PSSMHCpan: a novel PSSM-based software for predicting class I peptide-HLA binding affinity

被引:69
作者
Liu, Geng [1 ,2 ,3 ]
Li, Dongli [2 ,3 ]
Li, Zhang [1 ]
Qiu, Si [1 ,2 ]
Li, Wenhui [2 ]
Chao, Cheng-chi [2 ,3 ,4 ]
Yang, Naibo [2 ,3 ,4 ]
Li, Handong [2 ,4 ]
Cheng, Zhen [5 ]
Song, Xin [6 ]
Cheng, Le [2 ,3 ,7 ]
Zhang, Xiuqing [1 ,2 ]
Wang, Jian [2 ,8 ]
Yang, Huanming [2 ,8 ]
Ma, Kun [2 ]
Hou, Yong [2 ,3 ,9 ]
Li, Bo [2 ,3 ,10 ]
机构
[1] Univ Chinese Acad Sci, BGI Educ Ctr, Beishan Ind Zone, Main Bldg, Shenzhen 518083, Peoples R China
[2] BGI Shenzhen, Beishan Ind Zone, Main Bldg, Shenzhen 518083, Peoples R China
[3] BGI GenoImmune, East Lake New Technol Dev Zone, Gaoxing Rd, Wuhan 430079, Peoples R China
[4] Complete Genom Inc, 2071 Stierlin Court, Mountain View, CA 94043 USA
[5] Stanford Univ, Dept Radiol & Bio X Program, Mol Imaging Program Stanford, Montag Hall,355 Galvez St, Stanford, CA 94305 USA
[6] Kunming Med Univ, Tumor Hosp Yunnan Prov, Affiliated Hosp 3, Kunzhou Rd, Kunming 650100, Yunnan Province, Peoples R China
[7] BGI Yunnan, Kunming Hitech Dev Zone, Haiyuan North Rd, Kunming 650000, Yunnan Province, Peoples R China
[8] James D Watson Inst Genome Sci, Yuhang Tong Rd, Hangzhou 310058, Zhejiang, Peoples R China
[9] Univ Copenhagen, Dept Biol, Norregade 10,POB 2177, DK-1017 Copenhagen K, Denmark
[10] BGI Forens, Beishan Ind, Main Bldg, Shenzhen 518083, Peoples R China
关键词
Antitumor vaccine; peptide-HLA binding affinity; PSSMHCpan; neoantigen; MHC CLASS-I; HUMAN-LEUKOCYTE ANTIGENS; NEURAL-NETWORKS; DATABASE; BENCHMARKING; MOLECULES; NETMHCPAN; RECEPTOR; PROTEIN; SEQ;
D O I
10.1093/gigascience/gix017
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Predicting peptide binding affinity with human leukocyte antigen (HLA) is a crucial step in developing powerful antitumor vaccine for cancer immunotherapy. Currently available methods work quite well in predicting peptide binding affinity with HLA alleles such as HLA-A*0201, HLA-A*0101, and HLA-B*0702 in terms of sensitivity and specificity. However, quite a few types of HLA alleles that are present in the majority of human populations including HLA-A*0202, HLA-A*0203, HLA-A*6802, HLA-B*5101, HLA-B*5301, HLA-B*5401, and HLA-B*5701 still cannot be predicted with satisfactory accuracy using currently available methods. Furthermore, currently the most popularly used methods for predicting peptide binding affinity are inefficient in identifying neoantigens from a large quantity of whole genome and transcriptome sequencing data. Here we present a Position Specific Scoring Matrix (PSSM)-based software called PSSMHCpan to accurately and efficiently predict peptide binding affinity with a broad coverage of HLA class I alleles. We evaluated the performance of PSSMHCpan by analyzing 10-fold cross-validation on a training database containing 87 HLA alleles and obtained an average area under receiver operating characteristic curve (AUC) of 0.94 and accuracy (ACC) of 0.85. In an independent dataset (Peptide Database of Cancer Immunity) evaluation, PSSMHCpan is substantially better than the popularly used NetMHC-4.0, NetMHCpan-3.0, PickPocket, Nebula, and SMM with a sensitivity of 0.90, as compared to 0.74, 0.81, 0.77, 0.24, and 0.79. In addition, PSSMHCpan is more than 197 times faster than NetMHC-4.0, NetMHCpan-3.0, PickPocket, sNebula, and SMM when predicting neoantigens from 661 263 peptides from a breast tumor sample. Finally, we built a neoantigen prediction pipeline and identified 117 017 neoantigens from 467 cancer samples of various cancers from TCGA. PSSMHCpan is superior to the currently available methods in predicting peptide binding affinity with a broad coverage of HLA class I alleles.
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页数:11
相关论文
共 45 条
[1]   PSI-BLAST pseudocounts and the minimum description length principle [J].
Altschul, Stephen F. ;
Gertz, E. Michael ;
Agarwala, Richa ;
Schaffer, Alejandro A. ;
Yu, Yi-Kuo .
NUCLEIC ACIDS RESEARCH, 2009, 37 (03) :815-824
[2]   Gapped sequence alignment using artificial neural networks: application to the MHC class I system [J].
Andreatta, Massimo ;
Nielsen, Morten .
BIOINFORMATICS, 2016, 32 (04) :511-517
[3]  
Apweiler R, 2004, NUCLEIC ACIDS RES, V32, pD115, DOI [10.1093/nar/gkh131, 10.1093/nar/gkw1099]
[4]   Immunoinformatics and epitope prediction in the age of genomic medicine [J].
Backert, Linus ;
Kohlbacher, Oliver .
GENOME MEDICINE, 2015, 7
[5]   MHCBN: a comprehensive database of MHC binding and non-binding peptides [J].
Bhasin, M ;
Singh, H ;
Raghava, GPS .
BIOINFORMATICS, 2003, 19 (05) :665-666
[6]   A hybrid approach for predicting promiscuous MHC class I restricted T cell epitopes [J].
Bhasin, Manoi ;
Raghava, G. P. S. .
JOURNAL OF BIOSCIENCES, 2007, 32 (01) :31-42
[7]   HLA typing from RNA-Seq sequence reads [J].
Boegel, Sebastian ;
Loewer, Martin ;
Schaefer, Michael ;
Bukur, Thomas ;
de Graaf, Jos ;
Boisguerin, Valesca ;
Tuereci, Oezlem ;
Diken, Mustafa ;
Castle, John C. ;
Sahin, Ugur .
GENOME MEDICINE, 2012, 4
[8]   A dendritic cell vaccine increases the breadth and diversity of melanoma neoantigen-specific T cells [J].
Carreno, Beatriz M. ;
Magrini, Vincent ;
Becker-Hapak, Michelle ;
Kaabinejadian, Saghar ;
Hundal, Jasreet ;
Petti, Allegra A. ;
Ly, Amy ;
Lie, Wen-Rong ;
Hildebrand, William H. ;
Mardis, Elaine R. ;
Linette, Gerald P. .
SCIENCE, 2015, 348 (6236) :803-808
[9]   SVMHC:: a server for prediction of MHC-binding peptides [J].
Donnes, Pierre ;
Kohlbacher, Oliver .
NUCLEIC ACIDS RESEARCH, 2006, 34 :W194-W197
[10]   PREDICTION OF GENE STRUCTURE [J].
GUIGO, R ;
KNUDSEN, S ;
DRAKE, N ;
SMITH, T .
JOURNAL OF MOLECULAR BIOLOGY, 1992, 226 (01) :141-157