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

Molecular Dynamics as a Tool for Virtual Ligand Screening

Rational drug design is essential for new drugs to emerge, especially when the structure of a target protein or nucleic acid is known. To that purpose, high-throughput virtual ligand screening campaigns aim at discovering computationally new binding molecules or fragments to modulate particular biomolecular interactions or biological activities, related to a disease process. The structure-based virtual ligand screening process primarily relies on docking methods which allow predicting the binding of a molecule to a biological target structure with a correct conformation and the best possible affinity. The docking method itself is not sufficient as it suffers from several and crucial limitations (lack of full protein flexibility information, no solvation and ion effects, poor scoring functions, and unreliable molecular affinity estimation).At the interface of computer techniques and drug discovery, molecular dynamics (MD) allows introducing protein flexibility before or after a docking protocol, refining the structure of protein-drug complexes in the presence of water, ions, and even in membrane-like environments, describing more precisely the temporal evolution of the biological complex and ranking these complexes with more accurate binding energy calculations. In this chapter, we describe the up-to-date MD, which plays the role of supporting tools in the virtual ligand screening (VS) process.Without a doubt, using docking in combination with MD is an attractive approach in structure-based drug discovery protocols nowadays. It has proved its efficiency through many examples in the literature and is a powerful method to significantly reduce the amount of required wet experimentations (Tarcsay et al, J Chem Inf Model 53:2990-2999, 2013; Barakat et al, PLoS One 7:e51329, 2012; De Vivo et al, J Med Chem 59:4035-4061, 2016; Durrant, McCammon, BMC Biol 9:71-79, 2011; Galeazzi, Curr Comput Aided Drug Des 5:225-240, 2009; Hospital et al, Adv Appl Bioinforma Chem 8:37-47, 2015; Jiang et al, Molecules 20:12769-12786, 2015; Kundu et al, J Mol Graph Model 61:160-174, 2015; Mirza et al, J Mol Graph Model 66:99-107, 2016; Moroy et al, Future Med Chem 7:2317-2331, 2015; Naresh et al, J Mol Graph Model 61:272-280, 2015; Nichols et al, J Chem Inf Model 51:1439-1446, 2011; Nichols et al, Methods Mol Biol 819:93-103, 2012; Okimoto et al, PLoS Comput Biol 5:e1000528, 2009; Rodriguez-Bussey et al, Biopolymers 105:35-42, 2016; Sliwoski et al, Pharmacol Rev 66:334-395, 2014).

Site-directed modification of multifunctional lignocellulose-degrading enzymes of straw based on homologous modeling

Studying the straw lignocellulose strengthening mechanism during simultaneous degradation has important practical significance for improving resource utilization and reducing environmental pollution. In this paper, the degradation ability of four straw lignocellulose-degrading enzymes was evaluated by molecular docking and molecular dynamics. Using the significantly binds to straw lignocellulose-degrading enzyme as a template, a multifunctional lignocellulose-degrading enzyme 3CBH-1KS5-4XQD-1B85 was constructed based on amino acid recombination and homologous modeling. Five efficient degrading enzymes (3CBH-1, 3CBH-2, 3CBH-3, 3CBH-4, and 3CBH-5) were designed by site-directed mutagenesis of 3CBH-1KS5-4XQD-1B85 amino acid at position 346. Molecular dynamics showed that the degradation ability of 3CBH-1 was significant and it was 1.45 times higher than 3CBH-1KS5-4XQD-1B85. Moreover, the mechanism of enhanced degradability and the stability of the enzymes were explored. With the aid of Taguchi experiments, the suitable external environment for degrading straw was determined. In the presence of inhibitors (organic acids and phenolic compounds), the binding energy of 3CBH-1 (238.46 ± 30.96 kJ/mol) is 36.42% higher than that of 3CBH-1KS5-4XQD-1B85 (174.79 ± 20.35 kJ/mol) without external environmental stimulation. Based on homology modeling, this paper constructed a site-directed mutagenesis scheme of multifunctional enzymes, and the aim was to obtain multifunctional and efficient straw lignocellulose-degrading enzymes through protein engineering, which provided a feasible scheme for straw biodegradation.

Identification of structural scaffold from interbioscreen (IBS) database to inhibit 3CLpro, PLpro, and RdRp of SARS-CoV-2 using molecular docking and dynamic simulation studies

A novel coronavirus SARS-CoV-2 has caused a worldwide pandemic and remained a severe threat to the entire human population. Researchers worldwide are struggling to find an effective drug treatment to combat this deadly disease. Many FDA-approved drugs from varying inhibitory classes and plant-derived compounds are screened to combat this virus. Still, due to the lack of structural information and several mutations of this virus, initial drug discovery efforts have limited success. A high-resolution crystal structure of important proteins like the main protease (3CLpro) that are required for SARS-CoV-2 viral replication and polymerase (RdRp) and papain-like protease (PLpro) as a vital target in other coronaviruses still presents important targets for the drug discovery. With this knowledge, scaffold library of Interbioscreen (IBS) database was explored through molecular docking, MD simulation and postdynamic binding free energy studies. The 3D docking structures and simulation data for the IBS compounds was studied and articulated. The compounds were further evaluated for ADMET studies using QikProp and SwissADME tools. The results revealed that the natural compounds STOCK2N-00385, STOCK2N-00244, and STOCK2N-00331 interacted strongly with 3CLpro, PLpro, and RdRp, respectively, and ADMET data was also observed in the range of limits for almost all the compounds with few exceptions. Thus, it suggests that these compounds may be potential inhibitors of selected target proteins, or their structural scaffolds can be further optimized to obtain effective drug candidates for SARS-CoV-2. The findings of in-silico data need to be supported by in-vivo studies which could shed light on understanding the exact mode of inhibitory action.Communicated by Ramaswamy H. Sarma.

An Integrated Machine Learning Model To Spot Peptide Binding Pockets in 3D Protein Screening

The prediction of peptide-protein binding sites is of utmost importance to tackle the onset of severe neurodegenerative diseases and cancer. In this work, we detail a novel machine learning model based on Linear Discriminant Analysis (LDA) demonstrating to be highly predictive in detecting the putative protein binding regions of small peptides. Starting from 439 high-quality pockets derived from peptide-protein crystallographic complexes, three sets of well-established peptide-binding regions were first selected through a Partitioning Around Medoids (PAM) clustering algorithm based on morphological and energetic 3D GRID-MIF molecular descriptors. Next, the best combination between all the putative interacting peptide pockets and related GRID-MIF scores was automatically explored by using the LDA-based protocol implemented in BioGPS. This approach proved successful to recognize the actual interacting peptide regions (that is, AUC = 0.86 and partial ROC enrichment at 5% of 0.48) from all the other pockets of the protein. Validated on two external collections sets, including 445 and 347 crystallographic peptide-protein complexes, our LDA-based model could be effective to further run peptide-protein virtual screening campaigns.

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.

A Computational Study on the Interaction of NSP10 and NSP14: Unraveling the RNA Synthesis Proofreading Mechanism in SARS-CoV-2, SARS-CoV, and MERS-CoV

The interaction of exoribonuclease (ExoN) nonstructural protein (NSP14) with NSP10 co-factors is crucial for high-fidelity proofreading activity of coronavirus replication and transcription. Proofreading function is critical for maintaining the large genomes to ensure replication proficiency; therefore, while maintaining the viral replication fitness, quick resistance has been reported to the nucleotide analogue (NA) drugs. Therefore, targeting the NSP14 and NSP10 interacting interface with small molecules or peptides could be a better strategy to obstruct replication processes of coronaviruses (CoVs). A comparative study on the binding mechanism of NSP10 with the NSP14 ExoN domain of SARS-CoV-2, SARS-CoV, MERS-CoV, and four SARS-CoV-2 NSP14mutant complexes has been carried out. Protein-protein interaction (PPI) dynamics, per-residue binding free energy (BFE) analyses, and the identification of interface hotspot residues have been studied using molecular dynamics simulations and various computational tools. The BFE of the SARS-CoV NSP14-NSP10 complex was higher when compared to novel SARS-CoV-2 and MERS. However, SARS-CoV-2 NSP14mutant systems display a higher BFE as compared to the wild type (WT) but lower than SARS-CoV and MERS. Despite the high BFE, the SARS-CoV NSP14-NSP10 complex appears to be structurally more flexible in many regions especially the catalytic site, which is not seen in SARS-CoV-2 and its mutant or MERS complexes. The significantly high residue energy contribution of key interface residues and hotspots reveals that the high binding energy between NSP14 and NSP10 may enhance the functional activity of the proofreading complex, as the NSP10-NSP14 interaction is essential in maintaining the stability of the ExoN domain for the replicative fitness of CoVs. The factors discussed for SARS-CoV-2 complexes may be responsible for NSP14 ExoN having a high replication proficiency, significantly leading to the evolution of new variants of SARS-CoV-2. The NSP14 residues V66, T69, D126, and I201and eight residues of NSP10 (L16, F19, V21, V42, M44, H80, K93, and F96) are identified as common hotspots. Overall, the interface area, hotspot locations, bonded/nonbonded contacts, and energies between NSP14 and NSP10 may pave a way in designing potential inhibitors to disrupt NSP14-NSP10 interactions of CoVs especially SARS-CoV-2.

Protein-protein interaction of RdRp with its co-factor NSP8 and NSP7 to decipher the interface hotspot residues for drug targeting: A comparison between SARS-CoV-2 and SARS-CoV

In this study we explored the molecular mechanism of RdRp (Non-Structural Protein, NSP12) interaction with its co-factors NSP7 and NSP8 which is the main toolbox for RNA replication and transcription of SARS-CoV-2 and SARS-CoV. The replication complex is a heterotetramer consists of one NSP12, one NSP7 and two NSP8. Extensive molecular dynamics (MD) simulations were applied on both the heterotetramer complexes to generate the conformations and were used to estimate the MMPBSA binding free energy (BFE) and per-residue energy decomposition of NSP12-NSP8 and NSP12-NSP7 and NSP7-NSP8 complexes. The BFE of SARS-CoV-2 heterotetramer complex with its corresponding partner protein was significantly higher as compared to SARS-CoV. Interface hotspot residues were predicted using different methods implemented in KFC (Knowledge-based FADA and Contracts), HotRegion and Robetta web servers. Per-residue energy decomposition analysis showed that the predicted interface hotspot residues contribute more energy towards the formation of complexes and most of the predicted hotspot residues are clustered together. However, there is a slight difference in the residue-wise energy contribution in the interface NSPs on heterotetramer viral replication complex of both coronaviruses. While the overall replication complex of SARS-CoV-2 was found to be slightly flexible as compared to SARS-CoV. This difference in terms of structural flexibility/stability and energetic characteristics of interface residues including hotspots at PPI interface in the viral replication complexes may be the reason of higher rate of RNA replication of SARS-CoV-2 as compared to SARS-CoV. Overall, the interaction profile at PPI interface such as, interface area, hotspot residues, nature of bonds and energies between NSPs, may provide valuable insights in designing of small molecules or peptide/peptidomimetic ligands which can fit into the PPI interface to disrupt the interaction.

Interaction between a Novel Oligopeptide Fragment of the Human Neurotrophin Receptor TrkB Ectodomain D5 and the C-Terminal Fragment of Tetanus Neurotoxin

This article presents experimental evidence and computed molecular models of a potential interaction between receptor domain D5 of TrkB with the carboxyl-terminal domain of tetanus neurotoxin (Hc-TeNT). Computational simulations of a novel small cyclic oligopeptide are designed, synthesized, and tested for possible tetanus neurotoxin-D5 interaction. A hot spot of this protein-protein interaction is identified in analogy to the hitherto known crystal structures of the complex between neurotrophin and D5. Hc-TeNT activates the neurotrophin receptors, as well as its downstream signaling pathways, inducing neuroprotection in different stress cellular models. Based on these premises, we propose the Trk receptor family as potential proteic affinity receptors for TeNT. In vitro, Hc-TeNT binds to a synthetic TrkB-derived peptide and acts similar to an agonist ligand for TrkB, resulting in phosphorylation of the receptor. These properties are weakened by the mutagenesis of three residues of the predicted interaction region in Hc-TeNT. It also competes with Brain-derived neurotrophic factor, a native binder to human TrkB, for the binding to neural membranes, and for uptake in TrkB-positive vesicles. In addition, both molecules are located together in vivo at neuromuscular junctions and in motor neurons.

Improving Docking Power for Short Peptides Using Random Forest

In recent years, therapeutic peptides have gained a lot interest as demonstrated by the 60 peptides approved as drugs in major markets and 150+ peptides currently in clinical trials. However, while small molecule docking is routinely used in rational drug design efforts, docking peptides has proven challenging partly because docking scoring functions, developed and calibrated for small molecules, perform poorly for these molecules. Here, we present random forest classifiers trained to discriminate correctly docked peptides. We show that, for a testing set of 47 protein-peptide complexes, structurally dissimilar from the training set and previously used to benchmark AutoDock Vina's ability to dock short peptides, these random forest classifiers improve docking power from ∼25% for AutoDock scoring functions to an average of ∼70%. These results pave the way for peptide-docking success rates comparable to those of small molecule docking. To develop these classifiers, we compiled the ProptPep37_2021 data set, a curated, high-quality set of 322 crystallographic protein-peptides complexes annotated with structural similarity information. The data set also provides a collection of high-quality putative poses with a range of deviations from the crystallographic pose, providing correct and incorrect poses (i.e., decoys) of the peptide for each entry. The ProptPep37_2021 data set as well as the classifiers presented here are freely available.

Resources and computational strategies to advance small molecule SARS-CoV-2 discovery: Lessons from the pandemic and preparing for future health crises

There is an urgent need to identify new therapies that prevent SARS-CoV-2 infection and improve the outcome of COVID-19 patients. This pandemic has thus spurred intensive research in most scientific areas and in a short period of time, several vaccines have been developed. But, while the race to find vaccines for COVID-19 has dominated the headlines, other types of therapeutic agents are being developed. In this mini-review, we report several databases and online tools that could assist the discovery of anti-SARS-CoV-2 small chemical compounds and peptides. We then give examples of studies that combined in silico and in vitro screening, either for drug repositioning purposes or to search for novel bioactive compounds. Finally, we question the overall lack of discussion and plan observed in academic research in many countries during this crisis and suggest that there is room for improvement.

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.

Analyzing In Silico the Relationship Between the Activation of the Edema Factor and Its Interaction With Calmodulin

Molecular dynamics (MD) simulations have been recorded on the complex between the edema factor (EF) of Bacilllus anthracis and calmodulin (CaM), starting from a structure with the orthosteric inhibitor adefovir bound in the EF catalytic site. The starting structure has been destabilized by alternately suppressing different co-factors, such as adefovir ligand or ions, revealing several long-distance correlations between the conformation of CaM, the geometry of the CaM/EF interface, the enzymatic site and the overall organization of the complex. An allosteric communication between CaM/EF interface and the EF catalytic site, highlighted by these correlations, was confirmed by several bioinformatics approaches from the literature. A network of hydrogen bonds and stacking interactions extending from the helix V of of CaM, and the residues of the switches A, B and C, and connecting to catalytic site residues, is a plausible candidate for the mediation of allosteric communication. The greatest variability in volume between the different MD conditions was also found for cavities present at the EF/CaM interface and in the EF catalytic site. The similarity between the predictions from literature and the volume variability might introduce the volume variability as new descriptor of allostery.

Rapid in silico Design of Potential Cyclic Peptide Binders Targeting Protein-Protein Interfaces

Rational design of specific inhibitors of protein-protein interactions is desirable for drug design to control cellular signal transduction but also for studying protein-protein interaction networks. We have developed a rapid computational approach to rationally design cyclic peptides that potentially bind at desired regions of the interface of protein-protein complexes. The methodology is based on comparing the protein backbone structure of short peptide segments (epitopes) at the protein-protein interface with a collection of cyclic peptide backbone structures. A cyclic peptide that matches the backbone structure of the segment is used as a template for a binder by adapting the amino acid side chains to the side chains found in the target complex. For a small library of cyclic peptides with known high resolution structures we found for the majority (~82%) of 154 protein-protein complexes at least one very well fitting match for a cyclic peptide template to a protein-protein interface segment. The majority of the constructed protein-cyclic peptide complexes was very stable during Molecular Dynamics simulations and showed an interaction energy score that was typically more favorable compared to interaction scores of typical peptide-protein complexes. Our cPEPmatch approach could be a promising approach for rapid suggestion of cyclic peptide binders that could be tested experimentally and further improved by chemical modification.

Structure-Based Drug Design for Tuberculosis: Challenges Still Ahead

Structure-based and computer-aided drug design approaches are commonly considered to have been successful in the fields of cancer and antiviral drug discovery but not as much for antibacterial drug development. The search for novel anti-tuberculosis agents is indeed an emblematic example of this trend. Although huge efforts, by consortiums and groups worldwide, dramatically increased the structural coverage of the Mycobacterium tuberculosis proteome, the vast majority of candidate drugs included in clinical trials during the last decade were issued from phenotypic screenings on whole mycobacterial cells. We developed here three selected case studies, i.e., the serine/threonine (Ser/Thr) kinases—protein kinase (Pkn) B and PknG, considered as very promising targets for a long time, and the DNA gyrase of M. tuberculosis, a well-known, pharmacologically validated target. We illustrated some of the challenges that rational, target-based drug discovery programs in tuberculosis (TB) still have to face, and, finally, discussed the perspectives opened by the recent, methodological developments in structural biology and integrative techniques.

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.

Small molecules for modulating the localisation of the water channel aquaporin-2-disease relevance and perspectives for targeting local cAMP signalling

The tight spatial and temporal organisation of cyclic adenosine monophosphate (cAMP) signalling plays a key role in arginine-vasopressin (AVP)-mediated water reabsorption in renal collecting duct principal cells and in a plethora of other processes such as in the control of cardiac myocyte contractility. This review critically discusses in vitro- and cell-based screening strategies for the identification of small molecules that interfere with AVP/cAMP signalling in renal principal cells; it features phenotypic screening and approaches for targeting protein-protein interactions of A-kinase anchoring proteins (AKAPs), which organise local cAMP signalling hubs. The discovery of novel chemical entities for the modulation of local cAMP will not only provide tools for elucidating molecular mechanisms underlying cAMP signalling. Novel chemical entities can also serve as starting points for the development of novel drugs for the treatment of human diseases. Examples illustrate how screening for small molecules can pave the way to novel approaches for the treatment of certain forms of diabetes insipidus, a disease caused by defects in AVP-mediated water reabsorption.

Quantum Molecular Dynamics, Topological, Group Theoretical and Graph Theoretical Studies of Protein-Protein Interactions

BACKGROUND

Protein-protein interactions (PPIs) are becoming increasingly important as PPIs form the basis of multiple aggregation-related diseases such as cancer, Creutzfeldt-Jakob, and Alzheimer's diseases. This mini-review presents hybrid quantum molecular dynamics, quantum chemical, topological, group theoretical, graph theoretical, and docking studies of PPIs. We also show how these theoretical studies facilitate the discovery of some PPI inhibitors of therapeutic importance.

OBJECTIVE

The objective of this review is to present hybrid quantum molecular dynamics, quantum chemical, topological, group theoretical, graph theoretical, and docking studies of PPIs. We also show how these theoretical studies enable the discovery of some PPI inhibitors of therapeutic importance.

METHODS

This article presents a detailed survey of hybrid quantum dynamics that combines classical and quantum MD for PPIs. The article also surveys various developments pertinent to topological, graph theoretical, group theoretical and docking studies of PPIs and highlight how the methods facilitate the discovery of some PPI inhibitors of therapeutic importance.

RESULTS

It is shown that it is important to include higher-level quantum chemical computations for accurate computations of free energies and electrostatics of PPIs and Drugs with PPIs, and thus techniques that combine classical MD tools with quantum MD are preferred choices. Topological, graph theoretical and group theoretical techniques are shown to be important in studying large network of PPIs comprised of over 100,000 proteins where quantum chemical and other techniques are not feasible. Hence, multiple techniques are needed for PPIs.

CONCLUSION

Drug discovery and our understanding of complex PPIs require multifaceted techniques that involve several disciplines such as quantum chemistry, topology, graph theory, knot theory and group theory, thus demonstrating a compelling need for a multi-disciplinary approach to the problem.

Rational modulator design by exploitation of protein-protein complex structures

The horizon of drug discovery is currently expanding to target and modulate protein-protein interactions (PPIs) in globular proteins and intrinsically disordered proteins that are involved in various diseases. To either interrupt or stabilize PPIs, the 3D structure of target protein-protein (or protein-peptide) complexes can be exploited to rationally design PPI modulators (inhibitors or stabilizers) through structure-based molecular design. In this review, we present an overview of experimental and computational methods that can be used to determine 3D structures of protein-protein complexes. Several approaches including rational and in silico methods that can be applied to design peptides, peptidomimetics and small compounds by utilization of determined 3D protein-protein/peptide complexes are summarized and illustrated.

Design criteria for minimalist mimics of protein-protein interface segments

Small molecules that can interrupt or inhibit protein-protein interactions (PPIs) are valuable as probes in chemical biology and medicinal chemistry, but they are also notoriously difficult to develop. Design of non-peptidic small molecules that mimic amino acid side-chain interactions in PPIs ("minimalist mimics") is seen as a way to fast track discovery of PPI inhibitors. However, there has been little comment on general design criteria for minimalist mimics, even though such guidelines could steer construction of libraries to screen against multiple PPI targets. We hypothesized insight into general design criteria for minimalist mimics could be gained by comparing preferred conformations of typical minimalist mimic designs against side-chain orientations on a huge number of PPI interfaces. That thought led to this work which features nine minimalist mimic designs: one from the literature, and eight new "hypothetical" ones conceived by us. Simulated preferred conformers of these were systematically aligned with >240 000 PPI interfaces from the Protein Data Bank. Conclusions from those analyses did indeed reveal various design considerations that are discussed here. Surprisingly, this study also showed one of the minimalist mimic designs aligned on PPI interface segments more than 15 times more frequently than any other in the series (according to uniform standards described herein); reasons for this are also discussed.

Computational Opportunities and Challenges in Finding Cyclic Peptide Modulators of Protein-Protein Interactions

Peptide cyclization can improve stability, conformational constraint, and compactness. However, apart from beta-turn structures, which are well incorporated into cyclic peptides (CPs), many primary peptide structures and functions are markedly altered by cyclization. Accordingly, to mimic linear peptide interfaces with cyclic peptides, it can be beneficial to screen combinatorial cyclic peptide libraries. Computational methods have been developed to screen CPs, but face a number of challenges. Here, we review methods to develop in silico computational libraries, and the potential for screening naturally occurring libraries of CPs. The simplest and most rapid computational pharmacophore methods that estimate peptide three-dimensional structures to be screened versus targets are relatively easy to implement, and while the constraint on structure imposed by cyclization makes them more effective than the same approaches with linear peptides, there are a large number of limiting assumptions. In contrast, full molecular dynamics simulations of cyclic peptide structures not only are costly to implement, but also require careful attention to interpretation, so that not only is the computation time rate limiting, but the interpretation time is also rate limiting due to the analysis of the typically complex underlying conformational space of CPs. A challenge for the field of computational cyclic peptide screening is to bridge this gap effectively. Natural compound libraries of short cyclic peptides, and short cyclized regions of proteins, encoded in the genomes of many organisms present a potential treasure trove of novel functionality which may be screened via combined computational and experimental screening approaches.

Structure-Based Detection of Orthosteric and Allosteric Pockets at Protein-Protein Interfaces

Protein-protein interfaces represent challenging but very promising targets to discover novel drugs with exquisite specificity profiles. We herewith chart for the first time all biologically relevant protein-protein interfaces of known X-ray structure and detect potentially druggable cavities at and nearby the interface. These cavities are then converted in simple 3D pharmacophore queries for identifying potential modulators (inhibitors, stabilizers) of druggable interfaces.

Protein structure and computational drug discovery

The first protein structures revealed a complex web of weak interactions stabilising the three-dimensional shape of the molecule. Small molecule ligands were then found to exploit these same weak binding events to modulate protein function or act as substrates in enzymatic reactions. As the understanding of ligand–protein binding grew, it became possible to firstly predict how and where a particular small molecule might interact with a protein, and then to identify putative ligands for a specific protein site. Computer-aided drug discovery, based on the structure of target proteins, is now a well-established technique that has produced several marketed drugs. We present here an overview of the various methodologies being used for structure-based computer-aided drug discovery and comment on possible future developments in the field.

Inhibition of protein interactions: co-crystalized protein-protein interfaces are nearly as good as holo proteins in rigid-body ligand docking

Modulating protein interaction pathways may lead to the cure of many diseases. Known protein-protein inhibitors bind to large pockets on the protein-protein interface. Such large pockets are detected also in the protein-protein complexes without known inhibitors, making such complexes potentially druggable. The inhibitor-binding site is primary defined by the side chains that form the largest pocket in the protein-bound conformation. Low-resolution ligand docking shows that the success rate for the protein-bound conformation is close to the one for the ligand-bound conformation, and significantly higher than for the apo conformation. The conformational change on the protein interface upon binding to the other protein results in a pocket employed by the ligand when it binds to that interface. This proof-of-concept study suggests that rather than using computational pocket-opening procedures, one can opt for an experimentally determined structure of the target co-crystallized protein-protein complex as a starting point for drug design.

Theoretical Model of EphA2-Ephrin A1 Inhibition

This work aims at the theoretical description of EphA2-ephrin A1 inhibition by small molecules. Recently proposed ab initio-based scoring models, comprising long-range components of interaction energy, is tested on lithocholic acid class inhibitors of this protein–protein interaction (PPI) against common empirical descriptors. We show that, although limited to compounds with similar solvation energy, the ab initio model is able to rank the set of selected inhibitors more effectively than empirical scoring functions, aiding the design of novel compounds.

Molecular Dynamics as a Tool for Virtual Ligand Screening

Rational drug design is essential for new drugs to emerge, especially when the structure of a target protein or catalytic enzyme is known experimentally. To that purpose, high-throughput virtual ligand screening campaigns aim at discovering computationally new binding molecules or fragments to inhibit a particular protein interaction or biological activity. The virtual ligand screening process often relies on docking methods which allow predicting the binding of a molecule into a biological target structure with a correct conformation and the best possible affinity. The docking method itself is not sufficient as it suffers from several and crucial limitations (lack of protein flexibility information, no solvation effects, poor scoring functions, and unreliable molecular affinity estimation).At the interface of computer techniques and drug discovery, molecular dynamics (MD) allows introducing protein flexibility before or after a docking protocol, refining the structure of protein-drug complexes in the presence of water, ions and even in membrane-like environments, and ranking complexes with more accurate binding energy calculations. In this chapter we describe the up-to-date MD protocols that are mandatory supporting tools in the virtual ligand screening (VS) process. Using docking in combination with MD is one of the best computer-aided drug design protocols nowadays. It has proved its efficiency through many examples, described below.

Geometric Detection Algorithms for Cavities on Protein Surfaces in Molecular Graphics: A Survey

Detecting and analyzing protein cavities provides significant information about active sites for biological processes (e.g., protein-protein or protein-ligand binding) in molecular graphics and modeling. Using the three-dimensional structure of a given protein (i.e., atom types and their locations in 3D) as retrieved from a PDB (Protein Data Bank) file, it is now computationally viable to determine a description of these cavities. Such cavities correspond to pockets, clefts, invaginations, voids, tunnels, channels, and grooves on the surface of a given protein. In this work, we survey the literature on protein cavity computation and classify algorithmic approaches into three categories: evolution-based, energy-based, and geometry-based. Our survey focuses on geometric algorithms, whose taxonomy is extended to include not only sphere-, grid-, and tessellation-based methods, but also surface-based, hybrid geometric, consensus, and time-varying methods. Finally, we detail those techniques that have been customized for GPU (Graphics Processing Unit) computing.

Natural products used as a chemical library for protein-protein interaction targeted drug discovery

Protein-protein interactions (PPIs), which are essential for cellular processes, have been recognized as attractive therapeutic targets. Therefore, the construction of a PPI-focused chemical library is an inevitable necessity for future drug discovery. Natural products have been used as traditional medicines to treat human diseases for millennia; in addition, their molecular scaffolds have been used in diverse approved drugs and drug candidates. The recent discovery of the ability of natural products to inhibit PPIs led us to use natural products as a chemical library for PPI-targeted drug discovery. In this study, we collected natural products (NPDB) from non-commercial and in-house databases to analyze their similarities to small-molecule PPI inhibitors (iPPIs) and FDA-approved drugs by using eight molecular descriptors. Then, we evaluated the distribution of NPDB and iPPIs in the chemical space, represented by the molecular fingerprint and molecular scaffolds, to identify the promising scaffolds, which could interfere with PPIs. To investigate the ability of natural products to inhibit PPI targets, molecular docking was used. Then, we predicted a set of high-potency natural products by using the iPPI-likeness score based on a docking score-weighted model. These selected natural products showed high binding affinities to the PPI target, namely XIAP, which were validated in an in vitro experiment. In addition, the natural products with novel scaffolds might provide a promising starting point for further medicinal chemistry developments. Overall, our study shows the potency of natural products in targeting PPIs, which might help in the design of a PPI-focused chemical library for future drug discovery.

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.

Theoretical models of inhibitory activity for inhibitors of protein-protein interactions: targeting menin-mixed lineage leukemia with small molecules

A computationally affordable, non-empirical model based on electrostatic multipole and dispersion terms successfully predicts the binding affinity of inhibitors of menin–MLL protein–protein interactions.

Development and binding affinity predictions of inhibitors targeting protein–protein interactions (PPI) still represent a major challenge in drug discovery efforts. This work reports application of a predictive non-empirical model of inhibitory activity for PPI inhibitors, exemplified here for small molecules targeting the menin–mixed lineage leukemia (MLL) interaction. Systematic ab initio analysis of menin–inhibitor complexes was performed, revealing the physical nature of these interactions. Notably, the non-empirical protein–ligand interaction energy comprising electrostatic multipole and approximate dispersion terms (E(10)El,MTP + E_Das) produced a remarkable correlation with experimentally measured inhibitory activities and enabled accurate activity prediction for new menin–MLL inhibitors. Importantly, this relatively simple and computationally affordable non-empirical interaction energy model outperformed binding affinity predictions derived from commonly used empirical scoring functions. This study demonstrates high relevance of the non-empirical model we developed for binding affinity prediction of inhibitors targeting protein–protein interactions that are difficult to predict using empirical scoring functions.

Molecular Principles of Gene Fusion Mediated Rewiring of Protein Interaction Networks in Cancer

Gene fusions are common cancer-causing mutations, but the molecular principles by which fusion protein products affect interaction networks and cause disease are not well understood. Here, we perform an integrative analysis of the structural, interactomic, and regulatory properties of thousands of putative fusion proteins. We demonstrate that genes that form fusions (i.e., parent genes) tend to be highly connected hub genes, whose protein products are enriched in structured and disordered interaction-mediating features. Fusion often results in the loss of these parental features and the depletion of regulatory sites such as post-translational modifications. Fusion products disproportionately connect proteins that did not previously interact in the protein interaction network. In this manner, fusion products can escape cellular regulation and constitutively rewire protein interaction networks. We suggest that the deregulation of central, interaction-prone proteins may represent a widespread mechanism by which fusion proteins alter the topology of cellular signaling pathways and promote cancer.

Computational analysis of calculated physicochemical and ADMET properties of protein-protein interaction inhibitors

The modulation of PPIs by low molecular weight chemical compounds, particularly by orally bioavailable molecules, would be very valuable in numerous disease indications. However, it is known that PPI inhibitors (iPPIs) tend to have properties that are linked to poor Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) and in some cases to poor clinical outcomes. Previously reported in silico analyses of iPPIs have essentially focused on physicochemical properties but several other ADMET parameters would be important to assess. In order to gain new insights into the ADMET properties of iPPIs, computations were carried out on eight datasets collected from several databases. These datasets involve compounds targeting enzymes, GPCRs, ion channels, nuclear receptors, allosteric modulators, oral marketed drugs, oral natural product-derived marketed drugs and iPPIs. Several trends are reported that should assist the design and optimization of future PPI inhibitors, either for drug discovery endeavors or for chemical biology projects.

Novel effects of identified SNPs within the porcine Pregnancy-Associated Glycoprotein gene family (pPAGs) on the major reproductive traits in Hirschmann hybrid-line sows

This is the first study describing identification of SNPs within the multiple and polymorphic Pregnancy-Associated Glycoprotein gene family (PAGs) in the genome of the domestic pig (pPAGs). We identified pPAG-like (pPAG-L) genotypes in primiparous and multiparous farmed hybrid-line JSR Hirschmann (Hrn) sows (N=159), in which various novel associations with their phenotypes for the major reproductive traits have been discovered. Genomic DNA templates were isolated from the blood and different pPAG-L primers were used to amplify various regions by PCR. Electrophoretically-separated amplicons were selected, purified and sequenced. All identified SNPs were verified for possible pPAG2-L genotype associations with the major reproductive traits. In total, 196 SNPs were identified within the entire structure of the pPAG2-Ls, encompassing 9 exons and 8 (A-H) introns, resembling all aspartic proteinases. It was discovered that among all SNPs, one diplotype localized in exon 6 (657C>T/749G>C; pPAG2 ORF cDNA numbering; L34361) caused amino acid substitutions (Asp²²⁰→Asn and Ser²⁵⁰→Thr) in the polypeptide precursors and was associated with an increase in the number of live-born piglets (P≤0.05) in Hrn sows. In turn, co-localized SNP (504g>a; KF537535 numbering) in the intron F of the pPAG2-Ls, but only in the homozygotic genotype (gg), was associated with an increased number of live-born (P≤0.01) and weaned (P≤0.05) piglets in the Hrn sows. These results qualify the pPAG2-Ls as candidate genes of the main QTLs. The novel pPAG SNP profiles provide the basis for a diagnostic genotyping test required for early pre-selection of female/male piglets, presumably mainly useful in various breeding herds.

Specificity and stability of transient protein-protein interactions

Remarkable features that are achieved in a protein-protein complex to precise levels are stability and specificity. Deviation from the normal levels of specificity and stability, which is often caused by mutations, could result in disease conditions. Chemical nature, 3-D arrangement and dynamics of interface residues code for both specificity and stability. This article reviews roles of interfacial residues in transient protein-protein complexes. It is proposed that aside from hotspot residues conferring stability to the complex, a small set of 'rigid' residues at the interface that maintain conformation between complexed and uncomplexed forms, play a major role in conferring specificity. Exceptionally, 'super hotspot' residues, which confer both stability and specificity, are attractive sites for interaction with small molecule inhibitors.

Protein-Protein Interactions (PPIs) as an Alternative to Targeting the ATP Binding Site of Kinase

Most of the developed kinase inhibitor drugs are ATP competitive and suffer from drawbacks such as off-target kinase activity, development of resistance due to mutation in the ATP binding pocket and unfavorable intellectual property situations. Besides the ATP binding pocket, protein kinases have binding sites that are involved in Protein-Protein Interactions (PPIs); these PPIs directly or indirectly regulate the protein kinase activity. Of recent, small molecule inhibitors of PPIs are emerging as an alternative to ATP competitive agents. Rational design of inhibitors for kinase PPIs could be carried out using molecular modeling techniques. In silico tools available for the prediction of hot spot residues and cavities at the PPI sites and the means to utilize this information for the identification of inhibitors are discussed. Moreover, in silico studies to target the Aurora B-INCENP PPI sites are discussed in context. Overall, this chapter provides detailed in silico strategies that are available to the researchers for carrying out structure-based drug design of PPI inhibitors.

Recent contributions of structure-based drug design to the development of antibacterial compounds

According to a Pew Research study published in February 2015, there are 37 antibacterial programs currently in clinical trials in the United States. Protein structure-based methods for guiding small molecule design were used in at least 34 of these programs. Typically, this occurred at an early stage (drug discovery and/or lead optimization) prior to an Investigational New Drug (IND) application, although sometimes in retrospective studies to rationalize biological activity. Recognizing that structure-based methods are resource-intensive and often require specialized equipment and training, the NIAID has funded two Structural Genomics Centers to determine structures of infectious disease species proteins with the aim of supporting individual investigators' research programs with structural biology methods.

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.

Molecular docking as a popular tool in drug design, an in silico travel

New molecular modeling approaches, driven by rapidly improving computational platforms, have allowed many success stories for the use of computer-assisted drug design in the discovery of new mechanism-or structure-based drugs. In this overview, we highlight three aspects of the use of molecular docking. First, we discuss the combination of molecular and quantum mechanics to investigate an unusual enzymatic mechanism of a flavoprotein. Second, we present recent advances in anti-infectious agents' synthesis driven by structural insights. At the end, we focus on larger biological complexes made by protein-protein interactions and discuss their relevance in drug design. This review provides information on how these large systems, even in the presence of the solvent, can be investigated with the outlook of drug discovery.

Modulation of Protein-Protein Interactions for the Development of Novel Therapeutics

Protein-protein interactions (PPIs) underlie most biological processes. An increasing interest to investigate the unexplored potential of PPIs in drug discovery is driven by the need to find novel therapeutic targets for a whole range of diseases with a high unmet medical need. To date, PPI inhibition with small molecules is the mechanism that has most often been explored, resulting in significant progress towards drug development. However, also PPI stabilization is gradually gaining ground. In this review, we provide a focused overview of a number of PPIs that control critical regulatory pathways and constitute targets for the design of novel therapeutics. We discuss PPI-modulating small molecules that are already pursued in clinical trials. In addition, we review a number of PPIs that are still under preclinical investigation but for which preliminary data support their use as therapeutic targets.

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

Structure-Based Virtual Ligand Screening on the XRCC4/DNA Ligase IV Interface

The association of DNA Ligase IV (Lig4) with XRCC4 is essential for repair of DNA double-strand breaks (DSBs) by Non-homologous end-joining (NHEJ) in humans. DSBs cytotoxicity is largely exploited in anticancer therapy. Thus, NHEJ is an attractive target for strategies aimed at increasing the sensitivity of tumors to clastogenic anticancer treatments. However the high affinity of the XRCC4/Lig4 interaction and the extended protein-protein interface make drug screening on this target particularly challenging. Here, we conducted a pioneering study aimed at interfering with XRCC4/Lig4 assembly. By Molecular Dynamics simulation using the crystal structure of the complex, we first delineated the Lig4 clamp domain as a limited suitable target. Then, we performed in silico screening of ~95,000 filtered molecules on this Lig4 subdomain. Hits were evaluated by Differential Scanning Fluorimetry, Saturation Transfer Difference-NMR spectroscopy and interaction assays with purified recombinant proteins. In this way we identified the first molecule able to prevent Lig4 binding to XRCC4 in vitro. This compound has a unique tripartite interaction with the Lig4 clamp domain that suggests a starting chemotype for rational design of analogous molecules with improved affinity.

Ultra-High-Throughput Structure-Based Virtual Screening for Small-Molecule Inhibitors of Protein-Protein Interactions

Protein-protein interactions play important roles in virtually all cellular processes, making them enticing targets for modulation by small-molecule therapeutics: specific examples have been well validated in diseases ranging from cancer and autoimmune disorders, to bacterial and viral infections. Despite several notable successes, however, overall these remain a very challenging target class. Protein interaction sites are especially challenging for computational approaches, because the target protein surface often undergoes a conformational change to enable ligand binding: this confounds traditional approaches for virtual screening. Through previous studies, we demonstrated that biased "pocket optimization" simulations could be used to build collections of low-energy pocket-containing conformations, starting from an unbound protein structure. Here, we demonstrate that these pockets can further be used to identify ligands that complement the protein surface. To do so, we first build from a given pocket its "exemplar": a perfect, but nonphysical, pseudoligand that would optimally match the shape and chemical features of the pocket. In our previous studies, we used these exemplars to quantitatively compare protein surface pockets to one another. Here, we now introduce this exemplar as a template for pharmacophore-based screening of chemical libraries. Through a series of benchmark experiments, we demonstrate that this approach exhibits comparable performance as traditional docking methods for identifying known inhibitors acting at protein interaction sites. However, because this approach is predicated on ligand/exemplar overlays, and thus does not require explicit calculation of protein-ligand interactions, exemplar screening provides a tremendous speed advantage over docking: 6 million compounds can be screened in about 15 min on a single 16-core, dual-GPU computer. The extreme speed at which large compound libraries can be traversed easily enables screening against a "pocket-optimized" ensemble of protein conformations, which in turn facilitates identification of more diverse classes of active compounds for a given protein target.

Protein-protein interactions as drug targets

Modulation of protein-protein interactions (PPIs) is becoming increasingly important in drug discovery and chemical biology. While a few years ago this 'target class' was deemed to be largely undruggable an impressing number of publications and success stories now show that targeting PPIs with small, drug-like molecules indeed is a feasible approach. Here, we summarize the current state of small-molecule inhibition and stabilization of PPIs and review the active molecules from a structural and medicinal chemistry angle, especially focusing on the key examples of iNOS, LFA-1 and 14-3-3.

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.

Surfing the Protein-Protein Interaction Surface Using Docking Methods: Application to the Design of PPI Inhibitors

Blocking protein-protein interactions (PPI) using small molecules or peptides modulates biochemical pathways and has therapeutic significance. PPI inhibition for designing drug-like molecules is a new area that has been explored extensively during the last decade. Considering the number of available PPI inhibitor databases and the limited number of 3D structures available for proteins, docking and scoring methods play a major role in designing PPI inhibitors as well as stabilizers. Docking methods are used in the design of PPI inhibitors at several stages of finding a lead compound, including modeling the protein complex, screening for hot spots on the protein-protein interaction interface and screening small molecules or peptides that bind to the PPI interface. There are three major challenges to the use of docking on the relatively flat surfaces of PPI. In this review we will provide some examples of the use of docking in PPI inhibitor design as well as its limitations. The combination of experimental and docking methods with improved scoring function has thus far resulted in few success stories of PPI inhibitors for therapeutic purposes. Docking algorithms used for PPI are in the early stages, however, and as more data are available docking will become a highly promising area in the design of PPI inhibitors or stabilizers.