多肽色谱保留预测及其在蛋白质组学中的应用

基于串联质谱的蛋白质组学分析方法往往依赖于实际谱图和理论谱图的匹配打分，而大量共洗脱肽的干扰会降低多肽和蛋白的鉴定及定量的准确性。多肽保留时间预测可将多肽色谱保留行为转变为稳定独立的特征时间属性，作为多肽鉴定的辅助和验证指标，改善多肽鉴定的准确性。复杂体系中多肽色谱保留预测也对优化蛋白质组学测定条件、提高数据非依赖采集中质谱数据的检出率和重复性具有重要意义。本文针对未修饰多肽及修饰多肽常用的色谱保留预测方法（包括基于标准化索引、多肽分子模型、氨基酸残基参数和机器学习等）进行了综述，总结各种方法的原理及其特点，并对其在蛋白质组学中的应用及发展方向进行了展望。

Peptide retention prediction algorithm and its application in proteomics

Most of the proteomics analysis methods based on tandem mass spectrometry rely on the matching scoring of actual spectrum and theoretical spectrum, the interference of a large number of co-eluting peptides could cause error in the identification and quantification of peptides and proteins. Peptide retention time prediction, as a auxiliary and verification index of the peptide, can transition the chromatographic behavior into stable independent time attributes, and improve the accuracy of the peptide identification. Prediction of peptide chromatographic retention in complex systems is also of great significance for optimizing proteomics determination conditions and improving the detection rate and repeatability of mass spectrometry data in data-independent acquisition. This review focused on the chromatographic retention prediction method of unmodified peptides and modified peptides, summarizes the content, characteristics and limitations of four types of peptide retention time prediction methods based on standardized indexes, peptide molecular models, amino acid residue parameters, and machine learning, as well as their applications in proteomics, with a prospect of their future.