Suppr超能文献

AMBER 药物发现助推工具:用于生产自由能模拟设置和分析的自动化工作流程 (ProFESSA)。

AMBER Drug Discovery Boost Tools: Automated Workflow for Production Free-Energy Simulation Setup and Analysis (ProFESSA).

机构信息

Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, United States.

Donostia International Physics Center (DIPC), PK 1072, 20080 Donostia-San Sebastian, Spain.

出版信息

J Chem Inf Model. 2022 Dec 12;62(23):6069-6083. doi: 10.1021/acs.jcim.2c00879. Epub 2022 Nov 30.

Abstract

We report an automated workflow for production free-energy simulation setup and analysis (ProFESSA) using the GPU-accelerated AMBER free-energy engine with enhanced sampling features and analysis tools, part of the AMBER Drug Discovery Boost package that has been integrated into the AMBER22 release. The workflow establishes a flexible, end-to-end pipeline for performing alchemical free-energy simulations that brings to bear technologies, including new enhanced sampling features and analysis tools, to practical drug discovery problems. ProFESSA provides the user with top-level control of large sets of free-energy calculations and offers access to the following key functionalities: (1) automated setup of file infrastructure; (2) enhanced conformational and alchemical sampling with the ACES method; and (3) network-wide free-energy analysis with the optional imposition of cycle closure and experimental constraints. The workflow is applied to perform absolute and relative solvation free-energy and relative ligand-protein binding free-energy calculations using different atom-mapping procedures. Results demonstrate that the workflow is internally consistent and highly robust. Further, the application of a new network-wide Lagrange multiplier constraint analysis that imposes key experimental constraints substantially improves binding free-energy predictions.

摘要

我们报告了一个使用 GPU 加速 AMBER 自由能引擎的自动化工作流程,该引擎具有增强的采样功能和分析工具,是 AMBER Drug Discovery Boost 包的一部分,已集成到 AMBER22 版本中。该工作流程建立了一个灵活的、端到端的管道,用于执行原子替换自由能模拟,利用包括新的增强采样功能和分析工具在内的技术来解决实际的药物发现问题。ProFESSA 为用户提供了对大量自由能计算的顶级控制,并提供了以下关键功能:(1)文件基础设施的自动设置;(2)使用 ACES 方法进行增强构象和原子替换采样;(3)通过可选的循环闭合和实验约束进行全网自由能分析。该工作流程应用于使用不同的原子映射程序执行绝对和相对溶剂化自由能以及相对配体-蛋白结合自由能计算。结果表明,该工作流程具有内部一致性和高度稳健性。此外,应用新的全网拉格朗日乘子约束分析,施加关键实验约束,可显著提高结合自由能预测。

相似文献

引用本文的文献

1
Recent Developments in Amber Biomolecular Simulations.琥珀色生物分子模拟的最新进展。
J Chem Inf Model. 2025 Aug 11;65(15):7835-7843. doi: 10.1021/acs.jcim.5c01063. Epub 2025 Jul 29.
5
The Role of General Acid Catalysis in the Mechanism of an Alkyl Transferase Ribozyme.一般酸催化在烷基转移酶核酶机制中的作用。
ACS Catal. 2024 Oct 2;14(20):15294-15305. doi: 10.1021/acscatal.4c04571. eCollection 2024 Oct 18.
10
Alchemical Enhanced Sampling with Optimized Phase Space Overlap.具有优化相空间重叠的炼金术增强采样
J Chem Theory Comput. 2024 May 14;20(9):3935-3953. doi: 10.1021/acs.jctc.4c00251. Epub 2024 Apr 26.

本文引用的文献

1
ACES: Optimized Alchemically Enhanced Sampling.ACES:优化的化学增强采样法
J Chem Theory Comput. 2023 Jan 11. doi: 10.1021/acs.jctc.2c00697.

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验