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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. Availability and implementation The tool is available on GitLab at https://gitlab.pasteur.fr/InDeep/InDeep. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Human metapneumovirus (HMPV) causes severe respiratory diseases in young children. The HMPV RNA genome is encapsidated by the viral nucleoprotein (N), forming an RNA-N complex (NNuc), which serves as the template for genome replication and mRNA transcription by the RNA-dependent RNA polymerase (RdRp). The RdRp is formed by the association of the large polymerase subunit (L), which has RNA polymerase, capping, and methyltransferase activities, and the tetrameric phosphoprotein (P). P plays a central role in the RdRp complex by binding to NNuc and L, allowing the attachment of the L polymerase to the NNuc template. During infection these proteins concentrate in cytoplasmic inclusion bodies (IBs) where viral RNA synthesis occurs. By analogy to the closely related pneumovirus respiratory syncytial virus (RSV), it is likely that the formation of IBs depends on the interaction between HMPV P and NNuc, which has not been demonstrated yet. Here, we finely characterized the binding P-NNuc interaction domains by using recombinant proteins, combined with a functional assay for the polymerase complex activity, and the study of the recruitment of these proteins to IBs by immunofluorescence. We show that the last 6 C-terminal residues of HMPV P are necessary and sufficient for binding to NNuc and that P binds to the N-terminal domain of N (NNTD), and we identified conserved N residues critical for the interaction. Our results allowed us to propose a structural model for the HMPV P-NNuc interaction. [ABSTRACT FROM AUTHOR]

Motivation: One avenue to address the paucity of clinically testable targets is to reinvestigate the druggable genome by tackling complicated types of targets such as Protein-Protein Interactions (PPIs). Given the challenge to target those interfaces with small chemical compounds, it has become clear that learning from successful examples of PPI modulation is a powerful strategy. Freely accessible databases of PPI modulators that provide the community with tractable chemical and pharmacological data, as well as powerful tools to query them, are therefore essential to stimulate new drug discovery projects on PPI targets., Results: Here, we present the new version iPPI-DB, our manually curated database of PPI modulators. In this completely redesigned version of the database, we introduce a new web interface relying on crowdsourcing for the maintenance of the database. This interface was created to enable community contributions, whereby external experts can suggest new database entries. Moreover, the data model, the graphical interface, and the tools to query the database have been completely modernized and improved. We added new PPI modulators, new PPI targets and extended our focus to stabilizers of PPIs as well., Availability and Implementation: The iPPI-DB server is available at https://ippidb.pasteur.fr The source code for this server is available at https://gitlab.pasteur.fr/ippidb/ippidb-web/ and is distributed under GPL licence (http://www.gnu.org/licences/gpl). Queries can be shared through persistent links according to the FAIR data standards. Data can be downloaded from the website as csv files., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2021. Published by Oxford University Press.)