Suppr超能文献

PSSMHCpan:一种基于位置特异性打分矩阵(PSSM)的新型软件,用于预测I类肽与人类白细胞抗原(HLA)的结合亲和力。

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

作者信息

Liu Geng, Li Dongli, Li Zhang, Qiu Si, Li Wenhui, Chao Cheng-Chi, Yang Naibo, Li Handong, Cheng Zhen, Song Xin, Cheng Le, Zhang Xiuqing, Wang Jian, Yang Huanming, Ma Kun, Hou Yong, Li Bo

机构信息

BGI Education Center, University of Chinese Academy of Sciences, Main Building, Beishan Industrial Zone, Yantian District, Shenzhen 518083, China.

BGI-Shenzhen, Main Building, Beishan Industrial Zone, Yantian District, Shenzhen 518083, China.

出版信息

Gigascience. 2017 May 1;6(5):1-11. doi: 10.1093/gigascience/gix017.

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-A0201, HLA-A0101, and HLA-B0702 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-A0202, HLA-A0203, HLA-A6802, HLA-B5101, HLA-B5301, HLA-B5401, and HLA-B5701 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.

摘要

预测肽与人类白细胞抗原(HLA)的结合亲和力是开发用于癌症免疫治疗的强效抗肿瘤疫苗的关键步骤。目前可用的方法在预测肽与HLA等位基因(如HLA-A0201、HLA-A0101和HLA-B0702)的结合亲和力方面,在敏感性和特异性方面表现良好。然而,包括HLA-A0202、HLA-A0203、HLA-A6802、HLA-B5101、HLA-B5301、HLA-B5401和HLA-B5701在内的大多数人类群体中存在的相当一部分HLA等位基因,使用目前可用的方法仍无法以令人满意的准确性进行预测。此外,目前最常用的预测肽结合亲和力的方法在从大量全基因组和转录组测序数据中识别新抗原方面效率低下。在此,我们提出了一种基于位置特异性评分矩阵(PSSM)的软件,称为PSSMHCpan,以准确、高效地预测肽与广泛的HLA I类等位基因的结合亲和力。我们通过在包含87个HLA等位基因的训练数据库上进行10倍交叉验证来评估PSSMHCpan的性能,获得的平均受试者工作特征曲线下面积(AUC)为0.94,准确率(ACC)为0.85。在一个独立数据集(癌症免疫肽数据库)评估中,PSSMHCpan明显优于常用的NetMHC-4.0、NetMHCpan-3.0、PickPocket、Nebula和SMM,其敏感性为0.90,而NetMHC-4.0、NetMHCpan-3.0、PickPocket、Nebula和SMM的敏感性分别为0.74、0.81、0.77、0.24和0.79。此外,当从乳腺肿瘤样本的661263个肽中预测新抗原时,PSSMHCpan比NetMHC-4.0、NetMHCpan-3.0、PickPocket、sNebula和SMM快197倍以上。最后,我们构建了一个新抗原预测管道,并从TCGA的467个不同癌症的癌症样本中鉴定出117017个新抗原。PSSMHCpan在预测肽与广泛的HLA I类等位基因的结合亲和力方面优于目前可用的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/739a/5467046/7760c1e20fe2/gix017fig1.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验