Predicting small-molecule inhibition of protein complexes
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Motivation
Protein-Protein Interactions (PPIs) are crucial in biological processes and disease mechanisms, underscoring the importance of discovering PPI inhibitors in drug development. Machine learning can expedite this discovery process. Although machine learning techniques for predicting general compound inhibition are available, we are not aware of any that accurately forecast the inhibitory effect of a compound on a specific protein complex, utilizing inputs from both the compound and the protein complex.
Methods
We present the first targeted machine learning based predictor of small molecule based inhibition of protein complexes. Our proposed graph neural network integrates the structure of a protein complex, its protein-protein binding site or interface features and a compound’s SMILES representation to predict the potential of the given compound to inhibit the interaction between proteins in the given complex in a targeted manner.
Results
Validated on the 2p2i-DB-v2 database, encompassing 714 inhibitors across 23 complexes with over 12,000 instances, our model achieves superior predictive accuracy (cross-validation AUC-ROC of 0.86), outperforming established kernel methods and pre-trained neural networks. We further tested the predictive performance of our model on two independent external datasets – one collected from recent publications and another consisting of putative inhibitors of the SARS-CoV-2-Spike and Human-ACE2 protein complex with AUC-ROCs of 0.82 and 0.78, respectively. Our targeted predictor introduces a novel approach for PPI inhibitor discovery, laying foundational work for future advancements in addressing this complex and previously unexplored prediction challenge.
Availability
Code/supplementary material available: https://github.com/adibayaseen/PPI-Inhibitors
