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Current Medicinal Chemistry

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ISSN (Print): 0929-8673
ISSN (Online): 1875-533X

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Review Article

In Silico Immunogenicity Assessment of Therapeutic Peptides

Author(s): Wenzhen Li, Jinyi Wei, Qianhu Jiang, Yuwei Zhou, Xingru Yan, Changcheng Xiang* and Jian Huang*

Volume 31, Issue 26, 2024

Published on: 24 January, 2024

Page: [4100 - 4110] Pages: 11

DOI: 10.2174/0109298673264899231206093930

Price: $65

Abstract

The application of therapeutic peptides in clinical practice has significantly progressed in the past decades. However, immunogenicity remains an inevitable and crucial issue in the development of therapeutic peptides. The prediction of antigenic peptides presented by MHC class II is a critical approach to evaluating the immunogenicity of therapeutic peptides. With the continuous upgrade of algorithms and databases in recent years, the prediction accuracy has been significantly improved. This has made in silico evaluation an important component of immunogenicity assessment in therapeutic peptide development. In this review, we summarize the development of peptide-MHC-II binding prediction methods for antigenic peptides presented by MHC class II molecules and provide a systematic explanation of the most advanced ones, aiming to deepen our understanding of this field that requires particular attention.

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[1]
Lau, J.L.; Dunn, M.K. Therapeutic peptides: Historical perspectives, current development trends, and future directions. Bioorg. Med. Chem., 2018, 26(10), 2700-2707.
[http://dx.doi.org/10.1016/j.bmc.2017.06.052] [PMID: 28720325]
[2]
Fosgerau, K.; Hoffmann, T. Peptide therapeutics: Current status and future directions. Drug Discov. Today, 2015, 20(1), 122-128.
[http://dx.doi.org/10.1016/j.drudis.2014.10.003] [PMID: 25450771]
[3]
Ning, L.; Huang, J.; He, B.; Kang, J. An in silico immunogenicity analysis for PbHRH: An antiangiogenic peptibody by fusing HRH peptide and human IgG1 Fc fragment. Curr. Bioinform., 2020, 15(6), 547-553.
[http://dx.doi.org/10.2174/1574893614666190730104348]
[4]
Wang, L.; Wang, N.; Zhang, W.; Cheng, X.; Yan, Z.; Shao, G.; Wang, X.; Wang, R.; Fu, C. Therapeutic peptides: Current applications and future directions. Signal Transduct. Target. Ther., 2022, 7(1), 48.
[http://dx.doi.org/10.1038/s41392-022-00904-4] [PMID: 35165272]
[5]
Sharma, A.; Kumar, A.; de la Torre, B.G.; Albericio, F. Liquid-phase peptide synthesis (LPPS): A third wave for the preparation of peptides. Chem. Rev., 2022, 122(16), 13516-13546.
[http://dx.doi.org/10.1021/acs.chemrev.2c00132] [PMID: 35816287]
[6]
Jain, K.K. An overview of drug delivery systems. Methods Mol. Biol., 2020, 2059, 1-54.
[http://dx.doi.org/10.1007/978-1-4939-9798-5_1] [PMID: 31435914]
[7]
Muttenthaler, M.; King, G.F.; Adams, D.J.; Alewood, P.F. Trends in peptide drug discovery. Nat. Rev. Drug Discov., 2021, 20(4), 309-325.
[http://dx.doi.org/10.1038/s41573-020-00135-8] [PMID: 33536635]
[8]
Khalily, M.P.; Soydan, M. Peptide-based diagnostic and therapeutic agents: Where we are and where we are heading? Chem. Biol. Drug Des., 2023, 101(3), 772-793.
[http://dx.doi.org/10.1111/cbdd.14180] [PMID: 36366980]
[9]
Zhang, H.; Saravanan, K.M.; Wei, Y.; Jiao, Y.; Yang, Y.; Pan, Y.; Wu, X.; Zhang, J.Z.H. Deep learning-based bioactive therapeutic peptide generation and screening. J. Chem. Inf. Model., 2023, 63(3), 835-845.
[http://dx.doi.org/10.1021/acs.jcim.2c01485] [PMID: 36724090]
[10]
Jawa, V.; Cousens, L.P.; Awwad, M.; Wakshull, E.; Kropshofer, H.; De Groot, A.S. T-cell dependent immunogenicity of protein therapeutics: Preclinical assessment and mitigation. Clin. Immunol., 2013, 149(3), 534-555.
[http://dx.doi.org/10.1016/j.clim.2013.09.006] [PMID: 24263283]
[11]
Watts, C. Capture and processing of exogenous antigens for presentation on MHC molecules. Annu. Rev. Immunol., 1997, 15(1), 821-850.
[http://dx.doi.org/10.1146/annurev.immunol.15.1.821] [PMID: 9143708]
[12]
Vaisman-Mentesh, A.; Gutierrez-Gonzalez, M.; DeKosky, B.J.; Wine, Y. The molecular mechanisms that underlie the immune biology of anti-drug antibody formation following treatment with monoclonal antibodies. Front. Immunol., 2020, 11, 1951.
[http://dx.doi.org/10.3389/fimmu.2020.01951] [PMID: 33013848]
[13]
Ning, L.; He, B.; Zhou, P.; Derda, R.; Huang, J. Molecular design of peptide-Fc fusion drugs. Curr. Drug Metab., 2019, 20(3), 203-208.
[http://dx.doi.org/10.2174/1389200219666180821095355] [PMID: 30129406]
[14]
Rosenstock, J.; Balas, B.; Charbonnel, B.; Bolli, G.B.; Boldrin, M.; Ratner, R.; Balena, R. The fate of taspoglutide, a weekly GLP-1 receptor agonist, versus twice-daily exenatide for type 2 diabetes: the T-emerge 2 trial. Diabetes Care, 2013, 36(3), 498-504.
[http://dx.doi.org/10.2337/dc12-0709] [PMID: 23139373]
[15]
Groell, F.; Jordan, O.; Borchard, G. In vitro models for immunogenicity prediction of therapeutic proteins. Eur. J. Pharm. Biopharm., 2018, 130, 128-142.
[http://dx.doi.org/10.1016/j.ejpb.2018.06.008] [PMID: 29894817]
[16]
Zhou, Y.; Huang, Z.; Li, W.; Wei, J.; Jiang, Q.; Yang, W.; Huang, J. Deep learning in preclinical antibody drug discovery and development. Methods, 2023, 218, 57-71.
[http://dx.doi.org/10.1016/j.ymeth.2023.07.003] [PMID: 37454742]
[17]
Zhou, Y.; Huang, Z.; Gou, Y.; Liu, S.; Yang, W.; Zhang, H.; Dzisoo, A.M.; Huang, J. AB-Amy: machine learning aided amyloidogenic risk prediction of therapeutic antibody light chains. Antib. Ther., 2023, 6(3), 147-156.
[http://dx.doi.org/10.1093/abt/tbad007] [PMID: 37492587]
[18]
Zhou, Y.; Xie, S.; Yang, Y.; Jiang, L.; Liu, S.; Li, W.; Abagna, H.B.; Ning, L.; Huang, J. SSH2.0: A better tool for predicting the hydrophobic interaction risk of monoclonal antibody. Front. Genet., 2022, 13, 842127.
[http://dx.doi.org/10.3389/fgene.2022.842127] [PMID: 35368659]
[19]
Paul, S.; Kolla, R.V.; Sidney, J.; Weiskopf, D.; Fleri, W.; Kim, Y.; Peters, B.; Sette, A. Evaluating the immunogenicity of protein drugs by applying in vitro MHC binding data and the immune epitope database and analysis resource. Clin. Dev. Immunol., 2013, 2013, 1-7.
[http://dx.doi.org/10.1155/2013/467852] [PMID: 24222776]
[20]
Weaver, J.M.; Lazarski, C.A.; Richards, K.A.; Chaves, F.A.; Jenks, S.A.; Menges, P.R.; Sant, A.J. Immunodominance of CD4 T cells to foreign antigens is peptide intrinsic and independent of molecular context: implications for vaccine design. J. Immunol., 2008, 181(5), 3039-3048.
[http://dx.doi.org/10.4049/jimmunol.181.5.3039] [PMID: 18713974]
[21]
Lazarski, C.A.; Chaves, F.A.; Jenks, S.A.; Wu, S.; Richards, K.A.; Weaver, J.M.; Sant, A.J. The kinetic stability of MHC class II:peptide complexes is a key parameter that dictates immunodominance. Immunity, 2005, 23(1), 29-40.
[http://dx.doi.org/10.1016/j.immuni.2005.05.009] [PMID: 16039577]
[22]
Paul, S.; Grifoni, A.; Peters, B.; Sette, A. Major histocompatibility complex binding, eluted ligands, and immunogenicity: Benchmark testing and predictions. Front. Immunol., 2020, 10, 3151.
[http://dx.doi.org/10.3389/fimmu.2019.03151] [PMID: 32117208]
[23]
Buus, S.; Sette, A.; Colon, S.M.; Jenis, D.M.; Grey, H.M. Isolation and characterization of antigen-la complexes involved in T cell recognition. Cell, 1986, 47(6), 1071-1077.
[http://dx.doi.org/10.1016/0092-8674(86)90822-6] [PMID: 3490919]
[24]
Peters, B.; Bui, H.H.; Frankild, S.; Nielsen, M.; Lundegaard, C.; Kostem, E.; Basch, D.; Lamberth, K.; Harndahl, M.; Fleri, W.; Wilson, S.S.; Sidney, J.; Lund, O.; Buus, S.; Sette, A. A community resource benchmarking predictions of peptide binding to MHC-I molecules. PLOS Comput. Biol., 2006, 2(6), e65.
[http://dx.doi.org/10.1371/journal.pcbi.0020065] [PMID: 16789818]
[25]
Sette, A.; Buus, S.; Colon, S.; Smith, J.A.; Miles, C.; Grey, H.M. Structural characteristics of an antigen required for its interaction with Ia and recognition by T cells. Nature, 1987, 328(6129), 395-399.
[http://dx.doi.org/10.1038/328395a0] [PMID: 3497349]
[26]
Allen, P.M.; Matsueda, G.R.; Evans, R.J.; Dunbar, J.B., Jr; Marshall, G.R.; Unanue, E.R. Identification of the T-cell and Ia contact residues of a T-cell antigenic epitope. Nature, 1987, 327(6124), 713-715.
[http://dx.doi.org/10.1038/327713a0] [PMID: 2439915]
[27]
Hammer, J.; Bono, E.; Gallazzi, F.; Belunis, C.; Nagy, Z.; Sinigaglia, F. Precise prediction of major histocompatibility complex class II-peptide interaction based on peptide side chain scanning. J. Exp. Med., 1994, 180(6), 2353-2358.
[http://dx.doi.org/10.1084/jem.180.6.2353] [PMID: 7964508]
[28]
Rothbard, J.B.; Marshall, K.; Wilson, J.; Fugger, L.; Zaller, D. Prediction of peptide affinity to HLA DRB1*0401. Int. Arch. Allergy Immunol., 1994, 105(1), 1-7.
[http://dx.doi.org/10.1159/000236795] [PMID: 8086825]
[29]
Mendes, M.; Mahita, J.; Blazeska, N.; Greenbaum, J.; Ha, B.; Wheeler, K.; Wang, J.; Shackelford, D.; Sette, A.; Peters, B. IEDB-3D 2.0: Structural data analysis within the Immune Epitope Database. Protein Sci., 2023, 32(4), e4605.
[http://dx.doi.org/10.1002/pro.4605] [PMID: 36806329]
[30]
Vita, R.; Mahajan, S.; Overton, J.A.; Dhanda, S.K.; Martini, S.; Cantrell, J.R.; Wheeler, D.K.; Sette, A.; Peters, B. The immune epitope database (IEDB): 2018 update. Nucleic Acids Res., 2019, 47(D1), D339-D343.
[http://dx.doi.org/10.1093/nar/gky1006] [PMID: 30357391]
[31]
Schuler, M.M.; Nastke, M.D.; Stevanović, 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]
[32]
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(1), 238.
[http://dx.doi.org/10.1186/1471-2105-8-238] [PMID: 17608956]
[33]
Bui, H.H.; Sidney, J.; Peters, B.; Sathiamurthy, M.; Sinichi, A.; Purton, K.A.; Mothé, B.R.; Chisari, F.V.; Watkins, D.I.; Sette, A. Automated generation and evaluation of specific MHC binding predictive tools: ARB matrix applications. Immunogenetics, 2005, 57(5), 304-314.
[http://dx.doi.org/10.1007/s00251-005-0798-y] [PMID: 15868141]
[34]
Reche, P.A.; Glutting, J.P.; Reinherz, E.L. Prediction of MHC class I binding peptides using profile motifs. Hum. Immunol., 2002, 63(9), 701-709.
[http://dx.doi.org/10.1016/S0198-8859(02)00432-9] [PMID: 12175724]
[35]
Sturniolo, T.; Bono, E.; Ding, J.; Raddrizzani, L.; Tuereci, O.; Sahin, U.; Braxenthaler, M.; Gallazzi, F.; Protti, M.P.; Sinigaglia, F.; Hammer, J. Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices. Nat. Biotechnol., 1999, 17(6), 555-561.
[http://dx.doi.org/10.1038/9858] [PMID: 10385319]
[36]
Singh, H.; Raghava, G.P.S. ProPred: prediction of HLA-DR binding sites. Bioinformatics, 2001, 17(12), 1236-1237.
[http://dx.doi.org/10.1093/bioinformatics/17.12.1236] [PMID: 11751237]
[37]
Dönnes, P.; Kohlbacher, O. SVMHC: A server for prediction of MHC-binding peptides. Nucleic Acids Res, 2006, 34, 194-197.
[38]
Zhang, L.; Chen, Y.; Wong, H.S.; 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]
[39]
Wang, P.; Sidney, J.; Dow, C.; Mothé, B.; Sette, A.; Peters, B. A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach. PLOS Comput. Biol., 2008, 4(4), e1000048.
[http://dx.doi.org/10.1371/journal.pcbi.1000048] [PMID: 18389056]
[40]
Jani, S.P.; Kumar, S.P.; Mangukia, N.; Patel, S.K.; Pandya, H.A.; Rawal, R.M. MHC2AFFYPRED: A machine-learning approach to estimate affinity of MHC class II peptides based on structural interaction fingerprints. Proteins, 2023, 91(2), 277-289.
[http://dx.doi.org/10.1002/prot.26428] [PMID: 36116110]
[41]
Brusic, V.; Rudy, G.; Honeyman, G.; Hammer, J.; Harrison, L. Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network. Bioinformatics, 1998, 14(2), 121-130.
[http://dx.doi.org/10.1093/bioinformatics/14.2.121] [PMID: 9545443]
[42]
Noguchi, H.; Kato, R.; Hanai, T.; Matsubara, Y.; Honda, H.; Brusic, V.; Kobayashi, T. Hidden Markov model-based prediction of antigenic peptides that interact with MHC class II molecules. J. Biosci. Bioeng., 2002, 94(3), 264-270.
[http://dx.doi.org/10.1016/S1389-1723(02)80160-8] [PMID: 16233301]
[43]
Lata, S.; Bhasin, M.; Raghava, G.P.S. Application of machine learning techniques in predicting MHC binders. Methods Mol. Biol., 2007, 409, 201-215.
[http://dx.doi.org/10.1007/978-1-60327-118-9_14] [PMID: 18450002]
[44]
Karosiene, E.; Rasmussen, M.; Blicher, T.; Lund, O.; Buus, S.; Nielsen, M. NetMHCIIpan-3.0, a common pan-specific MHC class II prediction method including all three human MHC class II isotypes, HLA-DR, HLA-DP and HLA-DQ. Immunogenetics, 2013, 65(10), 711-724.
[http://dx.doi.org/10.1007/s00251-013-0720-y] [PMID: 23900783]
[45]
Nielsen, M.; Lundegaard, C.; Worning, P.; Lauemøller, S.L.; Lamberth, K.; Buus, S.; Brunak, S.; Lund, O. Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci., 2003, 12(5), 1007-1017.
[http://dx.doi.org/10.1110/ps.0239403] [PMID: 12717023]
[46]
Nielsen, M.; Lundegaard, C.; Blicher, T.; Peters, B.; Sette, A.; Justesen, S.; Buus, S.; Lund, O. 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]
[47]
Nielsen, M.; Lund, O. NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction. BMC Bioinformatics, 2009, 10(1), 296.
[http://dx.doi.org/10.1186/1471-2105-10-296] [PMID: 19765293]
[48]
Pertseva, M.; Gao, B.; Neumeier, D.; Yermanos, A.; Reddy, S.T. Applications of machine and deep learning in adaptive immunity. Annu. Rev. Chem. Biomol. Eng., 2021, 12(1), 39-62.
[http://dx.doi.org/10.1146/annurev-chembioeng-101420-125021] [PMID: 33852352]
[49]
Davies, M.N.; Sansom, C.E.; Beazley, C.; Moss, D.S. A novel predictive technique for the MHC class II peptide-binding interaction. Mol. Med., 2003, 9(9-12), 220-225.
[http://dx.doi.org/10.2119/2003-00032.Sansom] [PMID: 15208743]
[50]
Tong, J.C.; Zhang, G.L.; Tan, T.W.; August, J.T.; Brusic, V.; Ranganathan, S. Prediction of HLA-DQ3.2β ligands: Evidence of multiple registers in class II binding peptides. Bioinformatics, 2006, 22(10), 1232-1238.
[http://dx.doi.org/10.1093/bioinformatics/btl071] [PMID: 16510499]
[51]
Bordner, A.J. Towards universal structure-based prediction of class II MHC epitopes for diverse allotypes. PLoS One, 2010, 5(12), e14383.
[http://dx.doi.org/10.1371/journal.pone.0014383] [PMID: 21187956]
[52]
Atanasova, M.; Patronov, A.; Dimitrov, I.; Flower, D.R.; Doytchinova, I. EpiDOCK: A molecular docking-based tool for MHC class II binding prediction. Protein Eng. Des. Sel., 2013, 26(10), 631-634.
[http://dx.doi.org/10.1093/protein/gzt018] [PMID: 23661105]
[53]
Laimer, J.; Lackner, P. MHCII3D-Robust structure based prediction of MHC II binding peptides. Int. J. Mol. Sci., 2020, 22(1), 12.
[http://dx.doi.org/10.3390/ijms22010012] [PMID: 33374958]
[54]
Zhang, H.; Wang, P.; Papangelopoulos, N.; Xu, Y.; Sette, A.; Bourne, P.E.; Lund, O.; Ponomarenko, J.; Nielsen, M.; Peters, B. Limitations of Ab initio predictions of peptide binding to MHC class II molecules. PLoS One, 2010, 5(2), e9272.
[http://dx.doi.org/10.1371/journal.pone.0009272] [PMID: 20174654]
[55]
Doytchinova, I.; Petkov, P.; Dimitrov, I.; Atanasova, M.; Flower, D.R. HLA-DP2 binding prediction by molecular dynamics simulations. Protein Sci., 2011, 20(11), 1918-1928.
[http://dx.doi.org/10.1002/pro.732] [PMID: 21898654]
[56]
Greenbaum, J.; Sidney, J.; Chung, J.; Brander, C.; Peters, B.; Sette, A. Functional classification of class II human leukocyte antigen (HLA) molecules reveals seven different supertypes and a surprising degree of repertoire sharing across supertypes. Immunogenetics, 2011, 63(6), 325-335.
[http://dx.doi.org/10.1007/s00251-011-0513-0] [PMID: 21305276]
[57]
Shen, W.J.; Zhang, X.; Zhang, S.; Liu, C.; Cui, W. The utility of supertype clustering in prediction for class II MHC-peptide binding. Molecules, 2018, 23(11), 3034.
[http://dx.doi.org/10.3390/molecules23113034] [PMID: 30463372]
[58]
Zhang, L.; Udaka, K.; Mamitsuka, H.; Zhu, S. Toward more accurate pan-specific MHC-peptide binding prediction: A review of current methods and tools. Brief. Bioinform., 2012, 13(3), 350-364.
[http://dx.doi.org/10.1093/bib/bbr060] [PMID: 21949215]
[59]
Degoot, A.M.; Chirove, F.; Ndifon, W. Trans-Allelic model for prediction of peptide:MHC-II Interactions. Front. Immunol., 2018, 9, 1410.
[http://dx.doi.org/10.3389/fimmu.2018.01410] [PMID: 29988560]
[60]
Nielsen, M.; Justesen, S.; Lund, O.; Lundegaard, C.; Buus, S. NetMHCIIpan-2.0 - Improved pan-specific HLA-DR predictions using a novel concurrent alignment and weight optimization training procedure. Immunome Res., 2010, 6(1), 9.
[http://dx.doi.org/10.1186/1745-7580-6-9] [PMID: 21073747]
[61]
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), 641-650.
[http://dx.doi.org/10.1007/s00251-015-0873-y] [PMID: 26416257]
[62]
Bordner, A.J.; Mittelmann, H.D. MultiRTA: A simple yet reliable method for predicting peptide binding affinities for multiple class II MHC allotypes. BMC Bioinformatics, 2010, 11(1), 482.
[http://dx.doi.org/10.1186/1471-2105-11-482] [PMID: 20868497]
[63]
Bordner, A.J.; Mittelmann, H.D. Prediction of the binding affinities of peptides to class II MHC using a regularized thermodynamic model. BMC Bioinformatics, 2010, 11(1), 41.
[http://dx.doi.org/10.1186/1471-2105-11-41] [PMID: 20089173]
[64]
Zeng, H.; Gifford, D.K. Quantification of uncertainty in peptide-MHC binding prediction improves high-affinity peptide selection for therapeutic design. Cell Syst., 2019, 9(2), 159-166.e3.
[http://dx.doi.org/10.1016/j.cels.2019.05.004] [PMID: 31176619]
[65]
Shao, X.M.; Bhattacharya, R.; Huang, J.; Sivakumar, I.K.A.; Tokheim, C.; Zheng, L.; Hirsch, D.; Kaminow, B.; Omdahl, A.; Bonsack, M.; Riemer, A.B.; Velculescu, V.E.; Anagnostou, V.; Pagel, K.A.; Karchin, R. High-throughput prediction of MHC class I and II neoantigens with MHC nuggets. Cancer Immunol. Res., 2020, 8(3), 396-408.
[http://dx.doi.org/10.1158/2326-6066.CIR-19-0464] [PMID: 31871119]
[66]
Collobert, R. Natural Language Processing (almost) from Scratch. Pre-Print Server, 2011.
[67]
Venkatesh, G.; Grover, A.; Srinivasaraghavan, G.; Rao, S. MHCAttnNet: Predicting MHC-peptide bindings for MHC alleles classes I and II using an attention-based deep neural model. Bioinformatics, 2020, 36(1), i399-i406.
[http://dx.doi.org/10.1093/bioinformatics/btaa479] [PMID: 32657386]
[68]
Asgari, E.; Mofrad, M.R.K. Continuous distributed representation of biological sequences for deep proteomics and genomics. PLoS One, 2015, 10(11), e0141287.
[http://dx.doi.org/10.1371/journal.pone.0141287] [PMID: 26555596]
[69]
Matthew Deep contextualized word representations. pre-print server, 2018.
[70]
Devlin, J. BERT: Pre-training of deep bidirectional transformers for language understanding. Pre-Print Server, 2019.
[71]
Liu, Z. DeepSeqPanII: An interpretable recurrent neural network model with attention mechanism for peptide-hla class ii binding prediction. IEEE/ACM Trans Comput Biol Bioinform, 2022, 19(4), 2188-2196.
[http://dx.doi.org/10.1109/TCBB.2021.3074927]
[72]
You, R.; Qu, W.; Mamitsuka, H.; Zhu, S. DeepMHCII: A novel binding core-aware deep interaction model for accurate MHC-II peptide binding affinity prediction. Bioinformatics, 2022, 38(Suppl. 1), i220-i228.
[http://dx.doi.org/10.1093/bioinformatics/btac225] [PMID: 35758790]
[73]
Racle, J.; Michaux, J.; Rockinger, G.A.; Arnaud, M.; Bobisse, S.; Chong, C.; Guillaume, P.; Coukos, G.; Harari, A.; Jandus, C.; Bassani-Sternberg, M.; Gfeller, D. Robust prediction of HLA class II epitopes by deep motif deconvolution of immunopeptidomes. Nat. Biotechnol., 2019, 37(11), 1283-1286.
[http://dx.doi.org/10.1038/s41587-019-0289-6] [PMID: 31611696]
[74]
Reynisson, B.; Alvarez, B.; Paul, S.; Peters, B.; Nielsen, M. NetMHCpan-4.1 and NetMHCIIpan-4.0: Improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Res., 2020, 48(W1), W449-W454.
[http://dx.doi.org/10.1093/nar/gkaa379] [PMID: 32406916]
[75]
Heng, Y.; Kuang, Z.; Xie, W.; Lan, H.; Huang, S.; Chen, L.; Shi, T.; Xu, L.; Pan, X.; Mei, H. A simple pan-specific RNN model for predicting HLA-II binding peptides. Mol. Immunol., 2021, 139, 177-183.
[http://dx.doi.org/10.1016/j.molimm.2021.09.004] [PMID: 34555693]
[76]
Cheng, J.; Bendjama, K.; Rittner, K.; Malone, B. BERTMHC: Improved MHC–peptide class II interaction prediction with transformer and multiple instance learning. Bioinformatics, 2021, 37(22), 4172-4179.
[http://dx.doi.org/10.1093/bioinformatics/btab422] [PMID: 34096999]
[77]
Junet, V.; Daura, X. CNN-PepPred: an open-source tool to create convolutional NN models for the discovery of patterns in peptide sets-application to peptide–MHC class II binding prediction. Bioinformatics, 2021, 37(23), 4567-4568.
[http://dx.doi.org/10.1093/bioinformatics/btab687] [PMID: 34601583]
[78]
Rosenberg, A.S.; Sauna, Z.E. Immunogenicity assessment during the development of protein therapeutics. J. Pharm. Pharmacol., 2018, 70(5), 584-594.
[http://dx.doi.org/10.1111/jphp.12810] [PMID: 28872677]
[79]
Gupta, R.; Srivastava, D.; Sahu, M.; Tiwari, S.; Ambasta, R.K.; Kumar, P. Artificial intelligence to deep learning: Machine intelligence approach for drug discovery. Mol. Divers., 2021, 25(3), 1315-1360.
[http://dx.doi.org/10.1007/s11030-021-10217-3] [PMID: 33844136]
[80]
Holland, C.J.; Cole, D.K.; Godkin, A. Re-Directing CD4+ T cell responses with the flanking residues of MHC Class II-Bound Peptides: The core is not enough. Front. Immunol., 2013, 4, 172.
[http://dx.doi.org/10.3389/fimmu.2013.00172] [PMID: 23847615]
[81]
Jensen, K.K.; Andreatta, M.; Marcatili, P.; Buus, S.; Greenbaum, J.A.; Yan, Z.; Sette, A.; Peters, B.; Nielsen, M. 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]
[82]
Lee, C.H.; Huh, J.; Buckley, P.R.; Jang, M.; Pinho, M.P.; Fernandes, R.A.; Antanaviciute, A.; Simmons, A.; Koohy, H. A robust deep learning workflow to predict CD8 + T-cell epitopes. Genome Med., 2023, 15(1), 70.
[http://dx.doi.org/10.1186/s13073-023-01225-z] [PMID: 37705109]
[83]
Andreatta, M.; Trolle, T.; Yan, Z.; Greenbaum, J.A.; Peters, B.; Nielsen, M. An automated benchmarking platform for MHC class II binding prediction methods. Bioinformatics, 2018, 34(9), 1522-1528.
[http://dx.doi.org/10.1093/bioinformatics/btx820] [PMID: 29281002]
[84]
EL-Manzalawy, Y.; Dobbs, D.; Honavar, V. On evaluating MHC-II binding peptide prediction methods. PLoS One, 2008, 3(9), e3268.
[http://dx.doi.org/10.1371/journal.pone.0003268] [PMID: 18813344]
[85]
Lin, H.H. Evaluation of MHC-II peptide binding prediction servers: Applications for vaccine research. BMC Bioinformatics, 2008, 9(12), 22.
[http://dx.doi.org/10.1186/1471-2105-9-S12-S22]
[86]
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]
[87]
Nielsen, M.; Andreatta, M.; Peters, B.; Buus, S. Immunoinformatics: Predicting peptide–MHC binding. Annu. Rev. Biomed. Data Sci., 2020, 3(1), 191-215.
[http://dx.doi.org/10.1146/annurev-biodatasci-021920-100259] [PMID: 37427310]

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