The accurate description of protein binding sites is essential to the determination of similarity and the application of machine learning methods to relate the binding sites to observed functions. This work describes CAVIAR, a new open source tool for generating descriptors for binding sites, using protein structures in PDB and mmCIF format as well as trajectory frames from molecular dynamics simulations as input. The applicability of CAVIAR descriptors is showcased by computing machine learning predictions of binding site ligandability. The method can also automatically assign subcavities, even in the absence of a bound ligand. The defined subpockets mimic the empirical definitions used in medicinal chemistry projects. It is shown that the experimental binding affinity scales relatively well with the number of subcavities filled by the ligand, with compounds binding to more than three subcavities having nanomolar or better affinities to the target. The CAVIAR descriptors and methods can be used in any machine learning-based investigations of problems involving binding sites, from protein engineering to hit identification. The full software code is available on GitHub and a conda package is hosted on Anaconda cloud.

蛋白质结合位点的准确描述对于确定相似性和应用机器学习方法将结合位点与观察到的功能联系起来至关重要。这项工作描述了 CAVIAR，这是一种新的开源工具，用于生成结合位点的描述符，使用 PDB 和 mmCIF 格式的蛋白质结构以及来自分子动力学模拟的轨迹框架作为输入。CAVIAR 描述符的适用性通过结合位点配体性的计算机学习预测来展示。该方法还可以自动分配子腔，即使没有结合的配体。定义的 subpockets 模仿了药物化学项目中使用的经验定义。结果表明，实验结合亲和力随配体填充的子腔数的增加而相对良好，与三个以上亚腔结合的化合物对目标具有纳摩尔或更好的亲和力。CAVIAR 描述符和方法可用于任何基于机器学习的涉及结合位点的问题的调查，从蛋白质工程到命中识别。GitHub 上提供了完整的软件代码，并且在 Anaconda 云上托管了一个 conda 包。