CDS&E: D3SC: Developing A Molecular Mechanics Modeling Platform (MMMP) for Studying Molecular Interactions

CDS

• 批准号: 1955260
• 负责人: Junmei Wang
• 金额: $ 50万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1955260&HistoricalAwards=false
• 关键词: CDS& D3SC; Developing; Molecular; Mechanics

Junmei Wang of The University of Pittsburgh is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to develop a set of computational tools and build a publicly available platform to facilitate users to study biomolecular systems. The award is cofunded by the Office of Advanced Cyberinfrastructure. High quality molecular mechanics force field (MMFF) parameters are critical to the successful modeling and simulations of various molecular systems. However, users naïve to molecular modeling may find it a daunting task to obtain high-quality MMFF parameters and models without assistance. Dr. Wang and his team are conducting research to develop novel software tools, derive MMFF parameters, build high-quality models, and then integrate them into a freely accessible Molecular Mechanics Modeling Platform (MMMP). MMMP will help users from a broad range of disciplines to study molecular mechanisms of biomolecule-ligand interactions and to calculate the binding affinity accurately and efficiently with ease. Researchers from the drug discovery community can employ MMMP to increase the success rate on the discovery of drug candidates for combating a variety of diseases including the Coronavirus Disease 2019 (COVID-19). A major bottleneck for studying novel molecular systems is the availability, accuracy and validation of consistent molecular mechanics parameter sets. Dr. Junmei Wang is developing a Molecular Mechanics Modeling Platform (MMMP) which integrates force field parameters and residue topologies, novel online tools, and Application Programming Interfaces (APIs) to break the bottleneck. He is conducting research to (1) improve the atom type and bond type perception algorithm to handle arbitrary small molecules; (2) develop molecular mechanics model databases for non-standard amino acid/nucleic acid residues and co-crystallized ligands in the Protein Data Bank, and other compounds (3) develop and advance a software tool coined re-Affinity to bridge the the gap between the efficient docking methods and more computer resource-demanding yet more accurate free energy-based methods; (4) develop a physical, efficient and highly transferrable charge model which can significantly improve the accuracy of free energy calculations; and (5) create a user-friendly Graphic User Interface (GUI), ClickFF, which allows users to generate energy profiles, compare force fields, and optimize force field parameters for selected bonded force field parameters with a few clicks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

匹兹堡大学的王俊梅（Junmei Wang）获得了化学系化学理论、模型和计算方法项目的资助，开发了一套计算工具，并建立了一个公开可用的平台，以方便用户研究生物分子系统。该奖项由高级网络基础设施办公室共同资助。高质量的分子力学力场（MMFF）参数对各种分子体系的成功建模和模拟至关重要。然而，naïve分子建模用户可能会发现，在没有帮助的情况下获得高质量的MMFF参数和模型是一项艰巨的任务。王博士和他的团队正在进行研究，开发新的软件工具，推导MMFF参数，建立高质量的模型，然后将它们集成到一个免费访问的分子力学建模平台（MMMP）中。MMMP将帮助来自广泛学科的用户研究生物分子-配体相互作用的分子机制，并轻松准确有效地计算结合亲和力。药物发现界的研究人员可以利用MMMP来提高发现包括2019冠状病毒病（COVID-19）在内的多种疾病的候选药物的成功率。研究新分子系统的一个主要瓶颈是一致性分子力学参数集的可用性、准确性和有效性。王俊梅博士正在开发一个分子力学建模平台（MMMP），该平台集成了力场参数和残留物拓扑，新型在线工具和应用程序编程接口（api），以打破瓶颈。他正在进行以下研究：(1)改进原子类型和键类型感知算法以处理任意小分子；(2)开发蛋白质数据库中非标准氨基酸/核酸残基和共结晶配体的分子力学模型数据库，以及其他化合物；(3)开发并推进了re-Affinity软件工具，以弥补高效对接方法与更需要计算机资源但更精确的基于自由能的方法之间的差距；(4)建立了物理、高效、高可转移的电荷模型，显著提高了自由能计算的准确性；(5)创建一个用户友好的图形用户界面（GUI）， ClickFF，它允许用户生成能量概况，比较力场，并优化力场参数，选择键合力场参数，只需点击几下。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

In silico binding affinity prediction for metabotropic glutamate receptors using both endpoint free energy methods and a machine learning-based scoring function.

• DOI: 10.1039/d2cp01727j
• 发表时间: 2022-08-03
• 期刊: PHYSICAL CHEMISTRY CHEMICAL PHYSICS
• 影响因子: 3.3
• 作者: Zhai, Jingchen;He, Xibing;Sun, Yuchen;Wan, Zhuoya;Ji, Beihong;Liu, Shuhan;Li, Song;Wang, Junmei
• 通讯作者: Wang, Junmei

Development and Evaluation of Geometry Optimization Algorithms in Conjunction with ANI Potentials

• DOI: 10.1021/acs.jctc.1c01043
• 发表时间: 2022-01-12
• 期刊: JOURNAL OF CHEMICAL THEORY AND COMPUTATION
• 影响因子: 5.5
• 作者: Hao, Dongxiao;He, Xibing;Wang, Junmei
• 通讯作者: Wang, Junmei

Drug-Drug Interaction Between Oxycodone and Diazepam by a Combined in Silico Pharmacokinetic and Pharmacodynamic Modeling Approach.

• DOI: 10.1021/acschemneuro.0c00810
• 发表时间: 2021-05-19
• 期刊: ACS chemical neuroscience
• 影响因子: 5
• 作者: Ji B;Xue Y;Xu Y;Liu S;Gough AH;Xie XQ;Wang J
• 通讯作者: Wang J

In Silico Prediction of Pharmacokinetic Profile for Human Oral Drug Candidates Which Lack Clinical Pharmacokinetic Experiment Data.

缺乏临床药代动力学实验数据的人类口服候选药物的药代动力学特征的计算机预测。

• DOI: 10.1007/s13318-022-00758-9
• 发表时间: 2022
• 期刊: European journal of drug metabolism and pharmacokinetics
• 影响因子: 1.9
• 作者: Zhai,Jingchen;Ji,Beihong;Liu,Shuhan;Zhang,Yuzhao;Cai,Lianjin;Wang,Junmei
• 通讯作者: Wang,Junmei

Feeling the tension: the bacterial mechanosensitive channel of large conductance as a model system and drug target

• DOI: 10.1016/j.cophys.2022.100627
• 发表时间: 2022-12-27
• 期刊: CURRENT OPINION IN PHYSIOLOGY
• 影响因子: 2.5
• 作者: Wang，Junmei;Blount，Paul
• 通讯作者: Blount，Paul