Open Data-driven Infrastructure for Building Biomolecular Force Field for Predictive Biophysics and Drug Design

开放数据驱动的基础设施，用于构建用于预测生物物理学和药物设计的生物分子力场

• 批准号: 10166314
• 负责人: Michael R Shirts
• 金额: $ 22.5万
• 依托单位: UNIVERSITY OF COLORADO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-01 至 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10166314
• 关键词: Affinity; Binding; Biophysics; COVID-19; Collaborations; Computer Simulation; Computer software; Computing Methodologies; DNA; Development; Drug Design; Ecosystem; Engineering; Funding; Generations; Human; Individual; Industrialization; Infrastructure; Language; Learning; Libraries; Life; Machine Learning; Methods; Modeling; Molecular; Motion; Pharmaceutical Preparations; Pharmacologic Substance; Problem Solving; Property; Proteins; Pythons; RNA; Readability; Research; Research Personnel; Running; Sampling; Science; Scientist; Software Framework; System; Technology; Testing; Therapeutic; Time; Work; Writing; biomaterial interface; computing resources; data infrastructure; data modeling; design; experimental study; file format; improved; interoperability; molecular modeling; open data; simulation; small molecule; small molecule therapeutics; software infrastructure; sound; structured data; tool

PROJECT SUMMARY/ABSTRACT Molecular simulation is a powerful tool to predict the properties of biomolecules, interpret biophysical experiments, and design small molecules or biomolecules with therapeutic utility. However, a number of obstacles have impeded the development of quantitative, cloud-scale research workflows involving biomolecular simulation. Two main ob- stacles are the insufficient accuracy of current atomistic models for biomolecules and small molecule therapeutics and the lack of interoperability in simulation toolchains used in both academic and industrial biomolecular research. Our original R01, “Open Data-driven Infrastructure for Building Biomolecular Force Fields for Predictive Bio- physics and Drug Design,” seeks to solve the first problem. It helps fund our effort, the Open Force Field Initiative (https://openforcefield.org) to develop open, extensible, and shared software and data infrastructure, implementing statistically robust methods of parameterizing force fields and choosing new force fields in a statistically sound manner. This work is designed to create not just a new generation of force fields, but an open technology to continue advancing force field science. However, even with improved molecular models, putting together complete workflows of biomolecular simulations involves interfacing substantial numbers of different tools. However the majority of the existing molecular simulation workflows are mutually incompatible, with differing representations of the molecular models. The Open Force Field Initiative effort already includes the development of molecular data structures that we can ex- port into existing molecular simulation tools. We propose to extend the existing scope of our R01 to create an extensible common molecular simulation representation and translators to and from this representation. Such a set of tools will immediately make it significantly easier to combine the disparate workflows developed for different sets of molecular simulation tools. Researchers will be able to set up and build the biophysical simulations using their usual tools, but run and analyze them with currently incompatible tools, enabling better matching of computational resources and methods to problems. It will help avoid trapping in a single software framework, and enable combinations of functionalities previously impossible without substantial developer time and effort. We will (Aim 1) work with partners to generalize our modular, extensible object model for representing parameterized biomolecular systems in a manner that accommodates the force field terms currently supported by most popular biomolecular simulation packages. We will engineer it to be extensible to advanced interaction forms, such as polarizability and other multibody terms, and machine learning models for intermolecular forces. We will (Aim 2): enable easy conversion between components of molecular simulation workflows by allowing other molecular simulation packages to easily store their representations in this data model, developing converters that can import/export this object model to multiple popular file formats, focusing initially on OpenMM, AMBER, CHARMM, and GROMACS. We will demonstrate the utility of this interface in cloud-ready workflows.

项目总结/文摘