Literature DB >> 28327987

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

Geng Liu1,2,3, Dongli Li2,3, Zhang Li1, Si Qiu1,2, Wenhui Li2, Cheng-Chi Chao2,3,4, Naibo Yang2,3,4, Handong Li2,4, Zhen Cheng5, Xin Song6, Le Cheng2,3,7, Xiuqing Zhang1,2, Jian Wang2,8, Huanming Yang2,8, Kun Ma2, Yong Hou2,3,9, Bo Li2,3,10.   

Abstract

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.
© The Author 2017. Published by Oxford University Press.

Entities:  

Keywords:  Antitumor vaccine; PSSMHCpan; neoantigen; peptide-HLA binding affinity

Mesh:

Substances:

Year:  2017        PMID: 28327987      PMCID: PMC5467046          DOI: 10.1093/gigascience/gix017

Source DB:  PubMed          Journal:  Gigascience        ISSN: 2047-217X            Impact factor:   6.524


  44 in total

1.  UniProt: the Universal Protein knowledgebase.

Authors:  Rolf Apweiler; Amos Bairoch; Cathy H Wu; Winona C Barker; Brigitte Boeckmann; Serenella Ferro; Elisabeth Gasteiger; Hongzhan Huang; Rodrigo Lopez; Michele Magrane; Maria J Martin; Darren A Natale; Claire O'Donovan; Nicole Redaschi; Lai-Su L Yeh
Journal:  Nucleic Acids Res       Date:  2004-01-01       Impact factor: 16.971

2.  Hidden Markov model-based prediction of antigenic peptides that interact with MHC class II molecules.

Authors:  Hideki Noguchi; Ryuji Kato; Taizo Hanai; Yukari Matsubara; Hiroyuki Honda; Vladimir Brusic; Takeshi Kobayashi
Journal:  J Biosci Bioeng       Date:  2002       Impact factor: 2.894

3.  SYFPEITHI: database for searching and T-cell epitope prediction.

Authors:  Mathias M Schuler; Maria-Dorothea Nastke; Stefan Stevanovikć
Journal:  Methods Mol Biol       Date:  2007

4.  Automated benchmarking of peptide-MHC class I binding predictions.

Authors:  Thomas Trolle; Imir G Metushi; Jason A Greenbaum; Yohan Kim; John Sidney; Ole Lund; Alessandro Sette; Bjoern Peters; Morten Nielsen
Journal:  Bioinformatics       Date:  2015-02-25       Impact factor: 6.937

5.  Predicting immunogenic tumour mutations by combining mass spectrometry and exome sequencing.

Authors:  Mahesh Yadav; Suchit Jhunjhunwala; Qui T Phung; Patrick Lupardus; Joshua Tanguay; Stephanie Bumbaca; Christian Franci; Tommy K Cheung; Jens Fritsche; Toni Weinschenk; Zora Modrusan; Ira Mellman; Jennie R Lill; Lélia Delamarre
Journal:  Nature       Date:  2014-11-27       Impact factor: 49.962

6.  A community resource benchmarking predictions of peptide binding to MHC-I molecules.

Authors:  Bjoern Peters; Huynh-Hoa Bui; Sune Frankild; Morten Nielson; Claus Lundegaard; Emrah Kostem; Derek Basch; Kasper Lamberth; Mikkel Harndahl; Ward Fleri; Stephen S Wilson; John Sidney; Ole Lund; Soren Buus; Alessandro Sette
Journal:  PLoS Comput Biol       Date:  2006-06-09       Impact factor: 4.475

7.  Understanding and predicting binding between human leukocyte antigens (HLAs) and peptides by network analysis.

Authors:  Heng Luo; Hao Ye; Hui Ng; Leming Shi; Weida Tong; William Mattes; Donna Mendrick; Huixiao Hong
Journal:  BMC Bioinformatics       Date:  2015-09-25       Impact factor: 3.169

Review 8.  Immunoinformatics and epitope prediction in the age of genomic medicine.

Authors:  Linus Backert; Oliver Kohlbacher
Journal:  Genome Med       Date:  2015-11-20       Impact factor: 11.117

9.  HLA typing from RNA-Seq sequence reads.

Authors:  Sebastian Boegel; Martin Löwer; Michael Schäfer; Thomas Bukur; Jos de Graaf; Valesca Boisguérin; Ozlem Türeci; Mustafa Diken; John C Castle; Ugur Sahin
Journal:  Genome Med       Date:  2012-12-22       Impact factor: 11.117

10.  The immune epitope database (IEDB) 3.0.

Authors:  Randi Vita; James A Overton; Jason A Greenbaum; Julia Ponomarenko; Jason D Clark; Jason R Cantrell; Daniel K Wheeler; Joseph L Gabbard; Deborah Hix; Alessandro Sette; Bjoern Peters
Journal:  Nucleic Acids Res       Date:  2014-10-09       Impact factor: 16.971

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  25 in total

1.  A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction.

Authors:  Shutao Mei; Fuyi Li; André Leier; Tatiana T Marquez-Lago; Kailin Giam; Nathan P Croft; Tatsuya Akutsu; A Ian Smith; Jian Li; Jamie Rossjohn; Anthony W Purcell; Jiangning Song
Journal:  Brief Bioinform       Date:  2020-07-15       Impact factor: 11.622

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

Authors:  Geng Liu; Dongli Li; Zhang Li; Si Qiu; Wenhui Li; Cheng-Chi Chao; Naibo Yang; Handong Li; Zhen Cheng; Xin Song; Le Cheng; Xiuqing Zhang; Jian Wang; Huanming Yang; Kun Ma; Yong Hou; Bo Li
Journal:  Gigascience       Date:  2017-05-01       Impact factor: 6.524

3.  Prediction of Major Histocompatibility Complex Binding with Bilateral and Variable Long Short Term Memory Networks.

Authors:  Limin Jiang; Jijun Tang; Fei Guo; Yan Guo
Journal:  Biology (Basel)       Date:  2022-06-01

4.  Predicting MHC class I binder: existing approaches and a novel recurrent neural network solution.

Authors:  Limin Jiang; Hui Yu; Jiawei Li; Jijun Tang; Yan Guo; Fei Guo
Journal:  Brief Bioinform       Date:  2021-11-05       Impact factor: 13.994

Review 5.  Predicting Antigen Presentation-What Could We Learn From a Million Peptides?

Authors:  David Gfeller; Michal Bassani-Sternberg
Journal:  Front Immunol       Date:  2018-07-25       Impact factor: 7.561

6.  DeepSeqPan, a novel deep convolutional neural network model for pan-specific class I HLA-peptide binding affinity prediction.

Authors:  Zhonghao Liu; Yuxin Cui; Zheng Xiong; Alierza Nasiri; Ansi Zhang; Jianjun Hu
Journal:  Sci Rep       Date:  2019-01-28       Impact factor: 4.379

Review 7.  Nature of tumour rejection antigens in ovarian cancer.

Authors:  Muzamil Y Want; Amit A Lugade; Sebastiano Battaglia; Kunle Odunsi
Journal:  Immunology       Date:  2018-06-13       Impact factor: 7.215

8.  The molecular landscape of synchronous colorectal cancer reveals genetic heterogeneity.

Authors:  Xiangfeng Wang; Hu Fang; Yong Cheng; Lin Li; Xiaohui Sun; Tao Fu; Peide Huang; Anping Zhang; Zhimin Feng; Chunxue Li; Xuanlin Huang; Guangyan Li; Peina Du; Huanming Yang; Xiaodong Fang; Fan Li; Qiang Gao; Baohua Liu
Journal:  Carcinogenesis       Date:  2018-05-03       Impact factor: 4.944

9.  Recurrent Neoantigens in Colorectal Cancer as Potential Immunotherapy Targets.

Authors:  Chao Chen; Songming Liu; Ruokai Qu; Bo Li
Journal:  Biomed Res Int       Date:  2020-07-17       Impact factor: 3.411

10.  Predicting MHC-peptide binding affinity by differential boundary tree.

Authors:  Peiyuan Feng; Jianyang Zeng; Jianzhu Ma
Journal:  Bioinformatics       Date:  2021-07-12       Impact factor: 6.937

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