蛋白质中残基远程相互作用预测算法研究综述

蛋白质是由多个氨基酸残基顺序连接而成的长链.在天然状态下，蛋白质并不是无规则的自由状态，而是自发形成特定的空间结构，以执行其特定的生物学功能.驱动蛋白质形成特定空间结构的主要因素是残基间的非共价相互作用，包括疏水作用、静电相互作用、范德华力等.因此，对残基之间远程相互作用的准确预测将有助于对蛋白质空间结构的预测，进而有助于对蛋白质生物学功能的了解.在蛋白质进化过程，有相互作用残基对之间存在一种“共进化”模式，即当一个残基发生变异时，与其有相互作用的残基也要发生相应的变异，以维持相互作用，进而维持整体空间结构以及生物学功能.基于上述生物学观察，研究者开发了多个统计模型和算法以预测残基对之间的相互作用:1)概述残基之间远程相互作用的两大类基本预测算法，包括无监督学习方法和监督学习方法；2)使用蛋白质结构预测CASP比赛结果来客观比较上述各类算法的性能，分析各个算法的特点和优势；3)从生物学观察和统计模型2个角度分析总结了未来的发展趋势.

A Survey on Algorithms for Protein Contact Prediction

Proteins are large molecules consisting of a linear sequence of amino acids. In the natural environment, a protein spontaneously folds into specific tertiary structure to perform its biological functionality. The main factors that drive proteins to fold are interactions between residues, including hydrophobic interaction, Van der Waals’ force and electrostatic interactions. The interactions between residues usually lead to residue-residue contacts, and the prediction of residue-residue contacts should greatly facilitate understanding of protein structures and functionalities. A great variety of techniques have been proposed for residue-residue contacts prediction, including machine learning, statistical models, and linear programing. It should be pointed out that most of these techniques are based on the biological insight of co-evolution, i.e., during the evolutionary history of proteins, a residue’s mutation usually leads its contacting partner to mutate accordingly. In this review, we summarize the state-of-art algorithms in this field with emphasis on the construction of statistical models based on biological insights. We also present the evaluation of these algorithms using CASP (critical assessment of techniques for protein structure prediction) targets as well as popular benchmark datasets, and describe the trends in the field of protein contact prediction.