InDeep: 3D fully convolutional neural networks to assist in silico drug design on protein–protein interactions. Mallet, V., Checa Ruano, L., Moine Franel, A., Nilges, M., Druart, K., Bouvier, G., & Sperandio, O. Bioinformatics, December, 2021. ![InDeep: 3D fully convolutional neural networks to assist in silico drug design on protein–protein interactions [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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 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.The tool is available on GitLab at https://gitlab.pasteur.fr/InDeep/InDeep.Supplementary data are available at Bioinformatics online.
@article{mallet_indeep_2021,
title = {{InDeep}: {3D} fully convolutional neural networks to assist in silico drug design on protein–protein interactions},
issn = {1367-4803},
shorttitle = {{InDeep}},
url = {https://doi.org/10.1093/bioinformatics/btab849},
doi = {10.1093/bioinformatics/btab849},
abstract = {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 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.The tool is available on GitLab at https://gitlab.pasteur.fr/InDeep/InDeep.Supplementary data are available at Bioinformatics online.},
urldate = {2022-01-19},
journal = {Bioinformatics},
author = {Mallet, Vincent and Checa Ruano, Luis and Moine Franel, Alexandra and Nilges, Michael and Druart, Karen and Bouvier, Guillaume and Sperandio, Olivier},
month = dec,
year = {2021},
pages = {btab849},
}
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