Geometric and Topological Modeling and Computation of Biomolecular Structure, Function, and Dynamics

生物分子结构、功能和动力学的几何和拓扑建模与计算

• 批准号: 1721024
• 负责人: Guowei Wei
• 金额: $ 37.5万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1721024&HistoricalAwards=false
• 关键词: Geometric; Topological; Modeling; Computation; Biomolecular

A major feature of the biological science in the 21st century is its transition from qualitative and descriptive to quantitative and analytical. Experimental exploration of self-organizing biomolecular systems, such as viruses, molecular motors and proteins in Alzheimer's disease, has been a dominating driving force in scientific discovery and innovation in the past few decades. Unfortunately, quantitative understanding of biomolecular structure, function, and dynamics severely lags behind the pace of the experimental progress. Fundamental challenges that hinder the current quantitative understanding of biomolecular systems are their tremendous complexity and excessively large number of degrees of freedom. Most biological processes occur in water, which constitutes 65-90 percent human cell mass. An average human protein has about 5500 atoms, which, together with its surrounding water molecules, involve about 100,000 degrees of freedom. The dimensionality increases dramatically for subcellular organelles and multiprotein complexes. The real-time structure optimization, dynamic simulation, and function prediction of molecular motors and/or viruses in human cells are intractable with full-atom models at present. A crucial question is how to reduce the number of degrees of freedom, while retaining the fundamental physics in complex biological systems. This project addresses grand challenges in the structure, function, and dynamics of self-organizing biomolecular systems due to exceptionally massive data sets. These challenges are tackled through the introduction of a new mathematical models, together with advanced computational methods to deal with excessively large biomolecular data sets. This proposal offers innovative approaches to an important area in massive data analysis, dimensionality reduction, computational mathematics and mathematical modeling.The project addresses the aforementioned challenges by a number of geometric and topological approaches. First, a multiscale framework is proposed to reduce the dimensionality and number of degrees of freedom by a macroscopic continuum description of the aquatic environment, and a microscopic discrete description of biomolecules. Additionally, adaptive coarse-grained approach based on persistently stable manifolds is introduced to further reduce the dimensionality of excessively large biomolecular systems. A total free energy functional is introduced to bring the macroscopic surface tension and microscopic potential interactions on an equal footing. The differential geometry theory of surfaces is utilized to describe the interface between macroscopic and microscopic domains. Potential driven geometric flows are constructed to minimize the total free energy functional. Furthermore, evolutionary topology and total curvature are introduced to analyze the topology-function relationship of biomolecules. Frenet frames are utilized to characterize the local geometry and associated stable manifolds in dynamical data of biomolecular systems. Machine learning algorithms are proposed to extract stable manifolds. Finally, perturbation strategy is introduced to explore the persistence of stable manifolds, which provides the assurance for the reliability of the coarse grained model. In addition to promising and extensive preliminary results illustrating the power of this approach, extensive validation and application have been proposed to ensure that the proposed methodology yields robust and powerful tools for biomolecular structure optimization, function prediction and dynamical simulation.This project is funded by the Division of Mathematical Sciences with cofounding from the Division of Molecular and Cellular Biosciences.

世纪生物科学的一个重要特征是从定性描述向定量分析的转变。 自组织生物分子系统的实验探索，如病毒，分子马达和阿尔茨海默病中的蛋白质，在过去几十年中一直是科学发现和创新的主导动力。不幸的是，对生物分子结构、功能和动力学的定量理解严重滞后于实验进展的步伐。 阻碍当前对生物分子系统的定量理解的基本挑战是其巨大的复杂性和过多的自由度。 大多数生物过程都发生在水中，而水占人体细胞质量的65- 90%。 一个普通的人类蛋白质大约有5500个原子，加上周围的水分子，大约有10万个自由度。亚细胞器和多蛋白复合物的维数急剧增加。目前，全原子模型难以对人体细胞中的分子马达或病毒进行实时结构优化、动力学模拟和功能预测。一个关键的问题是如何减少自由度的数量，同时保留复杂生物系统的基本物理。该项目解决了由于异常庞大的数据集而导致的自组织生物分子系统的结构，功能和动力学方面的巨大挑战。这些挑战是通过引入新的数学模型来解决的，再加上先进的计算方法来处理过大的生物分子数据集。该方案为海量数据分析、降维、计算数学和数学建模等重要领域提供了创新性的方法，并通过几何和拓扑方法解决了上述挑战。首先，提出了一个多尺度框架，以减少维度和数量的自由度的宏观连续描述的水环境，和微观离散描述的生物分子。此外，自适应粗粒度的方法的基础上持续稳定的流形被引入到进一步降低过大的生物分子系统的维数。引入总自由能泛函，使宏观表面张力和微观势相互作用处于同等地位。利用曲面微分几何理论描述了宏观域和微观域之间的界面。势驱动的几何流构造，以最小化总自由能泛函。 在此基础上，引入演化拓扑和全曲率来分析生物分子的拓扑-功能关系。Frenet标架被用来描述生物分子系统动力学数据中的局部几何和相关的稳定流形。提出了机器学习算法来提取稳定流形。 最后，通过引入扰动策略来探索稳定流形的持久性，为粗粒度模型的可靠性提供了保证。除了有前途的和广泛的初步结果说明这种方法的力量，广泛的验证和应用已经提出，以确保所提出的方法产生强大的和强大的工具，生物分子结构优化，功能预测和动力学simulation.This项目是由数学科学部资助，从分子和细胞生物科学部共同创立。

项目成果

Breaking the polar‐nonpolar division in solvation free energy prediction

• DOI: 10.1002/jcc.25107
• 发表时间: 2018-02
• 期刊: Journal of Computational Chemistry
• 影响因子: 3
• 作者: Bao Wang;Chengzhang Wang;Kedi Wu;G. Wei

Decoding SARS-CoV-2 Transmission and Evolution and Ramifications for COVID-19 Diagnosis, Vaccine, and Medicine

• DOI: 10.1021/acs.jcim.0c00501
• 发表时间: 2020-12-28
• 期刊: JOURNAL OF CHEMICAL INFORMATION AND MODELING
• 影响因子: 5.6
• 作者: Wang, Rui;Hozumi, Yuta;Wei, Guo-Wei
• 通讯作者: Wei, Guo-Wei

Protein pocket detection via convex hull surface evolution and associated Reeb graph

• DOI: 10.1093/bioinformatics/bty598
• 发表时间: 2018-09
• 期刊: Bioinformatics
• 影响因子: 5.8
• 作者: Rundong Zhao;Zixuan Cang;Y. Tong;G. Wei

Divide-and-conquer strategy for large-scale Eulerian solvent excluded surface

大规模欧拉溶剂排除曲面的分而治之策略

• DOI: 10.4310/cis.2018.v18.n4.a5
• 发表时间: 2018
• 期刊: Communications in Information and Systems
• 影响因子: 0.9
• 作者: Zhao, Rundong;Wang, Menglun;Tong, Yiying;Wei, Guo-Wei
• 通讯作者: Wei, Guo-Wei

Generative network complex (GNC) for drug discovery.

• DOI: 10.4310/cis.2019.v19.n3.a2
• 发表时间: 2019
• 期刊: Communications in information and systems
• 影响因子: 0.9
• 作者: Grow C;Gao K;Nguyen DD;Wei GW
• 通讯作者: Wei GW