首页> 美国卫生研究院文献>GigaScience >PSSMHCpan: a novel PSSM-based software for predicting class I peptide-HLA binding affinity
【2h】

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

机译:PSSMHCpan:基于PSSM的新型软件可预测I类肽与HLA的结合亲和力

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。

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.
机译:预测与人白细胞抗原(HLA)的肽结合亲和力是开发用于癌症免疫疗法的强大抗肿瘤疫苗的关键步骤。就灵敏度和特异性而言,目前可用的方法在预测与HLA等位基因(例如HLA-A * 0201,HLA-A * 0101和HLA-B * 0702)的肽结合亲和力方面效果很好。但是,大多数人口中存在的HLA等位基因类型很多,包括HLA-A * 0202,HLA-A * 0203,HLA-A * 6802,HLA-B * 5101,HLA-B * 5301,HLA -B * 5401和HLA-B * 5701仍无法使用当前可用的方法以令人满意的精度进行预测。此外,目前最流行的用于预测肽结合亲和力的方法无法从大量的全基因组和转录组测序数据中鉴定新抗原。在这里,我们介绍一种称为PSSMHCpan的基于位置特定评分矩阵(PSSM)的软件,可准确有效地预测HLA I类等位基因的多肽结合亲和力。我们通过在包含87个HLA等位基因的训练数据库上分析10倍交叉验证来评估PSSMHCpan的性能,并得出接收器工作特征曲线(AUC)下的平均面积为0.94,准确度(ACC)为0.85。在独立的数据集(癌症免疫肽数据库)评估中,PSSMHCpan优于流行的NetMHC-4.0,NetMHCpan-3.0,PickPocket,Nebula和SMM,其灵敏度为0.90,而灵敏度分别为0.74、0.81、0.77, 0.24和0.79。此外,当从乳腺肿瘤样本中预测661 263肽的新抗原时,PSSMHCpan比NetMHC-4.0,NetMHCpan-3.0,PickPocket,sNebula和SMM快197倍以上。最后,我们建立了新抗原预测管道,并从TCGA的467种癌症样本中鉴定了117-017种新抗原。 PSSMHCpan在预测肽结合亲和力方面优于目前可用的方法,具有广泛的HLA I类等位基因。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号