[Paper Review] Martinize2 and Vermouth: Unified Framework for Topology Generation

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

Cell Image Analysis Techniques37 citations

TL;DR

Vermouth provides a unified Python framework for setting up Martini CG simulations, with Martinize2 extending MARTINI topology generation to handle protonation states, post-translational modifications, and non-protein molecules, demonstrated on large-scale protein structure conversions.

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.

Motivation & Objective

• Aim to automate and streamline Martini CG simulations for complex systems and high-throughput studies.

Proposed method

• Introduce Vermouth as an open-source Python library for preparing, running, and analyzing Martini simulations.
• Generalize Martinize to Martinize2 to automatically handle protein protonation states and post-translational modifications.
• Enable conversion of non-protein molecules (e.g., ligands) for CG modeling.
• Provide configurable options to tune structural biases such as elastic network constraints.
• Validate the framework through high-complexity benchmarks converting large protein structure databases to coarse-grained resolution.

Experimental results

Research questions

• RQ1 Can Vermouth serve as a unified framework for preparing, running, and analyzing Martini-based simulations of complex systems?
• RQ2 Does Martinize2 reliably handle protonation states, PTMs, and non-protein molecule conversion for CG Martini models?
• RQ3 What are the performance and quality checks when applying these tools to large-scale structure databases?
• RQ4 How do input structure quality checks impact reliability in high-throughput topology generation?

Key findings

• Martinize2 extends protein topology preparation to automatically manage protonation states and PTMs and to convert ligands and non-protein molecules for CG Martini simulations.
• Vermouth is presented as a unified, open-source framework for developing programs that prepare, run, and analyze Martini simulations of complex systems.
• The tools are demonstrated in two high-complexity benchmarks by converting the complete I-TASSER protein template database and a subset of 200,000 AlphaFold structures to CG resolution.
• The workflow emphasizes input-structure quality checks to safeguard high-throughput applications.