基于计算毒理的环境污染物-生物大分子的相互作用研究  

Computational toxicology studies on the interactions between environmental contaminants and biomacromolecules

作　　者：谭皓月 张荣 陈钦畅 张驰 郭婧 张效伟[1] 于红霞[1] 史薇[1,2] Haoyue Tan;Rong Zhang;Qinchang Chen;Chi Zhang;Jing Guo;Xiaowei Zhang;Hongxia Yu;Wei Shi(State Key Laboratory of Plution Control and Resources Reuse,School of Environment,Nanjing University Nanjing 210023,China;Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk,Nanjing 210023,China)

机构地区：[1]南京大学环境学院,污染控制与资源化研究国家重点实验室,南京210023 [2]江苏省生态环境保护化学品安全与健康风险研究重点实验室,南京210023

出　　处：《科学通报》2022年第35期4180-4191,共12页Chinese Science Bulletin

基　　金：国家自然科学基金(21922603);江苏省重点研发计划(社会发展)(BE2022837);国家重点研发计划(2018YFC1801604);江苏省环境保护科研项目(2021003,2018001);中央高校基础研究经费(021114380139);污染控制与资源再利用国家重点实验室基金(PCR-ZZ-202103)资助。

摘　　要：计算机技术的革新以及结构生物学和深度学习的爆发式发展,促使计算毒理学迅速应用到环境新污染物领域,通过研究环境污染物与生物大分子间的相互作用解析致毒机制并筛查高风险毒物.本文系统综述了用于解析环境污染物-生物大分子互作过程常用的计算毒理方法,包括分子对接、分子动力学模拟和机器学习建模;阐明了近年来3类计算毒理方法在环境毒理学领域的主要用途,包括环境污染物-生物大分子相互作用的构效关系研究与虚拟筛查应用;讨论了3类计算毒理方法在配体-受体结合、可解释性、计算效率、计算深度、生物学过程5个方面的优势与局限;展望了计算毒理技术与新型技术(如人工智能)结合后的未来应用和进一步潜力.Through manufacturing operations, product consumption, and drug administration, humans and wildlife are continuously exposed to environmental contaminants(ECs) throughout their lives. Faced with the potentially harmful effects of ECs on humans, regulatory agencies around the world require the integration of epidemiological, in vivo toxicological, and in vitro mechanistic data to provide the necessary information for hazard classification, labeling, and risk management of chemicals. However, animal studies have time-consuming and high-cost defects and ethical problems. High-throughput in vitro assays are also unable to provide systematic toxicological information for chemical hazard classification for over 100000 chemicals in commerce.Recently, computing resources and artificial intelligence have innovatively improved the accuracy and speed of machine learning(ML) algorithms, and the structural biology and deep learning(DL) algorithms(e.g., AlphaFold2 and AF2Complex) have incrementally resolved a large number of biomolecular crystal structures. Thus, the use of computational toxicology techniques in environmental toxicology has increased significantly. It is estimated that computational toxicology techniques can perform virtual screening of millions of compounds in a limited amount of time for the contaminant-biomolecule interaction process. Thus, computational toxicology techniques can reduce the initial experimental cost of identifying environmental emerging contaminants, increase information on the toxicity mechanisms of ECs, and improve the efficiency of hazard identification of ECs by regulatory authorities.This study systematically reviews computational toxicology techniques commonly used to analyze ECs-biomolecule interactions, including molecular docking, molecular dynamics(MD) simulation, and machine learning-based modelling.Molecular docking is a well-established molecular simulation method that explores the interactions between biomolecules and small molecules to predict their binding modes and binding

关 键 词：计算毒理学 分子对接 分子动力学模拟 机器学习 虚拟筛查 机制解析 

分 类 号：X171.5[环境科学与工程—环境科学]