集成技术2024，Vol.13Issue(2)：74-88,15.DOI:10.12146/j.issn.2095-3135.20230912001
基于AlphaFold数据库分析蛋白质进化中的统计规律
Uncovering the Statistical Trends of Protein Evolution with AlphaFold Database
摘要
Abstract
AlphaFold,which is developed by DeepMind,has made amazing advances in predicting protein structures for life sciences research.Using the vast structural predictions made possible by AlphaFold,a database of over 200 million proteins has been established.Such a database covers the complete proteomes of many organisms.This review outlines the most recent progresses in exploring protein evolution using statistical physical methods based on the AlphaFold database.Traditional protein evolution research often concentrates on the sequences or structures of proteins within the same family,using a narrow microscopic approach.With the new emergence of extensive protein structure predictions by AlphaFold,whereas scientists can expand their horizons to include vast assortments of proteins to make parallels with all proteins in different species and extract statistical trends through macroscopic observation.By comparing the proteins with similar chain lengths in over 40 model organisms,the statistical trends in protein evolution are discovered.For organisms with higher complexity,their constituent proteins present larger radii of gyration,higher flexibility,and higher segregation of hydrophobic and hydrophilic residues in both spatial and sequence.It is also validated by statistical physics analysis that higher organismal complexity correlates with higher functional specialization of constituent proteins.The findings in these studies connect molecular evolution to organism evolution,contributing to the understanding of the origin and evolution of lives.关键词
AlphaFold/蛋白质/进化/蛋白质动力学/简正模分析/统计物理Key words
AlphaFold/protein/evolution/protein dynamics/normal mode analysis/statistical physics分类
生物科学引用本文复制引用
夏辰亮,唐乾元..基于AlphaFold数据库分析蛋白质进化中的统计规律[J].集成技术,2024,13(2):74-88,15.基金项目
江苏省高等学校自然科学研究项目(22KJD14005) （22KJD14005）
香港研究资助局杰出青年学者计划(22302723) This work is supported by Natural Science Foundation of the Jiangsu Higher Education Institutions of China(22KJD14005)and Early Career Scheme(22302723)from Research Grants Council of Hong Kong （22302723）
