Protein interaction explorer (PIE): a comprehensive platform for navigating protein-protein interactions and ligand binding pockets.

SUMMARY: Protein Interaction Explorer (PIE) is a new web-based tool integrated to our database iPPI-DB, specifically crafted to support structure-based drug discovery initiatives focused on protein-protein interactions (PPIs). Drawing upon extensive structural data encompassing thousands of heterodimer complexes, including those with successful ligands, PIE provides a comprehensive suite of tools dedicated to aid decision-making in PPI drug discovery. PIE enables researchers/bioinformaticians to identify and characterize crucial factors such as the presence of binding pockets or functional binding sites at the interface, predicting hot spots, and foreseeing similar protein-embedded pockets for potential repurposing efforts. AVAILABILITY AND IMPLEMENTATION: PIE is user-friendly and readily accessible at https://ippidb.pasteur.fr/targetcentric/. It relies on the NGL visualizer.

Characterization of the Interaction Domains between the Phosphoprotein and the Nucleoprotein of Human Metapneumovirus.

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. IMPORTANCE Human metapneumovirus (HMPV) is a leading cause of severe respiratory infections in children but also affects human populations of all ages worldwide. Currently, no vaccine or efficient antiviral treatments are available for this pneumovirus. A better understanding of the molecular mechanisms involved in viral replication could help the design or discovery of specific antiviral compounds. In this work, we have investigated the interaction between two major viral proteins involved in HMPV RNA synthesis, the N and P proteins. We finely characterized their domains of interaction and identified a pocket on the surface of the N protein, a potential target of choice for the design of compounds interfering with N-P complexes and inhibiting viral replication.

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

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.

The iPPI-DB initiative: a community-centered database of protein-protein interaction modulators.

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.

Investigation of the Fuzzy Complex between RSV Nucleoprotein and Phosphoprotein to Optimize an Inhibition Assay by Fluorescence Polarization.

The interaction between Respiratory Syncytial Virus phosphoprotein P and nucleoprotein N is essential for the formation of the holo RSV polymerase that carries out replication. In vitro screening of antivirals targeting the N-P protein interaction requires a molecular interaction model, ideally consisting of a complex between N protein and a short peptide corresponding to the C-terminal tail of the P protein. However, the flexibility of C-terminal P peptides as well as their phosphorylation status play a role in binding and may bias the outcome of an inhibition assay. We therefore investigated binding affinities and dynamics of this interaction by testing two N protein constructs and P peptides of different lengths and composition, using nuclear magnetic resonance and fluorescence polarization (FP). We show that, although the last C-terminal Phe241 residue is the main determinant for anchoring P to N, only longer peptides afford sub-micromolar affinity, despite increasing mobility towards the N-terminus. We investigated competitive binding by peptides and small compounds, including molecules used as fluorescent labels in FP. Based on these results, we draw optimized parameters for a robust RSV N-P inhibition assay and validated this assay with the M76 molecule, which displays antiviral properties, for further screening of chemical libraries.

Phylogenetic analysis of Harmonin homology domains.

BACKGROUND: Harmonin Homogy Domains (HHD) are recently identified orphan domains of about 70 residues folded in a compact five alpha-helix bundle that proved to be versatile in terms of function, allowing for direct binding to a partner as well as regulating the affinity and specificity of adjacent domains for their own targets. Adding their small size and rather simple fold, HHDs appear as convenient modules to regulate protein-protein interactions in various biological contexts. Surprisingly, only nine HHDs have been detected in six proteins, mainly expressed in sensory neurons. RESULTS: Here, we built a profile Hidden Markov Model to screen the entire UniProtKB for new HHD-containing proteins. Every hit was manually annotated, using a clustering approach, confirming that only a few proteins contain HHDs. We report the phylogenetic coverage of each protein and build a phylogenetic tree to trace the evolution of HHDs. We suggest that a HHD ancestor is shared with Paired Amphipathic Helices (PAH) domains, a four-helix bundle partially sharing fold and functional properties. We characterized amino-acid sequences of the various HHDs using pairwise BLASTP scoring coupled with community clustering and manually assessed sequence features among each individual family. These sequence features were analyzed using reported structures as well as homology models to highlight structural motifs underlying HHDs fold. We show that functional divergence is carried out by subtle differences in sequences that automatized approaches failed to detect. CONCLUSIONS: We provide the first HHD databases, including sequences and conservation, phylogenic trees and a list of HHD variants found in the auditory system, which are available for the community. This case study highlights surprising phylogenetic properties found in orphan domains and will assist further studies of HHDs. We unveil the implication of HHDs in their various binding interfaces using conservation across families and a new protein-protein surface predictor. Finally, we discussed the functional consequences of three identified pathogenic HHD variants involved in Hoyeraal-Hreidarsson syndrome and of three newly reported pathogenic variants identified in patients suffering from Usher Syndrome.

iPPI-DB: an online database of modulators of protein-protein interactions.

In order to boost the identification of low-molecular-weight drugs on protein-protein interactions (PPI), it is essential to properly collect and annotate experimental data about successful examples. This provides the scientific community with the necessary information to derive trends about privileged physicochemical properties and chemotypes that maximize the likelihood of promoting a given chemical probe to the most advanced stages of development. To this end we have developed iPPI-DB (freely accessible at http://www.ippidb.cdithem.fr), a database that contains the structure, some physicochemical characteristics, the pharmacological data and the profile of the PPI targets of several hundreds modulators of protein-protein interactions. iPPI-DB is accessible through a web application and can be queried according to two general approaches: using physicochemical/pharmacological criteria; or by chemical similarity to a user-defined structure input. In both cases the results are displayed as a sortable and exportable datasheet with links to external databases such as Uniprot, PubMed. Furthermore each compound in the table has a link to an individual ID card that contains its physicochemical and pharmacological profile derived from iPPI-DB data. This includes information about its binding data, ligand and lipophilic efficiencies, location in the PPI chemical space, and importantly similarity with known drugs, and links to external databases like PubChem, and ChEMBL.

Privileged Substructures to Modulate Protein-Protein Interactions.

Given the difficulties to identify chemical probes that can modulate protein-protein interactions (PPIs), actors in the field have started to agree on the necessity to use PPI-tailored screening chemical collections. However, which type of scaffolds may promote the binding of compounds to PPI targets remains unclear. In this big data analysis, we have identified a list of privileged chemical substructures that are most often observed within inhibitors of PPIs. Using molecular frameworks as a way to perceive chemical substructures with the combination of an experimental and a machine-learning based predicted data set of iPPI compounds, we propose a list of privileged substructures in the form of scaffolds and chemical moieties that can be substantially chemically functionalized and do not present any toxicophore nor pan-assay interference (PAINS) alerts. We think that such chemical guidance will be valuable for medicinal chemists in their attempt to identify initial quality chemical probes on PPI targets.

FAF-Drugs3: a web server for compound property calculation and chemical library design.

Drug attrition late in preclinical or clinical development is a serious economic problem in the field of drug discovery. These problems can be linked, in part, to the quality of the compound collections used during the hit generation stage and to the selection of compounds undergoing optimization. Here, we present FAF-Drugs3, a web server that can be used for drug discovery and chemical biology projects to help in preparing compound libraries and to assist decision-making during the hit selection/lead optimization phase. Since it was first described in 2006, FAF-Drugs has been significantly modified. The tool now applies an enhanced structure curation procedure, can filter or analyze molecules with user-defined or eight predefined physicochemical filters as well as with several simple ADMET (absorption, distribution, metabolism, excretion and toxicity) rules. In addition, compounds can be filtered using an updated list of 154 hand-curated structural alerts while Pan Assay Interference compounds (PAINS) and other, generally unwanted groups are also investigated. FAF-Drugs3 offers access to user-friendly html result pages and the possibility to download all computed data. The server requires as input an SDF file of the compounds; it is open to all users and can be accessed without registration at http://fafdrugs3.mti.univ-paris-diderot.fr.

MTiOpenScreen: a web server for structure-based virtual screening.

Open screening endeavors play and will play a key role to facilitate the identification of new bioactive compounds in order to foster innovation and to improve the effectiveness of chemical biology and drug discovery processes. In this line, we developed the new web server MTiOpenScreen dedicated to small molecule docking and virtual screening. It includes two services, MTiAutoDock and MTiOpenScreen, allowing performing docking into a user-defined binding site or blind docking using AutoDock 4.2 and automated virtual screening with AutoDock Vina. MTiOpenScreen provides valuable starting collections for screening, two in-house prepared drug-like chemical libraries containing 150 000 PubChem compounds: the Diverse-lib containing diverse molecules and the iPPI-lib enriched in molecules likely to inhibit protein-protein interactions. In addition, MTiOpenScreen offers users the possibility to screen up to 5000 small molecules selected outside our two libraries. The predicted binding poses and energies of up to 1000 top ranked ligands can be downloaded. In this way, MTiOpenScreen enables researchers to apply virtual screening using different chemical libraries on traditional or more challenging protein targets such as protein-protein interactions. The MTiOpenScreen web server is free and open to all users at http://bioserv.rpbs.univ-paris-diderot.fr/services/MTiOpenScreen/.

Rational design of small molecules targeting the C2 domain of coagulation factor VIII.

The C domains of coagulation factors V (FV) and VIII (FVIII) are structurally conserved domains and share a common and essential function in membrane binding. In vivo regulation of thrombin formation strongly depends on the expression and regulation of the cofactor activities of FVIII and FV. With this study, we explored the possibility of inhibition of thrombin formation in full blood with small druglike molecules. Such compounds may serve as lead molecules for the development of a new type of orally available coagulation inhibitors that act by blocking the interaction between the C domains of FVIII and the membrane surface. We identified 9 novel molecules that are able to inhibit binding of the FVIII C2 domain to a model membrane by application of a combined ligand-based and target structure-based virtual screening approach that took into account the knowledge of a set of previously identified low-molecular-weight FVIII binders that were, however, not active in full blood. The half-maximal inhibitory concentration values of our newly identified compounds varied from 2.1 to 19.9 µM, of which 7 of 9 molecules did not appreciably inhibit FV membrane binding and were thus specific for FVIII. The most active bioactive compound showed activity in both plasma and in full blood.

[Chemical libraries dedicated to protein-protein interactions]. / Les chimiothèques ciblant les interactions protéine-protéine.

The identification of complete networks of protein-protein interactions (PPI) within a cell has contributed to major breakthroughs in understanding biological pathways, host-pathogen interactions and cancer development. As a consequence, PPI have emerged as a new class of promising therapeutic targets. However, they are still considered as a challenging class of targets for drug discovery programs. Recent successes have allowed the characterization of structural and physicochemical properties of protein-protein interfaces leading to a better understanding of how they can be disrupted with small molecule compounds. In addition, characterization of the profiles of PPI inhibitors has allowed the development of PPI-focused libraries. In this review, we present the current efforts at developing chemical libraries dedicated to these innovative targets.

Which three-dimensional characteristics make efficient inhibitors of protein-protein interactions?

The specific properties of protein-protein interactions (PPI) (flat, large and hydrophobic) make them harder to tackle with low-molecular-weight compounds. Learning from the properties of successful examples of PPI interface inhibitors (iPPI) at earlier stages of developments, has been pinpointed as a powerful strategy to circumvent this trend. To this end, we have computationally analyzed the bioactive conformations of iPPI and those of inhibitors of conventional targets (e.g enzymes) to highlight putative iPPI 3D characteristics. Most noticeably, the essential property revealed by this study illustrates how efficiently iPPI manages to bind to the hydrophobic patch often present at the core of protein interfaces. The newly identified properties were further confirmed as characteristics of iPPI using much larger data sets (e.g iPPI-DB, www.ippidb.cdithem.fr ). Interestingly, the absence of correlation of such properties with the hydrophobicity and the size of the compounds opens new ways to design potent iPPI with better pharmacokinetic features.

A comprehensive dataset of protein-protein interactions and ligand binding pockets for advancing drug discovery.

This dataset represents a collection of pocket-centric structural data related to protein-protein interactions (PPIs) and PPI-related ligand binding sites. The dataset includes high-quality structural information on more than 23,000 pockets, 3,700 proteins on more than 500 organisms, and nearly 3500 ligands that can aid researchers in the fields of bioinformatics, structural biology, and drug discovery. It encompasses a diverse set of PPI complexes with more than 1,700 unique protein families including some with associated ligands, enabling detailed investigations into molecular interactions at the atomic level. This article introduces an indispensable resource designed to unlock the full potential of PPIs while pioneering a novel metric for pocket similarity for hypothesizing protein partners repurposing.

The FAF-Drugs2 server: a multistep engine to prepare electronic chemical compound collections.

SUMMARY: The FAF-Drugs2 server is a web application that prepares chemical compound libraries prior to virtual screening or that assists hit selection/lead optimization before chemical synthesis or ordering. The FAF-Drugs2 web server is an enhanced version of the FAF-Drugs2 package that now includes Pan Assay Interference Compounds detection. This online toolkit has been designed through a user-centered approach with emphasis on user-friendliness. This is a unique online tool allowing to prepare large compound libraries with in house or user-defined filtering parameters. AVAILABILITY: The FAF-Drugs2 server is freely available at http://bioserv.rpbs.univ-paris-diderot.fr/FAF-Drugs/.

Designing focused chemical libraries enriched in protein-protein interaction inhibitors using machine-learning methods.

Protein-protein interactions (PPIs) may represent one of the next major classes of therapeutic targets. So far, only a minute fraction of the estimated 650,000 PPIs that comprise the human interactome are known with a tiny number of complexes being drugged. Such intricate biological systems cannot be cost-efficiently tackled using conventional high-throughput screening methods. Rather, time has come for designing new strategies that will maximize the chance for hit identification through a rationalization of the PPI inhibitor chemical space and the design of PPI-focused compound libraries (global or target-specific). Here, we train machine-learning-based models, mainly decision trees, using a dataset of known PPI inhibitors and of regular drugs in order to determine a global physico-chemical profile for putative PPI inhibitors. This statistical analysis unravels two important molecular descriptors for PPI inhibitors characterizing specific molecular shapes and the presence of a privileged number of aromatic bonds. The best model has been transposed into a computer program, PPI-HitProfiler, that can output from any drug-like compound collection a focused chemical library enriched in putative PPI inhibitors. Our PPI inhibitor profiler is challenged on the experimental screening results of 11 different PPIs among which the p53/MDM2 interaction screened within our own CDithem platform, that in addition to the validation of our concept led to the identification of 4 novel p53/MDM2 inhibitors. Collectively, our tool shows a robust behavior on the 11 experimental datasets by correctly profiling 70% of the experimentally identified hits while removing 52% of the inactive compounds from the initial compound collections. We strongly believe that this new tool can be used as a global PPI inhibitor profiler prior to screening assays to reduce the size of the compound collections to be experimentally screened while keeping most of the true PPI inhibitors. PPI-HitProfiler is freely available on request from our CDithem platform website, www.CDithem.com.

New machine learning and physics-based scoring functions for drug discovery.

Scoring functions are essential for modern in silico drug discovery. However, the accurate prediction of binding affinity by scoring functions remains a challenging task. The performance of scoring functions is very heterogeneous across different target classes. Scoring functions based on precise physics-based descriptors better representing protein-ligand recognition process are strongly needed. We developed a set of new empirical scoring functions, named DockTScore, by explicitly accounting for physics-based terms combined with machine learning. Target-specific scoring functions were developed for two important drug targets, proteases and protein-protein interactions, representing an original class of molecules for drug discovery. Multiple linear regression (MLR), support vector machine and random forest algorithms were employed to derive general and target-specific scoring functions involving optimized MMFF94S force-field terms, solvation and lipophilic interactions terms, and an improved term accounting for ligand torsional entropy contribution to ligand binding. DockTScore scoring functions demonstrated to be competitive with the current best-evaluated scoring functions in terms of binding energy prediction and ranking on four DUD-E datasets and will be useful for in silico drug design for diverse proteins as well as for specific targets such as proteases and protein-protein interactions. Currently, the MLR DockTScore is available at www.dockthor.lncc.br .

Tensin2 reduces intracellular phosphatidylinositol 3,4,5-trisphosphate levels at the plasma membrane.

Tensins are proposed cytoskeleton-regulating proteins. However, Tensin2 additionally inhibits Akt signalling and cell survival. Structural modelling of the Tensin2 phosphatase (PTPase) domain revealed an active site-like pocket receptive towards phosphoinositides. Tensin2-expressing HEK293 cells displayed negligible levels of plasma membrane phosphatidylinositol 3,4,5-trisphosphate (PtdIns(3,4,5)P(3)) under confocal microscopy. However, mock-transfected cells, and Tensin2 cells harbouring a putative phosphatase-inactivating mutation, exhibited significant PtdIns(3,4,5)P(3) levels, which decreased upon phosphatidylinositol 3-kinase inhibition with LY294002. In contrast, wtTensin3, mock and mutant cells were identical in membrane PtdIns(3,4,5)P(3) and Akt phosphorylation. In vitro lipid PTPase activity was however undetectable in isolated recombinant PTPase domains of both Tensins, indicating a possible loss of structural stability when expressed in isolation. In summary, we provide evidence that Tensin2, in addition to regulating cytoskeletal dynamics, influences phosphoinositide-Akt signalling through its PTPase domain.

How to choose relevant multiple receptor conformations for virtual screening: a test case of Cdk2 and normal mode analysis.

Better treatment of protein flexibility is essential in structure-based drug design projects such as virtual screening and protein-ligand docking. Diversity in ligand-binding mechanisms and receptor conformational changes makes it difficult to treat dynamic features of the receptor during the docking simulation. Thus, the use of pregenerated multiple receptor conformations is applied today in virtual screening studies. However, generation of a small relevant set of receptor conformations remains challenging. To address this problem, we propose a new protocol for the generation of multiple receptor conformations via normal mode analysis and for the selection of several receptor conformations suitable for docking/virtual screening. We validated this protocol on cyclin-dependent kinase 2, which possesses a binding site located at the interface between two subdomains and is known to undergo significant conformational changes in the active site region upon ligand binding. We believe that the suggested rules for the choice of suitable receptor conformations can be applied to other targets when dealing with in silico screening on flexible receptors.

New non-hydroxamic ADAMTS-5 inhibitors based on the 1,2,4-triazole-3-thiol scaffold.

In this Letter we describe the design, synthesis, screening, and optimization of a new family of ADAMTS-5 inhibitors. These inhibitors display an original 1,2,4-triazole-3-thiol scaffold as a putative zinc binding-group. In vitro results are rationalized by in silico docking of the compounds in ADAMTS-5's crystal structure.