[논문 리뷰] Martinize2 and Vermouth: Unified Framework for Topology Generation

P. Kroon, Fabian Grünewald외 6명|arXiv (Cornell University)|2022. 11. 29.

Cell Image Analysis Techniques인용 수 37

한 줄 요약

Vermouth는 Martini CG 시뮬레이션 설정을 위한 통합 Python 프레임워크를 제공하며, Martinize2는 MARTINI 토폴로지 생성을 확장하여 양성 상태(protonation states), 포스트트랜스레이션 수정(post-translational modifications), 및 비단백 분자를 처리하고 대규모 단백질 구조 변환에서 시연되었습니다.

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.

연구 동기 및 목표

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

제안 방법

• 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.

실험 결과

연구 질문

• 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?

주요 결과

• Martinize2는 단백질 토폴로지 준비를 자동으로 관리하는 양성화 상태와 PTMs를 처리하고 리간드 및 비단백 분자를 CG Martini 시뮬레이션으로 변환합니다.
• Vermouth는 복잡한 시스템의 Martini 시뮬레이션을 준비, 실행, 분석하기 위한 통합 오픈 소스 프레임워크로 제시됩니다.
• 도구는 두 가지 고난이도 벤치마크에서 완전한 I-TASSER 단백질 템플릿 데이터베이스와 200,000개의 AlphaFold 구조의 부분 집합을 CG 해상도로 변환하여 시연됩니다.
• 워크플로우는 고처리량 응용을 보장하기 위한 입력 구조 품질 검사를 강조합니다.