2P2I HUNTER: a tool for filtering orthosteric protein-protein interaction modulators via a dedicated support vector machine

A Multimodal Deep Learning Framework for Predicting PPI-Modulator Interactions

Protein-protein interactions (PPIs) are essential for various biological processes and diseases. However, most existing computational methods for identifying PPI modulators require either target structure or reference modulators, which restricts their applicability to novel PPI targets. To address this challenge, we propose MultiPPIMI, a sequence-based deep learning framework that predicts the interaction between any given PPI target and modulator. MultiPPIMI integrates multimodal representations of PPI targets and modulators and uses a bilinear attention network to capture intermolecular interactions. Experimental results on our curated benchmark data set show that MultiPPIMI achieves an average AUROC of 0.837 in three cold-start scenarios and an AUROC of 0.994 in the random-split scenario. Furthermore, the case study shows that MultiPPIMI can assist molecular docking simulations in screening inhibitors of Keap1/Nrf2 PPI interactions. We believe that the proposed method provides a promising way to screen PPI-targeted modulators.

Novel Antiviral Molecules against Ebola Virus Infection

Infection with Ebola virus (EBOV) is responsible for hemorrhagic fever in humans with a high mortality rate. Combined efforts of prevention and therapeutic intervention are required to tackle highly variable RNA viruses, whose infections often lead to outbreaks. Here, we have screened the 2P2I3D chemical library using a nanoluciferase-based protein complementation assay (NPCA) and isolated two compounds that disrupt the interaction of the EBOV protein fragment VP35IID with the N-terminus of the dsRNA-binding proteins PKR and PACT, involved in IFN response and/or intrinsic immunity, respectively. The two compounds inhibited EBOV infection in cell culture as well as infection by measles virus (MV) independently of IFN induction. Consequently, we propose that the compounds are antiviral by restoring intrinsic immunity driven by PACT. Given that PACT is highly conserved across mammals, our data support further testing of the compounds in other species, as well as against other negative-sense RNA viruses.

Using a stacked ensemble learning framework to predict modulators of protein-protein interactions

Identifying small molecule protein-protein interaction modulators (PPIMs) is a highly promising and meaningful research direction for drug discovery, cancer treatment, and other fields. In this study, we developed a stacking ensemble computational framework, SELPPI, based on a genetic algorithm and tree-based machine learning method for effectively predicting new modulators targeting protein-protein interactions. More specifically, extremely randomized trees (ExtraTrees), adaptive boosting (AdaBoost), random forest (RF), cascade forest, light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost) were used as basic learners. Seven types of chemical descriptors were taken as the input characteristic parameters. Primary predictions were obtained with each basic learner-descriptor pair. Then, the 6 methods mentioned above were used as meta learners and trained on the primary prediction in turn. The most efficient method was utilized as the meta learner. Finally, the genetic algorithm was used to select the optimal primary prediction output as the input of the meta learner for secondary prediction to obtain the final result. We systematically evaluated our model on the pdCSM-PPI datasets. To our knowledge, our model outperformed all existing models, which demonstrates its great power.

Computational approaches for the design of modulators targeting protein-protein interactions

BACKGROUND

Protein-protein interactions (PPIs) are intriguing targets for designing novel small-molecule inhibitors. The role of PPIs in various infectious and neurodegenerative disorders makes them potential therapeutic targets . Despite being portrayed as undruggable targets, due to their flat surfaces, disorderedness, and lack of grooves. Recent progresses in computational biology have led researchers to reconsider PPIs in drug discovery.

AREAS COVERED

In this review, we introduce in-silico methods used to identify PPI interfaces and present an in-depth overview of various computational methodologies that are successfully applied to annotate the PPIs. We also discuss several successful case studies that use computational tools to understand PPIs modulation and their key roles in various physiological processes.

EXPERT OPINION

Computational methods face challenges due to the inherent flexibility of proteins, which makes them expensive, and result in the use of rigid models. This problem becomes more significant in PPIs due to their flexible and flat interfaces. Computational methods like molecular dynamics (MD) simulation and machine learning can integrate the chemical structure data into biochemical and can be used for target identification and modulation. These computational methodologies have been crucial in understanding the structure of PPIs, designing PPI modulators, discovering new drug targets, and predicting treatment outcomes.

A Hybrid Docking and Machine Learning Approach to Enhance the Performance of Virtual Screening Carried out on Protein-Protein Interfaces

The modulation of protein-protein interactions (PPIs) by small chemical compounds is challenging. PPIs play a critical role in most cellular processes and are involved in numerous disease pathways. As such, novel strategies that assist the design of PPI inhibitors are of major importance. We previously reported that the knowledge-based DLIGAND2 scoring tool was the best-rescoring function for improving receptor-based virtual screening (VS) performed with the Surflex docking engine applied to several PPI targets with experimentally known active and inactive compounds. Here, we extend our investigation by assessing the vs. potential of other types of scoring functions with an emphasis on docking-pose derived solvent accessible surface area (SASA) descriptors, with or without the use of machine learning (ML) classifiers. First, we explored rescoring strategies of Surflex-generated docking poses with five GOLD scoring functions (GoldScore, ChemScore, ASP, ChemPLP, ChemScore with Receptor Depth Scaling) and with consensus scoring. The top-ranked poses were post-processed to derive a set of protein and ligand SASA descriptors in the bound and unbound states, which were combined to derive descriptors of the docked protein-ligand complexes. Further, eight ML models (tree, bagged forest, random forest, Bayesian, support vector machine, logistic regression, neural network, and neural network with bagging) were trained using the derivatized SASA descriptors and validated on test sets. The results show that many SASA descriptors are better than Surflex and GOLD scoring functions in terms of overall performance and early recovery success on the used dataset. The ML models were superior to all scoring functions and rescoring approaches for most targets yielding up to a seven-fold increase in enrichment factors at 1% of the screened collections. In particular, the neural networks and random forest-based ML emerged as the best techniques for this PPI dataset, making them robust and attractive vs. tools for hit-finding efforts. The presented results suggest that exploring further docking-pose derived SASA descriptors could be valuable for structure-based virtual screening projects, and in the present case, to assist the rational design of small-molecule PPI inhibitors.

pdCSM-PPI: Using Graph-Based Signatures to Identify Protein-Protein Interaction Inhibitors

Protein-protein interactions are promising sites for development of selective drugs; however, they have generally been viewed as challenging targets. Molecules targeting protein-protein interactions tend to be larger and more lipophilic than other drug-like molecules, mimicking the properties of interacting interfaces. Here, we propose a machine learning approach that uses a graph-based representation of small molecules to guide identification of inhibitors modulating protein-protein interactions, pdCSM-PPI. This approach was applied to 21 different PPI targets. We developed interaction-specific models that were able to accurately identify active compounds achieving MCC and F1 scores up to 1, and Pearson's correlations up to 0.87, outperforming previous approaches. Using insights from these individual models, we developed a generic protein-protein interaction modulator predictive model, which accurately predicted IC50 with a Pearson's correlation of 0.64 on a low redundancy blind test. Importantly, we were able to accurately identify active from inactive compounds, achieving an AUC of 0.77 and sensitivity and specificity of 76% and 78%, respectively. We believe pdCSM-PPI will be an important tool to help guide more efficient screening of new PPI inhibitors; it is freely available as an easy-to-use web server and API at http://biosig.unimelb.edu.au/pdcsm_ppi.

Exploring the chemical space of protein-protein interaction inhibitors through machine learning

Although protein-protein interactions (PPIs) have emerged as the basis of potential new therapeutic approaches, targeting intracellular PPIs with small molecule inhibitors is conventionally considered highly challenging. Driven by increasing research efforts, success rates have increased significantly in recent years. In this study, we analyze the physicochemical properties of 9351 non-redundant inhibitors present in the iPPI-DB and TIMBAL databases to define a computational model for active compounds acting against PPI targets. Principle component analysis (PCA) and k-means clustering were used to identify plausible PPI targets in regions of interest in the active group in the chemical space between active and inactive iPPI compounds. Notably, the uniquely defined active group exhibited distinct differences in activity compared with other active compounds. These results demonstrate that active compounds with regions of interest in the chemical space may be expected to provide insights into potential PPI inhibitors for particular protein targets.

SMMPPI: a machine learning-based approach for prediction of modulators of protein-protein interactions and its application for identification of novel inhibitors for RBD:hACE2 interactions in SARS-CoV-2

Small molecule modulators of protein–protein interactions (PPIs) are being pursued as novel anticancer, antiviral and antimicrobial drug candidates. We have utilized a large data set of experimentally validated PPI modulators and developed machine learning classifiers for prediction of new small molecule modulators of PPI. Our analysis reveals that using random forest (RF) classifier, general PPI Modulators independent of PPI family can be predicted with ROC-AUC higher than 0.9, when training and test sets are generated by random split. The performance of the classifier on data sets very different from those used in training has also been estimated by using different state of the art protocols for removing various types of bias in division of data into training and test sets. The family-specific PPIM predictors developed in this work for 11 clinically important PPI families also have prediction accuracies of above 90% in majority of the cases. All these ML-based predictors have been implemented in a freely available software named SMMPPI for prediction of small molecule modulators for clinically relevant PPIs like RBD:hACE2, Bromodomain_Histone, BCL2-Like_BAX/BAK, LEDGF_IN, LFA_ICAM, MDM2-Like_P53, RAS_SOS1, XIAP_Smac, WDR5_MLL1, KEAP1_NRF2 and CD4_gp120. We have identified novel chemical scaffolds as inhibitors for RBD_hACE PPI involved in host cell entry of SARS-CoV-2. Docking studies for some of the compounds reveal that they can inhibit RBD_hACE2 interaction by high affinity binding to interaction hotspots on RBD. Some of these new scaffolds have also been found in SARS-CoV-2 viral growth inhibitors reported recently; however, it is not known if these molecules inhibit the entry phase.

VSPrep: A KNIME Workflow for the Preparation of Molecular Databases for Virtual Screening

Drug discovery is a challenging and expensive field. Hence, novel in silico tools have been developed in early discovery stage to identify and prioritize novel molecules with suitable physicochemical properties. In many in silico drug design projects, molecular databases are screened by virtual screening tools to search for potential bioactive molecules. The preparation of the molecules is therefore a key step in the success of well-established techniques such as docking, similarity or pharmacophore searching. We review here the lists of several toolkits used in different steps during the cleaning of molecular databases, integrated within a KNIME workflow. During the first step of the automatic workflow, salts are removed, and mixtures are split to get one compound per entry. Then compounds with unwanted features are filtered. Duplicated entries are then deleted while considering stereochemistry. As a compromise between exhaustiveness and computational time, most distributed tautomers at physiological pH are computed. Additionally, various flags are applied to molecules by using either classical molecular descriptors, similarity search to known libraries or substructure search rules. Moreover, stereoisomers are enumerated depending on the unassigned chiral centers. Then, three-dimensional coordinates, and optionally conformers, are generated. This workflow has been already applied to several drug design projects and can be used for molecular database preparation upon request.

Overcoming challenges in developing small molecule inhibitors for GPVI and CLEC-2

GPVI and CLEC-2 have emerged as promising targets for long-term prevention of both arterial thrombosis and thrombo-inflammation with a decreased bleeding risk relative to current drugs. However, while there are potent blocking antibodies of both receptors, their protein nature comes with decreased bioavailability, making formulation for oral medication challenging. Small molecules are able to overcome these limitations, but there are many challenges in developing antagonists of nanomolar potency, which is necessary when considering the structural features that underlie the interaction of CLEC-2 and GPVI with their protein ligands. In this review, we describe current small-molecule inhibitors for both receptors and strategies to overcome such limitations, including considerations when it comes to in silico drug design and the importance of complex compound library selection.

Fr-PPIChem: An Academic Compound Library Dedicated to Protein-Protein Interactions

Protein-protein interactions (PPIs) mediate nearly every cellular process and represent attractive targets for modulating disease states but are challenging to target with small molecules. Despite this, several PPI inhibitors (iPPIs) have entered clinical trials, and a growing number of PPIs have become validated drug targets. However, high-throughput screening efforts still endure low hit rates mainly because of the use of unsuitable screening libraries. Here, we describe the collective effort of a French consortium to build, select, and store in plates a unique chemical library dedicated to the inhibition of PPIs. Using two independent predictive models and two updated databases of experimentally confirmed PPI inhibitors developed by members of the consortium, we built models based on different training sets, molecular descriptors, and machine learning methods. Independent statistical models were used to select putative PPI inhibitors from large commercial compound collections showing great complementarity. Medicinal chemistry filters were applied to remove undesirable structures from this set (such as PAINS, frequent hitters, and toxic compounds) and to improve drug likeness. The remaining compounds were subjected to a clustering procedure to reduce the final size of the library while maintaining its chemical diversity. In practice, the library showed a 46-fold activity rate enhancement when compared to a non-iPPI-enriched diversity library in high-throughput screening against the CD47-SIRPα PPI. The Fr-PPIChem library is plated in 384-well plates and will be distributed on demand to the scientific community as a powerful tool for discovering new chemical probes and early hits for the development of potential therapeutic drugs.

Discovery of Inhibitors for Proliferating Cell Nuclear Antigen Using a Computational-Based Linked-Multiple-Fragment Screen

Proliferating cell nuclear antigen (PCNA) is a central factor in DNA replication and repair pathways that plays an essential role in genome stability. The functional roles of PCNA are mediated through an extensive list of protein-protein interactions, each of which transmits specific information in protein assemblies. The flexibility at the PCNA-protein interaction interfaces offers opportunities for the discovery of functionally selective inhibitors of DNA repair pathways. Current fragment-based drug design methodologies can be limited by the flexibility of protein interfaces. These factors motivated an approach to defining compounds that could leverage previously identified subpockets on PCNA that are suitable for fragment-binding sites. Methodologies for screening multiple connected fragment-binding events in distinct subpockets are deployed to improve the selection of fragment combinations. A flexible backbone based on N-alkyl-glycine amides offers a scaffold to combinatorically link multiple fragments for in silico screening libraries that explore the diversity of subpockets at protein interfaces. This approach was applied to discover new potential inhibitors of DNA replication and repair that target PCNA in a multiprotein recognition site. The screens of the libraries were designed to computationally filter ligands based upon the fragments and positions to <1%, which were synthesized and tested for direct binding to PCNA. Molecular dynamics simulations also revealed distinct features of these novel molecules that block key PCNA-protein interactions. Furthermore, a Bayesian classifier predicted 15 of the 16 new inhibitors to be modulators of protein-protein interactions, demonstrating the method's utility as an effective screening filter. The cellular activities of example ligands with similar affinity for PCNA demonstrate unique properties for novel selective synergy with therapeutic DNA-damaging agents in drug-resistant contexts.

Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery

The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their "undruggable" binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery.

Structural insights into pharmacophore-assisted in silico identification of protein-protein interaction inhibitors for inhibition of human toll-like receptor 4 - myeloid differentiation factor-2 (hTLR4-MD-2) complex

Toll-like receptor 4 (TLR4) is a member of Toll-Like Receptors (TLRs) family that serves as a receptor for bacterial lipopolysaccharide (LPS). TLR4 alone cannot recognize LPS without aid of co-receptor myeloid differentiation factor-2 (MD-2). Binding of LPS with TLR4 forms a LPS-TLR4-MD-2 complex and directs downstream signaling for activation of immune response, inflammation and NF-κB activation. Activation of TLR4 signaling is associated with various pathophysiological consequences. Therefore, targeting protein-protein interaction (PPI) in TLR4-MD-2 complex formation could be an attractive therapeutic approach for targeting inflammatory disorders. The aim of present study was directed to identify small molecule PPI inhibitors (SMPPIIs) using pharmacophore mapping-based approach of computational drug discovery. Here, we had retrieved the information about the hot spot residues and their pharmacophoric features at both primary (TLR4-MD-2) and dimerization (MD-2-TLR4*) protein-protein interaction interfaces in TLR4-MD-2 homo-dimer complex using in silico methods. Promising candidates were identified after virtual screening, which may restrict TLR4-MD-2 protein-protein interaction. In silico off-target profiling over the virtually screened compounds revealed other possible molecular targets. Two of the virtually screened compounds (C11 and C15) were predicted to have an inhibitory concentration in μM range after HYDE assessment. Molecular dynamics simulation study performed for these two compounds in complex with target protein confirms the stability of the complex. After virtual high throughput screening we found selective hTLR4-MD-2 inhibitors, which may have therapeutic potential to target chronic inflammatory diseases.

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.

Genetic, structural, and chemical insights into the dual function of GRASP55 in germ cell Golgi remodeling and JAM-C polarized localization during spermatogenesis

Spermatogenesis is a dynamic process that is regulated by adhesive interactions between germ and Sertoli cells. Germ cells express the Junctional Adhesion Molecule-C (JAM-C, encoded by Jam3), which localizes to germ/Sertoli cell contacts. JAM-C is involved in germ cell polarity and acrosome formation. Using a proteomic approach, we demonstrated that JAM-C interacted with the Golgi reassembly stacking protein of 55 kDa (GRASP55, encoded by Gorasp2) in developing germ cells. Generation and study of Gorasp2-/- mice revealed that knock-out mice suffered from spermatogenesis defects. Acrosome formation and polarized localization of JAM-C in spermatids were altered in Gorasp2-/- mice. In addition, Golgi morphology of spermatocytes was disturbed in Gorasp2-/- mice. Crystal structures of GRASP55 in complex with JAM-C or JAM-B revealed that GRASP55 interacted via PDZ-mediated interactions with JAMs and induced a conformational change in GRASP55 with respect of its free conformation. An in silico pharmacophore approach identified a chemical compound called Graspin that inhibited PDZ-mediated interactions of GRASP55 with JAMs. Treatment of mice with Graspin hampered the polarized localization of JAM-C in spermatids, induced the premature release of spermatids and affected the Golgi morphology of meiotic spermatocytes.

VSPrep: A General KNIME Workflow for the Preparation of Molecules for Virtual Screening

Over the past decades, virtual screening has proved itself to be a valuable asset to identify new bioactive compounds. The vast majority of commonly used techniques can be described in three steps: pre-processing the dataset i. e. small (ligands) and eventually larger (receptors) molecules, execute the method and finally analyse the results. Hence, the preparation of ligands is a critical step for success of commonly used virtual screening approaches such as protein-ligand docking, similarity or pharmacophore search. We present here a new workflow, VSPrep, for the pre-processing of small molecules; it is based on freely accessible tools for academics and is integrated within the KNIME platform. It can be used to perform several chemoinformatics tasks such as molecular database cleaning, tautomer and stereoisomer enumeration, focused library design and conformer generation. Additionally, graphical reports of the results are provided to the user as a convenient analysis tool.

PPIMpred: a web server for high-throughput screening of small molecules targeting protein-protein interaction

PPIMpred is a web server that allows high-throughput screening of small molecules for targeting specific protein-protein interactions, namely Mdm2/P53, Bcl2/Bak and c-Myc/Max. Three different kernels of support vector machine (SVM), namely, linear, polynomial and radial basis function (RBF), and two other machine learning techniques including Naive Bayes and Random Forest were used to train the models. A fivefold cross-validation technique was used to measure the performance of these classifiers. The RBF kernel of SVM outperformed and/or was comparable with all other methods with accuracy values of 83%, 79% and 90% for Mdm2/P53, Bcl2/Bak and c-Myc/Max, respectively. About 80% of the predicted SVM scores of training/testing datasets from Mdm2/P53 and Bcl2/Bak have significant IC50 values and docking scores. The proposed models achieved an accuracy of 66-90% with blind sets. The three mentioned (Mdm2/P53, Bcl2/Bak and c-Myc/Max) proposed models were screened in a large dataset of 265 242 small chemicals from National Cancer Institute open database. To further realize the robustness of this approach, hits with high and random SVM scores were used for molecular docking in AutoDock Vina wherein the molecules with high and random predicted SVM scores yielded moderately significant docking scores (p-values < 0.1). In addition to the above-mentioned classification scheme, this web server also allows users to get the structural and chemical similarities with known chemical modulators or drug-like molecules based on Tanimoto coefficient similarity search algorithm. PPIMpred is freely available at http://bicresources.jcbose.ac.in/ssaha4/PPIMpred/.

Protein-Protein Interaction Inhibition (2P2I)-Oriented Chemical Library Accelerates Hit Discovery

Protein-protein interactions (PPIs) represent an enormous source of opportunity for therapeutic intervention. We and others have recently pinpointed key rules that will help in identifying the next generation of innovative drugs to tackle this challenging class of targets within the next decade. We used these rules to design an oriented chemical library corresponding to a set of diverse "PPI-like" modulators with cores identified as privileged structures in therapeutics. In this work, we purchased the resulting 1664 structurally diverse compounds and evaluated them on a series of representative protein-protein interfaces with distinct "druggability" potential using homogeneous time-resolved fluorescence (HTRF) technology. For certain PPI classes, analysis of the hit rates revealed up to 100 enrichment factors compared with nonoriented chemical libraries. This observation correlates with the predicted "druggability" of the targets. A specific focus on selectivity profiles, the three-dimensional (3D) molecular modes of action resolved by X-ray crystallography, and the biological activities of identified hits targeting the well-defined "druggable" bromodomains of the bromo and extraterminal (BET) family are presented as a proof-of-concept. Overall, our present study illustrates the potency of machine learning-based oriented chemical libraries to accelerate the identification of hits targeting PPIs. A generalization of this method to a larger set of compounds will accelerate the discovery of original and potent probes for this challenging class of targets.

Imbalance in chemical space: How to facilitate the identification of protein-protein interaction inhibitors

Protein-protein interactions (PPIs) play vital roles in life and provide new opportunities for therapeutic interventions. In this large data analysis, 3,300 inhibitors of PPIs (iPPIs) were compared to 17 reference datasets of collectively ~566,000 compounds (including natural compounds, existing drugs, active compounds on conventional targets, etc.) using a chemoinformatics approach. Using this procedure, we showed that comparable classes of PPI targets can be formed using either the similarity of their ligands or the shared properties of their binding cavities, constituting a proof-of-concept that not only can binding pockets be used to group PPI targets, but that these pockets certainly condition the properties of their corresponding ligands. These results demonstrate that matching regions in both chemical space and target space can be found. Such identified classes of targets could lead to the design of PPI-class-specific chemical libraries and therefore facilitate the development of iPPIs to the stage of drug candidates.

2P2Idb v2: update of a structural database dedicated to orthosteric modulation of protein-protein interactions

2P2Idb is a hand-curated structural database dedicated to protein–protein interactions with known small molecule orthosteric modulators. It compiles the structural information related to orthosteric inhibitors and their target [i.e. related 3D structures available in the RCSB Protein Data Bank (PDB)] and provides links to other useful databases. 2P2Idb includes all interactions for which both the protein–protein and protein–inhibitor complexes have been structurally characterized. Since its first release in 2010, the database has grown constantly and the current version contains 27 protein–protein complexes and 274 protein–inhibitor complexes corresponding to 242 unique small molecule inhibitors which represent almost a 5-fold increase compared to the previous version. A number of new data have been added, including new protein–protein complexes, binding affinities, molecular descriptors, precalculated interface parameters and links to other webservers. A new query tool has been implemented to search for inhibitors within the database using standard molecular descriptors. A novel version of the 2P2I-inspector tool has been implemented to calculate a series of physical and chemical parameters of the protein interfaces. Several geometrical parameters including planarity, eccentricity and circularity have been added as well as customizable distance cutoffs. This tool has also been extended to protein–ligand interfaces. The 2P2I database thus represents a wealth of structural source of information for scientists interested in the properties of protein–protein interactions and the design of protein–protein interaction modulators.

Database URL:http://2p2idb.cnrs-mrs.fr

Exploring Selective Inhibition of the First Bromodomain of the Human Bromodomain and Extra-terminal Domain (BET) Proteins

A midthroughput screening follow-up program targeting the first bromodomain of the human BRD4 protein, BRD4(BD1), identified an acetylated-mimic xanthine derivative inhibitor. This compound binds with an affinity in the low micromolar range yet exerts suitable unexpected selectivity in vitro against the other members of the bromodomain and extra-terminal domain (BET) family. A structure-based program pinpointed a role of the ZA loop, paving the way for the development of potent and selective BET-BRDi probes.

Computational probing protein-protein interactions targeting small molecules

MOTIVATION

With the booming of interactome studies, a lot of interactions can be measured in a high throughput way and large scale datasets are available. It is becoming apparent that many different types of interactions can be potential drug targets. Compared with inhibition of a single protein, inhibition of protein-protein interaction (PPI) is promising to improve the specificity with fewer adverse side-effects. Also it greatly broadens the drug target search space, which makes the drug target discovery difficult. Computational methods are highly desired to efficiently provide candidates for further experiments and hold the promise to greatly accelerate the discovery of novel drug targets.

RESULTS

Here, we propose a machine learning method to predict PPI targets in a genomic-wide scale. Specifically, we develop a computational method, named as PrePPItar, to Predict PPIs as drug targets by uncovering the potential associations between drugs and PPIs. First, we survey the databases and manually construct a gold-standard positive dataset for drug and PPI interactions. This effort leads to a dataset with 227 associations among 63 PPIs and 113 FDA-approved drugs and allows us to build models to learn the association rules from the data. Second, we characterize drugs by profiling in chemical structure, drug ATC-code annotation, and side-effect space and represent PPI similarity by a symmetrical S-kernel based on protein amino acid sequence. Then the drugs and PPIs are correlated by Kronecker product kernel. Finally, a support vector machine (SVM), is trained to predict novel associations between drugs and PPIs. We validate our PrePPItar method on the well-established gold-standard dataset by cross-validation. We find that all chemical structure, drug ATC-code, and side-effect information are predictive for PPI target. Moreover, we can increase the PPI target prediction coverage by integrating multiple data sources. Follow-up database search and pathway analysis indicate that our new predictions are worthy of future experimental validation.

CONCLUSION

In conclusion, PrePPItar can serve as a useful tool for PPI target discovery and provides a general heterogeneous data integrative framework.

AVAILABILITY AND IMPLEMENTATION

PrePPItar is available at http://doc.aporc.org/wiki/PrePPItar.

CONTACT

ycwang@nwipb.cas.cn or ywang@amss.ac.cn

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Computational Advances for the Development of Allosteric Modulators and Bitopic Ligands in G Protein-Coupled Receptors

Allosteric modulators of G protein-coupled receptors (GPCRs), which target at allosteric sites, have significant advantages against the corresponding orthosteric compounds including higher selectivity, improved chemical tractability or physicochemical properties, and reduced risk of receptor oversensitization. Bitopic ligands of GPCRs target both orthosteric and allosteric sites. Bitopic ligands can improve binding affinity, enhance subtype selectivity, stabilize receptors, and reduce side effects. Discovering allosteric modulators or bitopic ligands for GPCRs has become an emerging research area, in which the design of allosteric modulators is a key step in the detection of bitopic ligands. Radioligand binding and functional assays ([(35)S]GTPγS and ERK1/2 phosphorylation) are used to test the effects for potential modulators or bitopic ligands. High-throughput screening (HTS) in combination with disulfide trapping and fragment-based screening are used to aid the discovery of the allosteric modulators or bitopic ligands of GPCRs. When used alone, these methods are costly and can often result in too many potential drug targets, including false positives. Alternatively, low-cost and efficient computational approaches are useful in drug discovery of novel allosteric modulators and bitopic ligands to help refine the number of targets and reduce the false-positive rates. This review summarizes the state-of-the-art computational methods for the discovery of modulators and bitopic ligands. The challenges and opportunities for future drug discovery are also discussed.

[Chemical libraries dedicated to protein-protein interactions]

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.

Computational and biophysical approaches to protein-protein interaction inhibition of Plasmodium falciparum AMA1/RON2 complex

Invasion of the red blood cell by Plasmodium falciparum parasites requires formation of an electron dense circumferential ring called the Moving Junction (MJ). The MJ is anchored by a high affinity complex of two parasite proteins: Apical Membrane Antigen 1 (PfAMA1) displayed on the surface of the parasite and Rhoptry Neck Protein 2 that is discharged from the parasite and imbedded in the membrane of the host cell. Structural studies of PfAMA1 revealed a conserved hydrophobic groove localized to the apical surface that coordinates RON2 and invasion inhibitory peptides. In the present work, we employed computational and biophysical methods to identify competitive P. falciparum AMA1-RON2 inhibitors with the goal of exploring the 'druggability' of this attractive antimalarial target. A virtual screen followed by molecular docking with the PfAMA1 crystal structure was performed using an eight million compound collection that included commercial molecules, the ChEMBL malaria library and approved drugs. The consensus approach resulted in the selection of inhibitor candidates. We also developed a fluorescence anisotropy assay using a modified inhibitory peptide to experimentally validate the ability of the selected compounds to inhibit the AMA1-RON2 interaction. Among those, we identified one compound that displayed significant inhibition. This study offers interesting clues to improve the throughput and reliability of screening for new drug leads.

In silico design of low molecular weight protein-protein interaction inhibitors: Overall concept and recent advances

Protein-protein interactions (PPIs) are carrying out diverse functions in living systems and are playing a major role in the health and disease states. Low molecular weight (LMW) "drug-like" inhibitors of PPIs would be very valuable not only to enhance our understanding over physiological processes but also for drug discovery endeavors. However, PPIs were deemed intractable by LMW chemicals during many years. But today, with the new experimental and in silico technologies that have been developed, about 50 PPIs have already been inhibited by LMW molecules. Here, we first focus on general concepts about protein-protein interactions, present a consensual view about ligandable pockets at the protein interfaces and the possibilities of using fast and cost effective structure-based virtual screening methods to identify PPI hits. We then discuss the design of compound collections dedicated to PPIs. Recent financial analyses of the field suggest that LMW PPI modulators could be gaining momentum over biologics in the coming years supporting further research in this area.

Flexibility and small pockets at protein-protein interfaces: New insights into druggability

The transient assembly of multiprotein complexes mediates many aspects of cell regulation and signalling in living organisms. Modulation of the formation of these complexes through targeting protein-protein interfaces can offer greater selectivity than the inhibition of protein kinases, proteases or other post-translational regulatory enzymes using substrate, co-factor or transition state mimetics. However, capitalising on protein-protein interaction interfaces as drug targets has been hindered by the nature of interfaces that tend to offer binding sites lacking the well-defined large cavities of classical drug targets. In this review we posit that interfaces formed by concerted folding and binding (disorder-to-order transitions on binding) of one partner and other examples of interfaces where a protein partner is bound through a continuous epitope from a surface-exposed helix, flexible loop or chain extension may be more tractable for the development of "orthosteric", competitive chemical modulators; these interfaces tend to offer small-volume but deep pockets and/or larger grooves that may be bound tightly by small chemical entities. We discuss examples of such protein-protein interaction interfaces for which successful chemical modulators are being developed.

Rational design of protein–protein interaction inhibitors

Low molecular weight compound competing for the binding of the p53 tumor suppressor to the MDM2 oncoprotein.

Drug-Like Protein-Protein Interaction Modulators: Challenges and Opportunities for Drug Discovery and Chemical Biology

[Formula: see text] 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.

Focused chemical libraries--design and enrichment: an example of protein-protein interaction chemical space

One of the many obstacles in the development of new drugs lies in the limited number of therapeutic targets and in the quality of screening collections of compounds. In this review, we present general strategies for building target-focused chemical libraries with a particular emphasis on protein-protein interactions (PPIs). We describe the chemical spaces spanned by nine commercially available PPI-focused libraries and compare them to our 2P2I3D academic library, dedicated to orthosteric PPI modulators. We show that although PPI-focused libraries have been designed using different strategies, they share common subspaces. PPI inhibitors are larger and more hydrophobic than standard drugs; however, an effort has been made to improve the drug-likeness of focused chemical libraries dedicated to this challenging class of targets.