MDFit: automated molecular simulations workflow enables high throughput assessment of ligands-protein dynamics

IF 3 3区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY
Alexander C. Brueckner, Benjamin Shields, Palani Kirubakaran, Alexander Suponya, Manoranjan Panda, Shana L. Posy, Stephen Johnson, Sirish Kaushik Lakkaraju
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引用次数: 0

Abstract

Molecular dynamics (MD) simulation is a powerful tool for characterizing ligand–protein conformational dynamics and offers significant advantages over docking and other rigid structure-based computational methods. However, setting up, running, and analyzing MD simulations continues to be a multi-step process making it cumbersome to assess a library of ligands in a protein binding pocket using MD. We present an automated workflow that streamlines setting up, running, and analyzing Desmond MD simulations for protein–ligand complexes using machine learning (ML) models. The workflow takes a library of pre-docked ligands and a prepared protein structure as input, sets up and runs MD with each protein–ligand complex, and generates simulation fingerprints for each ligand. Simulation fingerprints (SimFP) capture protein–ligand compatibility, including stability of different ligand-pocket interactions and other useful metrics that enable easy rank-ordering of the ligand library for pocket optimization. SimFPs from a ligand library are used to build & deploy ML models that predict binding assay outcomes and automatically infer important interactions. Unlike relative free-energy methods that are constrained to assess ligands with high chemical similarity, ML models based on SimFPs can accommodate diverse ligand sets. We present two case studies on how SimFP helps delineate structure–activity relationship (SAR) trends and explain potency differences across matched-molecular pairs of (1) cyclic peptides targeting PD-L1 and (2) small molecule inhibitors targeting CDK9.

Abstract Image

MDFit:自动分子模拟工作流程,可对配体-蛋白质动力学进行高通量评估。
分子动力学(MD)模拟是表征配体-蛋白质构象动力学的强大工具,与对接和其他基于刚性结构的计算方法相比具有显著优势。然而,MD 模拟的设置、运行和分析仍然是一个多步骤的过程,因此使用 MD 评估蛋白质结合口袋中的配体库非常麻烦。我们介绍了一种自动化工作流程,它能利用机器学习(ML)模型简化蛋白质配体复合物的德斯蒙德 MD 模拟的设置、运行和分析。该工作流程以预对接配体库和准备好的蛋白质结构为输入,设置并运行每个蛋白质配体复合物的 MD,并生成每个配体的模拟指纹。模拟指纹(SimFP)可以捕捉蛋白质-配体的兼容性,包括不同配体-口袋相互作用的稳定性和其他有用的指标,便于对配体库进行排序,以优化口袋。配体库中的 SimFPs 可用于构建和部署 ML 模型,以预测结合试验结果并自动推断重要的相互作用。与受限于评估化学相似性高的配体的相对自由能方法不同,基于 SimFPs 的 ML 模型可以适应多种配体集。我们介绍了两个案例研究,说明 SimFP 如何帮助划定结构-活性关系(SAR)趋势,并解释(1)靶向 PD-L1 的环肽和(2)靶向 CDK9 的小分子抑制剂的匹配分子对之间的效力差异。
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来源期刊
Journal of Computer-Aided Molecular Design
Journal of Computer-Aided Molecular Design 生物-计算机:跨学科应用
CiteScore
8.00
自引率
8.60%
发文量
56
审稿时长
3 months
期刊介绍: The Journal of Computer-Aided Molecular Design provides a form for disseminating information on both the theory and the application of computer-based methods in the analysis and design of molecules. The scope of the journal encompasses papers which report new and original research and applications in the following areas: - theoretical chemistry; - computational chemistry; - computer and molecular graphics; - molecular modeling; - protein engineering; - drug design; - expert systems; - general structure-property relationships; - molecular dynamics; - chemical database development and usage.
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