Combining Docking Pose Rank and Structure with Deep Learning Improves Protein-Ligand Binding Mode Prediction over a Baseline Docking Approach

By Joseph A. Morrone, J. Weber, Tien Huynh, Heng Luo, W. Cornell. 2019

80 citations recorded by the source corpus.

We present a simple, modular graph-based convolutional neural network that takes structural information from protein-ligand complexes as input to generate models for activity and binding mode prediction. Complex structures are generated by a standard docking procedure and fed into a dual-graph architecture that includes separate sub-networks for the ligand bonded topology and the ligand-protein contact map. Recent work has indicated that dataset bias drives many past promising results derived from combining deep learning and docking. Our dual-graph network allows contributions from ligand identity that give rise to such biases to be distinguished from effects of protein-ligand interactions on classification. We show that our neural network is capable of learning from protein structural information when, as in the case of binding mode prediction, an unbiased dataset is constructed. We next develop a deep learning model for binding mode prediction that uses docking ranking as input in combination with docking structures. This strategy mirrors past consensus models and outperforms a baseline docking program (AutoDock Vina) in a variety of tests, including on cross-docking datasets that mimic real-world docking use cases. Furthermore, the magnitudes of network predictions serve as reliable measures of model confidence.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction (2022, background): “In self-docking setting, when the compound configuration is unknown, we add a local atomic structures (LAS) mask to the configuration loss to allow for compound flexibility while enforcing basic geometric constraint,Lconfiguration = ∑m j ∑m k′ 1(j, k ′)(|D̂c jk′−D jk′ |) where 1(j, k′) = 1 when comp”
EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction (2022, methodology): “…afﬁnity prediction from the 3D complex (McNutt et al., 2021; Fran-coeur et al., 2020; Ragoza et al., 2017; Wallach et al., 2015; Lim et al., 2019; Morrone et al., 2020; Jiang et al., 2021; Shen et al., 2021; Jastrzebski et al., 2020; Bao et al., 2021; Torng & Altman, 2019a; Li et al., 2021).”
Machine-learning methods for ligand-protein molecular docking. (2021, methodology): “Even though molecule representation as a graph is more instinctive, the authors in [55] use a graph to represent the interaction between a ligand and a target.”