Title: Integrating Protein Dynamics into Structure-Based Drug Design via Full-Atom Stochastic Flows Show Chinese title

Title: 通过全原子随机流将蛋白质动力学整合到基于结构的药物设计中

Abstract: The dynamic nature of proteins, influenced by ligand interactions, is essential for comprehending protein function and progressing drug discovery. Traditional structure-based drug design (SBDD) approaches typically target binding sites with rigid structures, limiting their practical application in drug development. While molecular dynamics simulation can theoretically capture all the biologically relevant conformations, the transition rate is dictated by the intrinsic energy barrier between them, making the sampling process computationally expensive. To overcome the aforementioned challenges, we propose to use generative modeling for SBDD considering conformational changes of protein pockets. We curate a dataset of apo and multiple holo states of protein-ligand complexes, simulated by molecular dynamics, and propose a full-atom flow model (and a stochastic version), named DynamicFlow, that learns to transform apo pockets and noisy ligands into holo pockets and corresponding 3D ligand molecules. Our method uncovers promising ligand molecules and corresponding holo conformations of pockets. Additionally, the resultant holo-like states provide superior inputs for traditional SBDD approaches, playing a significant role in practical drug discovery. Show Chinese abstract

Abstract: 蛋白质的动态特性受配体相互作用的影响，这对于理解蛋白质功能和推进药物发现至关重要。 传统的基于结构的药物设计（SBDD）方法通常针对具有刚性结构的结合位点，这限制了它们在药物开发中的实际应用。 虽然分子动力学模拟理论上可以捕捉所有生物相关的构象，但过渡速率由它们之间的固有能量屏障决定，使得采样过程计算成本高昂。 为克服上述挑战，我们提出考虑蛋白质口袋构象变化的生成建模方法用于SBDD。 我们整理了一个包含蛋白质-配体复合物的无配体状态和多个有配体状态的数据集，这些状态通过分子动力学模拟获得，并提出了一种全原子流模型（以及一种随机版本），称为DynamicFlow，该模型学习将无配体口袋和噪声配体转换为有配体口袋和相应的三维配体分子。 我们的方法揭示了有前景的配体分子及其口袋的有配体构象。 此外，产生的类似有配体的状态为传统SBDD方法提供了更优的输入，在实际药物发现中发挥了重要作用。