Sesame用生成模型高效预测蛋白质口袋构象，提升药物虚拟筛选精度。
Sesame: Opening the door to protein pockets
- 基于生成模型预测配体结合时的蛋白质口袋构象变化。
- 在极低计算成本下生成更利于配体结合的口袋结构。
- 适合需要大规模虚拟筛选的药物研发团队使用。
分子对接是药物发现的核心，依赖高分辨率配体结合结构以实现准确预测。然而，获取这些结构通常成本高昂且耗时，限制了其可用性。相比之下，无配体结构更易获得，但因为空置口袋的几何形状不利于配体结合，导致对接性能下降。传统方法如分子动力学模拟虽可人工诱导构象变化，但计算成本过高。本文提出Sesame，一种生成模型，能以极低计算开销高效预测配体结合所需的构象变化。通过生成更适于配体结合的口袋几何结构，Sesame为虚拟筛选流程提供可扩展的解决方案。
原文摘要 · Abstract (English)
Molecular docking is a cornerstone of drug discovery, relying on high-resolution ligand-bound structures to achieve accurate predictions. However, obtaining these structures is often costly and time-intensive, limiting their availability. In contrast, ligand-free structures are more accessible but suffer from reduced docking performance due to pocket geometries being less suited for ligand accommodation in apo structures. Traditional methods for artificially inducing these conformations, such as molecular dynamics simulations, are computationally expensive. In this work, we introduce Sesame, a generative model designed to predict this conformational change efficiently. By generating geometries better suited for ligand accommodation at a fraction of the computational cost, Sesame aims to provide a scalable solution for improving virtual screening workflows.
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