DOI: 10.1515/mr-2023-0038
In silico protein function prediction:the rise of machine learning-based approaches
Jiaxiao Chen1
Zhonghui Gu2
Luhua Lai3
Jianfeng Pei4
1.Center for Quantitative Biology,Academy for Advanced Interdisciplinary Studies,Peking University,Beijing,China2.Peking-Tsinghua Center for Life Sciences,Academy for Advanced Interdisciplinary Studies,Peking University,Beijing,China3.Center for Quantitative Biology,Academy for Advanced Interdisciplinary Studies,Peking University,Beijing,China;Peking-Tsinghua Center for Life Sciences,Academy for Advanced Interdisciplinary Studies,Peking University,Beijing,China;BNLMS,College of Chemistry and Molecular Engineering,Peking University,Beijing,China;Research Unit of Drug Design Method,Chinese Academy of Medical Sciences(2021RU014),Beijing,China4.Center for Quantitative Biology,Academy for Advanced Interdisciplinary Studies,Peking University,Beijing,100871,China;Research Unit of Drug Design Method,Chinese Academy of Medical Sciences(2021RU014),Beijing,100871,China
摘要：Proteins function as integral actors in essential life processes,rendering the realm of protein research a fundamental domain that possesses the potential to propel advancements in pharmaceuticals and disease investiga-tion.Within the context of protein research,an imperious demand arises to uncover protein functionalities and untan-gle intricate mechanistic underpinnings.Due to the exorbi-tant costs and limited throughput inherent in experimental investigations,computational models offer a promising alter-native to accelerate protein function annotation.In recent years,protein pre-training models have exhibited note-worthy advancement across multiple prediction tasks.This advancement highlights a notable prospect for effectively tackling the intricate downstream task associated with protein function prediction.In this review,we elucidate the historical evolution and research paradigms of computational methods for predicting protein function.Subsequently,we summarize the progress in protein and molecule represen-tation as well as feature extraction techniques.Furthermore,we assess the performance of machine learning-based algorithms across various objectives in protein function prediction,thereby offering a comprehensive perspective on the progress within this field.
机标关键词：predictionproteinfunctionriseapproacheslearning-basedmachinesilico
论文发表日期：2023-12-28
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：24( 487-510 )
英文信息