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使用基于片段的量子化学计算蛋白质-配体相互作用能的收敛协议

Convergent Protocols for Computing Protein-Ligand Interaction Energies Using Fragment-Based Quantum Chemistry.

作者信息

Bowling Paige E, Broderick Dustin R, Herbert John M

机构信息

Biophysics Graduate Program, The Ohio State University, Columbus, Ohio 43210, United States.

Department of Chemistry & Biochemistry, The Ohio State University, Columbus, Ohio 43210, United States.

出版信息

J Chem Theory Comput. 2025 Jan 28;21(2):951-966. doi: 10.1021/acs.jctc.4c01429. Epub 2025 Jan 2.

Abstract

Fragment-based quantum chemistry methods offer a means to sidestep the steep nonlinear scaling of electronic structure calculations so that large molecular systems can be investigated using high-level methods. Here, we use fragmentation to compute protein-ligand interaction energies in systems with several thousand atoms, using a new software platform for managing fragment-based calculations that implements a screened many-body expansion. Convergence tests using a minimal-basis semiempirical method (HF-3c) indicate that two-body calculations, with single-residue fragments and simple hydrogen caps, are sufficient to reproduce interaction energies obtained using conventional supramolecular electronic structure calculations, to within 1 kcal/mol at about 1% of the computational cost. We also demonstrate that the HF-3c results are illustrative of trends obtained with density functional theory in basis sets up to augmented quadruple-ζ quality. Strategic deployment of fragmentation facilitates the use of converged biomolecular model systems alongside high-quality electronic structure methods and basis sets, bringing quantum chemistry to systems of hitherto unimaginable size. This will be useful for generation of high-quality training data for machine learning applications.

摘要

基于片段的量子化学方法提供了一种手段,可避开电子结构计算中急剧的非线性缩放,从而能够使用高级方法研究大分子系统。在此,我们利用片段化来计算含有数千个原子的系统中的蛋白质-配体相互作用能,使用了一个新的软件平台来管理基于片段的计算,该平台实现了一种筛选多体展开。使用最小基半经验方法(HF-3c)进行的收敛性测试表明,采用单残基片段和简单氢封端的二体计算足以重现使用传统超分子电子结构计算获得的相互作用能,在约1%的计算成本下,误差在1千卡/摩尔以内。我们还证明,HF-3c结果说明了在高达增强四重ζ质量的基组中用密度泛函理论获得的趋势。片段化的策略性部署有助于将收敛的生物分子模型系统与高质量电子结构方法和基组一起使用,将量子化学应用于迄今难以想象大小的系统。这对于为机器学习应用生成高质量训练数据将是有用的。

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本文引用的文献

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Delocalization error poisons the density-functional many-body expansion.离域误差破坏了密度泛函多体展开。
Chem Sci. 2024 Oct 30;15(47):19893-19906. doi: 10.1039/d4sc05955g. eCollection 2024 Dec 4.

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