Protein Structure Prediction:Challenges,Advances,and the Shift of Research Paradigms  

作　　者：Bin Huang Lupeng Kong Chao Wang Fusong Ju Qi Zhang Jianwei Zhu Tiansu Gong Haicang Zhang Chungong Yu Wei-Mou Zheng Dongbo Bu 

机构地区：[1]Key Laboratory of Intelligent Information Processing,Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China [2]University of Chinese Academy of Sciences,Beijing 100049,China [3]Changping Laboratory,Beijing 102206,China [4]Microsoft Research AI4Science,Beijing 100080,China [5]Huawei Noah’s Ark Lab,Wuhan 430206,China [6]Zhongke Big Data Academy,Zhengzhou 450046,China [7]Institute of Theoretical Physics,Chinese Academy of Sciences,Beijing 100190,China

出　　处：《Genomics, Proteomics & Bioinformatics》2023年第5期913-925,共13页基因组蛋白质组与生物信息学报（英文版）

基　　金：the National Key R&D Program of China(Grant No.2020YFA0907000)lthe National Natural Science Foundation of China(Grant Nos.32271297,62072435,31770775,and 31671369)for providing financial support for this study and publication charges.

摘　　要：Protein structure prediction is an interdisciplinary research topic that has attracted researchers from multiple fields,including biochemistry,medicine,physics,mathematics,and computer science.These researchers adopt various research paradigms to attack the same structure prediction problem:biochemists and physicists attempt to reveal the principles governing protein folding;mathematicians,especially statisticians,usually start from assuming a probability distribution of protein structures given a target sequence and then find the most likely structure,while computer scientists formulate protein structure prediction as an optimization problem-finding the structural conformation with the lowest energy or minimizing the difference between predicted structure and native structure.These research paradigms fall into the two statistical modeling cultures proposed by Leo Breiman,namely,data modeling and algorithmic modeling.Recently,we have also witnessed the great success of deep learning in protein structure prediction.In this review,we present a survey of the efforts for protein structure prediction.We compare the research paradigms adopted by researchers from different fields,with an emphasis on the shift of research paradigms in the era of deep learning.In short,the algorithmic modeling techniques,especially deep neural networks,have considerably improved the accuracy of protein structure prediction;however,theories interpreting the neural networks and knowledge on protein folding are still highly desired.

关 键 词：Protein folding Protein structure prediction Deep learning TRANSFORMER Language model 

分 类 号：Q51[生物学—生物化学]