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/摘要 分子模拟是预测生物分子性质，解释生物物理实验， 并设计具有治疗效用的小分子或生物分子。然而，一些障碍阻碍了 定量的、云规模的研究工作的发展涉及生物分子模拟。两个主要目标- 障碍是目前用于生物分子和小分子治疗的原子模型不够精确 以及在学术和工业生物分子研究中使用的模拟工具链缺乏互操作性。 我们最初的R 01，“开放式数据驱动的基础设施，用于构建预测性生物分子力场， 物理学和药物设计，“寻求解决第一个问题。它为我们的努力提供资金，开放力场倡议 （https：//openforce field.org）开发开放、可扩展和共享的软件和数据基础设施，实现 在统计上稳健的方法参数化力场和选择新的力场在统计上健全 方式这项工作的目的不仅是创造新一代的力场，而且是一种开放的技术， 继续推进力场科学。 然而，即使有了改进的分子模型，把完整的生物分子模拟工作流程放在一起， 涉及连接大量不同的工具。然而，大多数现有的分子 模拟工作流程是相互不兼容的，具有不同的分子模型表示。 开放力场计划的努力已经包括了分子数据结构的开发，我们可以从分子数据结构中提取信息。 移植到现有的分子模拟工具中。我们建议扩大R 01的现有范围， 可扩展的通用分子模拟表示和到该表示和来自该表示的翻译器。 有了这样一套工具，就能立即显著地简化联合收割机的工作流程， 不同的分子模拟工具研究人员将能够建立和建立生物物理模拟 使用他们常用的工具，但运行和分析他们与目前不兼容的工具，使更好的匹配， 计算资源和解决问题的方法。它将有助于避免陷入单一的软件框架， 无需大量开发人员时间和精力即可实现以前不可能实现的功能组合。 我们将（目标1）与合作伙伴合作，推广我们的模块化、可扩展的对象模型， 参数化的生物分子系统的方式，以适应目前支持的力场条款 最流行的生物分子模拟软件包。我们将把它设计成可扩展的高级交互 形式，如极化率和其他多体项，以及分子间力的机器学习模型。我们 将（目标2）：通过允许在分子模拟工作流程的组件之间进行轻松转换， 其他分子模拟软件包，以方便地存储在这个数据模型，开发转换器， 可以将此对象模型导入/导出为多种流行的文件格式，最初专注于OpenMM，AMBER， Charmm和Gromacs。我们将在云就绪工作流程中演示此接口的实用性。