借助机器学习加速可靠的蛋白质-药物系统的多尺度量子细化。

Accelerating reliable multiscale quantum refinement of protein-drug systems enabled by machine learning.

机构信息

Shenzhen Grubbs Institute, Department of Chemistry and Guangdong Provincial Key Laboratory of Catalysis, Southern University of Science and Technology, Shenzhen, 518055, China.

出版信息

Nat Commun. 2024 May 16;15(1):4181. doi: 10.1038/s41467-024-48453-4.

Biomacromolecule structures are essential for drug development and biocatalysis. Quantum refinement (QR) methods, which employ reliable quantum mechanics (QM) methods in crystallographic refinement, showed promise in improving the structural quality or even correcting the structure of biomacromolecules. However, vast computational costs and complex quantum mechanics/molecular mechanics (QM/MM) setups limit QR applications. Here we incorporate robust machine learning potentials (MLPs) in multiscale ONIOM(QM:MM) schemes to describe the core parts (e.g., drugs/inhibitors), replacing the expensive QM method. Additionally, two levels of MLPs are combined for the first time to overcome MLP limitations. Our unique MLPs+ONIOM-based QR methods achieve QM-level accuracy with significantly higher efficiency. Furthermore, our refinements provide computational evidence for the existence of bonded and nonbonded forms of the Food and Drug Administration (FDA)-approved drug nirmatrelvir in one SARS-CoV-2 main protease structure. This study highlights that powerful MLPs accelerate QRs for reliable protein-drug complexes, promote broader QR applications and provide more atomistic insights into drug development.

生物大分子结构对于药物开发和生物催化至关重要。量子精修（QR）方法在晶体学精修中采用可靠的量子力学（QM）方法，有望提高结构质量甚至纠正生物大分子的结构。然而，巨大的计算成本和复杂的量子力学/分子力学（QM/MM）设置限制了 QR 的应用。在这里，我们将强大的机器学习势能（MLP）纳入多尺度 ONIOM（QM：MM）方案中，以描述核心部分（例如药物/抑制剂），取代昂贵的 QM 方法。此外，首次结合了两个层次的 MLP 来克服 MLP 的限制。我们独特的基于 MLP+ONIOM 的 QR 方法以显著更高的效率实现了 QM 级精度。此外，我们的精修为一种 SARS-CoV-2 主要蛋白酶结构中 FDA 批准药物奈玛特韦的键合和非键合形式的存在提供了计算证据。这项研究强调了强大的 MLP 可以加速可靠的蛋白质-药物复合物的 QR，促进更广泛的 QR 应用，并为药物开发提供更多的原子见解。