Exploring and Learning the Universe of Protein Allostery Using Artificial Intelligence Augmented Biophysical and Computational Approaches

doi:10.1021/acs.jcim.2c01634

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	Exploring and Learning the Universe of Protein Allostery Using Artificial Intelligence Augmented Biophysical and Computational Approaches
	Agajanian, Steve 1; Alshahrani, Mohammed 1; Bai, Fang2 ; Tao, Peng 3; Verkhivker, Gennady M.1,4
	2023-02-01
发表期刊	JOURNAL OF CHEMICAL INFORMATION AND MODELING
ISSN	1549-9596
EISSN	1549-960X
卷号	63 期号:5 页码:1413-1428
发表状态	已发表
DOI	10.1021/acs.jcim.2c01634
摘要	Allosteric mechanisms are commonly employed regulatory tools used by proteins to orchestrate complex biochemical processes and control communications in cells. The quantitative understanding and characterization of allosteric molecular events are among major challenges in modern biology and require integration of innovative computational experimental approaches to obtain atomistic-level knowledge of the allosteric states, interactions, and dynamic conformational landscapes. The growing body of computational and experimental studies empowered by emerging artificial intelligence (AI) technologies has opened up new paradigms for exploring and learning the universe of protein allostery from first principles. In this review we analyze recent developments in high-throughput deep mutational scanning of allosteric protein functions; applications and latest adaptations of Alpha-fold structural prediction methods for studies of protein dynamics and allostery; new frontiers in integrating machine learning and enhanced sampling techniques for characterization of allostery; and recent advances in structural biology approaches for studies of allosteric systems. We also highlight recent computational and experimental studies of the SARS-CoV-2 spike (S) proteins revealing an important and often hidden role of allosteric regulation driving functional conformational changes, binding interactions with the host receptor, and mutational escape mechanisms of S proteins which are critical for viral infection. We conclude with a summary and outlook of future directions suggesting that AI-augmented biophysical and computer simulation approaches are beginning to transform studies of protein allostery toward systematic characterization of allosteric landscapes, hidden allosteric states, and mechanisms which may bring about a new revolution in molecular biology and drug discovery.
关键词	biology drug allosteric mechanisms artificial intelligence protein allostery first principles high-throughput deep mutational scanning allosteric drug design machine learning structural prediction methods SARS-CoV-2
URL	查看原文
收录类别	SCI ; EI
语种	英语
资助项目	National Institute of General Medical Sciences of the National Institutes of Health[R15GM122013] ; Kay Family Foundation[A20-0032]
WOS研究方向	Pharmacology & Pharmacy ; Chemistry ; Computer Science
WOS类目	Chemistry, Medicinal ; Chemistry, Multidisciplinary ; Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications
WOS记录号	WOS:000938209400001
出版者	AMER CHEMICAL SOC
EI入藏号	20230913654348
EI主题词	Coronavirus
EI分类号	461.4 Ergonomics and Human Factors Engineering ; 461.6 Medicine and Pharmacology ; 461.9 Biology ; 801.4 Physical Chemistry ; 804.1 Organic Compounds
原始文献类型	Journal article (JA)
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/286842
专题	免疫化学研究所免疫化学研究所_PI研究组_白芳组
通讯作者	Verkhivker, Gennady M.
作者单位	1.Chapman Univ, Schmid Coll Sci & Technol, Keck Ctr Sci & Engn, Grad Program Computat & Data Sci, Orange, CA 92866 USA 2.Shanghai Tech Univ, Shanghai Inst Adv Immunochem Studies, Sch Life Sci & Technol & Informat Sci & Technol, Shanghai 201210, Peoples R China 3.Southern Methodist Univ, Ctr Res Comp, Ctr Drug Discovery Design & Delivery CD4, Dept Chem, Dallas, TX 75205 USA 4.Chapman Univ Sch Pharm, Dept Biomed & Pharmaceut Sci, Irvine, CA 92618 USA
推荐引用方式 GB/T 7714	Agajanian, Steve,Alshahrani, Mohammed,Bai, Fang,et al. Exploring and Learning the Universe of Protein Allostery Using Artificial Intelligence Augmented Biophysical and Computational Approaches[J]. JOURNAL OF CHEMICAL INFORMATION AND MODELING,2023,63(5):1413-1428.
APA	Agajanian, Steve,Alshahrani, Mohammed,Bai, Fang,Tao, Peng,&Verkhivker, Gennady M..(2023).Exploring and Learning the Universe of Protein Allostery Using Artificial Intelligence Augmented Biophysical and Computational Approaches.JOURNAL OF CHEMICAL INFORMATION AND MODELING,63(5),1413-1428.
MLA	Agajanian, Steve,et al."Exploring and Learning the Universe of Protein Allostery Using Artificial Intelligence Augmented Biophysical and Computational Approaches".JOURNAL OF CHEMICAL INFORMATION AND MODELING 63.5(2023):1413-1428.