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/.

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Fundamental processes in living cells are largely controlled by macromolecular interactions and among them, protein–protein interactions (PPIs) have a critical role while their dysregulations can contribute to the pathogenesis of numerous diseases. Although PPIs were considered as attractive pharmaceutical targets already some years ago, they have been thus far largely unexploited for therapeutic interventions with low molecular weight compounds. Several limiting factors, from technological hurdles to conceptual barriers, are known, which, taken together, explain why research in this area has been relatively slow. However, this last decade, the scientific community has challenged the dogma and became more enthusiastic about the modulation of PPIs with small drug-like molecules. In fact, several success stories were reported both, at the preclinical and clinical stages. In this review article, written for the 2014 International Summer School in Chemoinformatics (Strasbourg, France), we discuss in silico tools (essentially post 2012) and databases that can assist the design of low molecular weight PPI modulators (these tools can be found at www.vls3d.com). We first introduce the field of protein–protein interaction research, discuss key challenges and comment recently reported in silico packages, protocols and databases dedicated to PPIs. Then, we illustrate how in silico methods can be used and combined with experimental work to identify PPI modulators.

Protein-protein interactions (PPI) are involved in vital cellular processes and are therefore associated to a growing number of diseases. But working with them as therapeutic targets comes with some major hurdles that require substantial mutations from our way to design drugs on historical targets such as enzymes and G-Protein Coupled Receptor (GPCR). Among the numerous ways we could improve our methodologies to maximize the potential of developing new chemical entities on PPI targets, is the fundamental question of what type of compounds should we use to identify the first hits and among which chemical space should we navigate to optimize them to the drug candidate stage. In this review article, we cover different aspects on PPI but with the aim to gain some insights into the specific nature of the chemical space of PPI inhibitors. We describe the work of different groups to highlight such properties and discuss their respective approach. We finally discuss a case study in which we describe the properties of a set of 115 PPI inhibitors that we compare to a reference set of 1730 enzyme inhibitors. This case study highlights interesting properties such as the unfortunate price that still needs to be paid by PPI inhibitors in terms of molecular weight, hydrophobicity, and aromaticity in order to reach a critical level of activity. But it also shows that not all PPI targets are equivalent, and that some PPI targets can demonstrate a better druggability by illustrating the better drug likeness of their associated inhibitors.

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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.

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.

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