Department of Statistics, University of California, Berkeley, CA 94720, USA.
Pac Symp Biocomput. 2022;27:22-33.
There is significant interest in developing machine learning methods to model protein-ligand interactions but a scarcity of experimentally resolved protein-ligand structures to learn from. Protein self-contacts are a much larger source of structural data that could be leveraged, but currently it is not well understood how this data source differs from the target domain. Here, we characterize the 3D geometric patterns of protein self-contacts as probability distributions. We then present a flexible statistical framework to assess the transferability of these patterns to protein-ligand contacts. We observe that the level of transferability from protein self-contacts to protein-ligand contacts depends on contact type, with many contact types exhibiting high transferability. We then demonstrate the potential of leveraging information from these geometric patterns to aid in ligand pose-selection problems in protein-ligand docking. We publicly release our extracted data on geometric interaction patterns to enable further exploration of this problem.
人们对开发机器学习方法来模拟蛋白质-配体相互作用非常感兴趣，但可用于学习的实验解析的蛋白质-配体结构却很少。蛋白质自身接触是一个更大的结构数据来源，可以加以利用，但目前尚不清楚这种数据源与目标域有何不同。在这里，我们将蛋白质自身接触的 3D 几何模式描述为概率分布。然后，我们提出了一个灵活的统计框架来评估这些模式向蛋白质-配体接触的可转移性。我们观察到，从蛋白质自身接触到蛋白质-配体接触的可转移性水平取决于接触类型，许多接触类型表现出高度的可转移性。然后，我们展示了利用这些几何模式中的信息来辅助蛋白质-配体对接中配体构象选择问题的潜力。我们公开发布了我们从几何相互作用模式中提取的数据，以促进对这一问题的进一步探索。
