摘要
由DeepMind开发的AlphaFold在蛋白质结构预测领域取得了前所未有的巨大突破,对生命科学的研究产生了革命性的影响。基于大规模的结构预测,AlphaFold结构预测数据库得以建立,它包含2亿多种蛋白,并覆盖了数十种物种的完整蛋白质组。该综述介绍了在“后AlphaFold时代”利用统计物理方法研究蛋白质进化问题的一些最新进展。传统的蛋白质进化研究往往关注同一个家族的蛋白质序列或者结构(微观视角),而随着AlphaFold预测的海量蛋白质结构的出现,研究者可以把视角扩展到大量蛋白质的集合,甚至是直接对比不同物种体内的全部蛋白质,从中挖掘统计趋势(宏观视角)。基于AlphaFold数据库,通过对比40多种模式生物体内相似链长的蛋白质,研究者发现了蛋白质分子进化中的统计规律。随着物种复杂性的提高,蛋白质结构将趋向于更高的柔性和模块化程度,蛋白质序列将趋向于出现更显著的亲疏水片段分隔,蛋白质的功能专一性也不断提高。这些基于AlphaFold的统计研究在分子进化和物种进化之间建立了联系,有助于理解生物复杂性的演化。
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.
作者
夏辰亮
唐乾元
XIA Chenliang;TANG Qianyuan(Department of Mathematics and Physics,Sanjiang University,Nanjing 210012,China;Department of Physics,Hong Kong Baptist University,Hong Kong SAR 999077,China)
出处
《集成技术》
2024年第2期74-88,共15页
Journal of Integration Technology
基金
江苏省高等学校自然科学研究项目(22KJD14005)
香港研究资助局杰出青年学者计划(22302723)。
