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Guo Z, Yamaguchi R. Machine learning methods for protein-protein binding affinity prediction in protein design. FRONTIERS IN BIOINFORMATICS 2022; 2:1065703. [PMID: 36591334 PMCID: PMC9800603 DOI: 10.3389/fbinf.2022.1065703] [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: 10/10/2022] [Accepted: 12/01/2022] [Indexed: 12/23/2022] Open
Abstract
Protein-protein interactions govern a wide range of biological activity. A proper estimation of the protein-protein binding affinity is vital to design proteins with high specificity and binding affinity toward a target protein, which has a variety of applications including antibody design in immunotherapy, enzyme engineering for reaction optimization, and construction of biosensors. However, experimental and theoretical modelling methods are time-consuming, hinder the exploration of the entire protein space, and deter the identification of optimal proteins that meet the requirements of practical applications. In recent years, the rapid development in machine learning methods for protein-protein binding affinity prediction has revealed the potential of a paradigm shift in protein design. Here, we review the prediction methods and associated datasets and discuss the requirements and construction methods of binding affinity prediction models for protein design.
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Affiliation(s)
- Zhongliang Guo
- Division of Cancer Systems Biology, Aichi Cancer Center Research Institute, Nagoya, Aichi, Japan
| | - Rui Yamaguchi
- Division of Cancer Systems Biology, Aichi Cancer Center Research Institute, Nagoya, Aichi, Japan,Division of Cancer Informatics, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan,*Correspondence: Rui Yamaguchi,
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2
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Yurina V, Adianingsih OR. Predicting epitopes for vaccine development using bioinformatics tools. Ther Adv Vaccines Immunother 2022; 10:25151355221100218. [PMID: 35647486 PMCID: PMC9130818 DOI: 10.1177/25151355221100218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 04/14/2022] [Indexed: 11/20/2022] Open
Abstract
Epitope-based DNA vaccine development is one application of bioinformatics or
in silico studies, that is, computational methods,
including mathematical, chemical, and biological approaches, which are widely
used in drug development. Many in silico studies have been
conducted to analyze the efficacy, safety, toxicity effects, and interactions of
drugs. In the vaccine design process, in silico studies are
performed to predict epitopes that could trigger T-cell and B-cell reactions
that would produce both cellular and humoral immune responses. Immunoinformatics
is the branch of bioinformatics used to study the relationship between immune
responses and predicted epitopes. Progress in immunoinformatics has been rapid
and has led to the development of a variety of tools that are used for the
prediction of epitopes recognized by B cells or T cells as well as the antigenic
responses. However, the in silico approach to vaccine design is
still relatively new; thus, this review is aimed at increasing understanding of
the importance of in silico studies in the design of vaccines
and thereby facilitating future research in this field.
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Affiliation(s)
- Valentina Yurina
- Department of Pharmacy, Medical Faculty, Universitas Brawijaya, Jalan Veteran, Malang 65145, East Java, Indonesia
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3
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Evaluation of potential MHC-I allele-specific epitopes in Zika virus proteins and the effects of mutations on peptide-MHC-I interaction studied using in silico approaches. Comput Biol Chem 2021; 92:107459. [PMID: 33636637 DOI: 10.1016/j.compbiolchem.2021.107459] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 02/06/2021] [Accepted: 02/12/2021] [Indexed: 11/24/2022]
Abstract
Zika virus (ZIKV) infection is a global health concern due to its association with microcephaly and neurological complications. The development of a T-cell vaccine is important to combat this disease. In this study, we propose ZIKV major histocompatibility complex I (MHC-I) epitopes based on in silico screening consensus followed by molecular docking, PRODIGY, and molecular dynamics (MD) simulation analyses. The effects of the reported mutations on peptide-MHC-I (pMHC-I) complexes were also evaluated. In general, our data indicate an allele-specific peptide-binding human leukocyte antigen (HLA) and potential epitopes. For HLA-B44, we showed that the absence of acidic residue Glu at P2, due to the loss of the electrostatic interaction with Lys45, has a negative impact on the pMHC-I complex stability and explains the low free energy estimated for the immunodominant peptide E-4 (IGVSNRDFV). Our MD data also suggest the deleterious effects of acidic residue Asp at P1 on the pMHC-I stability of HLA-B8 due to destabilization of the α-helix and β-strand. Free energy estimation further indicated that the mutation from Val to Ala at P9 of peptide E-247 (DAHAKRQTV), which was found exclusively in microcephaly samples, did not reduce HLA-B8 affinity. In contrast, the mutation from Thr to Pro at P2 of the peptide NS5-832 (VTKWTDIPY) decreased the interaction energy, number of intermolecular interactions, and adversely affected its binding mode with HLA-A1. Overall, our findings are important with regard to the design of T-cell peptide vaccines and for understanding how ZIKV escapes recognition by CD8 + T-cells.
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4
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Fischer DS, Wu Y, Schubert B, Theis FJ. Predicting antigen specificity of single T cells based on TCR CDR3 regions. Mol Syst Biol 2020; 16:e9416. [PMID: 32779888 PMCID: PMC7418512 DOI: 10.15252/msb.20199416] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 07/13/2020] [Accepted: 07/22/2020] [Indexed: 11/12/2022] Open
Abstract
It has recently become possible to simultaneously assay T-cell specificity with respect to large sets of antigens and the T-cell receptor sequence in high-throughput single-cell experiments. Leveraging this new type of data, we propose and benchmark a collection of deep learning architectures to model T-cell specificity in single cells. In agreement with previous results, we found that models that treat antigens as categorical outcome variables outperform those that model the TCR and antigen sequence jointly. Moreover, we show that variability in single-cell immune repertoire screens can be mitigated by modeling cell-specific covariates. Lastly, we demonstrate that the number of bound pMHC complexes can be predicted in a continuous fashion providing a gateway to disentangle cell-to-dextramer binding strength and receptor-to-pMHC affinity. We provide these models in the Python package TcellMatch to allow imputation of antigen specificities in single-cell RNA-seq studies on T cells without the need for MHC staining.
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Affiliation(s)
- David S Fischer
- Institute of Computational BiologyHelmholtz Zentrum MünchenNeuherbergGermany
- TUM School of Life Sciences WeihenstephanTechnical University of MunichFreisingGermany
| | - Yihan Wu
- Institute of Computational BiologyHelmholtz Zentrum MünchenNeuherbergGermany
| | - Benjamin Schubert
- Institute of Computational BiologyHelmholtz Zentrum MünchenNeuherbergGermany
- Department of MathematicsTechnical University of MunichGarching bei MünchenGermany
| | - Fabian J Theis
- Institute of Computational BiologyHelmholtz Zentrum MünchenNeuherbergGermany
- TUM School of Life Sciences WeihenstephanTechnical University of MunichFreisingGermany
- Department of MathematicsTechnical University of MunichGarching bei MünchenGermany
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5
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Antunes DA, Abella JR, Devaurs D, Rigo MM, Kavraki LE. Structure-based Methods for Binding Mode and Binding Affinity Prediction for Peptide-MHC Complexes. Curr Top Med Chem 2019; 18:2239-2255. [PMID: 30582480 DOI: 10.2174/1568026619666181224101744] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2018] [Revised: 11/29/2018] [Accepted: 12/08/2018] [Indexed: 12/26/2022]
Abstract
Understanding the mechanisms involved in the activation of an immune response is essential to many fields in human health, including vaccine development and personalized cancer immunotherapy. A central step in the activation of the adaptive immune response is the recognition, by T-cell lymphocytes, of peptides displayed by a special type of receptor known as Major Histocompatibility Complex (MHC). Considering the key role of MHC receptors in T-cell activation, the computational prediction of peptide binding to MHC has been an important goal for many immunological applications. Sequence- based methods have become the gold standard for peptide-MHC binding affinity prediction, but structure-based methods are expected to provide more general predictions (i.e., predictions applicable to all types of MHC receptors). In addition, structural modeling of peptide-MHC complexes has the potential to uncover yet unknown drivers of T-cell activation, thus allowing for the development of better and safer therapies. In this review, we discuss the use of computational methods for the structural modeling of peptide-MHC complexes (i.e., binding mode prediction) and for the structure-based prediction of binding affinity.
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Affiliation(s)
- Dinler A Antunes
- Computer Science Department, Rice University, Houston, TX, United States
| | - Jayvee R Abella
- Computer Science Department, Rice University, Houston, TX, United States
| | - Didier Devaurs
- Computer Science Department, Rice University, Houston, TX, United States
| | - Maurício M Rigo
- School of Medicine, Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
| | - Lydia E Kavraki
- Computer Science Department, Rice University, Houston, TX, United States
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6
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Kar P, Ruiz-Perez L, Arooj M, Mancera RL. Current methods for the prediction of T-cell epitopes. Pept Sci (Hoboken) 2018. [DOI: 10.1002/pep2.24046] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Prattusha Kar
- School of Pharmacy and Biomedical Sciences; Curtin Health Innovation Research Institute and Curtin Institute for Computation, Curtin University; Perth Western Australia 6845 Australia
| | - Lanie Ruiz-Perez
- School of Pharmacy and Biomedical Sciences; Curtin Health Innovation Research Institute and Curtin Institute for Computation, Curtin University; Perth Western Australia 6845 Australia
| | - Mahreen Arooj
- School of Pharmacy and Biomedical Sciences; Curtin Health Innovation Research Institute and Curtin Institute for Computation, Curtin University; Perth Western Australia 6845 Australia
| | - Ricardo L. Mancera
- School of Pharmacy and Biomedical Sciences; Curtin Health Innovation Research Institute and Curtin Institute for Computation, Curtin University; Perth Western Australia 6845 Australia
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7
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Kenn M, Ribarics R, Ilieva N, Cibena M, Karch R, Schreiner W. Spatiotemporal multistage consensus clustering in molecular dynamics studies of large proteins. MOLECULAR BIOSYSTEMS 2017; 12:1600-14. [PMID: 26978458 DOI: 10.1039/c5mb00879d] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
The aim of this work is to find semi-rigid domains within large proteins as reference structures for fitting molecular dynamics trajectories. We propose an algorithm, multistage consensus clustering, MCC, based on minimum variation of distances between pairs of Cα-atoms as target function. The whole dataset (trajectory) is split into sub-segments. For a given sub-segment, spatial clustering is repeatedly started from different random seeds, and we adopt the specific spatial clustering with minimum target function: the process described so far is stage 1 of MCC. Then, in stage 2, the results of spatial clustering are consolidated, to arrive at domains stable over the whole dataset. We found that MCC is robust regarding the choice of parameters and yields relevant information on functional domains of the major histocompatibility complex (MHC) studied in this paper: the α-helices and β-floor of the protein (MHC) proved to be most flexible and did not contribute to clusters of significant size. Three alleles of the MHC, each in complex with ABCD3 peptide and LC13 T-cell receptor (TCR), yielded different patterns of motion. Those alleles causing immunological allo-reactions showed distinct correlations of motion between parts of the peptide, the binding cleft and the complementary determining regions (CDR)-loops of the TCR. Multistage consensus clustering reflected functional differences between MHC alleles and yields a methodological basis to increase sensitivity of functional analyses of bio-molecules. Due to the generality of approach, MCC is prone to lend itself as a potent tool also for the analysis of other kinds of big data.
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Affiliation(s)
- Michael Kenn
- Section of Biosimulation and Bioinformatics, Center for Medical Statistics, Informatics and Intelligent Systems (CeMSIIS), Medical University of Vienna, Spitalgasse 23, A-1090 Vienna, Austria.
| | - Reiner Ribarics
- Section of Biosimulation and Bioinformatics, Center for Medical Statistics, Informatics and Intelligent Systems (CeMSIIS), Medical University of Vienna, Spitalgasse 23, A-1090 Vienna, Austria.
| | - Nevena Ilieva
- Institute of Information and Communication Technologies (IICT), Bulgarian Academy of Sciences, 25A, Acad. G. Bonchev Str., Sofia 1113, Bulgaria
| | - Michael Cibena
- Section of Biosimulation and Bioinformatics, Center for Medical Statistics, Informatics and Intelligent Systems (CeMSIIS), Medical University of Vienna, Spitalgasse 23, A-1090 Vienna, Austria.
| | - Rudolf Karch
- Section of Biosimulation and Bioinformatics, Center for Medical Statistics, Informatics and Intelligent Systems (CeMSIIS), Medical University of Vienna, Spitalgasse 23, A-1090 Vienna, Austria.
| | - Wolfgang Schreiner
- Section of Biosimulation and Bioinformatics, Center for Medical Statistics, Informatics and Intelligent Systems (CeMSIIS), Medical University of Vienna, Spitalgasse 23, A-1090 Vienna, Austria.
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8
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Eccleston RC, Wan S, Dalchau N, Coveney PV. The Role of Multiscale Protein Dynamics in Antigen Presentation and T Lymphocyte Recognition. Front Immunol 2017; 8:797. [PMID: 28740497 PMCID: PMC5502259 DOI: 10.3389/fimmu.2017.00797] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2016] [Accepted: 06/22/2017] [Indexed: 12/15/2022] Open
Abstract
T lymphocytes are stimulated when they recognize short peptides bound to class I proteins of the major histocompatibility complex (MHC) protein, as peptide-MHC complexes. Due to the diversity in T-cell receptor (TCR) molecules together with both the peptides and MHC proteins they bind to, it has been difficult to design vaccines and treatments based on these interactions. Machine learning has made some progress in trying to predict the immunogenicity of peptide sequences in the context of specific MHC class I alleles but, as such approaches cannot integrate temporal information and lack explanatory power, their scope will always be limited. Here, we advocate a mechanistic description of antigen presentation and TCR activation which is explanatory, predictive, and quantitative, drawing on modeling approaches that collectively span several length and time scales, being capable of furnishing reliable biological descriptions that are difficult for experimentalists to provide. It is a form of multiscale systems biology. We propose the use of chemical rate equations to describe the time evolution of the foreign and host proteins to explain how the original proteins end up being presented on the cell surface as peptide fragments, while we invoke molecular dynamics to describe the key binding processes on the molecular level, including those of peptide-MHC complexes with TCRs which lie at the heart of the immune response. On each level, complementary methods based on machine learning are available, and we discuss the relationship between these divergent approaches. The pursuit of predictive mechanistic modeling approaches requires experimentalists to adapt their work so as to acquire, store, and expose data that can be used to verify and validate such models.
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Affiliation(s)
- R Charlotte Eccleston
- Centre for Computational Science, Department of Chemistry, University College London, London, United Kingdom
| | - Shunzhou Wan
- Centre for Computational Science, Department of Chemistry, University College London, London, United Kingdom
| | | | - Peter V Coveney
- Centre for Computational Science, Department of Chemistry, University College London, London, United Kingdom
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9
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Oyarzun P, Kobe B. Computer-aided design of T-cell epitope-based vaccines: addressing population coverage. Int J Immunogenet 2015. [PMID: 26211755 DOI: 10.1111/iji.12214] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Epitope-based vaccines (EVs) make use of short antigen-derived peptides corresponding to immune epitopes, which are administered to trigger a protective humoral and/or cellular immune response. EVs potentially allow for precise control over the immune response activation by focusing on the most relevant - immunogenic and conserved - antigen regions. Experimental screening of large sets of peptides is time-consuming and costly; therefore, in silico methods that facilitate T-cell epitope mapping of protein antigens are paramount for EV development. The prediction of T-cell epitopes focuses on the peptide presentation process by proteins encoded by the major histocompatibility complex (MHC). Because different MHCs have different specificities and T-cell epitope repertoires, individuals are likely to respond to a different set of peptides from a given pathogen in genetically heterogeneous human populations. In addition, protective immune responses are only expected if T-cell epitopes are restricted by MHC proteins expressed at high frequencies in the target population. Therefore, without careful consideration of the specificity and prevalence of the MHC proteins, EVs could fail to adequately cover the target population. This article reviews state-of-the-art algorithms and computational tools to guide EV design through all the stages of the process: epitope prediction, epitope selection and vaccine assembly, while optimizing vaccine immunogenicity and coping with genetic variation in humans and pathogens.
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Affiliation(s)
- P Oyarzun
- Biotechnology Centre, Facultad de Ingeniería y Tecnología, Universidad San Sebastián, Concepción, Chile
| | - B Kobe
- School of Chemistry and Molecular Biosciences, Institute for Molecular Bioscience and Australian Infectious Diseases Research Centre, University of Queensland, Brisbane, QLD, Australia
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10
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Molecular dynamics, monte carlo simulations, and langevin dynamics: a computational review. BIOMED RESEARCH INTERNATIONAL 2015; 2015:183918. [PMID: 25785262 PMCID: PMC4345249 DOI: 10.1155/2015/183918] [Citation(s) in RCA: 76] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/28/2014] [Accepted: 11/05/2014] [Indexed: 01/08/2023]
Abstract
Macromolecular structures, such as neuraminidases, hemagglutinins, and monoclonal antibodies, are not rigid entities. Rather, they are characterised by their flexibility, which is the result of the interaction and collective motion of their constituent atoms. This conformational diversity has a significant impact on their physicochemical and biological properties. Among these are their structural stability, the transport of ions through the M2 channel, drug resistance, macromolecular docking, binding energy, and rational epitope design. To assess these properties and to calculate the associated thermodynamical observables, the conformational space must be efficiently sampled and the dynamic of the constituent atoms must be simulated. This paper presents algorithms and techniques that address the abovementioned issues. To this end, a computational review of molecular dynamics, Monte Carlo simulations, Langevin dynamics, and free energy calculation is presented. The exposition is made from first principles to promote a better understanding of the potentialities, limitations, applications, and interrelations of these computational methods.
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Abstract
Immunoinformatics focuses on modeling immune responses for better understanding of the immune system and in many cases for proposing agents able to modify the immune system. The most classical of these agents are vaccines derived from living organisms such as smallpox or polio. More modern vaccines comprise recombinant proteins, protein domains, and in some cases peptides. Generating a vaccine from peptides however requires technologies and concepts very different from classical vaccinology. Immunoinformatics therefore provides the computational tools to propose peptides suitable for formulation into vaccines. This chapter introduces the essential biological concepts affecting design and efficacy of peptide vaccines and discusses current methods and workflows applied to design successful peptide vaccines using computers.
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Affiliation(s)
- Johannes Söllner
- Emergentec Biodevelopment GmbH, Gersthofer Straße 29-31, 1180, Vienna, Austria,
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12
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Singaravelu M, Selvan A, Anishetty S. Molecular dynamics simulations of lectin domain of FimH and immunoinformatics for the design of potential vaccine candidates. Comput Biol Chem 2014; 52:18-24. [DOI: 10.1016/j.compbiolchem.2014.08.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2014] [Revised: 08/11/2014] [Accepted: 08/11/2014] [Indexed: 02/04/2023]
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Large scale characterization of the LC13 TCR and HLA-B8 structural landscape in reaction to 172 altered peptide ligands: a molecular dynamics simulation study. PLoS Comput Biol 2014; 10:e1003748. [PMID: 25101830 PMCID: PMC4125040 DOI: 10.1371/journal.pcbi.1003748] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2014] [Accepted: 05/28/2014] [Indexed: 12/29/2022] Open
Abstract
The interplay between T cell receptors (TCRs) and peptides bound by major histocompatibility complexes (MHCs) is one of the most important interactions in the adaptive immune system. Several previous studies have computationally investigated their structural dynamics. On the basis of these simulations several structural and dynamical properties have been proposed as effectors of the immunogenicity. Here we present the results of a large scale Molecular Dynamics simulation study consisting of 100 ns simulations of 172 different complexes. These complexes consisted of all possible point mutations of the Epstein Barr Virus peptide FLRGRAYGL bound by HLA-B*08:01 and presented to the LC13 TCR. We compare the results of these 172 structural simulations with experimental immunogenicity data. We found that simulations with more immunogenic peptides and those with less immunogenic peptides are in fact highly similar and on average only minor differences in the hydrogen binding footprints, interface distances, and the relative orientation between the TCR chains are present. Thus our large scale data analysis shows that many previously suggested dynamical and structural properties of the TCR/peptide/MHC interface are unlikely to be conserved causal factors for peptide immunogenicity.
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14
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Abstract
Peptides fulfill many roles in immunology, yet none are more important than their role as immunogenic epitopes driving the adaptive immune response, our ultimate bulwark against infectious disease. Peptide epitopes are mediated primarily by their interaction with major histocompatibility complexes (T-cell epitopes) and antibodies (B-cell epitopes). As pathogen genomes continue to be revealed, both experimental and computational epitope mapping are becoming crucial tools in vaccine discovery. Immunoinformatics offers many tools, techniques and approaches for in silico epitope characterization, which is capable of greatly accelerating epitope design.
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15
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Abstract
A large volume of data relevant to immunology research has accumulated due to sequencing of genomes of the human and other model organisms. At the same time, huge amounts of clinical and epidemiologic data are being deposited in various scientific literature and clinical records. This accumulation of the information is like a goldmine for researchers looking for mechanisms of immune function and disease pathogenesis. Thus the need to handle this rapidly growing immunological resource has given rise to the field known as immunoinformatics. Immunoinformatics, otherwise known as computational immunology, is the interface between computer science and experimental immunology. It represents the use of computational methods and resources for the understanding of immunological information. It not only helps in dealing with huge amount of data but also plays a great role in defining new hypotheses related to immune responses. This chapter reviews classical immunology, different databases, and prediction tool. Further, it briefly describes applications of immunoinformatics in reverse vaccinology, immune system modeling, and cancer diagnosis and therapy. It also explores the idea of integrating immunoinformatics with systems biology for the development of personalized medicine. All these efforts save time and cost to a great extent.
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Affiliation(s)
- Namrata Tomar
- Machine Intelligence Unit, Indian Statistical Institute, 203 B.T. Road, Kolkata, 700108, India,
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16
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Jawa V, Cousens LP, Awwad M, Wakshull E, Kropshofer H, De Groot AS. T-cell dependent immunogenicity of protein therapeutics: Preclinical assessment and mitigation. Clin Immunol 2013; 149:534-55. [PMID: 24263283 DOI: 10.1016/j.clim.2013.09.006] [Citation(s) in RCA: 181] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2013] [Revised: 09/13/2013] [Accepted: 09/14/2013] [Indexed: 02/07/2023]
Abstract
Protein therapeutics hold a prominent and rapidly expanding place among medicinal products. Purified blood products, recombinant cytokines, growth factors, enzyme replacement factors, monoclonal antibodies, fusion proteins, and chimeric fusion proteins are all examples of therapeutic proteins that have been developed in the past few decades and approved for use in the treatment of human disease. Despite early belief that the fully human nature of these proteins would represent a significant advantage, adverse effects associated with immune responses to some biologic therapies have become a topic of some concern. As a result, drug developers are devising strategies to assess immune responses to protein therapeutics during both the preclinical and the clinical phases of development. While there are many factors that contribute to protein immunogenicity, T cell- (thymus-) dependent (Td) responses appear to play a critical role in the development of antibody responses to biologic therapeutics. A range of methodologies to predict and measure Td immune responses to protein drugs has been developed. This review will focus on the Td contribution to immunogenicity, summarizing current approaches for the prediction and measurement of T cell-dependent immune responses to protein biologics, discussing the advantages and limitations of these technologies, and suggesting a practical approach for assessing and mitigating Td immunogenicity.
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17
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Ferber M, Zoete V, Michielin O. T-cell receptors binding orientation over peptide/MHC class I is driven by long-range interactions. PLoS One 2012; 7:e51943. [PMID: 23251658 PMCID: PMC3522592 DOI: 10.1371/journal.pone.0051943] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2012] [Accepted: 11/08/2012] [Indexed: 11/19/2022] Open
Abstract
Crystallographic data about T-Cell Receptor - peptide - major histocompatibility complex class I (TCRpMHC) interaction have revealed extremely diverse TCR binding modes triggering antigen recognition. Understanding the molecular basis that governs TCR orientation over pMHC is still a considerable challenge. We present a simplified rigid approach applied on all non-redundant TCRpMHC crystal structures available. The CHARMM force field in combination with the FACTS implicit solvation model is used to study the role of long-distance interactions between the TCR and pMHC. We demonstrate that the sum of the coulomb interactions and the electrostatic solvation energies is sufficient to identify two orientations corresponding to energetic minima at 0° and 180° from the native orientation. Interestingly, these results are shown to be robust upon small structural variations of the TCR such as changes induced by Molecular Dynamics simulations, suggesting that shape complementarity is not required to obtain a reliable signal. Accurate energy minima are also identified by confronting unbound TCR crystal structures to pMHC. Furthermore, we decompose the electrostatic energy into residue contributions to estimate their role in the overall orientation. Results show that most of the driving force leading to the formation of the complex is defined by CDR1,2/MHC interactions. This long-distance contribution appears to be independent from the binding process itself, since it is reliably identified without considering neither short-range energy terms nor CDR induced fit upon binding. Ultimately, we present an attempt to predict the TCR/pMHC binding mode for a TCR structure obtained by homology modeling. The simplicity of the approach and the absence of any fitted parameters make it also easily applicable to other types of macromolecular protein complexes.
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Affiliation(s)
- Mathias Ferber
- Multidisciplinary Oncology Center, Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Vincent Zoete
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
- * E-mail: (VZ); (OM)
| | - Olivier Michielin
- Multidisciplinary Oncology Center, Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
- * E-mail: (VZ); (OM)
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18
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Huang G, Geng J, Wang R, Chen L. Identification of a New Cytotoxic T-Cell Epitope p675 of Human Telomerase Reverse Transcriptase. Cancer Biother Radiopharm 2012; 27:600-5. [PMID: 22917214 DOI: 10.1089/cbr.2012.1193] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Affiliation(s)
- Guichun Huang
- Medical Oncology Department of Jinling Hospital, Medical School of Nanjing University, Nanjing, People's Republic of China
| | - Jian Geng
- Medical Oncology Department of Jinling Hospital, Medical School of Nanjing University, Nanjing, People's Republic of China
| | - Rui Wang
- Medical Oncology Department of Jinling Hospital, Medical School of Nanjing University, Nanjing, People's Republic of China
| | - Longbang Chen
- Medical Oncology Department of Jinling Hospital, Medical School of Nanjing University, Nanjing, People's Republic of China
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19
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Mahmood MDI, Matsuo Y, Neya S, Hoshino T. Computational analysis on the binding of epitope peptide to human leukocyte antigen class I molecule A*2402 subtype. Chem Pharm Bull (Tokyo) 2012; 59:1254-62. [PMID: 21963635 DOI: 10.1248/cpb.59.1254] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Immunological response induced by small amino peptide has attracted much recent attention in the field of immunotherapy. Wilms' tumor (WT1) protein is one of the potent tumor antigens inducing immunological response in mouse and human, because WT1 is over expressed in many types of leukemia and various kinds of solid tumors. A 9-mer peptide encoded in WT1 protein (CMTWNQMNL; amino acid 235-243) is known to serve as antigenic peptide for human leukocyte antigen (HLA)-A*2402 molecule. It was reported that the replacement of the second amino residue, which is deeply responsible for the peptide binding to HLA, induced strong immunological response compared to the natural peptide. In this study, 19 kinds of single amino substitutions were introduced at position 2 of this 9-mer WT1 peptide. We performed molecular dynamics simulation on the complex of each of WT1 epitope peptides and HLA-β2 micro globulin (β2m) molecule, and subsequently estimated the binding affinity using molecular mechanics/generalized-Born surface area method combined with normal mode analysis. Our computation indicated that the peptide containing M2Y or M2W mutation showed high binding affinity to the HLA-β2m molecule as well as the natural peptide. We have also examined the role of the residue at position 2 in peptide binding to HLA-β2m. The calculation showed that van der Waals interaction between the side chain of the residue at position 2 and hydrophobic residues inside B-pocket of HLA are important. These findings will be helpful to search other potent peptides that will enhance strong immunological response specific to HLA-A*2402 molecule.
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Affiliation(s)
- M D Iqbal Mahmood
- Graduate School of Pharmaceutical Sciences, Chiba University, Chiba, Japan
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Stavrakoudis A. Molecular dynamics study of the human insulin B peptide SHLVEALYLVCGERGG complexed with HLA-DQ8 reveals important hydrogen bond interactions. MOLECULAR SIMULATION 2011. [DOI: 10.1080/08927022.2011.566607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Insights into the structure of the LC13 TCR/HLA-B8-EBV peptide complex with molecular dynamics simulations. Cell Biochem Biophys 2011; 60:283-95. [DOI: 10.1007/s12013-011-9151-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Stavrakoudis A. Cis-transisomerization of the Epstein-Barr virus determinant peptide EENLLDFVRF after the DM1 TCR recognition of the HLA-B*4405/peptide complex. FEBS Lett 2010; 585:485-91. [DOI: 10.1016/j.febslet.2010.12.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2010] [Revised: 11/23/2010] [Accepted: 12/08/2010] [Indexed: 10/18/2022]
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