InDeep: 3D fully convolutional neural networks to assist in silico drug design on protein–protein interactions

Vincent Mallet (1, 2) , Luis Checa Ruano (1, 3) , Alexandra Moine Franel (1, 3) , Michael Nilges (1) , Karen Druart (1) , Guillaume Bouvier (1) , Olivier Sperandio (1)

1 Bioinformatique structurale - Structural Bioinformatics
2 CBIO - Centre de Bioinformatique
3 Collège Doctoral

Motivation: Protein-protein interactions (PPIs) are key elements in numerous biological pathways and the subject of a growing number of drug discovery projects including against infectious diseases. Designing drugs on PPI targets remains a difficult task and requires extensive efforts to qualify a given interaction as an eligible target. To this end, besides the evident need to determine the role of PPIs in disease-associated pathways and their experimental characterization as therapeutics targets, prediction of their capacity to be bound by other protein partners or modulated by future drugs is of primary importance. Results: We present InDeep, a tool for predicting functional binding sites within proteins that could either host protein epitopes or future drugs. Leveraging deep learning on a curated dataset of PPIs, this tool can proceed to enhanced functional binding site predictions either on experimental structures or along molecular dynamics trajectories. The benchmark of InDeep demonstrates that our tool outperforms state-of-the-art ligandable binding sites predictors when assessing PPI targets but also conventional targets. This offers new opportunities to assist drug design projects on PPIs by identifying pertinent binding pockets at or in the vicinity of PPI interfaces.

Domaines: Informatique [cs]

Vincent Mallet, Luis Checa Ruano, Alexandra Moine Franel, Michael Nilges, Karen Druart, et al.. InDeep: 3D fully convolutional neural networks to assist in silico drug design on protein–protein interactions. Bioinformatics, 2021, 38 (5), pp.1261 - 1268. ⟨10.1093/bioinformatics/btab849⟩. ⟨pasteur-04102799⟩