DeeplyTough: Learning Structural Comparison of Protein Binding Sites.,Journal of Chemical Information and Modeling

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DeeplyTough: Learning Structural Comparison of Protein Binding Sites.
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2020-02-05 , DOI: 10.1021/acs.jcim.9b00554
Martin Simonovsky _{1,

2,

3} , Joshua Meyers ₁

Affiliation

Protein pocket matching, or binding site comparison, is of importance in drug discovery. Identification of similar binding pockets can help guide efforts for hit-finding, understanding polypharmacology, and characterization of protein function. The design of pocket matching methods has traditionally involved much intuition and has employed a broad variety of algorithms and representations of the input protein structures. We regard the high heterogeneity of past work and the recent availability of large-scale benchmarks as an indicator that a data-driven approach may provide a new perspective. We propose DeeplyTough, a convolutional neural network that encodes a three-dimensional representation of protein pockets into descriptor vectors that may be compared efficiently in an alignment-free manner by computing pairwise Euclidean distances. The network is trained with supervision (i) to provide similar pockets with similar descriptors, (ii) to separate the descriptors of dissimilar pockets by a minimum margin, and (iii) to achieve robustness to nuisance variations. We evaluate our method using three large-scale benchmark datasets, on which it demonstrates excellent performance for held-out data coming from the training distribution and competitive performance when the trained network is required to generalize to datasets constructed independently. DeeplyTough is available at https://github.com/BenevolentAI/DeeplyTough.

中文翻译：

DeeplyTough：学习蛋白质结合位点的结构比较。

蛋白质口袋匹配或结合位点比较在药物发现中很重要。鉴定相似的结合口袋可以帮助指导寻找结果，了解多元药理学和表征蛋白质功能的工作。传统上，口袋匹配法的设计涉及很多直觉，并采用了各种各样的算法和输入蛋白质结构的表示形式。我们将过去工作的高度异质性和大规模基准的最新可用性视为数据驱动方法可能提供新视角的指标。我们提出了DeeplyTough，这是一种将蛋白质袋的三维表示编码为描述符向量的卷积神经网络，可以通过计算成对的欧几里得距离以无比对的方式有效地对其进行比较。该网络在监督下进行训练（i）为相似的口袋提供相似的描述符，（ii）以最小的余量将不相似的口袋的描述符分开，以及（iii）实现对扰动变化的鲁棒性。我们使用三个大型基准数据集评估了我们的方法，当需要训练网络将其推广到独立构建的数据集时，该方法论证了来自训练分布和竞争绩效的数据保持出色的性能。DeeplyTough可从https://github.com/BenevolentAI/DeeplyTough获得。我们使用三个大型基准数据集评估了我们的方法，当需要训练网络将其推广到独立构建的数据集时，该方法论证了来自训练分布和竞争绩效的数据保持出色的性能。DeeplyTough可从https://github.com/BenevolentAI/DeeplyTough获得。我们使用三个大型基准数据集评估了我们的方法，当需要训练网络将其推广到独立构建的数据集时，该方法论证了来自训练分布和竞争绩效的数据保持出色的性能。DeeplyTough可从https://github.com/BenevolentAI/DeeplyTough获得。

更新日期：2020-02-05

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