[论文解读] Martinize2 and Vermouth: Unified Framework for Topology Generation

P. Kroon, Fabian Grünewald+6 more|arXiv (Cornell University)|Nov 29, 2022

Cell Image Analysis Techniques被引用 37

一句话总结

Vermouth 提供一个统一的 Python 框架，用于建立 Martini CG 仿真，其中 Martinize2 扩展 MARTINI 拓扑生成以处理质子化状态、翻译后修饰以及非蛋白质分子，在大规模蛋白质结构转换中得到证明。

ABSTRACT

Ongoing advances in force field and computer hardware development enable the use of molecular dynamics (MD) to simulate increasingly complex systems with the ultimate goal of reaching cellular complexity. At the same time, rational design by high-throughput (HT) simulations is another forefront of MD. In these areas, the Martini coarse-grained force field, especially the latest version (i.e. v3), is being actively explored because it offers an enhanced spatial-temporal resolution. However, the automation tools for preparing simulations with the Martini force field, accompanying the previous version, were not designed for HT simulations or studies of complex cellular systems. Therefore, they become a major limiting factor. To address these shortcomings, we present the open-source Vermouth python library. Vermouth is designed to become the unified framework for developing programs, which prepare, run, and analyze Martini simulations of complex systems. To demonstrate the power of the Vermouth library, the Martinize2 program is showcased as a generalization of the martinize script, originally aimed to set up simulations of proteins. In contrast to the previous version, Martinize2 automatically handles protonation states in proteins and post-translation modifications, offers more options to fine-tune structural biases such as the elastic network (EN), and can convert non-protein molecules such as ligands. Finally, Martinize2 is used in two high-complexity benchmarks. The entire I-TASSER protein template database as well as a subset of 200,000 structures from the AlphaFold Protein Structure Database are converted to CG resolution and we illustrate how the checks on input structure quality can safeguard high-throughput applications.

研究动机与目标

• 旨在实现对复杂系统和高通量研究的 Martini CG 仿真自动化与简化流程。

提出的方法

• 引入 Vermouth 作为一个用于准备、运行和分析 Martini 仿真 的开源 Python 库。
• 将 Martinize 泛化为 Martinize2，以自动处理蛋白质的质子化状态和翻译后修饰。
• 使非蛋白质分子（如配体）能够进行 CG 建模的转换。
• 提供可配置选项以调整结构偏差，如弹性网络约束。
• 通过将大型蛋白质结构数据库转换为粗粒化分辨率的高复杂性基准测试来验证该框架。

实验结果

研究问题

• RQ1 Vermouth 能否作为一个统一的框架，用于准备、运行和分析复杂系统的 Martini 基于仿真？
• RQ2 Martinize2 是否能够可靠地处理质子化状态、PTMs 以及非蛋白质分子转换以用于 CG Martini 模型？
• RQ3 在将这些工具应用于大规模结构数据库时，性能和质量检查有哪些？
• RQ4 输入结构质量检查如何影响高通量拓扑生成的可靠性？

主要发现

• Martinize2 将蛋白质拓扑准备扩展为自动管理质子化状态和 PTMs，并将配体与非蛋白质分子转换为 CG Martini 仿真。
• Vermouth 被介绍为一个统一的、开源的框架，用于开发用于准备、运行和分析复杂系统的 Martini 仿真程序。
• 通过将完整的 I-TASSER 蛋白质模板数据库和 200,000 AlphaFold 结构的子集转换为 CG 分辨率，在两个高复杂性基准中演示了这些工具。
• 该工作流强调输入结构质量检查，以保障高通量应用的可靠性。