Lee Changsoo, Won Jonghun, Ryu Seongok, Yang Jinsol, Jung Nuri, Park Hahnbeom, Seok Chaok
Department of Chemistry, Seoul National University, Seoul 08826, Republic of Korea.
Galux Inc., Seoul 08738, Republic of Korea.
J Chem Theory Comput. 2024 Aug 7. doi: 10.1021/acs.jctc.4c00385.
With the recent introduction of deep learning techniques into the prediction of biomolecular structures, structure prediction performance has significantly improved, and the potential for biomedical applications has increased considerably. The prediction of protein-ligand complex structures, applicable to the atomistic understanding of biomolecular functions and the effective design of drug molecules, has also improved with the introduction of deep learning. In this paper, it is demonstrated that docking performance can be greatly enhanced by training an energy function that encapsulates physical effects using deep learning within the framework of the traditional protein-ligand docking method. The advantage of this method, called GalaxyDock-DL, lies in its minimal overfitting to the training data compared to several existing deep learning-based protein-ligand docking methods. Unlike some recent deep learning methods, it does not use information about known binding pocket center positions. Instead, the results of this docking method show a systematic dependence on the physical properties of the target protein-ligand complexes such as atomic thermal fluctuations and binding affinity. GalaxyDock-DL utilizes the global optimization technique of the conventional protein-ligand docking method, GalaxyDock, and a neural network energy function trained to stabilize the native state compared to non-native states, just as physical free energy does. This physical principle-based approach suggests directions not only for future structure prediction involving structurally flexible biomolecular complexes but also for predicting binding affinity, thereby providing guidance for the effective design of biofunctional ligands.
随着深度学习技术最近被引入生物分子结构预测领域，结构预测性能有了显著提升，生物医学应用潜力也大幅增加。蛋白质 - 配体复合物结构预测对于从原子层面理解生物分子功能以及有效设计药物分子具有重要意义，随着深度学习的引入，其预测能力也得到了提高。本文表明，在传统蛋白质 - 配体对接方法的框架内，通过深度学习训练一个封装物理效应的能量函数，可以极大地提升对接性能。这种名为GalaxyDock-DL的方法的优势在于，与现有的几种基于深度学习的蛋白质 - 配体对接方法相比，它对训练数据的过拟合程度最小。与一些近期的深度学习方法不同，它不使用关于已知结合口袋中心位置的信息。相反，这种对接方法的结果显示出对目标蛋白质 - 配体复合物的物理性质（如原子热涨落和结合亲和力）的系统性依赖。GalaxyDock-DL利用了传统蛋白质 - 配体对接方法GalaxyDock的全局优化技术，以及一个经过训练的神经网络能量函数，该函数与物理自由能一样，能够稳定天然状态相对于非天然状态。这种基于物理原理的方法不仅为涉及结构灵活的生物分子复合物的未来结构预测指明了方向，也为预测结合亲和力提供了指导，从而为生物功能配体的有效设计提供了依据。
