蛋白质中残基远程相互作用预测算法研究综述

张海仓; 高玉娟; 邓明华; 郑伟谋; 卜东波

doi:10.7544/issn1000-1239.2017.20151076

蛋白质中残基远程相互作用预测算法研究综述

¹(中国科学院计算技术研究所北京 100190)
²(中国科学院大学北京 100049)
³(北京大学定量生物学中心北京 100871)
⁴(北京大学数学科学学院北京 100871)
⁵(北京大学统计科学中心北京 100871)
⁶(中国科学院理论物理研究所北京 100190) (zhanghaicang@ict.ac.cn)

基金项目: 国家“九七三”重点基础研究发展计划基金项目(2012CB316502,2015CB910303)；国家自然科学基金项目(11175224,11121403,31270834,61272318,31171262,31428012,31471246)；中国科学院理论物理研究所理论物理国家重点实验室开放工程项目(Y4KF171CJ1) This work was supported by the National Basic Research Program of China (973 Program) (2012CB316502, 2015CB910303), the National Natural Science Foundation of China (11175224, 11121403, 31270834, 61272318, 31171262, 31428012, 31471246), and the Open Project Program of State Key Laboratory of the Institute of Theoretical Physics, Chinese Academy of Sciences (Y4KF171CJ1).

详细信息

中图分类号: TP399
计量
- 文章访问数: 1922
- HTML全文浏览量: 2
- PDF下载量: 1100
出版历程
- 发布日期: 2016-12-31

A Survey on Algorithms for Protein Contact Prediction

¹(Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190)
²(University of Chinese Academy of Sciences, Beijing 100049)
³(Centre for Quantitative Biology, Peking University, Beijing 100871)
⁴(School of Mathematical Sciences, Peking University, Beijing 100871)
⁵(Center for Statistical Sciences, Peking University, Beijing 100871)
⁶(Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190)

摘要

摘要: 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.
- 残基远程相互作用预测 /
- 蛋白质三级结构预测 /
- 图模型 /
- 共进化 /
- 机器学习
Abstract: 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.
- protein contact prediction /
- protein tertiary structure prediction /
- graphical model /
- co-evolution /
- machine learning

HTML全文

参考文献(0)

施引文献(11)

期刊类型引用(7)

1.	黄玲，黄镇伟，黄梓源，关灿荣，高月芳，王昌栋. 图卷积宽度跨域推荐系统. 计算机研究与发展. 2024(07): 1713-1729 . 本站查看
2.	杨玲玲. 基于HM与LWR算法的电子设备MCS推荐优化. 山西电子技术. 2024(04): 22-24 . 百度学术
3.	郑升旻，付晓东. 利用混合Plackett-Luce模型的不完整序数偏好预测. 计算机应用. 2024(10): 3105-3113 . 百度学术
4.	杜兆芳. 基于协同排序学习算法的移动群智感知任务推荐. 电子产品世界. 2023(09): 64-66+70 . 百度学术
5.	朱丽丽. 随机森林算法下列表级排序学习推荐系统设计. 淮阴工学院学报. 2023(05): 62-68 . 百度学术
6.	曹玉红，赵乙，陈佳桦. 兼容异构数据的稳定评估模型. 小型微型计算机系统. 2021(09): 2011-2016 . 百度学术
7.	林子楠，刘杜钢，潘微科，明仲. 面向推荐系统中有偏和无偏一元反馈建模的三任务变分自编码器. 信息安全学报. 2021(05): 110-127 . 百度学术