In Silico-In Vitro Screening of Protein-Protein Interactions: Towards the Next Generation of Therapeutics

Integrated Network Pharmacology, Molecular Docking and Animal Experiment to Explore the Efficacy and Potential Mechanism of Baiyu Decoction Against Ulcerative Colitis by Enema

Background

Baiyu Decoction (BYD), a clinical prescription of traditional Chinese medicine, has been proven to be valuable for treating ulcerative colitis (UC) by enema. However, the mechanism of BYD against UC remains unclear.

Purpose

A combination of bioinformatics methods including network pharmacology and molecular docking and animal experiments were utilized to investigate the potential mechanism of BYD in the treatment of UC.

Materials and Methods

Firstly, the representative compounds of each herb in BYD were detected by liquid chromatography-mass spectrometry. Subsequently, we predicted the core targets and potential pathways of BYD for treating UC through network pharmacology. And rat colitis model was established with dextran sodium sulfate. UC rats were subjected to BYD enema administration, during which we recorded body weight changes, disease activity index, and colon length to assess the effectiveness of BYD. Besides, quantitative real-time PCR, western blotting, ELISA and immunofluorescence were used to detect intestinal inflammatory factors, intestinal barrier biomarkers and TOLL-like receptor pathway in rats. Finally, the core components and targets of BYD were subjected to molecular docking so as to further validate the results of network pharmacology.

Results

A total of 41 active compositions and 203 targets related to BYD-UC were subjected to screening. The results of bioinformatics analysis showed that quercetin and kaempferol may be the main compounds. Additionally, AKT1, IL-6, TP53, TNF and IL-1β were regarded as potential therapeutic targets. KEGG results explained that TOLL-like receptor pathway might play a pivotal role in BYD protecting against UC. In addition, animal experiments and molecular docking validated the network pharmacology results. BYD enema treatment can reduce body weight loss, lower disease activity index score, reverse colon shortening, relieve intestinal inflammation, protect intestinal barrier, and inhibit TOLL-like receptor pathway in UC rats. Besides, molecular docking suggested that quercetin and kaempferol docked well with TLR4, AKT1, IL-6, TP53.

Conclusion

Utilizing network pharmacology, animal studies, and molecular docking, enema therapy with BYD was confirmed to have anti-UC efficacy by alleviating intestinal inflammation, protecting the intestinal barrier, and inhibiting the TOLL-like receptor pathway. Researchers should focus not only on oral medications but also on the rectal administration of medications in furtherance of the cure of ulcerative colitis.

Exploring the molecular mechanism of Gan Shuang granules for the treatment of non-alcoholic steatohepatitis using network pharmacology, molecular docking, and experimental verification

Background: With the gradual increase in prevalence in recent years, non-alcoholic steatohepatitis (NASH) has become one of the significant health problems that urgently needs to be addressed worldwide. GanShuang Granules (GSG) is derived from the classical Chinese formula Xiaoyao San and mainly used in the clinical treatment of chronic liver diseases. Objective: In this study, we aim to gain a deeper insight into the inhibiting effects of GSG on non-alcoholic fatty liver disease (NAFLD) rats and preliminarily elucidate the underlying intervention mechanisms. Methods: First, High performance liquid chromatography (UHPLC-Q/Orbitrap-MS/MS) was used for the active compounds prediction in GSG. Then the data was mapped to mzCloud database. The targets corresponding to GSG compounds were collected from public databases, along with disease genes for NAFLD. The core targets and molecular mechanisms of GSG for NAFLD treatment were predicted by protein-protein interaction (PPI) network, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses. Molecular docking of the core target-component interactions was simulated using AutoDock Vina software. The effect of GSG on NASH rats was evaluated by pathological staining and analysis of various index results. Finally, the candidate targets were further validated by ELISA and western blot (WB) analyses. Results: Combining UHPLC-Q/Orbitrap-MS/MS data analysis and public database data, a total of 346 cross-targets were obtained, corresponding to 81 compounds. The subnetwork with an MCODE score of 53.623 is a potential core target group for this study. GO and KEGG enrichment analyses showed that the targets of GSG in NAFLD were mostly related to oxidative stress, the NF-κB signaling pathway, and the apoptosis signaling pathway. By integrating the results of network pharmacology analysis, the core objectives of this study mainly include AKT1, CASP9, TNF, and CASP8. The core ingredients are related to resveratrol and fisetin. The molecular docking results indicated key binding activity between AKT1-fisetin, AKT1-Resveratrol, and CASP8-fisetin. Moreover, GSG could improve the inflammatory status and restore the abnormal lipid accumulation of NAFLD/NASH liver, and these levels are further verified by pathological staining and detection of related indicators. Mechanistically, GSG could regulate protein expression levels in the liver for P65, p-P65, IKB, p-IKB, IKK, caspase-3, -8, -9, and cytochrome C, etc. It reflects the inhibitory effect of GSG on the NF-κB/IκB signaling pathway. Conclusion: Our results suggested that GSG demonstrated therapeutic effects on NAFLD/NASH rats, and these may be mainly reflected in the inhibitory effects on the NF-κB/IκB signaling pathway and its downstream inflammation and apoptosis signals.

Exploring the potential mechanism of Fritiliariae Irrhosae Bulbus on ischemic stroke based on network pharmacology and experimental validation

Objective: To study the potential targets and molecular mechanisms of Fritiliariae Irrhosae Bulbus (FIB) in the treatment of ischemic strokes based on a network pharmacology strategy, with a combination of molecular docking and animal experiments.

Methods: The active components and targets of FIB were screened by TCMSP database and TCMIP database, and the related targets of ischemic strokes were screened by GeneCards, OMIM, CTD, and DrugBank, then the intersection targets of the two were taken. The protein interaction network was constructed by STRING, the PPI network diagram was drawn by using Cytoscape software, and the key targets of FIB treatment of ischemic strokes were analyzed by MCODE. The DAVID database was used for GO and KEGG enrichment analysis, and the potential pathway of FIB against ischemic strokes was obtained. Molecular docking was performed by using AutoDock Tools 1.5.6 software. Finally, a mouse model of ischemic stroke was established, and the results of network pharmacology were verified by in vivo experiments. Realtime Polymerase Chain Reaction was used to detect the expression levels of relevant mRNAs in the mouse brain tissue. Western blot was used to detect the expression levels of related proteins in the mouse brain tissue.

Results: 13 kinds of active components of FIB were screened, 31 targets were found in the intersection of FIB and ischemic strokes, 10 key targets were obtained by MCODE analysis, 236 biological processes were involved in GO enrichment analysis, and key targets of KEGG enrichment analysis were mainly concentrated in Neuroactive light receptor interaction, Calcium signaling pathway, Cholinergic synapse, Hepatitis B, Apoptosis—multiple specifications, Pathways in cancer and other significantly related pathways. There was good binding activity between the screened main active components and target proteins when molecular docking was performed. Animal experiments showed that the infarct volume of brain tissue in the FIB treatment group was considerably reduced. RT-qPCR and the results of Western Blot showed that FIB could inhibit the expression of active-Caspase3, HSP90AA1, phosphorylated C-JUN, and COX2.

Conclusion: Based on network pharmacology, the effect of FIB in the treatment of ischemic strokes was discussed through the multi-component-multi-target-multi-pathway. The therapeutic effect and potential mechanisms of FIB on ischemic strokes were preliminarily explored, which provided a ground work for further researches on the pharmacodynamic material basis, mechanism of action and clinical application.

Peptidomimetics designed to bind to RAS effector domain are promising cancer therapeutic compounds

Oncogenic RAS proteins are important for driving tumour formation, and for maintenance of the transformed phenotype, and thus their relevance as a cancer therapeutic target is undeniable. We focused here on obtaining peptidomimetics, which have good pharmacological properties, to block Ras–effector interaction. Computational analysis was used to identify hot spots of RAS relevant for these interactions and to screen a library of peptidomimetics. Nine compounds were synthesized and assayed for their activity as RAS inhibitors in cultured cells. Most of them induced a reduction in ERK and AKT activation by EGF, a marker of RAS activity. The most potent inhibitor disrupted Raf and PI3K interaction with oncogenic KRAS, corroborating its mechanism of action as an inhibitor of protein–protein interactions, and thus validating our computational methodology. Most interestingly, improvement of one of the compounds allowed us to obtain a peptidomimetic that decreased the survival of pancreatic cancer cell lines harbouring oncogenic KRAS.

Antimicrobial peptides with cell-penetrating activity as prophylactic and treatment drugs

Health is fundamental for the development of individuals and evolution of species. In that sense, for human societies is relevant to understand how the human body has developed molecular strategies to maintain health. In the present review, we summarize diverse evidence that support the role of peptides in this endeavor. Of particular interest to the present review are antimicrobial peptides (AMP) and cell-penetrating peptides (CPP). Different experimental evidence indicates that AMP/CPP are able to regulate autophagy, which in turn regulates the immune system response. AMP also assists in the establishment of the microbiota, which in turn is critical for different behavioral and health aspects of humans. Thus, AMP and CPP are multifunctional peptides that regulate two aspects of our bodies that are fundamental to our health: autophagy and microbiota. While it is now clear the multifunctional nature of these peptides, we are still in the early stages of the development of computational strategies aimed to assist experimentalists in identifying selective multifunctional AMP/CPP to control nonhealthy conditions. For instance, both AMP and CPP are computationally characterized as amphipatic and cationic, yet none of these features are relevant to differentiate these peptides from non-AMP or non-CPP. The present review aims to highlight current knowledge that may facilitate the development of AMP’s design tools for preventing or treating illness.

Myostatin and its Regulation: A Comprehensive Review of Myostatin Inhibiting Strategies

Myostatin (MSTN) is a well-reported negative regulator of muscle growth and a member of the transforming growth factor (TGF) family. MSTN has important functions in skeletal muscle (SM), and its crucial involvement in several disorders has made it an important therapeutic target. Several strategies based on the use of natural compounds to inhibitory peptides are being used to inhibit the activity of MSTN. This review delivers an overview of the current state of knowledge about SM and myogenesis with particular emphasis on the structural characteristics and regulatory functions of MSTN during myogenesis and its involvements in various muscle related disorders. In addition, we review the diverse approaches used to inhibit the activity of MSTN, especially in silico approaches to the screening of natural compounds and the design of novel short peptides derived from proteins that typically interact with MSTN.

Molecular docking analysis of two bioactive molecules KLUF10 and KLUF13 isolated from the marine bacteria Micrococcus sp. OUS9 with TNF alpha

Tumor necrosis factor-alpha (TNF-α) is known to be linked with tumor. Therefore, it is of interest to document the Molecular docking analysis of two bioactive molecules KLUF10 and KLUF13 isolated from the marine bacteria Micrococcus sp. OUS9 with TNF alpha. We report the molecular interactions of KLUF10 and KLUF13 with TNF alpha.

Network Pharmacology Prediction and Molecular Docking-Based Strategy to Discover the Potential Pharmacological Mechanism of Huai Hua San Against Ulcerative Colitis

Background

Huai Hua San (HHS), a famous Traditional Chinese Medicine (TCM) formula, has been widely applied in treating ulcerative colitis (UC). However, the interaction of bioactives from HHS with the targets involved in UC has not been elucidated yet.

Aim

A network pharmacology-based approach combined with molecular docking and in vitro validation was performed to determine the bioactives, key targets, and potential pharmacological mechanism of HHS against UC.

Materials and Methods

Bioactives and potential targets of HHS, as well as UC-related targets, were retrieved from public databases. Crucial bioactive ingredients, potential targets, and signaling pathways were acquired through bioinformatics analysis, including protein-protein interaction (PPI), as well as the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Subsequently, molecular docking was carried out to predict the combination of active compounds with core targets. Lastly, in vitro experiments were conducted to further verify the findings.

Results

A total of 28 bioactive ingredients of HHS and 421 HHS-UC-related targets were screened. Bioinformatics analysis revealed that quercetin, luteolin, and nobiletin may be potential candidate agents. JUN, TP53, and ESR1 could become potential therapeutic targets. PI3K-AKT signaling pathway might play an important role in HHS against UC. Moreover, molecular docking suggested that quercetin, luteolin, and nobiletin combined well with JUN, TP53, and ESR1, respectively. Cell experiments showed that the most important ingredient of HHS, quercetin, could inhibit the levels of inflammatory factors and phosphorylated c-Jun, as well as PI3K-Akt signaling pathway in LPS-induced RAW264.7 cells, which further confirmed the prediction by network pharmacology strategy and molecular docking.

Conclusion

Our results comprehensively illustrated the bioactives, potential targets, and molecular mechanism of HHS against UC. It also provided a promising strategy to uncover the scientific basis and therapeutic mechanism of TCM formulae in treating diseases.

Residue-based pharmacophore approaches to study protein-protein interactions

This review focuses on pharmacophore approaches in researching protein interfaces that bind protein ligands. Pharmacophore descriptions of binding interfaces that employ molecular dynamics simulation can account for effects of solvation and conformational flexibility. In addition, these calculations provide an approximation to entropic considerations and as such, a better approximation of the free energy of binding. Residue-based pharmacophore approaches can facilitate a variety of drug discovery tasks such as the identification of receptor-ligand partners, identifying their binding poses, designing protein interfaces for selectivity, or defining a reduced mutational combinatorial exploration for subsequent experimental engineering techniques by orders of magnitudes.

Understanding the Targeting Mechanisms of Multi-Specific Biologics in Immunotherapy with Multiscale Modeling

Immunotherapeutics are frequently associated with adverse side effects due to the elicitation of global immune modulation. To lower the risk of these side effects, recombinant DNA technology is employed to enhance the selectivity of cell targeting by genetically fusing different biomolecules, yielding new species referred to as multi-specific biologics. The design of new multi-specific biologics is a central challenge for the realization of new immunotherapies. To understand the molecular determinants responsible for regulating the binding between multi-specific biologics and surface-bound membrane receptors, we developed a multiscale computational framework that integrates various simulation approaches covering different timescales and spatial resolutions. Our model system of multi-specific biologics contains two natural ligands of immune receptors, which are covalently tethered by a peptide linker. Using this method, a number of interesting features of multi-specific biologics were identified. Our study therefore provides an important strategy to design the next-generation biologics for immunotherapy.

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Two proteins are connected by different linkers as a model of bispecific biologics

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Conformational dynamics of biologics are captured by microsecond MD simulations

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Coarse-grained simulations are used to test binding between biologics and receptors

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Biologics with long and flexible linkers are more efficient in targeting receptors

Recent Advances in Molecular Docking for the Research and Discovery of Potential Marine Drugs

Marine drugs have long been used and exhibit unique advantages in clinical practices. Among the marine drugs that have been approved by the Food and Drug Administration (FDA), the protein–ligand interactions, such as cytarabine–DNA polymerase, vidarabine–adenylyl cyclase, and eribulin–tubulin complexes, are the important mechanisms of action for their efficacy. However, the complex and multi-targeted components in marine medicinal resources, their bio-active chemical basis, and mechanisms of action have posed huge challenges in the discovery and development of marine drugs so far, which need to be systematically investigated in-depth. Molecular docking could effectively predict the binding mode and binding energy of the protein–ligand complexes and has become a major method of computer-aided drug design (CADD), hence this powerful tool has been widely used in many aspects of the research on marine drugs. This review introduces the basic principles and software of the molecular docking and further summarizes the applications of this method in marine drug discovery and design, including the early virtual screening in the drug discovery stage, drug target discovery, potential mechanisms of action, and the prediction of drug metabolism. In addition, this review would also discuss and prospect the problems of molecular docking, in order to provide more theoretical basis for clinical practices and new marine drug research and development.

Structure based design of inhibitory peptides targeting ornithine decarboxylase dimeric interface and in vitro validation in human retinoblastoma Y79 cells

Polyamine synthesis in human cells is initiated by catalytic action of Ornithine decarboxylase (ODC) on Ornithine. Elevated levels of polyamines are manifested proliferating cancer cells and are found to promote tumour cell adhesion. Di-flouro methyl orninthine is a known inhibitor of ODC, however its usage is limited due its low affinity quick clearance and incompetent cellular uptake, thus posing a need for potential inhibitors. Currently, peptides are substituting drugs, as these are highly selective, specific and potent. Hence, in this study, the interacting interfaces of native homodimeric form of ODC and its heterodimer with Antizyme were probed to design inhibitory peptides targeting ODC. The designed peptides were validated for structural fitness by extensive molecular dynamics simulations and Circular dichroism studies. Finally, these peptides were validated in Y79 retinoblastoma cells for impact on ODC activity, cytotoxicity cell cycle and cell adhesion. On collective analysis, Peptide3 (Pep 3) and Peptide4 (Pep 4) were found to be potentially targeting ODC, as these peptides showed significant decrease in intracellular polyamine levels, cell adhesion and cell cycle perturbation in Y79 cells. Thus, Pep 3 and Pep 4 shall be favourably considered as therapeutic agents for targeting ODC mediated proliferation in retinoblastoma.Communicated by Ramaswamy H. Sarma.

Development of R7BP inhibitors through cross-linking coupled mass spectrometry and integrated modeling

Protein-protein interaction (PPI) networks are known to be valuable targets for therapeutic intervention; yet the development of PPI modulators as next-generation drugs to target specific vertices, edges, and hubs has been impeded by the lack of structural information of many of the proteins and complexes involved. Building on recent advancements in cross-linking mass spectrometry (XL-MS), we describe an effective approach to obtain relevant structural data on R7BP, a master regulator of itch sensation, and its interfaces with other proteins in its network. This approach integrates XL-MS with a variety of modeling techniques to successfully develop antibody inhibitors of the R7BP and RGS7/Gβ5 duplex interaction. Binding and inhibitory efficiency are studied by surface plasmon resonance spectroscopy and through an R7BP-derived dominant negative construct. This approach may have broader applications as a tool to facilitate the development of PPI modulators in the absence of crystal structures or when structural information is limited.

In Silico Target Druggability Assessment: From Structural to Systemic Approaches

This chapter will focus on today's in silico direct and indirect approaches to assess therapeutic target druggability. The direct approach tries to infer from the 3D structure the capacity of the target protein to bind small molecule in order to modulate its biological function. Algorithms to recognize and characterize the quality of the ligand interaction sites whether within buried protein cavities or within large protein-protein interface will be reviewed in the first part of the paper. In the case a ligand-binding site is already identified, indirect aspects of target druggability can be assessed. These indirect approaches focus first on target promiscuity and the potential difficulties in developing specific drugs. It is based on large-scale comparison of protein-binding sites. The second aspect concerns the capacity of the target to induce resistant pathway once it is inhibited or activated by a drug. The emergence of drug-resistant pathways can be assessed through systemic analysis of biological networks implementing metabolism and/or cell regulation signaling.

Evaluations of the Absolute and Relative Free Energies for Antidepressant Binding to the Amino Acid Membrane Transporter LeuT with Free Energy Simulations

The binding of ligands to protein receptors with high affinity and specificity is central to many cellular processes. The quest for the development of computational models capable of accurately evaluating binding affinity remains one of the main goals of modern computational biophysics. In this work, free energy perturbation/molecular dynamics simulations were used to evaluate absolute and relative binding affinity for three different antidepressants to a sodium-dependent membrane transporter, LeuT, a bacterial homologue of human serotonin and dopamine transporters. Dysfunction of these membrane transporters in mammals has been implicated in multiple diseases of the nervous system, including bipolar disorder and depression. Furthermore, these proteins are key targets for antidepressants including fluoxetine (aka Prozac) and tricyclic antidepressants known to block transport activity. In addition to being clinically relevant, this system, where multiple crystal structures are readily available, represents an ideal testing ground for methods used to study the molecular mechanisms of ligand binding to membrane proteins. We discuss possible pitfalls and different levels of approximation required to evaluate binding affinity, such as the dependence of the computed affinities on the strength of constraints and the sensitivity of the computed affinities to the particular partial charges derived from restrained electrostatic potential fitting of quantum mechanics electrostatic potential. Finally, we compare the effects of different constraint schemes on the absolute and relative binding affinities obtained from free energy simulations.

State-of-the-art strategies for targeting protein-protein interactions by small-molecule inhibitors

Targeting protein-protein interactions (PPIs) has emerged as a viable approach in modern drug discovery. However, the identification of small molecules enabling us to effectively interrupt their interactions presents significant challenges. In the recent past, significant advances have been made in the development of new biological and chemical strategies to facilitate the discovery process of small-molecule PPI inhibitors. This review aims to highlight the state-of-the-art technologies and the achievements made recently in this field. The "hot spots" of PPIs have been proved to be critical for small molecules to bind. Three strategies including screening, designing, and synthetic approaches have been explored for discovering PPI inhibitors by targeting the "hot spots". Although the classic high throughput screening approach can be used, fragment screening, fragment-based drug design and newly improved virtual screening are demonstrated to be more effective in the discovery of PPI inhibitors. In addition to screening approaches, design strategies including anchor-based and small molecule mimetics of secondary structures involved in PPIs have become powerful tools as well. Finally, constructing new chemically spaced libraries with high diversity and complexity is becoming an important area of interest for PPI inhibitors. The successful cases from the recent five year studies are used to illustrate how these approaches are implemented to uncover and optimize small molecule PPI inhibitors and notably some of them have become promising therapeutics.

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.

The virtual heart as a platform for screening drug cardiotoxicity

To predict the safety of a drug at an early stage in its development is a major challenge as there is a lack of in vitro heart models that correlate data from preclinical toxicity screening assays with clinical results. A biophysically detailed computer model of the heart, the virtual heart, provides a powerful tool for simulating drug–ion channel interactions and cardiac functions during normal and disease conditions and, therefore, provides a powerful platform for drug cardiotoxicity screening. In this article, we first review recent progress in the development of theory on drug–ion channel interactions and mathematical modelling. Then we propose a family of biomarkers that can quantitatively characterize the actions of a drug on the electrical activity of the heart at multi‐physical scales including cellular and tissue levels. We also conducted some simulations to demonstrate the application of the virtual heart to assess the pro‐arrhythmic effects of cisapride and amiodarone. Using the model we investigated the mechanisms responsible for the differences between the two drugs on pro‐arrhythmogenesis, even though both prolong the QT interval of ECGs. Several challenges for further development of a virtual heart as a platform for screening drug cardiotoxicity are discussed.

Linked Articles

This article is part of a themed section on Chinese Innovation in Cardiovascular Drug Discovery. To view the other articles in this section visit http://dx.doi.org/10.1111/bph.2015.172.issue-23

Rational design of small-molecule stabilizers of spermine synthase dimer by virtual screening and free energy-based approach

Snyder-Robinson Syndrome (SRS) is a rare mental retardation disorder which is caused by the malfunctioning of an enzyme, the spermine synthase (SMS), which functions as a homo-dimer. The malfunctioning of SMS in SRS patients is associated with several identified missense mutations that occur away from the active site. This investigation deals with a particular SRS-causing mutation, the G56S mutation, which was shown computationally and experimentally to destabilize the SMS homo-dimer and thus to abolish SMS enzymatic activity. As a proof-of-concept, we explore the possibility to restore the enzymatic activity of the malfunctioning SMS mutant G56S by stabilizing the dimer through small molecule binding at the mutant homo-dimer interface. For this purpose, we designed an in silico protocol that couples virtual screening and a free binding energy-based approach to identify potential small-molecule binders on the destabilized G56S dimer, with the goal to stabilize it and thus to increase SMS G56S mutant activity. The protocol resulted in extensive list of plausible stabilizers, among which we selected and tested 51 compounds experimentally for their capability to increase SMS G56S mutant enzymatic activity. In silico analysis of the experimentally identified stabilizers suggested five distinctive chemical scaffolds. This investigation suggests that druggable pockets exist in the vicinity of the mutation sites at protein-protein interfaces which can be used to alter the disease-causing effects by small molecule binding. The identified chemical scaffolds are drug-like and can serve as original starting points for development of lead molecules to further rescue the disease-causing effects of the Snyder-Robinson syndrome for which no efficient treatment exists up to now.

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.

Oncogenic protein interfaces: small molecules, big challenges

Historically, targeting protein-protein interactions with small molecules was not thought possible because the corresponding interfaces were considered mostly flat and featureless and therefore 'undruggable'. Instead, such interactions were targeted with larger molecules, such as peptides and antibodies. However, the past decade has seen encouraging breakthroughs through the refinement of existing techniques and the development of new ones, together with the identification and exploitation of unexpected aspects of protein-protein interaction surfaces. In this Review, we describe some of the latest techniques to discover modulators of protein-protein interactions and how current drug discovery approaches have been adapted to successfully target these interfaces.

Structure-based design of small-molecule protein–protein interaction modulators: the story so far

As the pivotal role of protein–protein interactions in cell growth, transcriptional activity, intracellular trafficking, signal transduction and pathological conditions has been assessed, experimental and in silico strategies have been developed to design protein–protein interaction modulators. State-of-the-art structure-based design methods, mainly pharmacophore modeling and docking, which have succeeded in the identification of enzyme inhibitors, receptor agonists and antagonists, and new tools specifically conceived to target protein–protein interfaces (e.g., hot-spot and druggable pocket prediction methods) have been applied in the search for small-molecule protein–protein interaction modulators. Many successful applications of structure-based design approaches that, despite the challenge of targeting protein–protein interfaces with small molecules, have led to the identification of micromolar and submicromolar hits are reviewed here.

Computational analysis of protein-protein interfaces involving an alpha helix: insights for terphenyl-like molecules binding

Background

Protein-Protein Interactions (PPIs) are key for many cellular processes. The characterization of PPI interfaces and the prediction of putative ligand binding sites and hot spot residues are essential to design efficient small-molecule modulators of PPI. Terphenyl and its derivatives are small organic molecules known to mimic one face of protein-binding alpha-helical peptides. In this work we focus on several PPIs mediated by alpha-helical peptides.

Method

We performed computational sequence- and structure-based analyses in order to evaluate several key physicochemical and surface properties of proteins known to interact with alpha-helical peptides and/or terphenyl and its derivatives.

Results

Sequence-based analysis revealed low sequence identity between some of the analyzed proteins binding alpha-helical peptides. Structure-based analysis was performed to calculate the volume, the fractal dimension roughness and the hydrophobicity of the binding regions. Besides the overall hydrophobic character of the binding pockets, some specificities were detected. We showed that the hydrophobicity is not uniformly distributed in different alpha-helix binding pockets that can help to identify key hydrophobic hot spots.

Conclusions

The presence of hydrophobic cavities at the protein surface with a more complex shape than the entire protein surface seems to be an important property related to the ability of proteins to bind alpha-helical peptides and low molecular weight mimetics. Characterization of similarities and specificities of PPI binding sites can be helpful for further development of small molecules targeting alpha-helix binding proteins.

Structure-based target druggability assessment

The focus of this chapter is on the important concepts behind the in silico techniques that are used today to assess target druggability. The first step of the assessment consists of finding cavity space in the protein using 2D and/or 3D topological concepts. These concepts underlie the geometry and energy-based pocketfinder algorithms. Analysis pursues on the physico-chemical complementarity between the binding site and the drug like molecule. Geometrical and molecular flexibility aspect are also included in this assessment. The presence of hot interaction spots are shown to be particularly important for targeting protein-protein interactions. Finally, binding site promiscuity can be assessed by large scale structural comparison with other targets. Common chemical features amongst protein cavities can predict potential cross-reactivity with unwanted targets.

Targeting the proangiogenic VEGF-VEGFR protein-protein interface with drug-like compounds by in silico and in vitro screening

Protein-protein interactions play a central role in medicine, and their modulation with small organic compounds remains an enormous challenge. Because it has been noted that the macromolecular complexes modulated to date have a relatively pronounced binding cavity at the interface, we decided to perform screening experiments over the vascular endothelial growth factor receptor (VEGFR), a validated target for antiangiogenic treatments with a very flat interface. We focused the study on the VEGFR-1 D2 domain, and 20 active compounds were identified. These small compounds contained a (3-carboxy-2-ureido)thiophen unit and had IC(50) values in the low micromolar range. The most potent compound inhibited the VEGF-induced VEGFR-1 transduction pathways. Our findings suggest that our best hit may be a promising scaffold to probe this macromolecular complex and for the development of treatments of VEGFR-1-dependent diseases.

AMMOS software: method and application

Recent advances in computational sciences enabled extensive use of in silico methods in projects at the interface between chemistry and biology. Among them virtual ligand screening, a modern set of approaches, facilitates hit identification and lead optimization in drug discovery programs. Most of these approaches require the preparation of the libraries containing small organic molecules to be screened or a refinement of the virtual screening results. Here we present an overview of the open source AMMOS software, which is a platform performing an automatic procedure that allows for a structural generation and optimization of drug-like molecules in compound collections, as well as a structural refinement of protein-ligand complexes to assist in silico screening exercises.

Tyrosine kinase syk non-enzymatic inhibitors and potential anti-allergic drug-like compounds discovered by virtual and in vitro screening

In the past decade, the spleen tyrosine kinase (Syk) has shown a high potential for the discovery of new treatments for inflammatory and autoimmune disorders. Pharmacological inhibitors of Syk catalytic site bearing therapeutic potential have been developed, with however limited specificity towards Syk. To address this topic, we opted for the design of drug-like compounds that could impede the interaction of Syk with its cellular partners while maintaining an active kinase protein. To achieve this challenging task, we used the powerful potential of intracellular antibodies for the modulation of cellular functions in vivo, combined to structure-based in silico screening. In our previous studies, we reported the anti-allergic properties of the intracellular antibody G4G11. With the aim of finding functional mimics of G4G11, we developed an Antibody Displacement Assay and we isolated the drug-like compound C-13, with promising in vivo anti-allergic activity. The likely binding cavity of this compound is located at the close vicinity of G4G11 epitope, far away from the catalytic site of Syk. Here we report the virtual screen of a collection of 500,000 molecules against this new cavity, which led to the isolation of 1000 compounds subsequently evaluated for their in vitro inhibitory effects using the Antibody Displacement Assay. Eighty five compounds were selected and evaluated for their ability to inhibit the liberation of allergic mediators from mast cells. Among them, 10 compounds inhibited degranulation with IC₅₀ values ≤ 10 µM. The most bioactive compounds combine biological activity, significant inhibition of antibody binding and strong affinity for Syk. Moreover, these molecules show a good potential for oral bioavailability and are not kinase catalytic site inhibitors. These bioactive compounds could be used as starting points for the development of new classes of non-enzymatic inhibitors of Syk and for drug discovery endeavour in the field of inflammation related disorders.

Exploring NMR ensembles of calcium binding proteins: perspectives to design inhibitors of protein-protein interactions

Background

Disrupting protein-protein interactions by small organic molecules is nowadays a promising strategy employed to block protein targets involved in different pathologies. However, structural changes occurring at the binding interfaces make difficult drug discovery processes using structure-based drug design/virtual screening approaches. Here we focused on two homologous calcium binding proteins, calmodulin and human centrin 2, involved in different cellular functions via protein-protein interactions, and known to undergo important conformational changes upon ligand binding.

Results

In order to find suitable protein conformations of calmodulin and centrin for further structure-based drug design/virtual screening, we performed in silico structural/energetic analysis and molecular docking of terphenyl (a mimicking alpha-helical molecule known to inhibit protein-protein interactions of calmodulin) into X-ray and NMR ensembles of calmodulin and centrin. We employed several scoring methods in order to find the best protein conformations. Our results show that docking on NMR structures of calmodulin and centrin can be very helpful to take into account conformational changes occurring at protein-protein interfaces.

Conclusions

NMR structures of protein-protein complexes nowadays available could efficiently be exploited for further structure-based drug design/virtual screening processes employed to design small molecule inhibitors of protein-protein interactions.

Proteome-wide prediction of overlapping small molecule and protein binding sites using structure

Small molecules that modulate protein-protein interactions are of great interest for chemical biology and therapeutics. Here I present a structure-based approach to predict 'bi-functional' sites able to bind both small molecule ligands and proteins, in proteins of unknown structure. First, I develop a homology-based annotation method that transfers binding sites of known three-dimensional structure onto protein sequences, predicting residues in ligand and protein binding sites with estimated true positive rates of 98% and 88%, respectively, at 1% false positive rates. Applying this method to the human proteome predicts 8463 proteins with bi-functional residues and correctly recovers the targets of known interaction modulators. Proteins with significantly (p < 0.01) more bi-functional residues than expected were found to be enriched in regulatory and depleted in metabolism functions. Finally, I demonstrate the utility of the method by describing examples of predicted overlap and evidence of their biological and therapeutic relevance. The results suggest that combining the structures of known binding sites with established fold detection algorithms can predict regions of protein-protein interfaces that are amenable to small molecule modulation. Open-source software and the results for several complete proteomes are available at http://pibase.janelia.org/homolobind.

Druggable pockets and binding site centric chemical space: a paradigm shift in drug discovery

Detection, comparison and analyses of binding pockets are pivotal to structure-based drug design endeavors, from hit identification, screening of exosites and de-orphanization of protein functions to the anticipation of specific and non-specific binding to off- and anti-targets. Here, we analyze protein-ligand complexes and discuss methods that assist binding site identification, prediction of druggability and binding site comparison. The full potential of pockets is yet to be harnessed, and we envision that better understanding of the pocket space will have far-reaching implications in the field of drug discovery, such as the design of pocket-specific compound libraries and scoring functions.

Enhancements of screening collections to address areas of unmet medical need: an industry perspective

The past 20 years have witnessed an impressive expansion of the 'drug space'; defined as the intersection of the Medicinal Chemistry space and the Biologically Active space relevant in the quest for new treatments for disease. Despite the success of known lead discovery tactics, areas of unmet medical need are often linked to challenging or novel targets and are poorly served by current screening collections. A successful strategy to fill the gaps is to diversify the approaches taken in the enhancement of screening collections. Possible strategies include investments through proven methods, exploring areas of chemical space previously neglected (e.g. hydrophilic compounds, natural product mimics), and applying tactics to the lead discovery process that are complementary to HTS (e.g. fragment based screening or multidisciplinary team efforts to tackle new target classes).

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

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

A dynamic target-based pharmacophoric model mapping the CD4 binding site on HIV-1 gp120 to identify new inhibitors of gp120-CD4 protein-protein interactions

A dynamic target-based pharmacophoric model mapping the CD4 binding site on HIV-1 gp120 was built and used to identify new hits able to inhibit gp120-CD4 protein-protein interactions. Two compounds showed micromolar inhibition of HIV-1 replication in cells attributable to an interference with the entry step of infection, by direct interaction with gp120. Inactivity of compounds toward a M475I strain suggested specific contacts with the Phe43 cavity of gp120.

Fpocket: an open source platform for ligand pocket detection

Background

Virtual screening methods start to be well established as effective approaches to identify hits, candidates and leads for drug discovery research. Among those, structure based virtual screening (SBVS) approaches aim at docking collections of small compounds in the target structure to identify potent compounds. For SBVS, the identification of candidate pockets in protein structures is a key feature, and the recent years have seen increasing interest in developing methods for pocket and cavity detection on protein surfaces.

Results

Fpocket is an open source pocket detection package based on Voronoi tessellation and alpha spheres built on top of the publicly available package Qhull. The modular source code is organised around a central library of functions, a basis for three main programs: (i) Fpocket, to perform pocket identification, (ii) Tpocket, to organise pocket detection benchmarking on a set of known protein-ligand complexes, and (iii) Dpocket, to collect pocket descriptor values on a set of proteins. Fpocket is written in the C programming language, which makes it a platform well suited for the scientific community willing to develop new scoring functions and extract various pocket descriptors on a large scale level. Fpocket 1.0, relying on a simple scoring function, is able to detect 94% and 92% of the pockets within the best three ranked pockets from the holo and apo proteins respectively, outperforming the standards of the field, while being faster.

Conclusion

Fpocket provides a rapid, open source and stable basis for further developments related to protein pocket detection, efficient pocket descriptor extraction, or drugablity prediction purposes. Fpocket is freely available under the GNU GPL license at .

Modeling effects of human single nucleotide polymorphisms on protein-protein interactions

A large set of three-dimensional structures of 264 protein-protein complexes with known nonsynonymous single nucleotide polymorphisms (nsSNPs) at the interface was built using homology-based methods. The nsSNPs were mapped on the proteins' structures and their effect on the binding energy was investigated with CHARMM force field and continuum electrostatic calculations. Two sets of nsSNPs were studied: disease annotated Online Mendelian Inheritance in Man (OMIM) and nonannotated (non-OMIM). It was demonstrated that OMIM nsSNPs tend to destabilize the electrostatic component of the binding energy, in contrast with the effect of non-OMIM nsSNPs. In addition, it was shown that the change of the binding energy upon amino acid substitutions is not related to the conservation of the net charge, hydrophobicity, or hydrogen bond network at the interface. The results indicate that, generally, the effect of nsSNPs on protein-protein interactions cannot be predicted from amino acids' physico-chemical properties alone, since in many cases a substitution of a particular residue with another amino acid having completely different polarity or hydrophobicity had little effect on the binding energy. Analysis of sequence conservation showed that nsSNP at highly conserved positions resulted in a large variance of the binding energy changes. In contrast, amino acid substitutions corresponding to nsSNPs at nonconserved positions, on average, were not found to have a large effect on binding affinity. pKa calculations were performed and showed that amino acid substitutions could change the wild-type proton uptake/release and thus resulting in different pH-dependence of the binding energy.

Screening Outside the Catalytic Site: Inhibition of Macromolecular Inter-actions Through Structure-Based Virtual Ligand Screening Experiments

During these last 15 years, drug discovery strategies have essentially focused on identifying small molecules able to inhibit catalytic sites. However, other mechanisms could be targeted. Protein-protein interactions play crucial roles in a number of biological processes, and, as such, their disruption or stabilization is becoming an area of intense activity. Along the same line, inhibition of protein-membrane could be of major importance in several disease indications. Despite the many challenges associated with the development of such classes of interaction modulators, there has been considerable success in the recent years. Importantly, through the existence of protein hot-spots and the presence of druggable pockets at the macromolecular interfaces or in their vicinities, it has been possible to find small molecule effectors using a variety of screening techniques, including combined virtual ligand-in vitro screening strategy. Indeed such in silico-in vitro protocols emerge as the method of choice to facilitate our quest of novel drug-like compounds or of mechanistic probes aiming at facilitating the understanding of molecular reactions involved in the Health and Disease process. In this review, we comment recent successes of combined in silico-in vitro screening methods applied to modulating macromolecular interactions with a special emphasis on protein-membrane interactions.