Abstract Proteins are natural modular objects usually composed of several domains, each domain bearing a specific function that is mediated through its surface, which is accessible to vicinal molecules. This draws attention to an understudied characteristic of protein structures: surface, that is mostly unexploited by protein structure comparison methods. In the present work, we evaluated the performance of six shape comparison methods, among which three are based on machine learning, to distinguish between 588 multi-domain proteins and to recreate the evolutionary relationships at the proteinand species levels of the SCOPe database. The six groups that participated in the challenge submitted a total of 15 sets of results. We observed that the performance of all the methods significantly decreases at the species level, suggesting that shape-only protein comparison is challenging for closely related proteins. Even if the dataset is limited in size (only 588 proteins are considered whereas more than 160,000 protein structures are experimentally solved), we think that this work provides useful insights into the current shape comparison methods performance, and highlights possible limitations to large-scale applications due to the computational cost.

中文翻译：

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SHREC2020 赛道：多域蛋白质形状检索挑战

摘要 蛋白质是天然的模块化对象，通常由几个结构域组成，每个结构域都具有通过其表面介导的特定功能，邻近分子可以访问该功能。这引起了人们对蛋白质结构的一个未充分研究的特征的关注：表面，这主要是蛋白质结构比较方法未开发的。在目前的工作中，我们评估了六种形状比较方法的性能，其中三种基于机器学习，以区分 588 种多域蛋白质，并在 SCOPe 数据库的蛋白质和物种水平上重建进化关系。参加挑战的六个小组共提交了15组结果。我们观察到所有方法的性能在物种水平上显着下降，表明仅形状蛋白质比较对于密切相关的蛋白质具有挑战性。即使数据集的大小有限（仅考虑 588 种蛋白质，而实验解决了 160,000 多种蛋白质结构），我们认为这项工作提供了对当前形状比较方法性能的有用见解，并强调了对大规模应用的可能限制由于计算成本。