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© Research
Publication : Bioinformatics (Oxford, England)

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

Scientific Fields
Diseases
Organisms
Applications
Technique

Published in Bioinformatics (Oxford, England) - 15 Dec 2021

Mallet V, Checa Ruano L, Moine Franel A, Nilges M, Druart K, Bouvier G, Sperandio O,

Link to Pubmed [PMID] – 34908131

Link to DOI – 10.1093/bioinformatics/btab849

Bioinformatics 2021 Dec; ():

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.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 data set 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.The tool is available on GitLab at https://gitlab.pasteur.fr/InDeep/InDeep.

https://pubmed.ncbi.nlm.nih.gov/34908131