Synergistic integration of topology and machine learning for the predictions of protein-ligand binding affinities and mutation impacts

拓扑和机器学习的协同集成，用于预测蛋白质-配体结合亲和力和突变影响

• 批准号: 9756427
• 负责人: Guowei Wei
• 金额: $ 31.93万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9756427
• 关键词: 3-Dimensional; Address; Affinity; Architecture; Big Data; Binding; Binding Proteins; Bioinformatics; Biological; Biological Sciences; Biophysics; Characteristics; Chemicals; Classification; Complex; Computer software; DNA Sequence; Data; Data Analyses; Data Science; Data Set; Databases; Development; Diagnosis; Dimensions; Drug Design; Electrostatics; Elements; Free Energy; Freedom; Future Generations; Geometry; Handwriting; Image Analysis; Induced Mutation; Ions; Learning; Ligand Binding; Ligands; Lipids; Machine Learning; Medical; Membrane; Membrane Proteins; Metals; Methodology; Methods; Mutation; Physics; Plant Roots; Proteins; Psychological Transfer; Site; Speech; System; Techniques; Thermodynamics; Work; algebraic topology; base; cofactor; data warehouse; deep learning; deep learning algorithm; direct application; high dimensionality; improved; innovation; language processing; learning algorithm; learning strategy; machine learning algorithm; metallicity; models and simulation; multi-task learning; multitask; mutant; neglect; next generation; search engine; tool; trend; user-friendly

Project Summary Fundamental challenges that hinder the current understanding of biomolecular systems are their tremendous complexity, high dimensionality and excessively large data sets associated with their geometric modeling and simulations. These challenges call for innovative strategies for handling massive biomolecular datasets. Topology, in contrast to geometry, provides a unique tool for dimensionality reduction and data simplification. However, traditional topology typically incurs with excessive reduction in geometric information. Persistent homology is a new branch of topology that is able to bridge traditional topology and geometry, but suffers from neglecting biological information. Built upon PI’s recent work in the topological data analysis of biomolecules, this project will explore how to integrate topological data analysis and machine learning to significantly improve the current state-of-the-art predictions of protein-ligand binding and mutation impact established in the PI’s preliminary studies. These improvements will be achieved through developing physics-embedded topological methodologies and advanced deep learning architectures for tackling heterogeneous biomolecular data sets arising from a variety of physical and biological considerations. Finally, the PI will establish robust databases and online servers for the proposed predictions.

项目摘要 阻碍目前对生物分子系统的理解的根本挑战是它们的 巨大的复杂性，高维度和与其相关的超大数据集， 几何建模和仿真。这些挑战需要创新的应对策略 海量生物分子数据集与几何学不同，拓扑学提供了一种独特的工具， 降维和数据简化。然而，传统拓扑通常会导致 几何信息的过度减少。持久同调是拓扑学的一个新的分支 它能够弥合传统的拓扑学和几何学，但却忽视了生物学， 信息.基于PI最近在生物分子拓扑数据分析方面的工作， 该项目将探索如何将拓扑数据分析和机器学习相结合， 改进当前对蛋白质-配体结合和突变影响的最新预测 在PI的初步研究中确定。这些改进将通过以下方式实现： 开发物理嵌入式拓扑方法和高级深度学习 用于处理由各种物理过程产生的异质生物分子数据集的体系结构 和生物学考虑。最后，PI将建立强大的数据库和在线服务器 对于提出的预测。