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

项目摘要/摘要 分子模拟是预测生物分子性质、解释生物物理实验、 设计具有治疗作用的小分子或生物分子。然而，一些障碍阻碍了这一进程。 涉及生物分子模拟的云尺度定量研究工作fl的发展。两个主要目标- 堆积是目前用于生物分子和小分子治疗的原子模型不够准确的fi 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。 我们的原创R01《用于构建用于预测生物的生物分子力场的开放数据驱动的基础设施》 物理学和药物设计，“寻求解决fi的第一个问题。它帮助资助我们的努力，开放力场倡议 (https://openforcefield.org)开发开放、可扩展和共享的软件和数据基础设施，实施 统计稳健的力fi域参数化法和选择新的力fi域 举止。这项工作的目的不仅是创造新一代的力量fi场，而且是一种开放的技术，以 继续推进科学进步的力量fi。 然而，即使有了改进的分子模型，将生物分子模拟的完整工作flOWS结合在一起 涉及大量不同工具的接口。然而，现有的大多数分子 模拟工作flOW是相互不兼容的，具有不同的分子模型表示。 开放力场倡议的努力已经包括开发分子数据结构，我们可以- 移植到现有的分子模拟工具中。我们建议扩大R01的现有范围，以建立一个 可扩展的通用分子模拟表示以及与该表示来往的翻译器。 这样一套工具将立即显著地使fi更容易地组合为fl操作系统开发的完全不同的工作 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，但使用当前不兼容的工具运行和分析它们，从而更好地匹配 计算资源和解决问题的方法。它将有助于避免陷入单一软件框架中，并且 支持以前在没有开发人员大量时间和精力的情况下无法实现的功能组合。 我们将(目标1)与合作伙伴一起概括我们的模块化、可扩展的对象模型，以表示 以一种适应当前支持的力fi项的方式的参数化生物分子系统 最流行的生物分子模拟软件。我们将把它设计成可扩展到高级交互 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 Will(目标2)：通过允许在分子模拟工作fl的组件之间轻松转换 其他分子模拟包可以轻松地将其表示存储在此数据模型中，从而开发转换器 可以将此对象模型导入/导出为多种流行的fiLE格式，最初侧重于OpenMM、琥珀 ChARMM和GROMACS。我们将演示此接口在云就绪工作fl操作系统中的实用程序。

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.