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. 2022 Jun 29;12(7):919.
doi: 10.3390/biom12070919.

A Physics-Guided Neural Network for Predicting Protein-Ligand Binding Free Energy: From Host-Guest Systems to the PDBbind Database

Sahar Cain  1 , Ali Risheh  2 , Negin Forouzesh  1
Affiliations

A Physics-Guided Neural Network for Predicting Protein-Ligand Binding Free Energy: From Host-Guest Systems to the PDBbind Database

Sahar Cain et al. Biomolecules. .

Abstract

Calculation of protein-ligand binding affinity is a cornerstone of drug discovery. Classic implicit solvent models, which have been widely used to accomplish this task, lack accuracy compared to experimental references. Emerging data-driven models, on the other hand, are often accurate yet not fully interpretable and also likely to be overfitted. In this research, we explore the application of Theory-Guided Data Science in studying protein-ligand binding. A hybrid model is introduced by integrating Graph Convolutional Network (data-driven model) with the GBNSR6 implicit solvent (physics-based model). The proposed physics-data model is tested on a dataset of 368 complexes from the PDBbind refined set and 72 host-guest systems. Results demonstrate that the proposed Physics-Guided Neural Network can successfully improve the "accuracy" of the pure data-driven model. In addition, the "interpretability" and "transferability" of our model have boosted compared to the purely data-driven model. Further analyses include evaluating model robustness and understanding relationships between the physical features.

Keywords: binding free energy; graph convolutional network; implicit solvent model.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Workflow of the proposed PGNN model. The sample input structure, the complex of the BIR domain of MLIAP and GDC0152, is selected from the PDBbind refined set [58]. After featurization, the structure-based features along with the adjacency matrix A enter the network. The results of the first network iteration are the model variable M. The output of the physics-based model is vector P, which is concatenated with M. The resulting vector goes through several iterations before entering the final dense layer.
Figure 2
Figure 2
Distribution of ΔΔG values of PDBbind refined set vs. our sample dataset.
Figure 3
Figure 3
Validation loss (val loss) and training loss (train loss) per epoch for GraphConv, AtomicConv (data-driven) and PGNN (hybrid) models on the PDBbind dataset. (a) GraphConv model; (b) AtomicConv model; (c) PGNN model.
Figure 4
Figure 4
Validation loss (val loss) and training loss (train loss) per epoch for the GraphConv model (data-driven) and the PGNN model (hybrid) on the host–guest dataset. (a) GraphConv model; (b) PGNN model.
Figure 5
Figure 5
Error in calculating ΔΔG values on data with added noise compared to the original data.
Figure 6
Figure 6
Correlation heatmap between the physics–based features of PDBbind structures.
See this image and copyright information in PMC

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Grants and funding

This research was partially funded by the National Science Foundation (NSF) Grant No. 2136095 to N.F.

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