A Review on the Methods of Peptide-MHC Binding Prediction

Author(s): Yang Liu, Xia-hui Ouyang, Zhi-Xiong Xiao, Le Zhang*, Yang Cao*

Journal Name: Current Bioinformatics

Volume 15 , Issue 8 , 2020

Become EABM
Become Reviewer
Call for Editor

Graphical Abstract:


Background: T lymphocyte achieves an immune response by recognizing antigen peptides (also known as T cell epitopes) through major histocompatibility complex (MHC) molecules. The immunogenicity of T cell epitopes depends on their source and stability in combination with MHC molecules. The binding of the peptide to MHC is the most selective step, so predicting the binding affinity of the peptide to MHC is the principal step in predicting T cell epitopes. The identification of epitopes is of great significance in the research of vaccine design and T cell immune response.

Objective: The traditional method for identifying epitopes is to synthesize and test the binding activity of peptide by experimental methods, which is not only time-consuming, but also expensive. In silico methods for predicting peptide-MHC binding emerge to pre-select candidate peptides for experimental testing, which greatly saves time and costs. By summarizing and analyzing these methods, we hope to have a better insight and provide guidance for future directions.

Methods: Up to now, a number of methods have been developed to predict the binding ability of peptides to MHC based on various principles. Some of them employ matrix models or machine learning models based on the sequence characteristic embedded in peptides or MHC to predict the binding ability of peptides to MHC. Some others utilize the three-dimensional structural information of peptides or MHC, for example, by extracting three-dimensional structural information to construct a feature matrix or machine learning model, or directly using protein structure prediction, molecular docking to predict the binding mode of peptides and MHC.

Results: Although the methods in predicting peptide-MHC binding based on the feature matrix or machine learning model can achieve high-throughput prediction, the accuracy of which depends heavily on the sequence characteristic of confirmed binding peptides. In addition, it cannot provide insights into the mechanism of antigen specificity. Therefore, such methods have certain limitations in practical applications. Methods in predicting peptide-MHC binding based on structural prediction or molecular docking are computationally intensive compared to the methods based on feature matrix or machine learning model and the challenge is how to predict a reliable structural model.

Conclusion: This paper reviews the principles, advantages and disadvantages of the methods of peptide-MHC binding prediction and discussed the future directions to achieve more accurate predictions.

Keywords: Peptide, major histocompatibility complex (MHC), T cell epitope, prediction, molecular docking, immune.

Goswami M, Hourigan CS. Novel Antigen Targets for Immunotherapy of Acute Myeloid Leukemia. Curr Drug Targets 2017; 18(3): 296-303.
[http://dx.doi.org/10.2174/1389450116666150223120005] [PMID: 25706110]
Ettari R, Previti S, Bitto A, Grasso S, Zappalà M. Immunoproteasome-selective inhibitors: a promising strategy to treat hematologic malignancies, autoimmune and inflammatory diseases. Curr Med Chem 2016; 23(12): 1217-38.
[http://dx.doi.org/10.2174/0929867323666160318173706] [PMID: 26965184]
Neefjes J, Jongsma MLM, Paul P, Bakke O. Towards a systems understanding of MHC class I and MHC class II antigen presentation. Nat Rev Immunol 2011; 11(12): 823-36.
[http://dx.doi.org/10.1038/nri3084] [PMID: 22076556]
Wieczorek M, Abualrous ET, Sticht J, et al. Major histocompatibility complex (MHC) class I and MHC class II proteins: conformational plasticity in antigen presentation. Front Immunol 2017; 8: 292.
[http://dx.doi.org/10.3389/fimmu.2017.00292] [PMID: 28367149]
Cresswell P, Ackerman AL, Giodini A, Peaper DR, Wearsch PA. Mechanisms of MHC class I-restricted antigen processing and cross-presentation. Immunol Rev 2005; 207: 145-57.
[http://dx.doi.org/10.1111/j.0105-2896.2005.00316.x] [PMID: 16181333]
Blum JS, Wearsch PA, Cresswell P. Pathways of antigen processing. Annu Rev Immunol 2013; 31: 443-73.
[http://dx.doi.org/10.1146/annurev-immunol-032712-095910] [PMID: 23298205]
Bonneaud C, Pérez-Tris J, Federici P, Chastel O, Sorci G. Major histocompatibility alleles associated with local resistance to malaria in a passerine. Evolution 2006; 60(2): 383-9.
[http://dx.doi.org/10.1111/j.0014-3820.2006.tb01114.x] [PMID: 16610328]
Ayala García MA, González Yebra B, López Flores AL, Guaní Guerra E. The major histocompatibility complex in transplantation. J Transplant 2012; 2012842141
[http://dx.doi.org/10.1155/2012/842141] [PMID: 22778908]
Robinson J, Halliwell JA, McWilliam H, Lopez R, Parham P, Marsh SGE. The IMGT/HLA database. Nucleic Acids Res 2013; 41(Database issue): D1222-7.
[PMID: 23080122]
Shiina T, Hosomichi K, Inoko H, Kulski JK. The HLA genomic loci map: expression, interaction, diversity and disease. J Hum Genet 2009; 54(1): 15-39.
[http://dx.doi.org/10.1038/jhg.2008.5] [PMID: 19158813]
Rammensee HG, Friede T, Stevanoviíc S. MHC ligands and peptide motifs: first listing. Immunogenetics 1995; 41(4): 178-228.
[http://dx.doi.org/10.1007/BF00172063] [PMID: 7890324]
Lafuente EM, Reche PA. Prediction of MHC-peptide binding: a systematic and comprehensive overview. Curr Pharm Des 2009; 15(28): 3209-20.
[http://dx.doi.org/10.2174/138161209789105162] [PMID: 19860671]
Halling-Brown M, Shaban R, Frampton D, et al. Proteins accessible to immune surveillance show significant T-cell epitope depletion: Implications for vaccine design. Mol Immunol 2009; 46(13): 2699-705.
[http://dx.doi.org/10.1016/j.molimm.2009.05.027] [PMID: 19560824]
Hewitt EW. The MHC class I antigen presentation pathway: strategies for viral immune evasion. Immunology 2003; 110(2): 163-9.
[http://dx.doi.org/10.1046/j.1365-2567.2003.01738.x] [PMID: 14511229]
Rammensee H, Bachmann J, Emmerich NPN, Bachor OA, Stevanović S. SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics 1999; 50(3-4): 213-9.
[http://dx.doi.org/10.1007/s002510050595] [PMID: 10602881]
Schuler MM, Nastke M-D, Stevanovikć S. SYFPEITHI: database for searching and T-cell epitope prediction. Methods Mol Biol 2007; 409: 75-93.
[http://dx.doi.org/10.1007/978-1-60327-118-9_5] [PMID: 18449993]
Sturniolo T, Bono E, Ding J, et al. Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices. Nat Biotechnol 1999; 17(6): 555-61.
[http://dx.doi.org/10.1038/9858] [PMID: 10385319]
Mustafa AS, Shaban FA. ProPred analysis and experimental evaluation of promiscuous T-cell epitopes of three major secreted antigens of Mycobacterium tuberculosis. Tuberculosis (Edinb) 2006; 86(2): 115-24.
[http://dx.doi.org/10.1016/j.tube.2005.05.001] [PMID: 16039905]
Zhang L, Chen Y, Wong HS, Zhou S, Mamitsuka H, Zhu S. TEPITOPEpan: extending TEPITOPE for peptide binding prediction covering over 700 HLA-DR molecules. PLoS One 2012; 7(2)e30483
[http://dx.doi.org/10.1371/journal.pone.0030483] [PMID: 22383964]
Reche PA, Glutting JP, Reinherz EL. Prediction of MHC class I binding peptides using profile motifs. Hum Immunol 2002; 63(9): 701-9.
[http://dx.doi.org/10.1016/S0198-8859(02)00432-9] [PMID: 12175724]
Singh H, Raghava GPS. ProPred1: prediction of promiscuous MHC Class-I binding sites. Bioinformatics 2003; 19(8): 1009-14.
[http://dx.doi.org/10.1093/bioinformatics/btg108] [PMID: 12761064]
Singh H, Raghava GPS. ProPred: prediction of HLA-DR binding sites. Bioinformatics 2001; 17(12): 1236-7.
[http://dx.doi.org/10.1093/bioinformatics/17.12.1236] [PMID: 11751237]
Noguchi H, Kato R, Hanai T, et al. Hidden Markov model-based prediction of antigenic peptides that interact with MHC class II molecules. J Biosci Bioeng 2002; 94(3): 264-70.
[http://dx.doi.org/10.1016/S1389-1723(02)80160-8] [PMID: 16233301]
Kato R, Noguchi H, Honda H, Kobayashi T. Hidden Markov model-based approach as the first screening of binding peptides that interact with MHC class II molecules. Enzyme Microb Technol 2003; 33(4): 472-81.
Mamitsuka H. Predicting peptides that bind to MHC molecules using supervised learning of hidden Markov models. Proteins 1998; 33(4): 460-74.
[http://dx.doi.org/10.1002/(SICI)1097-0134(19981201)33:4<460:AID-PROT2>3.0.CO;2-M] [PMID: 9849933]
Yu K, Petrovsky N, Schönbach C, Koh JY, Brusic V. Methods for prediction of peptide binding to MHC molecules: a comparative study. Mol Med 2002; 8(3): 137-48.
[http://dx.doi.org/10.1007/BF03402006] [PMID: 12142545]
Buus S, Lauemøller SL, Worning P, et al. Sensitive quantitative predictions of peptide-MHC binding by a ‘Query by Committee’ artificial neural network approach. Tissue Antigens 2003; 62(5): 378-84.http://www.ncbi.nlm.nih.gov/pubmed/14617044
[http://dx.doi.org/10.1034/j.1399-0039.2003.00112.x] [PMID: 14617044]
Nielsen M, Lundegaard C, Worning P, et al. Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci 2003; 12(5): 1007-17.
[http://dx.doi.org/10.1110/ps.0239403] [PMID: 12717023]
Hoof I, Peters B, Sidney J, et al. NetMHCpan, a method for MHC class I binding prediction beyond humans. Immunogenetics 2009; 61(1): 1-13.
[http://dx.doi.org/10.1007/s00251-008-0341-z] [PMID: 19002680]
Nielsen M, Lundegaard C, Lund O. Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method. BMC Bioinformatics 2007; 8: 238.
[http://dx.doi.org/10.1186/1471-2105-8-238] [PMID: 17608956]
Andreatta M, Karosiene E, Rasmussen M, Stryhn A, Buus S, Nielsen M. Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification. Immunogenetics 2015; 67(11-12): 641-50.
[http://dx.doi.org/10.1007/s00251-015-0873-y] [PMID: 26416257]
Nielsen M, Lundegaard C, Worning P, et al. Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach. Bioinformatics 2004; 20(9): 1388-97.
[http://dx.doi.org/10.1093/bioinformatics/bth100] [PMID: 14962912]
Lundegaard C, Lund O, Nielsen M. Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers. Bioinformatics 2008; 24(11): 1397-8.
[http://dx.doi.org/10.1093/bioinformatics/btn128] [PMID: 18413329]
Andreatta M, Nielsen M. Gapped sequence alignment using artificial neural networks: application to the MHC class I system. Bioinformatics 2016; 32(4): 511-7.
[http://dx.doi.org/10.1093/bioinformatics/btv639] [PMID: 26515819]
Jurtz V, Paul S, Andreatta M, Marcatili P, Peters B, Nielsen M. NetMHCpan-4.0: Improved peptide-MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data. J Immunol 2017; 199(9): 3360-8.
[http://dx.doi.org/10.4049/jimmunol.1700893] [PMID: 28978689]
Zhang H, Lundegaard C, Nielsen M. Pan-specific MHC class I predictors: a benchmark of HLA class I pan-specific prediction methods. Bioinformatics 2009; 25(1): 83-9.
[http://dx.doi.org/10.1093/bioinformatics/btn579] [PMID: 18996943]
Nielsen M, Lundegaard C, Blicher T, et al. Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: NetMHCIIpan. PLOS Comput Biol 2008; 4(7)e1000107
[http://dx.doi.org/10.1371/journal.pcbi.1000107] [PMID: 18604266]
Jensen KK, Andreatta M, Marcatili P, et al. Improved methods for predicting peptide binding affinity to MHC class II molecules. Immunology 2018; 154(3): 394-406.
[http://dx.doi.org/10.1111/imm.12889] [PMID: 29315598]
Nielsen M, Andreatta M. NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets. Genome Med 2016; 8(1): 33.
[http://dx.doi.org/10.1186/s13073-016-0288-x] [PMID: 27029192]
Wan J, Liu W, Xu Q, Ren Y, Flower DR, Li T. SVRMHC prediction server for MHC-binding peptides. BMC Bioinformatics 2006; 7: 463.
[http://dx.doi.org/10.1186/1471-2105-7-463] [PMID: 17059589]
Dönnes P, Kohlbacher O. SVMHC: a server for prediction of MHC-binding peptides. Nucleic Acids Res 2006; 34: 194-7.
Cui J, Han LY, Lin HH, et al. MHC-BPS: MHC-binder prediction server for identifying peptides of flexible lengths from sequence-derived physicochemical properties. Immunogenetics 2006; 58(8): 607-13.
[http://dx.doi.org/10.1007/s00251-006-0117-2] [PMID: 16832638]
Zhao Y, Pinilla C, Valmori D, Martin R, Simon R. Application of support vector machines for T-cell epitopes prediction. Bioinformatics 2003; 19(15): 1978-84.
[http://dx.doi.org/10.1093/bioinformatics/btg255] [PMID: 14555632]
Liu W, Meng X, Xu Q, Flower DR, Li T. Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models. BMC Bioinformatics 2006; 7: 182.
[http://dx.doi.org/10.1186/1471-2105-7-182] [PMID: 16579851]
Lata S, Bhasin M, Raghava GPS. Application of machine learning techniques in predicting MHC binders. Methods Mol Biol 2007; 409: 201-15.
[http://dx.doi.org/10.1007/978-1-60327-118-9_14] [PMID: 18450002]
Das R, Baker D. Macromolecular modeling with rosetta. Annu Rev Biochem 2008; 77: 363-82.
[http://dx.doi.org/10.1146/annurev.biochem.77.062906.171838] [PMID: 18410248]
Zhang Y. I-TASSER server for protein 3D structure prediction. BMC Bioinformatics 2008; 9: 40.
[http://dx.doi.org/10.1186/1471-2105-9-40] [PMID: 18215316]
Yang J, Zhang Y. I-TASSER server: new development for protein structure and function predictions. Nucleic Acids Res 2015; 43(W1)W174-81
[http://dx.doi.org/10.1093/nar/gkv342] [PMID: 25883148]
London N, Raveh B, Cohen E, Fathi G, Schueler-Furman O. Rosetta FlexPepDock web server--high resolution modeling of peptide-protein interactions. Nucleic Acids Res 2011; 39(Web Server issue): 249-53.
Raveh B, London N, Zimmerman L, Schueler-Furman O. Rosetta FlexPepDock ab-initio: simultaneous folding, docking and refinement of peptides onto their receptors. PLoS One 2011; 6(4)e18934
[http://dx.doi.org/10.1371/journal.pone.0018934] [PMID: 21572516]
Schueler-Furman O, Altuvia Y, Sette A, Margalit H. Structure-based prediction of binding peptides to MHC class I molecules: application to a broad range of MHC alleles. Protein Sci 2000; 9(9): 1838-46.
[http://dx.doi.org/10.1110/ps.9.9.1838] [PMID: 11045629]
Dudek NL, Maier S, Chen ZJ, et al. T cell epitopes of the La/SSB autoantigen in humanized transgenic mice expressing the HLA class II haplotype DRB1*0301/DQB1*0201. Arthritis Rheum 2007; 56(10): 3387-98.
[http://dx.doi.org/10.1002/art.22870] [PMID: 17907193]
Lee H, Heo L, Lee MS, Seok C. GalaxyPepDock: a protein-peptide docking tool based on interaction similarity and energy optimization. Nucleic Acids Res 2015; 43(W1)W431-5
[http://dx.doi.org/10.1093/nar/gkv495] [PMID: 25969449]
Khan JM, Ranganathan S. pDOCK: a new technique for rapid and accurate docking of peptide ligands to Major Histocompatibility Complexes. Immunome Res 2010; 6(Suppl. 1): S2.
[http://dx.doi.org/10.1186/1745-7580-6-S1-S2] [PMID: 20875153]
Antunes DA, Devaurs D, Moll M, Lizée G, Kavraki LE. General prediction of peptide-MHC binding modes using incremental docking: a proof of concept. Sci Rep 2018; 8(1): 4327.
[http://dx.doi.org/10.1038/s41598-018-22173-4] [PMID: 29531253]
Rigo MM, Antunes DA, Vaz de Freitas M, et al. DockTope: a Web-based tool for automated pMHC-I modelling. Sci Rep 2015; 5: 18413.
[http://dx.doi.org/10.1038/srep18413] [PMID: 26674250]
Lundegaard C, Lamberth K, Harndahl M, Buus S, Lund O, Nielsen M. NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8-11. Nucleic Acids Res 2008; 36: 509-12.
Nielsen M, Lund O. NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction. BMC Bioinformatics 2009; 10: 296.
[http://dx.doi.org/10.1186/1471-2105-10-296] [PMID: 19765293]
Buus S. Description and prediction of peptide-MHC binding: the ‘human MHC project’. Curr Opin Immunol 1999; 11(2): 209-13.
[http://dx.doi.org/10.1016/S0952-7915(99)80035-1] [PMID: 10322158]
Bhasin M, Raghava GPS. A hybrid approach for predicting promiscuous MHC class I restricted T cell epitopes. J Biosci 2007; 32(1): 31-42.
[http://dx.doi.org/10.1007/s12038-007-0004-5] [PMID: 17426378]
Reche PA, Reinherz EL. PEPVAC: a web server for multi-epitope vaccine development based on the prediction of supertypic MHC ligands. Nucleic Acids Res 2005; 33: 138-42.
Peters B, Tong W, Sidney J, Sette A, Weng Z. Examining the independent binding assumption for binding of peptide epitopes to MHC-I molecules. Bioinformatics 2003; 19(14): 1765-72.
[http://dx.doi.org/10.1093/bioinformatics/btg247] [PMID: 14512347]
Bui HH, Sidney J, Peters B, et al. Automated generation and evaluation of specific MHC binding predictive tools: ARB matrix applications. Immunogenetics 2005; 57(5): 304-14.
[http://dx.doi.org/10.1007/s00251-005-0798-y] [PMID: 15868141]
Boutet E, Lieberherr D, Tognolli M, Schneider M, Bairoch A. UniProtKB/Swiss-Prot. Methods Mol Biol 2007; 406: 89-112.
[PMID: 18287689]
Powley EH, Cameron KS. Organizational healing: Lived virtuousness amidst organizational crisis. J Manag Spiritual Relig 2006; 13-33.
Bhasin M, Raghava GPS. Prediction of CTL epitopes using QM, SVM and ANN techniques. Vaccine 2004; 22(23-24): 3195-204.
[http://dx.doi.org/10.1016/j.vaccine.2004.02.005] [PMID: 15297074]
Bhasin M, Raghava GPS. SVM based method for predicting HLA-DRB1*0401 binding peptides in an antigen sequence. Bioinformatics 2004; 20(3): 421-3.
[http://dx.doi.org/10.1093/bioinformatics/btg424] [PMID: 14960470]
Joachims T. Making large-scale support vector machine learning practical Adv kernel methods Support vector Learn 1999.
Wang PW, Lin CJ. Support vector machines. Data Classif Algorithms Appl 2014.
Byvatov E, Schneider G. Support vector machine applications in bioinformatics. Appl Bioinformatics 2003; 2(2): 67-77.
[PMID: 15130823]
Madzarov G, Gjorgjevikj D, Chorbev I. A Multi-class SVM Classifier Utilizing Binary Decision Tree Support vector machines for pattern recognition. Informatica 2009; 33: 233-41.
Liu W, Wan J, Meng X, Flower DR, Li T. In silico prediction of peptide-MHC binding affinity using SVRMHC. Methods Mol Biol 2007; 409: 283-91.
[http://dx.doi.org/10.1007/978-1-60327-118-9_20] [PMID: 18450008]
Gopalakrishnan B, Roques BP. Do antigenic peptides have a unique sense of direction inside the MHC binding groove? A molecular modelling study. FEBS Lett 1992; 303(2-3): 224-8.
[http://dx.doi.org/10.1016/0014-5793(92)80525-L] [PMID: 1376698]
Ding YH, Smith KJ, Garboczi DN, Utz U, Biddison WE, Wiley DC. Two human T cell receptors bind in a similar diagonal mode to the HLA-A2/Tax peptide complex using different TCR amino acids. Immunity 1998; 8(4): 403-11.
[http://dx.doi.org/10.1016/S1074-7613(00)80546-4] [PMID: 9586631]
Ghosh P, Amaya M, Mellins E, Wiley DC. The structure of an intermediate in class II MHC maturation: CLIP bound to HLA-DR3. Nature 1995; 378(6556): 457-62.
[http://dx.doi.org/10.1038/378457a0] [PMID: 7477400]
Madden DR. The three-dimensional structure of peptide-MHC complexes. Annu Rev Immunol 1995; 13: 587-622.
[http://dx.doi.org/10.1146/annurev.iy.13.040195.003103] [PMID: 7612235]
Madden DR, Gorga JC, Strominger JL, Wiley DC. The three-dimensional structure of HLA-B27 at 2.1 A resolution suggests a general mechanism for tight peptide binding to MHC. Cell 1992; 70(6): 1035-48.
[http://dx.doi.org/10.1016/0092-8674(92)90252-8] [PMID: 1525820]
Kosmopoulou A, Vlassi M, Stavrakoudis A, Sakarellos C, Sakarellos-Daitsiotis M. T-cell epitopes of the La/SSB autoantigen: prediction based on the homology modeling of HLA-DQ2/DQ7 with the insulin-B peptide/HLA-DQ8 complex. J Comput Chem 2006; 27(9): 1033-44.
[http://dx.doi.org/10.1002/jcc.20422] [PMID: 16639700]
Zhang GL, Khan AM, Srinivasan KN, August JT, Brusic V. MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides Nucleic Acids Res 2005; 33(Web Server issue): 172-9.
Zhao W, Sher X. Systematically benchmarking peptide-MHC binding predictors: From synthetic to naturally processed epitopes. PLOS Comput Biol 2018; 14(11)e1006457
[http://dx.doi.org/10.1371/journal.pcbi.1006457] [PMID: 30408041]
Dias R, de Azevedo WF Jr. Molecular docking algorithms. Curr Drug Targets 2008; 9(12): 1040-7.
[http://dx.doi.org/10.2174/138945008786949432] [PMID: 19128213]
Xavier MM, Heck GS, Avila MB, et al. SAnDReS a Computational Tool for Statistical Analysis of Docking Results and Development of Scoring Functions. Comb Chem High Throughput Screen 2016; 19(10): 801-12.
[http://dx.doi.org/10.2174/1386207319666160927111347] [PMID: 27686428]
Heberlé G, de Azevedo WF Jr. Bio-inspired algorithms applied to molecular docking simulations. Curr Med Chem 2011; 18(9): 1339-52.
[http://dx.doi.org/10.2174/092986711795029573] [PMID: 21366530]
Goodsell DS, Morris GM, Olson AJ. Automated docking of flexible ligands: applications of AutoDock. J Mol Recognit 1996; 9(1): 1-5.
[http://dx.doi.org/10.1002/(SICI)1099-1352(199601)9:1<1:AID-JMR241>3.0.CO;2-6] [PMID: 8723313]
Friesner RA, Banks JL, Murphy RB, et al. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 2004; 47(7): 1739-49.
[http://dx.doi.org/10.1021/jm0306430] [PMID: 15027865]
Halgren TA, Murphy RB, Friesner RA, et al. Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 2004; 47(7): 1750-9.
[http://dx.doi.org/10.1021/jm030644s] [PMID: 15027866]
Verdonk ML, Cole JC, Hartshorn MJ, Murray CW, Taylor RD. Improved protein-ligand docking using Gold. Proteins 2003; 52(4): 609-23.
[http://dx.doi.org/10.1002/prot.10465] [PMID: 12910460]
Abagyan R, Totrov M, Kuznetsov D. ICM-A new method for protein modeling and design: Applications to docking and structure prediction from the distorted native conformation. J Comput Chem 1994; 15: 488-506.
Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 2010; 31(2): 455-61.
[PMID: 19499576]
Vita R, Overton JA, Greenbaum JA, et al. The immune epitope database (IEDB) 3.0. Nucleic Acids Res 2015; 43(Database issue): D405-12.
[http://dx.doi.org/10.1093/nar/gku938] [PMID: 25300482]
Moghram BA, Nabil E, Badr A. Ab-initio conformational epitope structure prediction using genetic algorithm and SVM for vaccine design. Comput Methods Programs Biomed 2018; 153: 161-70.
[http://dx.doi.org/10.1016/j.cmpb.2017.10.011] [PMID: 29157448]
Zhang Y, Skolnick J. TM-align: a protein structure alignment algorithm based on the TM-score. Nucleic Acids Res 2005; 33(7): 2302-9.
[http://dx.doi.org/10.1093/nar/gki524] [PMID: 15849316]
Stranzl T, Larsen MV, Lundegaard C, Nielsen M. NetCTLpan: pan-specific MHC class I pathway epitope predictions. Immunogenetics 2010; 62(6): 357-68.
[http://dx.doi.org/10.1007/s00251-010-0441-4] [PMID: 20379710]
Larsen MV, Lundegaard C, Lamberth K, Buus S, Lund O, Nielsen M. Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction. BMC Bioinformatics 2007; 8: 424.
[http://dx.doi.org/10.1186/1471-2105-8-424] [PMID: 17973982]
Nielsen M, Lundegaard C, Lund O, Keşmir C. The role of the proteasome in generating cytotoxic T-cell epitopes: insights obtained from improved predictions of proteasomal cleavage. Immunogenetics 2005; 57(1-2): 33-41.
[http://dx.doi.org/10.1007/s00251-005-0781-7] [PMID: 15744535]
Keşmir C, Nussbaum AK, Schild H, Detours V, Brunak S. Prediction of proteasome cleavage motifs by neural networks. Protein Eng 2002; 15(4): 287-96.
[http://dx.doi.org/10.1093/protein/15.4.287] [PMID: 11983929]
Peters B, Bulik S, Tampe R, Van Endert PM, Holzhütter H-G. Identifying MHC class I epitopes by predicting the TAP transport efficiency of epitope precursors. J Immunol 2003; 171(4): 1741-9.
[http://dx.doi.org/10.4049/jimmunol.171.4.1741] [PMID: 12902473]
Bhasin M, Lata S, Raghava GP. TAPPred prediction of TAP-binding peptides in antigens. Methods Mol Biol 2007; 409: 381-6.
[http://dx.doi.org/10.1007/978-1-60327-118-9_28] [PMID: 18450016]
Tenzer S, Peters B, Bulik S, et al. Modeling the MHC class I pathway by combining predictions of proteasomal cleavage, TAP transport and MHC class I binding. Cell Mol Life Sci 2005; 62(9): 1025-37.
[http://dx.doi.org/10.1007/s00018-005-4528-2] [PMID: 15868101]
Zeng H, Gifford DK. DeepLigand: accurate prediction of MHC class I ligands using peptide embedding. Bioinformatics 2019; 35(14): i278-83.
[http://dx.doi.org/10.1093/bioinformatics/btz330] [PMID: 31510651]
de Azevedo WF Jr, Dias R. Experimental approaches to evaluate the thermodynamics of protein-drug interactions. Curr Drug Targets 2008; 9(12): 1071-6.
[http://dx.doi.org/10.2174/138945008786949441] [PMID: 19128217]
F. de Azevedo W. Molecular Dynamics Simulations of Protein Targets Identified in Mycobacterium tuberculosis. Curr Med Chem 2011; 18(9): 1353-66.

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2020
Page: [878 - 888]
Pages: 11
DOI: 10.2174/1574893615999200429122801
Price: $65

Article Metrics

PDF: 20