containing WD40 domains play important roles in the formation of multiprotein complexes. Little is known about WD40 proteins in the malaria parasite. This report contains the initial description of a WD40 protein that is unique to the genus Plasmodium and possibly closely related genera. The N-terminal portion of this protein consists of seven WD40 repeats that are highly conserved in all Plasmodium species. Following the N-terminal region is a central region that is conserved within the major Plasmodium clades, such as parasites of great apes, monkeys, rodents, and birds, but partially conserved across all Plasmodium species. This central region contains extensive low-complexity sequence and is predicted to have a disordered structure. Proteins with disordered structure generally function in molecular interactions. The C-terminal region is semi-conserved across all Plasmodium species and has no notable features. This WD40 repeat protein likely functions in some aspect of parasite biology that is unique to Plasmodium and this uniqueness makes the protein a possible target for therapeutic intervention.
Collapse
Affiliation(s)
- Gladys T Cortés
- Departamento de Salud Pública, Facultad de Medicina, Grupo Biologia Celular, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Martha Margarita Gonzalez Beltran
- Ex alumna de la Maestría en Ciencias-Bioquímica, Departamento de Química, Facultad de Ciencias, Universidad Nacional de Colombia, Grupo UNIMOL, Bogotá, Colombia
| | - Claudio J Gómez-Alegría
- Departamento de Farmacia, Facultad de Ciencias, Universidad Nacional de Colombia, Grupo UNIMOL, Bogotá, Colombia
| | - Mark F Wiser
- Department of Tropical Medicine, Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street, Suite 2301, New Orleans, LA, 70112-2824, USA.
| |
Collapse
|
29
|
Peacock T, Chain B. Information-Driven Docking for TCR-pMHC Complex Prediction. Front Immunol 2021; 12:686127. [PMID: 34177934 PMCID: PMC8219952 DOI: 10.3389/fimmu.2021.686127] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 05/07/2021] [Indexed: 12/16/2022] Open
Abstract
T cell receptor (TCR) recognition of peptides presented by major histocompatibility complex (MHC) molecules is a fundamental process in the adaptive immune system. An understanding of this recognition process at the molecular level is crucial for TCR based therapeutics and vaccine design. The broad nature of TCR diversity and cross-reactivity presents a challenge for traditional structural resolution. Computational modelling of TCR-pMHC complexes offers an efficient alternative. This study compares the ability of four general-purpose docking platforms (ClusPro, LightDock, ZDOCK and HADDOCK) to make use of varying levels of binding interface information for accurate TCR-pMHC modelling. Each platform was tested on an expanded benchmark set of 44 TCR-pMHC docking cases. In general, HADDOCK is shown to be the best performer. Docking strategy guidance is provided to obtain the best models for each platform for future research. The TCR-pMHC docking cases used in this study can be downloaded from https://github.com/innate2adaptive/ExpandedBenchmark.
Collapse
Affiliation(s)
- Thomas Peacock
- Division of Infection and Immunity, University College London, London, United Kingdom.,The UCL Centre for Computation, Mathematics and Physics in the Life Sciences and Experimental Biology (CoMPLEX), Department Computer Science, University College London, London, United Kingdom
| | - Benny Chain
- Division of Infection and Immunity, University College London, London, United Kingdom
| |
Collapse
|
30
|
Wong WK, Marks C, Leem J, Lewis AP, Shi J, Deane CM. TCRBuilder: multi-state T-cell receptor structure prediction. Bioinformatics 2020; 36:3580-3581. [PMID: 32181809 DOI: 10.1093/bioinformatics/btaa194] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 02/21/2020] [Accepted: 03/13/2020] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION T-cell receptors (TCRs) are immune proteins that primarily target peptide antigens presented by the major histocompatibility complex. They tend to have lower specificity and affinity than their antibody counterparts, and their binding sites have been shown to adopt multiple conformations, which is potentially an important factor for their polyspecificity. None of the current TCR-modelling tools predict this variability which limits our ability to accurately predict TCR binding. RESULTS We present TCRBuilder, a multi-state TCR structure prediction tool. Given a paired αβTCR sequence, TCRBuilder returns a model or an ensemble of models covering the potential conformations of the binding site. This enables the analysis of structurally driven polyspecificity in TCRs, which is not possible with existing tools. AVAILABILITY AND IMPLEMENTATION http://opig.stats.ox.ac.uk/resources. CONTACT deane@stats.ox.ac.uk. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Wing Ki Wong
- Department of Statistics, University of Oxford, Oxford OX1 3LB, UK
| | - Claire Marks
- Department of Statistics, University of Oxford, Oxford OX1 3LB, UK
| | - Jinwoo Leem
- Department of Statistics, University of Oxford, Oxford OX1 3LB, UK
| | - Alan P Lewis
- Data and Computational Sciences, GlaxoSmithKline Research and Development, Stevenage SG1 2NY, UK
| | - Jiye Shi
- Department of Chemistry, UCB Pharma, Slough SL1 3WE, UK
| | | |
Collapse
|
31
|
Norman RA, Ambrosetti F, Bonvin AMJJ, Colwell LJ, Kelm S, Kumar S, Krawczyk K. Computational approaches to therapeutic antibody design: established methods and emerging trends. Brief Bioinform 2020; 21:1549-1567. [PMID: 31626279 PMCID: PMC7947987 DOI: 10.1093/bib/bbz095] [Citation(s) in RCA: 113] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 06/07/2019] [Accepted: 07/05/2019] [Indexed: 12/31/2022] Open
Abstract
Antibodies are proteins that recognize the molecular surfaces of potentially noxious molecules to mount an adaptive immune response or, in the case of autoimmune diseases, molecules that are part of healthy cells and tissues. Due to their binding versatility, antibodies are currently the largest class of biotherapeutics, with five monoclonal antibodies ranked in the top 10 blockbuster drugs. Computational advances in protein modelling and design can have a tangible impact on antibody-based therapeutic development. Antibody-specific computational protocols currently benefit from an increasing volume of data provided by next generation sequencing and application to related drug modalities based on traditional antibodies, such as nanobodies. Here we present a structured overview of available databases, methods and emerging trends in computational antibody analysis and contextualize them towards the engineering of candidate antibody therapeutics.
Collapse
|
32
|
Vujovic M, Degn KF, Marin FI, Schaap-Johansen AL, Chain B, Andresen TL, Kaplinsky J, Marcatili P. T cell receptor sequence clustering and antigen specificity. Comput Struct Biotechnol J 2020; 18:2166-2173. [PMID: 32952933 PMCID: PMC7473833 DOI: 10.1016/j.csbj.2020.06.041] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 06/25/2020] [Accepted: 06/27/2020] [Indexed: 11/17/2022] Open
Abstract
There has been increasing interest in the role of T cells and their involvement in cancer, autoimmune and infectious diseases. However, the nature of T cell receptor (TCR) epitope recognition at a repertoire level is not yet fully understood. Due to technological advances a plethora of TCR sequences from a variety of disease and treatment settings has become readily available. Current efforts in TCR specificity analysis focus on identifying characteristics in immune repertoires which can explain or predict disease outcome or progression, or can be used to monitor the efficacy of disease therapy. In this context, clustering of TCRs by sequence to reflect biological similarity, and especially to reflect antigen specificity have become of paramount importance. We review the main TCR sequence clustering methods and the different similarity measures they use, and discuss their performance and possible improvement. We aim to provide guidance for non-specialists who wish to use TCR repertoire sequencing for disease tracking, patient stratification or therapy prediction, and to provide a starting point for those aiming to develop novel techniques for TCR annotation through clustering.
Collapse
Affiliation(s)
- Milena Vujovic
- DTU HealthTech, Department of Health Technology, Technical University of Denmark, Ørsteds Plads, Building 345C, DK-2800 Kgs. Lyngby, Denmark
| | - Kristine Fredlund Degn
- DTU HealthTech, Department of Health Technology, Technical University of Denmark, Ørsteds Plads, Building 345C, DK-2800 Kgs. Lyngby, Denmark
| | - Frederikke Isa Marin
- DTU HealthTech, Department of Health Technology, Technical University of Denmark, Ørsteds Plads, Building 345C, DK-2800 Kgs. Lyngby, Denmark
| | - Anna-Lisa Schaap-Johansen
- DTU HealthTech, Department of Health Technology, Technical University of Denmark, Ørsteds Plads, Building 345C, DK-2800 Kgs. Lyngby, Denmark
| | - Benny Chain
- UCL Division of Infection and Immunity, University College London, Wing 3.2, Cruciform Building, Gower Street, London WC1E 6BT, United Kingdom
| | - Thomas Lars Andresen
- DTU HealthTech, Department of Health Technology, Technical University of Denmark, Ørsteds Plads, Building 345C, DK-2800 Kgs. Lyngby, Denmark
| | - Joseph Kaplinsky
- Ludwig Institute for Cancer Research Ltd, University of Oxford, Nuffield Department of Medicine, Old Road Campus Research Building, Roosevelt Drive, Oxford OX3 7DQ, United Kingdom
| | - Paolo Marcatili
- DTU HealthTech, Department of Health Technology, Technical University of Denmark, Ørsteds Plads, Building 345C, DK-2800 Kgs. Lyngby, Denmark
| |
Collapse
|
33
|
Teraguchi S, Saputri DS, Llamas-Covarrubias MA, Davila A, Diez D, Nazlica SA, Rozewicki J, Ismanto HS, Wilamowski J, Xie J, Xu Z, Loza-Lopez MDJ, van Eerden FJ, Li S, Standley DM. Methods for sequence and structural analysis of B and T cell receptor repertoires. Comput Struct Biotechnol J 2020; 18:2000-2011. [PMID: 32802272 PMCID: PMC7366105 DOI: 10.1016/j.csbj.2020.07.008] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 07/08/2020] [Accepted: 07/08/2020] [Indexed: 02/07/2023] Open
Abstract
B cell receptors (BCRs) and T cell receptors (TCRs) make up an essential network of defense molecules that, collectively, can distinguish self from non-self and facilitate destruction of antigen-bearing cells such as pathogens or tumors. The analysis of BCR and TCR repertoires plays an important role in both basic immunology as well as in biotechnology. Because the repertoires are highly diverse, specialized software methods are needed to extract meaningful information from BCR and TCR sequence data. Here, we review recent developments in bioinformatics tools for analysis of BCR and TCR repertoires, with an emphasis on those that incorporate structural features. After describing the recent sequencing technologies for immune receptor repertoires, we survey structural modeling methods for BCR and TCRs, along with methods for clustering such models. We review downstream analyses, including BCR and TCR epitope prediction, antibody-antigen docking and TCR-peptide-MHC Modeling. We also briefly discuss molecular dynamics in this context.
Collapse
Affiliation(s)
- Shunsuke Teraguchi
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Dianita S. Saputri
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Mara Anais Llamas-Covarrubias
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
- Departamento de Biología Molecular y Genómica, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara, Mexico
| | - Ana Davila
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Diego Diez
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Sedat Aybars Nazlica
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - John Rozewicki
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Hendra S. Ismanto
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Jan Wilamowski
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Jiaqi Xie
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Zichang Xu
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | | | - Floris J. van Eerden
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Songling Li
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| | - Daron M. Standley
- Immunology Frontier Research Center, Osaka University, 3-1 Yamadaoka, Suita, Japan
- Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Japan
| |
Collapse
|
34
|
Uncovering the Tumor Antigen Landscape: What to Know about the Discovery Process. Cancers (Basel) 2020; 12:cancers12061660. [PMID: 32585818 PMCID: PMC7352969 DOI: 10.3390/cancers12061660] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 06/11/2020] [Accepted: 06/20/2020] [Indexed: 12/14/2022] Open
Abstract
According to the latest available data, cancer is the second leading cause of death, highlighting the need for novel cancer therapeutic approaches. In this context, immunotherapy is emerging as a reliable first-line treatment for many cancers, particularly metastatic melanoma. Indeed, cancer immunotherapy has attracted great interest following the recent clinical approval of antibodies targeting immune checkpoint molecules, such as PD-1, PD-L1, and CTLA-4, that release the brakes of the immune system, thus reviving a field otherwise poorly explored. Cancer immunotherapy mainly relies on the generation and stimulation of cytotoxic CD8 T lymphocytes (CTLs) within the tumor microenvironment (TME), priming T cells and establishing efficient and durable anti-tumor immunity. Therefore, there is a clear need to define and identify immunogenic T cell epitopes to use in therapeutic cancer vaccines. Naturally presented antigens in the human leucocyte antigen-1 (HLA-I) complex on the tumor surface are the main protagonists in evocating a specific anti-tumor CD8+ T cell response. However, the methodologies for their identification have been a major bottleneck for their reliable characterization. Consequently, the field of antigen discovery has yet to improve. The current review is intended to define what are today known as tumor antigens, with a main focus on CTL antigenic peptides. We also review the techniques developed and employed to date for antigen discovery, exploring both the direct elution of HLA-I peptides and the in silico prediction of epitopes. Finally, the last part of the review analyses the future challenges and direction of the antigen discovery field.
Collapse
|
35
|
Dhanda SK, Mahajan S, Paul S, Yan Z, Kim H, Jespersen MC, Jurtz V, Andreatta M, Greenbaum JA, Marcatili P, Sette A, Nielsen M, Peters B. IEDB-AR: immune epitope database-analysis resource in 2019. Nucleic Acids Res 2020; 47:W502-W506. [PMID: 31114900 PMCID: PMC6602498 DOI: 10.1093/nar/gkz452] [Citation(s) in RCA: 210] [Impact Index Per Article: 52.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 05/01/2019] [Accepted: 05/10/2019] [Indexed: 11/13/2022] Open
Abstract
The Immune Epitope Database Analysis Resource (IEDB-AR, http://tools.iedb.org/) is a companion website to the IEDB that provides computational tools focused on the prediction and analysis of B and T cell epitopes. All of the tools are freely available through the public website and many are also available through a REST API and/or a downloadable command-line tool. A virtual machine image of the entire site is also freely available for non-commercial use and contains most of the tools on the public site. Here, we describe the tools and functionalities that are available in the IEDB-AR, focusing on the 10 new tools that have been added since the last report in the 2012 NAR webserver edition. In addition, many of the tools that were already hosted on the site in 2012 have received updates to newest versions, including NetMHC, NetMHCpan, BepiPred and DiscoTope. Overall, this IEDB-AR update provides a substantial set of updated and novel features for epitope prediction and analysis.
Collapse
Affiliation(s)
- Sandeep Kumar Dhanda
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA
| | - Swapnil Mahajan
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA
| | - Sinu Paul
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA
| | - Zhen Yan
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA
| | - Haeuk Kim
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA
| | | | - Vanessa Jurtz
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Massimo Andreatta
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark.,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Argentina
| | - Jason A Greenbaum
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA
| | - Paolo Marcatili
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Alessandro Sette
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA.,Department of Medicine, University of California, San Diego, CA 92122, USA
| | - Morten Nielsen
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark.,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Argentina
| | - Bjoern Peters
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA.,Department of Medicine, University of California, San Diego, CA 92122, USA
| |
Collapse
|
36
|
Marks C, Deane CM. How repertoire data are changing antibody science. J Biol Chem 2020; 295:9823-9837. [PMID: 32409582 DOI: 10.1074/jbc.rev120.010181] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 04/28/2020] [Indexed: 12/13/2022] Open
Abstract
Antibodies are vital proteins of the immune system that recognize potentially harmful molecules and initiate their removal. Mammals can efficiently create vast numbers of antibodies with different sequences capable of binding to any antigen with high affinity and specificity. Because they can be developed to bind to many disease agents, antibodies can be used as therapeutics. In an organism, after antigen exposure, antibodies specific to that antigen are enriched through clonal selection, expansion, and somatic hypermutation. The antibodies present in an organism therefore report on its immune status, describe its innate ability to deal with harmful substances, and reveal how it has previously responded. Next-generation sequencing technologies are being increasingly used to query the antibody, or B-cell receptor (BCR), sequence repertoire, and the amount of BCR data in public repositories is growing. The Observed Antibody Space database, for example, currently contains over a billion sequences from 68 different studies. Repertoires are available that represent both the naive state (i.e. antigen-inexperienced) and that after immunization. This wealth of data has created opportunities to learn more about our immune system. In this review, we discuss the many ways in which BCR repertoire data have been or could be exploited. We highlight its utility for providing insights into how the naive immune repertoire is generated and how it responds to antigens. We also consider how structural information can be used to enhance these data and may lead to more accurate depictions of the sequence space and to applications in the discovery of new therapeutics.
Collapse
Affiliation(s)
- Claire Marks
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Charlotte M Deane
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
37
|
Abstract
Our immune system plays a key role in health and disease as it is capable of responding to foreign antigens as well as acquired antigens from cancer cells. Latter are caused by somatic mutations, the so-called neoepitopes, and might be recognized by T cells if they are presented by HLA molecules on the surface of cancer cells. Personalized mutanome vaccines are a class of customized immunotherapies, which is dependent on the detection of individual cancer-specific tumor mutations and neoepitope (i.e., prediction, followed by a rational vaccine design, before on-demand production. The development of next generation sequencing (NGS) technologies and bioinformatic tools allows a large-scale analysis of each parameter involved in this process. Here, we provide an overview of the bioinformatic aspects involved in the design of personalized, neoantigen-based vaccines, including the detection of mutations and the subsequent prediction of potential epitopes, as well as methods for associated biomarker research, such as high-throughput sequencing of T-cell receptors (TCRs), followed by data analysis and the bioinformatics quantification of immune cell infiltration in cancer samples.
Collapse
Affiliation(s)
- Christoph Holtsträter
- TRON-Translationale Onkologie an der Universitätsmedizin der Johannes Gutenberg-Universität Mainz gemeinnützige GmbH, Freiligrathstraße, Mainz, Germany
| | - Barbara Schrörs
- TRON-Translationale Onkologie an der Universitätsmedizin der Johannes Gutenberg-Universität Mainz gemeinnützige GmbH, Freiligrathstraße, Mainz, Germany
| | - Thomas Bukur
- TRON-Translationale Onkologie an der Universitätsmedizin der Johannes Gutenberg-Universität Mainz gemeinnützige GmbH, Freiligrathstraße, Mainz, Germany
| | - Martin Löwer
- TRON-Translationale Onkologie an der Universitätsmedizin der Johannes Gutenberg-Universität Mainz gemeinnützige GmbH, Freiligrathstraße, Mainz, Germany.
| |
Collapse
|
38
|
Martini S, Nielsen M, Peters B, Sette A. The Immune Epitope Database and Analysis Resource Program 2003-2018: reflections and outlook. Immunogenetics 2019; 72:57-76. [PMID: 31761977 PMCID: PMC6970984 DOI: 10.1007/s00251-019-01137-6] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Accepted: 10/12/2019] [Indexed: 12/12/2022]
Abstract
The Immune Epitope Database and Analysis Resource (IEDB) contains information related to antibodies and T cells across an expansive scope of research fields (infectious diseases, allergy, autoimmunity, and transplantation). Capture and representation of the data to reflect growing scientific standards and techniques have required continual refinement of our rigorous curation and query and reporting processes beginning with the automated classification of over 28 million PubMed abstracts, and resulting in easily searchable data from over 20,000 published manuscripts. Data related to MHC binding and elution, nonpeptidics, natural processing, receptors, and 3D structure is first captured through manual curation and subsequently maintained through recuration to reflect evolving scientific standards. Upon promotion to the free, public database, users can query and export records of specific relevance via the online web portal which undergoes iterative development to best enable efficient data access. In parallel, the companion Analysis Resource site hosts a variety of tools that assist in the bioinformatic analyses of epitopes and related structures, which can be applied to IEDB-derived and independent datasets alike. Available tools are classified into two categories: analysis and prediction. Analysis tools include epitope clustering, sequence conservancy, and more, while prediction tools cover T and B cell epitope binding, immunogenicity, and TCR/BCR structures. In addition to these tools, benchmarking servers which allow for unbiased performance comparison are also offered. In order to expand and support the user-base of both the database and Analysis Resource, the research team actively engages in community outreach through publication of ongoing work, conference attendance and presentations, hosting of user workshops, and the provision of online help. This review provides a description of the IEDB database infrastructure, curation and recuration processes, query and reporting capabilities, the Analysis Resource, and our Community Outreach efforts, including assessment of the impact of the IEDB across the research community.
Collapse
Affiliation(s)
- Sheridan Martini
- Division of Vaccine Discovery, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA, 92037, USA.
| | - Morten Nielsen
- Department Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark.,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Bjoern Peters
- Division of Vaccine Discovery, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA, 92037, USA.,Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Alessandro Sette
- Division of Vaccine Discovery, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA, 92037, USA.,Department of Medicine, University of California San Diego, La Jolla, CA, USA
| |
Collapse
|
39
|
Mösch A, Raffegerst S, Weis M, Schendel DJ, Frishman D. Machine Learning for Cancer Immunotherapies Based on Epitope Recognition by T Cell Receptors. Front Genet 2019; 10:1141. [PMID: 31798635 PMCID: PMC6878726 DOI: 10.3389/fgene.2019.01141] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Accepted: 10/21/2019] [Indexed: 12/30/2022] Open
Abstract
In the last years, immunotherapies have shown tremendous success as treatments for multiple types of cancer. However, there are still many obstacles to overcome in order to increase response rates and identify effective therapies for every individual patient. Since there are many possibilities to boost a patient's immune response against a tumor and not all can be covered, this review is focused on T cell receptor-mediated therapies. CD8+ T cells can detect and destroy malignant cells by binding to peptides presented on cell surfaces by MHC (major histocompatibility complex) class I molecules. CD4+ T cells can also mediate powerful immune responses but their peptide recognition by MHC class II molecules is more complex, which is why the attention has been focused on CD8+ T cells. Therapies based on the power of T cells can, on the one hand, enhance T cell recognition by introducing TCRs that preferentially direct T cells to tumor sites (so called TCR-T therapy) or through vaccination to induce T cells in vivo. On the other hand, T cell activity can be improved by immune checkpoint inhibition or other means that help create a microenvironment favorable for cytotoxic T cell activity. The manifold ways in which the immune system and cancer interact with each other require not only the use of large omics datasets from gene, to transcript, to protein, and to peptide but also make the application of machine learning methods inevitable. Currently, discovering and selecting suitable TCRs is a very costly and work intensive in vitro process. To facilitate this process and to additionally allow for highly personalized therapies that can simultaneously target multiple patient-specific antigens, especially neoepitopes, breakthrough computational methods for predicting antigen presentation and TCR binding are urgently required. Particularly, potential cross-reactivity is a major consideration since off-target toxicity can pose a major threat to patient safety. The current speed at which not only datasets grow and are made available to the public, but also at which new machine learning methods evolve, is assuring that computational approaches will be able to help to solve problems that immunotherapies are still facing.
Collapse
Affiliation(s)
- Anja Mösch
- Department of Bioinformatics, Wissenschaftszentrum Weihenstephan, Technische Universität München, Freising, Germany
- Medigene Immunotherapies GmbH, a subsidiary of Medigene AG, Planegg, Germany
| | - Silke Raffegerst
- Medigene Immunotherapies GmbH, a subsidiary of Medigene AG, Planegg, Germany
| | - Manon Weis
- Medigene Immunotherapies GmbH, a subsidiary of Medigene AG, Planegg, Germany
| | - Dolores J. Schendel
- Medigene Immunotherapies GmbH, a subsidiary of Medigene AG, Planegg, Germany
| | - Dmitrij Frishman
- Department of Bioinformatics, Wissenschaftszentrum Weihenstephan, Technische Universität München, Freising, Germany
| |
Collapse
|
40
|
Wong WK, Leem J, Deane CM. Comparative Analysis of the CDR Loops of Antigen Receptors. Front Immunol 2019; 10:2454. [PMID: 31681328 PMCID: PMC6803477 DOI: 10.3389/fimmu.2019.02454] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Accepted: 10/01/2019] [Indexed: 12/24/2022] Open
Abstract
The adaptive immune system uses two main types of antigen receptors: T-cell receptors (TCRs) and antibodies. While both proteins share a globally similar β-sandwich architecture, TCRs are specialized to recognize peptide antigens in the binding groove of the major histocompatibility complex, while antibodies can bind an almost infinite range of molecules. For both proteins, the main determinants of target recognition are the complementarity-determining region (CDR) loops. Five of the six CDRs adopt a limited number of backbone conformations, known as the "canonical classes"; the remaining CDR (β3in TCRs and H3 in antibodies) is more structurally diverse. In this paper, we first update the definition of canonical forms in TCRs, build an auto-updating sequence-based prediction tool (available at http://opig.stats.ox.ac.uk/resources) and demonstrate its application on large scale sequencing studies. Given the global similarity of TCRs and antibodies, we then examine the structural similarity of their CDRs. We find that TCR and antibody CDRs tend to have different length distributions, and where they have similar lengths, they mostly occupy distinct structural spaces. In the rare cases where we found structural similarity, the underlying sequence patterns for the TCR and antibody version are different. Finally, where multiple structures have been solved for the same CDR sequence, the structural variability in TCR loops is higher than that in antibodies, suggesting TCR CDRs are more flexible. These structural differences between TCR and antibody CDRs may be important to their different biological functions.
Collapse
|
41
|
Jensen KK, Rantos V, Jappe EC, Olsen TH, Jespersen MC, Jurtz V, Jessen LE, Lanzarotti E, Mahajan S, Peters B, Nielsen M, Marcatili P. TCRpMHCmodels: Structural modelling of TCR-pMHC class I complexes. Sci Rep 2019; 9:14530. [PMID: 31601838 PMCID: PMC6787230 DOI: 10.1038/s41598-019-50932-4] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Accepted: 09/09/2019] [Indexed: 01/30/2023] Open
Abstract
The interaction between the class I major histocompatibility complex (MHC), the peptide presented by the MHC and the T-cell receptor (TCR) is a key determinant of the cellular immune response. Here, we present TCRpMHCmodels, a method for accurate structural modelling of the TCR-peptide-MHC (TCR-pMHC) complex. This TCR-pMHC modelling pipeline takes as input the amino acid sequence and generates models of the TCR-pMHC complex, with a median Cα RMSD of 2.31 Å. TCRpMHCmodels significantly outperforms TCRFlexDock, a specialised method for docking pMHC and TCR structures. TCRpMHCmodels is simple to use and the modelling pipeline takes, on average, only two minutes. Thanks to its ease of use and high modelling accuracy, we expect TCRpMHCmodels to provide insights into the underlying mechanisms of TCR and pMHC interactions and aid in the development of advanced T-cell-based immunotherapies and rational design of vaccines. The TCRpMHCmodels tool is available at http://www.cbs.dtu.dk/services/TCRpMHCmodels/.
Collapse
Affiliation(s)
| | - Vasileios Rantos
- Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby, Denmark.,Centre for Structural Systems Biology (CSSB), DESY and European Molecular Biology Laboratory, Notkestrasse 85, 22607, Hamburg, Germany
| | - Emma Christine Jappe
- Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby, Denmark.,Evaxion Biotech, Bredgade 34E, 1260, Copenhagen, Denmark
| | - Tobias Hegelund Olsen
- Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby, Denmark
| | | | - Vanessa Jurtz
- Department of Bioinformatics and Data Mining, Novo Nordisk A/S, 2760, Måløv, Denmark
| | - Leon Eyrich Jessen
- Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Esteban Lanzarotti
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Swapnil Mahajan
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA
| | - Bjoern Peters
- Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA.,University of California San Diego, Department of Medicine, La Jolla, CA 92037, USA
| | - Morten Nielsen
- Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby, Denmark.,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Paolo Marcatili
- Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby, Denmark.
| |
Collapse
|
42
|
Lanzarotti E, Marcatili P, Nielsen M. T-Cell Receptor Cognate Target Prediction Based on Paired α and β Chain Sequence and Structural CDR Loop Similarities. Front Immunol 2019; 10:2080. [PMID: 31555288 PMCID: PMC6724566 DOI: 10.3389/fimmu.2019.02080] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Accepted: 08/16/2019] [Indexed: 12/21/2022] Open
Abstract
T-cell receptors (TCR) mediate immune responses recognizing peptides in complex with major histocompatibility complexes (pMHC) displayed on the surface of cells. Resolving the challenge of predicting the cognate pMHC target of a TCR would benefit many applications in the field of immunology, including vaccine design/discovery and the development of immunotherapies. Here, we developed a model for prediction of TCR targets based on similarity to a database of TCRs with known targets. Benchmarking the model on a large set of TCRs with known target, we demonstrated how the predictive performance is increased (i) by focusing on CDRs rather than the full length TCR protein sequences, (ii) by incorporating information from paired α and β chains, and (iii) integrating information for all 6 CDR loops rather than just CDR3. Finally, we show how integration of the structure of CDR loops, as obtained through homology modeling, boosts the predictive power of the model, in particular in situations where no high-similarity TCRs are available for the query. These findings demonstrate that TCRs that bind to the same target also share, to a very high degree, sequence, and structural features. This observation has profound impact for future development of prediction models for TCR-pMHC interactions and for the use of such models for the rational design of T cell based therapies.
Collapse
Affiliation(s)
- Esteban Lanzarotti
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Paolo Marcatili
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Morten Nielsen
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina.,Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| |
Collapse
|
43
|
Leem J, de Oliveira SHP, Krawczyk K, Deane CM. STCRDab: the structural T-cell receptor database. Nucleic Acids Res 2019; 46:D406-D412. [PMID: 29087479 PMCID: PMC5753249 DOI: 10.1093/nar/gkx971] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2017] [Accepted: 10/09/2017] [Indexed: 01/16/2023] Open
Abstract
The Structural T–cell Receptor Database (STCRDab; http://opig.stats.ox.ac.uk/webapps/stcrdab) is an online resource that automatically collects and curates TCR structural data from the Protein Data Bank. For each entry, the database provides annotations, such as the α/β or γ/δ chain pairings, major histocompatibility complex details, and where available, antigen binding affinities. In addition, the orientation between the variable domains and the canonical forms of the complementarity-determining region loops are also provided. Users can select, view, and download individual or bulk sets of structures based on these criteria. Where available, STCRDab also finds antibody structures that are similar to TCRs, helping users explore the relationship between TCRs and antibodies.
Collapse
Affiliation(s)
- Jinwoo Leem
- Department of Statistics, University of Oxford, 24-29 St Giles, Oxford, OX1 3LB, UK
| | | | - Konrad Krawczyk
- Department of Statistics, University of Oxford, 24-29 St Giles, Oxford, OX1 3LB, UK
| | - Charlotte M Deane
- Department of Statistics, University of Oxford, 24-29 St Giles, Oxford, OX1 3LB, UK
| |
Collapse
|
44
|
Gowthaman R, Pierce BG. TCRmodel: high resolution modeling of T cell receptors from sequence. Nucleic Acids Res 2019; 46:W396-W401. [PMID: 29790966 PMCID: PMC6030954 DOI: 10.1093/nar/gky432] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Accepted: 05/07/2018] [Indexed: 02/07/2023] Open
Abstract
T cell receptors (TCRs), along with antibodies, are responsible for specific antigen recognition in the adaptive immune response, and millions of unique TCRs are estimated to be present in each individual. Understanding the structural basis of TCR targeting has implications in vaccine design, autoimmunity, as well as T cell therapies for cancer. Given advances in deep sequencing leading to immune repertoire-level TCR sequence data, fast and accurate modeling methods are needed to elucidate shared and unique 3D structural features of these molecules which lead to their antigen targeting and cross-reactivity. We developed a new algorithm in the program Rosetta to model TCRs from sequence, and implemented this functionality in a web server, TCRmodel. This web server provides an easy to use interface, and models are generated quickly that users can investigate in the browser and download. Benchmarking of this method using a set of nonredundant recently released TCR crystal structures shows that models are accurate and compare favorably to models from another available modeling method. This server enables the community to obtain insights into TCRs of interest, and can be combined with methods to model and design TCR recognition of antigens. The TCRmodel server is available at: http://tcrmodel.ibbr.umd.edu/.
Collapse
Affiliation(s)
- Ragul Gowthaman
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA.,Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA.,University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, Baltimore, MD 21201, USA
| | - Brian G Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA.,Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA.,University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, Baltimore, MD 21201, USA
| |
Collapse
|
45
|
Jespersen MC, Peters B, Nielsen M, Marcatili P. BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic Acids Res 2019; 45:W24-W29. [PMID: 28472356 PMCID: PMC5570230 DOI: 10.1093/nar/gkx346] [Citation(s) in RCA: 902] [Impact Index Per Article: 180.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2017] [Accepted: 04/20/2017] [Indexed: 02/07/2023] Open
Abstract
Antibodies have become an indispensable tool for many biotechnological and clinical applications. They bind their molecular target (antigen) by recognizing a portion of its structure (epitope) in a highly specific manner. The ability to predict epitopes from antigen sequences alone is a complex task. Despite substantial effort, limited advancement has been achieved over the last decade in the accuracy of epitope prediction methods, especially for those that rely on the sequence of the antigen only. Here, we present BepiPred-2.0 (http://www.cbs.dtu.dk/services/BepiPred/), a web server for predicting B-cell epitopes from antigen sequences. BepiPred-2.0 is based on a random forest algorithm trained on epitopes annotated from antibody-antigen protein structures. This new method was found to outperform other available tools for sequence-based epitope prediction both on epitope data derived from solved 3D structures, and on a large collection of linear epitopes downloaded from the IEDB database. The method displays results in a user-friendly and informative way, both for computer-savvy and non-expert users. We believe that BepiPred-2.0 will be a valuable tool for the bioinformatics and immunology community.
Collapse
Affiliation(s)
- Martin Closter Jespersen
- Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby 2800, Denmark
| | - Bjoern Peters
- La Jolla Institute for Allergy and Immunology, La Jolla, CA 92037, USA
| | - Morten Nielsen
- Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby 2800, Denmark.,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Paolo Marcatili
- Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby 2800, Denmark
| |
Collapse
|
46
|
Jespersen MC, Mahajan S, Peters B, Nielsen M, Marcatili P. Antibody Specific B-Cell Epitope Predictions: Leveraging Information From Antibody-Antigen Protein Complexes. Front Immunol 2019; 10:298. [PMID: 30863406 PMCID: PMC6399414 DOI: 10.3389/fimmu.2019.00298] [Citation(s) in RCA: 83] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 02/05/2019] [Indexed: 11/13/2022] Open
Abstract
B-cells can neutralize pathogenic molecules by targeting them with extreme specificity using receptors secreted or expressed on their surface (antibodies). This is achieved via molecular interactions between the paratope (i.e., the antibody residues involved in the binding) and the interacting region (epitope) of its target molecule (antigen). Discerning the rules that define this specificity would have profound implications for our understanding of humoral immunogenicity and its applications. The aim of this work is to produce improved, antibody-specific epitope predictions by exploiting features derived from the antigens and their cognate antibodies structures, and combining them using statistical and machine learning algorithms. We have identified several geometric and physicochemical features that are correlated in interacting paratopes and epitopes, used them to develop a Monte Carlo algorithm to generate putative epitopes-paratope pairs, and train a machine-learning model to score them. We show that, by including the structural and physicochemical properties of the paratope, we improve the prediction of the target of a given B-cell receptor. Moreover, we demonstrate a gain in predictive power both in terms of identifying the cognate antigen target for a given antibody and the antibody target for a given antigen, exceeding the results of other available tools.
Collapse
Affiliation(s)
- Martin Closter Jespersen
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Swapnil Mahajan
- La Jolla Institute for Allergy and Immunology, Center for Infectious Disease, Allergy and Asthma Research, La Jolla, CA, United States
| | - Bjoern Peters
- La Jolla Institute for Allergy and Immunology, Center for Infectious Disease, Allergy and Asthma Research, La Jolla, CA, United States
| | - Morten Nielsen
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark.,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Paolo Marcatili
- Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark
| |
Collapse
|
47
|
Long X, Jeliazkov JR, Gray JJ. Non-H3 CDR template selection in antibody modeling through machine learning. PeerJ 2019; 7:e6179. [PMID: 30648015 PMCID: PMC6330961 DOI: 10.7717/peerj.6179] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Accepted: 11/28/2018] [Indexed: 12/13/2022] Open
Abstract
Antibodies are proteins generated by the adaptive immune system to recognize and counteract a plethora of pathogens through specific binding. This adaptive binding is mediated by structural diversity in the six complementary determining region (CDR) loops (H1, H2, H3, L1, L2 and L3), which also makes accurate structural modeling of CDRs challenging. Both homology and de novo modeling approaches have been used; to date, the former has achieved greater accuracy for the non-H3 loops. The homology modeling of non-H3 CDRs is more accurate because non-H3 CDR loops of the same length and type can be grouped into a few structural clusters. Most antibody-modeling suites utilize homology modeling for the non-H3 CDRs, differing only in the alignment algorithm and how/if they utilize structural clusters. While RosettaAntibody and SAbPred do not explicitly assign query CDR sequences to clusters, two other approaches, PIGS and Kotai Antibody Builder, utilize sequence-based rules to assign CDR sequences to clusters. While the manually curated sequence rules can identify better structural templates, because their curation requires extensive literature search and human effort, they lag behind the deposition of new antibody structures and are infrequently updated. In this study, we propose a machine learning approach (Gradient Boosting Machine [GBM]) to learn the structural clusters of non-H3 CDRs from sequence alone. The GBM method simplifies feature selection and can easily integrate new data, compared to manual sequence rule curation. We compare the classification results using the GBM method to that of RosettaAntibody in a 3-repeat 10-fold cross-validation (CV) scheme on the cluster-annotated antibody database PyIgClassify and we observe an improvement in the classification accuracy of the concerned loops from 84.5% ± 0.24% to 88.16% ± 0.056%. The GBM models reduce the errors in specific cluster membership misclassifications when the involved clusters have relatively abundant data. Based on the factors identified, we suggest methods that can enrich structural classes with sparse data to further improve prediction accuracy in future studies.
Collapse
Affiliation(s)
- Xiyao Long
- Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - Jeliazko R Jeliazkov
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, United States of America
| | - Jeffrey J Gray
- Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, United States of America.,Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, United States of America.,Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, United States of America.,Institute for Nanobiotechnology, Johns Hopkins University, Baltimore, MD, United States of America
| |
Collapse
|
48
|
Li S, Wilamowski J, Teraguchi S, van Eerden FJ, Rozewicki J, Davila A, Xu Z, Katoh K, Standley DM. Structural Modeling of Lymphocyte Receptors and Their Antigens. Methods Mol Biol 2019; 2048:207-229. [PMID: 31396940 DOI: 10.1007/978-1-4939-9728-2_17] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Structural modeling plays a key role in protein function prediction on a genome-wide scale. For B and T lymphocyte receptors, the critical functional question is: which antigens and epitopes are targeted? With emerging B cell receptor (BCR) and T cell receptor (TCR) sequencing methods improving in both breadth and depth, there is a growing need for methods that can help answer this question. Since lymphocyte-antigen recognition depends on complementarity, structural modeling is likely to play an important role in understanding antigen specificity and affinity. In the case of BCRs, such modeling methods have a long history in the study and design of antibodies. However, for TCRs there are relatively few publicly available modeling tools, and, to our knowledge, none that incorporate interaction between TCRs and peptide-MHC (pMHC) complexes. Here, we provide a web-based tool, ImmuneScape ( https://sysimm.org/immune-scape/ ), to carry out TCR-pMHC modeling as a first step toward structure-based function prediction.
Collapse
Affiliation(s)
- Songling Li
- Research Institute for Microbial Diseases, Osaka University, Osaka, Japan
| | - Jan Wilamowski
- Research Institute for Microbial Diseases, Osaka University, Osaka, Japan
| | - Shunsuke Teraguchi
- Research Institute for Microbial Diseases, Osaka University, Osaka, Japan.,Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | | | - John Rozewicki
- Research Institute for Microbial Diseases, Osaka University, Osaka, Japan.,Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Ana Davila
- Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Zichang Xu
- Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Kazutaka Katoh
- Research Institute for Microbial Diseases, Osaka University, Osaka, Japan.,Immunology Frontier Research Center, Osaka University, Osaka, Japan
| | - Daron M Standley
- Research Institute for Microbial Diseases, Osaka University, Osaka, Japan. .,Immunology Frontier Research Center, Osaka University, Osaka, Japan.
| |
Collapse
|
49
|
Usmani SS, Kumar R, Bhalla S, Kumar V, Raghava GPS. In Silico Tools and Databases for Designing Peptide-Based Vaccine and Drugs. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2018; 112:221-263. [PMID: 29680238 DOI: 10.1016/bs.apcsb.2018.01.006] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The prolonged conventional approaches of drug screening and vaccine designing prerequisite patience, vigorous effort, outrageous cost as well as additional manpower. Screening and experimentally validating thousands of molecules for a specific therapeutic property never proved to be an easy task. Similarly, traditional way of vaccination includes administration of either whole or attenuated pathogen, which raises toxicity and safety issues. Emergence of sequencing and recombinant DNA technology led to the epitope-based advanced vaccination concept, i.e., small peptides (epitope) can stimulate specific immune response. Advent of bioinformatics proved to be an adjunct in vaccine and drug designing. Genomic study of pathogens aid to identify and analyze the protective epitope. A number of in silico tools have been developed to design immunotherapy as well as peptide-based drugs in the last two decades. These tools proved to be a catalyst in drug and vaccine designing. This review solicits therapeutic peptide databases as well as in silico tools developed for designing peptide-based vaccine and drugs.
Collapse
Affiliation(s)
- Salman Sadullah Usmani
- Center for Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India; Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India
| | - Rajesh Kumar
- Center for Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India; Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India
| | - Sherry Bhalla
- Center for Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India
| | - Vinod Kumar
- Center for Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India; Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India
| | - Gajendra P S Raghava
- Center for Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India; Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India.
| |
Collapse
|
50
|
Yokota R, Kaminaga Y, Kobayashi TJ. Quantification of Inter-Sample Differences in T-Cell Receptor Repertoires Using Sequence-Based Information. Front Immunol 2017; 8:1500. [PMID: 29187849 PMCID: PMC5694755 DOI: 10.3389/fimmu.2017.01500] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2017] [Accepted: 10/24/2017] [Indexed: 11/13/2022] Open
Abstract
Inter-sample comparisons of T-cell receptor (TCR) repertoires are crucial for gaining a better understanding of the immunological states determined by different collections of T cells from different donor sites, cell types, and genetic and pathological backgrounds. For quantitative comparison, most previous studies utilized conventional methods in ecology, which focus on TCR sequences that overlap between pairwise samples. Some recent studies attempted another approach that is categorized into Poisson abundance models using the abundance distribution of observed TCR sequences. However, these methods ignore the details of the measured sequences and are consequently unable to identify sub-repertoires that might have important contributions to the observed inter-sample differences. Moreover, the sparsity of sequence data due to the huge diversity of repertoires hampers the performance of these methods, especially when few overlapping sequences exist. In this paper, we propose a new approach for REpertoire COmparison in Low Dimensions (RECOLD) based on TCR sequence information, which can estimate the low-dimensional structure by embedding the pairwise sequence dissimilarities in high-dimensional sequence space. The inter-sample differences between repertoires are then quantified by information-theoretic measures among the distributions of data estimated in the embedded space. Using datasets of mouse and human TCR repertoires, we demonstrate that RECOLD can accurately identify the inter-sample hierarchical structures, which have a good correspondence with our intuitive understanding about sample conditions. Moreover, for the dataset of transgenic mice that have strong restrictions on the diversity of their repertoires, our estimated inter-sample structure was consistent with the structure estimated by previous methods based on abundance or overlapping sequence information. For the dataset of human healthy donors and Sézary syndrome patients, our method also showed robust estimation performance even under the condition of high sparsity in TCR sequences, while previous studies failed to estimate the structure. In addition, we identified the sequences that contribute to the pairwise-sample differences between the repertoires with the different genetic backgrounds of mice. Such identification of the sequences contributing to variation in immune cell repertoires may provide substantial insight for the development of new immunotherapies and vaccines.
Collapse
Affiliation(s)
- Ryo Yokota
- Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
| | - Yuki Kaminaga
- Department of Electrical Engineering and Information Systems, Graduate School of Engineering, The University of Tokyo, Tokyo, Japan
| | - Tetsuya J Kobayashi
- Institute of Industrial Science, The University of Tokyo, Tokyo, Japan.,Department of Electrical Engineering and Information Systems, Graduate School of Engineering, The University of Tokyo, Tokyo, Japan.,PRESTO, Japan Science and Technology Agency (JST), Saitama, Japan
| |
Collapse
|