Detecting and analysing protein cavities provides significant information about active sites for biological processes (e.g. protein–protein or protein–ligand binding) in molecular graphics and modelling. Using the three‐dimensional (3D) structure of a given protein (i.e. atom types and their locations in 3D) as retrieved from a PDB (Protein Data Bank) file, it is now computationally viable to determine a description of these cavities. Such cavities correspond to pockets, clefts, invaginations, voids, tunnels, channels and grooves on the surface of a given protein. In this work, we survey the literature on protein cavity computation and classify algorithmic approaches into three categories: evolution‐based, energy‐based and geometry‐based. Our survey focuses on geometric algorithms, whose taxonomy is extended to include not only sphere‐, grid‐ and tessellation‐based methods, but also surface‐based, hybrid geometric, consensus and time‐varying methods. Finally, we detail those techniques that have been customized for GPU (graphics processing unit) computing.

中文翻译：

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分子图形中蛋白质表面空腔的几何检测算法：一项调查


检测和分析蛋白质空腔可以提供有关分子图形和建模中生物过程（例如蛋白质-蛋白质或蛋白质-配体结合）活性位点的重要信息。使用从 PDB（蛋白质数据库）文件检索到的给定蛋白质的三维 (3D) 结构（即原子类型及其在 3D 中的位置），现在可以通过计算来确定这些空腔的描述。这些空腔对应于给定蛋白质表面上的口袋、裂缝、内陷、空隙、隧道、通道和凹槽。在这项工作中，我们调查了有关蛋白质腔计算的文献，并将算法方法分为三类：基于进化、基于能量和基于几何。我们的调查重点是几何算法，其分类法不仅包括基于球体、网格和曲面细分的方法，还包括基于表面、混合几何、共识和时变的方法。最后，我们详细介绍了那些为 GPU（图形处理单元）计算定制的技术。