用随机流模型生成蛋白构象变化，提升药物设计精度。
Integrating Protein Dynamics into Structure-Based Drug Design via Full-Atom Stochastic Flows
- 构建全原子随机流模型，从无配体状态生成有配体的动态构象。
- 在多个蛋白复合物上验证，能有效生成合理配体与对应口袋结构。
- 适合需要考虑蛋白柔性的药物研发人员使用。
蛋白质的动态特性受配体结合影响，对理解其功能和推动药物发现至关重要。传统基于结构的药物设计（SBDD）通常针对刚性结合位点，限制了实际应用。虽然分子动力学模拟理论上可捕捉所有生物相关构象，但因能垒导致采样效率低，计算成本高。为此，我们提出一种生成建模方法，用于考虑蛋白口袋构象变化的SBDD。我们构建了一个包含多个无配体（apo）和有配体（holo）状态的蛋白-配体复合物数据集，这些状态通过分子动力学模拟获得。我们提出一种全原子流模型（及随机版本），命名为DynamicFlow，能够将无配体口袋和含噪声的配体转化为对应的有配体口袋及三维配体分子。该方法成功生成了潜在的配体分子及其对应的有配体构象。此外，生成的类似有配体状态可作为传统SBDD方法的优质输入，在实际药物发现中具有重要意义。
原文摘要 · Abstract (English)
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.
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