小柯机器人

大型多肽组数据库可提高HLA I型表位预测
2019-12-18 13:20

美国麻省理工学院和哈佛大学的博德研究所Derin B. Keskin、Catherine J. Wu、Steven A. Carr、Nir Hacohen等研究人员合作建立了一个大型多肽组数据库,从而提高了对大部分人群中HLA I型表位的预测。相关论文2019年12月16日在线发表于国际学术期刊《自然—生物技术》。

为了能够在大部分人口中预测与内源性HLA I类相关的肽,研究人员使用质谱分析了从95种HLA-A、-B、-C和-G单等位基因细胞系洗脱的185000多个肽。研究人员确定了每个HLA等位基因的典型肽模体、不同等位基因的独特且共享的结合亚模体以及与不同肽段长度相关的独特模体。通过将这些数据与转录本丰度和肽加工相结合,研究人员开发了HLAthena,其为内源肽呈递提供了等位基因和长度特异性以及泛等位基因泛长度预测模型。与现有工具相比,这些模型预测的内源性HLA I类相关配体的阳性预测值具有1.5倍提升,并正确鉴定了在11种患者来源肿瘤细胞系中实验观察到的超过75%的HLA结合肽。

据了解,HLA表位的预测对于癌症免疫疗法和疫苗的开发很重要。但是,当前的预测算法的预测能力有限,部分原因是它们没有在涵盖广泛HLA等位基因的高质量抗原决定簇数据集上进行训练。

附:英文原文

Title: A large peptidome dataset improves HLA class I epitope prediction across most of the human population

Author: Siranush Sarkizova, Susan Klaeger, Phuong M. Le, Letitia W. Li, Giacomo Oliveira, Hasmik Keshishian, Christina R. Hartigan, Wandi Zhang, David A. Braun, Keith L. Ligon, Pavan Bachireddy, Ioannis K. Zervantonakis, Jennifer M. Rosenbluth, Tamara Ouspenskaia, Travis Law, Sune Justesen, Jonathan Stevens, William J. Lane, Thomas Eisenhaure, Guang Lan Zhang, Karl R. Clauser, Nir Hacohen, Steven A. Carr, Catherine J. Wu, Derin B. Keskin

Issue&Volume: 2019-12-16

Abstract: Prediction of HLA epitopes is important for the development of cancer immunotherapies and vaccines. However, current prediction algorithms have limited predictive power, in part because they were not trained on high-quality epitope datasets covering a broad range of HLA alleles. To enable prediction of endogenous HLA class I-associated peptides across a large fraction of the human population, we used mass spectrometry to profile >185,000 peptides eluted from 95 HLA-A, -B, -C and -G mono-allelic cell lines. We identified canonical peptide motifs per HLA allele, unique and shared binding submotifs across alleles and distinct motifs associated with different peptide lengths. By integrating these data with transcript abundance and peptide processing, we developed HLAthena, providing allele-and-length-specific and pan-allele-pan-length prediction models for endogenous peptide presentation. These models predicted endogenous HLA class I-associated ligands with 1.5-fold improvement in positive predictive value compared with existing tools and correctly identified >75% of HLA-bound peptides that were observed experimentally in 11 patient-derived tumor cell lines.

DOI: 10.1038/s41587-019-0322-9

Source: https://www.nature.com/articles/s41587-019-0322-9

Nature Biotechnology:《自然—生物技术》,创刊于1996年。隶属于施普林格·自然出版集团,最新IF:68.164
官方网址:https://www.nature.com/nbt/
投稿链接:https://mts-nbt.nature.com/cgi-bin/main.plex


本期文章:《自然—生物技术》:Online/在线发表

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