Small molecular weight protein–protein interaction antagonists—an insurmountable challenge?

Small Molecule Antagonists of the DNA Repair ERCC1/XPA Protein-Protein Interaction

The DNA excision repair protein ERCC1 and the DNA damage sensor protein, XPA are highly overexpressed in patient samples of cisplatin-resistant solid tumors including lung, bladder, ovarian, and testicular cancer. The repair of cisplatin-DNA crosslinks is dependent upon nucleotide excision repair (NER) that is modulated by protein-protein binding interactions of ERCC1, the endonuclease, XPF, and XPA. Thus, inhibition of their function is a potential therapeutic strategy for the selective sensitization of tumors to DNA-damaging platinum-based cancer therapy. Here, we report on new small-molecule antagonists of the ERCC1/XPA protein-protein interaction (PPI) discovered using a high-throughput competitive fluorescence polarization binding assay. We discovered a unique structural class of thiopyridine-3-carbonitrile PPI antagonists that block a truncated XPA polypeptide from binding to ERCC1. Preliminary hit-to-lead studies from compound 1 reveal structure-activity relationships (SAR) and identify lead compound 27 o with an EC50 of 4.7 μM. Furthermore, chemical shift perturbation mapping by NMR confirms that 1 binds within the same site as the truncated XPA67-80 peptide. These novel ERCC1 antagonists are useful chemical biology tools for investigating DNA damage repair pathways and provide a good starting point for medicinal chemistry optimization as therapeutics for sensitizing tumors to DNA damaging agents and overcoming resistance to platinum-based chemotherapy.

PPII-AEAT: Prediction of protein-protein interaction inhibitors based on autoencoders with adversarial training

Protein-protein interactions (PPIs) have shown increasing potential as novel drug targets. The design and development of small molecule inhibitors targeting specific PPIs are crucial for the prevention and treatment of related diseases. Accordingly, effective computational methods are highly desired to meet the emerging need for the large-scale accurate prediction of PPI inhibitors. However, existing machine learning models rely heavily on the manual screening of features and lack generalizability. Here, we propose a new PPI inhibitor prediction method based on autoencoders with adversarial training (named PPII-AEAT) that can adaptively learn molecule representation to cope with different PPI targets. First, Extended-connectivity fingerprints and Mordred descriptors are employed to extract the primary features of small molecular compounds. Then, an autoencoder architecture is trained in three phases to learn high-level representations and predict inhibitory scores. We evaluate PPII-AEAT on nine PPI targets and two different tasks, including the PPI inhibitor identification task and inhibitory potency prediction task. The experimental results show that our proposed PPII-AEAT outperforms state-of-the-art methods.

High-Quality Conformer Generation with CONFORGE: Algorithm and Performance Assessment

Knowledge of the putative bound-state conformation of a molecule is an essential prerequisite for the successful application of many computer-aided drug design methods that aim to assess or predict its capability to bind to a particular target receptor. An established approach to predict bioactive conformers in the absence of receptor structure information is to sample the low-energy conformational space of the investigated molecules and derive representative conformer ensembles that can be expected to comprise members closely resembling possible bound-state ligand conformations. The high relevance of such conformer generation functionality led to the development of a wide panel of dedicated commercial and open-source software tools throughout the last decades. Several published benchmarking studies have shown that open-source tools usually lag behind their commercial competitors in many key aspects. In this work, we introduce the open-source conformer ensemble generator CONFORGE, which aims at delivering state-of-the-art performance for all types of organic molecules in drug-like chemical space. The ability of CONFORGE and several well-known commercial and open-source conformer ensemble generators to reproduce experimental 3D structures as well as their computational efficiency and robustness has been assessed thoroughly for both typical drug-like molecules and macrocyclic structures. For small molecules, CONFORGE clearly outperformed all other tested open-source conformer generators and performed at least equally well as the evaluated commercial generators in terms of both processing speed and accuracy. In the case of macrocyclic structures, CONFORGE achieved the best average accuracy among all benchmarked generators, with RDKit's generator coming close in second place.

A Carbohydrate-based Synthetic Approach to Diverse Structurally and Stereochemically Complex Chiral Polyheterocycles

Chiral polyheterocycles are one of the most frequently encountered scaffolds in natural products and in current drugs repertoire. A carbohydrate-based diversity oriented synthetic (DOS) approach has been employed for gaining access to many structurally diverse and stereochemically complex rigid polyheterocyclic molecules with multiple chiral hydroxyl groups to enhance aqueous solubility. Inexpensive chiral pool of D-Glucose has been judiciously exploited to get access of complex chiral polyheterocyclic structures using inexpensive, common achiral reagents and domino-Knoevenagel hetero-Diels-Alder (DKHDA) reaction as one of the key synthetic tools. Stereochemistry of newly generated stereocenters of polycyclic structures are unambiguously determined through NMR and X-ray crystallographic study. A chemoinformatic comparison (PCA and PMI) with 40 branded blockbuster drugs showed that newly generated polyheterocycles have good three-dimensional scaffold diversity and most of these pass the Lipinski filter of drug-likeness.

Limonin as a Starting Point for the Construction of Compounds with High Scaffold Diversity

Structurally complex natural products have been a fruitful source for the discovery and development of new drugs. In an effort to construct a compound collection populated by architecturally complex members with unique scaffolds, we have used the natural product limonin as a starting point. Limonin is an abundant triterpenoid natural product and, through alteration of its heptacyclic core ring system using short synthetic sequences, a collection of 98 compounds was created, including multiple members with novel ring systems. The reactions leveraged in the construction of these compounds include novel ring cleavage, rearrangements, and cyclizations, and this work is highlighted by the discovery of a novel B-ring cleavage reaction, a unique B/C-ring rearrangement, an atypical D-ring cyclization, among others. Computational analysis shows that 52 different scaffolds/ring systems were produced during the course of this work, of which 36 are unprecedented. Phenotypic screening and structure-activity relationships identified compounds with activity against a panel of cancer cell lines.

Benchmark of Generic Shapes for Macrocycles

Macrocycles target proteins that are otherwise considered undruggable because of a lack of hydrophobic cavities and the presence of extended featureless surfaces. Increasing efforts by computational chemists have developed effective software to overcome the restrictions of torsional and conformational freedom that arise as a consequence of macrocyclization. Moloc is an efficient algorithm, with an emphasis on high interactivity, and has been constantly updated since 1986 by drug designers and crystallographers of the Roche biostructural community. In this work, we have benchmarked the shape-guided algorithm using a dataset of 208 macrocycles, carefully selected on the basis of structural complexity. We have quantified the accuracy, diversity, speed, exhaustiveness, and sampling efficiency in an automated fashion and we compared them with four commercial (Prime, MacroModel, molecular operating environment, and molecular dynamics) and four open-access (experimental-torsion distance geometry with additional “basic knowledge” alone and with Merck molecular force field minimization or universal force field minimization, Cambridge Crystallographic Data Centre conformer generator, and conformator) packages. With three-quarters of the database processed below the threshold of high ring accuracy, Moloc was identified as having the highest sampling efficiency and exhaustiveness without producing thousands of conformations, random ring splitting into two half-loops, and possibility to interactively produce globular or flat conformations with diversity similar to Prime, MacroModel, and molecular dynamics. The algorithm and the Python scripts for full automatization of these parameters are freely available for academic use.

Identification of Synthetic Activators of Cancer Cell Migration by Hybrid Deep Learning

Deep convolutional neural networks (CNNs) are a method of choice for image recognition. Herein a hybrid CNN approach is presented for molecular pattern recognition in drug discovery. Using self-organizing map images of molecular pharmacophores as input, CNN models were trained to identify chemokine receptor CXCR4 modulators with high accuracy. This machine learning classifier identified first-in-class synthetic CXCR4 full agonists. The receptor-activating effects were confirmed by intracellular cAMP response and in a phenotypic spheroid invasion assay of medulloblastoma cell invasion. Additional macromolecular targets of the small molecules were predicted in silico and tested in vitro, revealing modulatory effects on dopamine receptors and CCR1. These results positively advocate the applicability of molecular image recognition by CNNs to ligand-based virtual compound screening, and demonstrate the complementarity of machine intelligence and human expert knowledge.

Optimized Inhibitors of MDM2 via an Attempted Protein-Templated Reductive Amination

Innovative and efficient hit-identification techniques are required to accelerate drug discovery. Protein-templated fragment ligations represent a promising strategy in early drug discovery, enabling the target to assemble and select its binders from a pool of building blocks. Development of new protein-templated reactions to access a larger structural diversity and expansion of the variety of targets to demonstrate the scope of the technique are of prime interest for medicinal chemists. Herein, we present our attempts to use a protein-templated reductive amination to target protein-protein interactions (PPIs), a challenging class of drug targets. We address a flexible pocket, which is difficult to achieve by structure-based drug design. After careful analysis we did not find one of the possible products in the kinetic target-guided synthesis (KTGS) approach, however subsequent synthesis and biochemical evaluation of each library member demonstrated that all the obtained molecules inhibit MDM2. The most potent library member (Ki =0.095 μm) identified is almost as active as Nutlin-3, a potent inhibitor of the p53-MDM2 PPI.

Re-Engineering of Yohimbine's Biological Activity through Ring Distortion: Identification and Structure-Activity Relationships of a New Class of Antiplasmodial Agents

Select natural products are ideal starting points for ring distortion, or the dramatic altering of inherently complex molecules through short synthetic pathways, to generate an array of novel compounds with diverse skeletal architectures. A major goal of our ring distortion approach is to re-engineer the biological activity of indole alkaloids to identify new compounds with diverse biological activities in areas of significance to human health and medicine. In this study, we re-engineered the biological activity of the indole alkaloid yohimbine through ring rearrangement and ring cleavage synthesis pathways to discover new series of antiplasmodial agents. One new compound, Y7j, was found to demonstrate good potency against chloroquine-resistant Plasmodium falciparum Dd2 cells (EC₅₀ = 0.33 μM) without eliciting cytotoxicity against HepG2 cells (EC₅₀ > 40 μM). Y7j demonstrated stage-specific action against parasites at the late ring/trophozoite stage. A series of analogues was synthesized to gain structure-activity relationship insights, and we learned that both benzyl groups of Y7j are required for activity and fine-tuning of antiplasmodial activities could be accomplished by changing substitution patterns on the benzyl moieties. This study demonstrates the potential for ring distortion to drive new discoveries and change paradigms in chemical biology and drug discovery.

Hitting on the move: Targeting intrinsically disordered protein states of the MDM2-p53 interaction

Intrinsically disordered proteins are an emerging class of proteins without a folded structure and currently disorder-based drug targeting remains a challenge. p53 is the principal regulator of cell division and growth whereas MDM2 consists its main negative regulator. The MDM2-p53 recognition is a dynamic and multistage process that amongst other, employs the dissociation of a transient α-helical N-terminal ''lid'' segment of MDM2 from the proximity of the p53-complementary interface. Several small molecule inhibitors have been reported to inhibit the formation of the p53-MDM2 complex with the vast majority mimicking the p53 residues Phe19, Trp23 and Leu26. Recently, we have described the transit from the 3-point to 4-point pharmacophore model stabilizing this intrinsically disordered N-terminus by increasing the binding affinity by a factor of 3. Therefore, we performed a thorough SAR analysis, including chiral separation of key compound which was evaluated by FP and 2D NMR. Finally, p53-specific anti-cancer activity towards p53-wild-type cancer cells was observed for several representative compounds.

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.

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

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

Toward Small-Molecule Inhibition of Protein-Protein Interactions: General Aspects and Recent Progress in Targeting Costimulatory and Coinhibitory (Immune Checkpoint) Interactions

Protein-Protein Interactions (PPIs) that are part of the costimulatory and coinhibitory (immune checkpoint) signaling are critical for adequate T cell response and are important therapeutic targets for immunomodulation. Biologics targeting them have already achieved considerable clinical success in the treatment of autoimmune diseases or transplant recipients (e.g., abatacept, belatacept, and belimumab) as well as cancer (e.g., ipilimumab, nivolumab, pembrolizumab, atezolizumab, durvalumab, and avelumab). In view of such progress, there have been only relatively limited efforts toward developing small-molecule PPI inhibitors (SMPPIIs) targeting these cosignaling interactions, possibly because they, as all other PPIs, are difficult to target by small molecules and were not considered druggable. Nevertheless, substantial progress has been achieved during the last decade. SMPPIIs proving the feasibility of such approaches have been identified through various strategies for a number of cosignaling interactions including CD40-CD40L, OX40-OX40L, BAFFR-BAFF, CD80-CD28, and PD-1-PD-L1s. Here, after an overview of the general aspects and challenges of SMPPII-focused drug discovery, we review them briefly together with relevant structural, immune-signaling, physicochemical, and medicinal chemistry aspects. While so far only a few of these SMPPIIs have shown activity in animal models (DRI-C21045 for CD40-D40L, KR33426 for BAFFR-BAFF) or reached clinical development (RhuDex for CD80-CD28, CA-170 for PD-1-PD-L1), there is proof-of-principle evidence for the feasibility of such approaches in immunomodulation. They can result in products that are easier to develop/ manufacture and are less likely to be immunogenic or encounter postmarket safety events than corresponding biologics, and, contrary to them, can even become orally bioavailable.

Harnessing the Properties of Natural Products

Natural products (NPs) have been used as traditional medicines since antiquity. With more than 10⁶⁰ estimated compounds with molecular weights less than 500 Da representing chemical space, NPs occupy a very small percentage; however, they are significantly overrepresented in biologically relevant chemical space. The classical approach concentrates on identifying one or more NPs with biological activity from a source organism. There is much more to be learned from NPs than we can discover this narrow view. In this review, we discuss ways to harness the global properties of NPs.

Scaffold hopping via ANCHOR.QUERY: β-lactams as potent p53-MDM2 antagonists†

Using the pharmacophore-based virtual screening platform ANCHOR.QUERY, we morphed our recently described Ugi-4CR scaffold towards a β-lactam scaffold with potent p53-MDM2 antagonizing activities. 2D-HSQC and FP measurements confirm potent MDM2 binding. Molecular modeling studies are used to understand the observed SAR in the β-lactam series.

A Tryptoline Ring-Distortion Strategy Leads to Complex and Diverse Biologically Active Molecules from the Indole Alkaloid Yohimbine

High-throughput screening (HTS) is the primary driver to current drug-discovery efforts. New therapeutic agents that enter the market are a direct reflection of the structurally simple compounds that make up screening libraries. Unlike medically relevant natural products (e.g., morphine), small molecules currently being screened have a low fraction of sp³ character and few, if any, stereogenic centers. Although simple compounds have been useful in drugging certain biological targets (e.g., protein kinases), more sophisticated targets (e.g., transcription factors) have largely evaded the discovery of new clinical agents from screening collections. Herein, a tryptoline ring-distortion strategy is described that enables the rapid synthesis of 70 complex and diverse compounds from yohimbine (1); an indole alkaloid. The compounds that were synthesized had architecturally complex and unique scaffolds, unlike 1 and other scaffolds. These compounds were subjected to phenotypic screens and reporter gene assays, leading to the identification of new compounds that possessed various biological activities, including antiproliferative activities against cancer cells with functional hypoxia-inducible factors, nitric oxide inhibition, and inhibition and activation of the antioxidant response element. This tryptoline ring-distortion strategy can begin to address diversity problems in screening libraries, while occupying biologically relevant chemical space in areas critical to human health.

Identification of a diverse synthetic abietane diterpenoid library for anticancer activity

A diverse natural-product-like synthetic abietane diterpenoid library contains about 56 compounds were obtained, and evaluated for their potential in vitro cytotoxic or antitumor activity against A549, PC-3 and SKOV-3 tumor cell lines by SRB assay. Treatment of A549 cells with the most potent compound ketone 19 showed induction of apoptosis, as revealed by JC-1 mitochondrial membrane potential staining, TUNNEL assay, western blotting analysis and flow cytometry assay.

Diversity-oriented synthetic strategy for developing a chemical modulator of protein-protein interaction

Diversity-oriented synthesis (DOS) can provide a collection of diverse and complex drug-like small molecules, which is critical in the development of new chemical probes for biological research of undruggable targets. However, the design and synthesis of small-molecule libraries with improved biological relevance as well as maximized molecular diversity represent a key challenge. Herein, we employ functional group-pairing strategy for the DOS of a chemical library containing privileged substructures, pyrimidodiazepine or pyrimidine moieties, as chemical navigators towards unexplored bioactive chemical space. To validate the utility of this DOS library, we identify a new small-molecule inhibitor of leucyl-tRNA synthetase-RagD protein-protein interaction, which regulates the amino acid-dependent activation of mechanistic target of rapamycin complex 1 signalling pathway. This work highlights that privileged substructure-based DOS strategy can be a powerful research tool for the construction of drug-like compounds to address challenging biological targets.

Mapping Protein-Protein Interactions of the Resistance-Related Bacterial Zeta Toxin-Epsilon Antitoxin Complex (ε₂ζ₂) with High Affinity Peptide Ligands Using Fluorescence Polarization

Toxin–antitoxin systems constitute a native survival strategy of pathogenic bacteria and thus are potential targets of antibiotic drugs. Here, we target the Zeta–Epsilon toxin–antitoxin system, which is responsible for the stable maintenance of certain multiresistance plasmids in Gram-positive bacteria. Peptide ligands were designed on the basis of the ε₂ζ₂ complex. Three α helices of Zeta forming the protein–protein interaction (PPI) site were selected and peptides were designed conserving the residues interacting with Epsilon antitoxin while substituting residues binding intramolecularly to other parts of Zeta. Designed peptides were synthesized with an N-terminal fluoresceinyl-carboxy-residue for binding assays and provided active ligands, which were used to define the hot spots of the ε₂ζ₂ complex. Further shortening and modification of the binding peptides provided ligands with affinities <100 nM, allowing us to determine the most relevant PPIs and implement a robust competition binding assay.

The impact of α-hydrazino acids embedded in short fluorescent peptides on peptide interactions with DNA and RNA

A series of novel hydrazino-based peptidomimetics and analogues comprising N-terminal lysine and C-terminal phenanthridinyl-l-alanine were prepared. The presented results demonstrate the up to now unknown possibility to finely modulate peptide interactions with DNA/RNA by α-hydrazino group insertion and how the different positioning of two α-hydrazino groups in peptides controls binding to various double stranded and single stranded DNA and RNA. All peptidomimetics bind with 1-10 micromolar affinity to ds-DNA/RNA, whereby the binding mode is a combination of electrostatic interactions and hydrophobic interactions within DNA/RNA grooves. Insertion of the α-hydrazino group into the peptide systematically decreased its fluorimetric response to DNA/RNA binding in the order: mono-hydrazino < alternating-hydrazino < sequential-hydrazino group. Binding studies of ss-polynucleotides suggest intercalation of phenanthridine between polynucleotide bases, whereby affinity and fluorimetric response decrease with the number of α-hydrazino groups in the peptide sequence. Particularly interesting was the interaction of two sequential α-hydrazino acids-peptidomimetic with poly rG, characterised by a specific strong increase of CD bands, while all other peptide/ssRNA combinations gave only a CD-band decrease. All mentioned interactions could also be reversibly controlled by adjusting the pH, due to the protonation of the fluorophore.

Motif mediated protein-protein interactions as drug targets

Protein-protein interactions (PPI) are involved in virtually every cellular process and thus represent an attractive target for therapeutic interventions. A significant number of protein interactions are frequently formed between globular domains and short linear peptide motifs (DMI). Targeting these DMIs has proven challenging and classical approaches to inhibiting such interactions with small molecules have had limited success. However, recent new approaches have led to the discovery of potent inhibitors, some of them, such as Obatoclax, ABT-199, AEG-40826 and SAH-p53-8 are likely to become approved drugs. These novel inhibitors belong to a wide range of different molecule classes, ranging from small molecules to peptidomimetics and biologicals. This article reviews the main reasons for limited success in targeting PPIs, discusses how successful approaches overcome these obstacles to discovery promising inhibitors for human protein double minute 2 (HDM2), B-cell lymphoma 2 (Bcl-2), X-linked inhibitor of apoptosis protein (XIAP), and provides a summary of the promising approaches currently in development that indicate the future potential of PPI inhibitors in drug discovery.

Developing structure-activity relationships from an HTS hit for inhibition of the Cks1-Skp2 protein-protein interaction

Structure-activity relationships have been developed around 5-bromo-8-toluylsulfonamidoquinoline 1 a hit compound in an assay for the interaction of the E3 ligase Skp2 with Cks1, part of the SCF ligase complex. Disruption of this protein-protein interaction results in higher levels of CDK inhibitor p27, which can act as a tumor suppressor. The results of the SAR developed highlight the relationship between the sulfonamide and quinoline nitrogen, while also suggesting that an aryl substituent at the 5-position of the quinoline ring contributes to the potency in the interaction assay. Compounds showing potency in the interaction assay result in greater levels of p27 and have been shown to inhibit cell growth of two p27 sensitive tumor cell lines.

Peptide and small molecule inhibitors of the Keap1-Nrf2 protein-protein interaction

The transcription factor nuclear factor erythroid-2-related factor 2 (Nrf2) up-regulates the expression of a range of cytoprotective enzymes with antioxidant response elements in their promoter regions and thus can protect cells against oxidative damage. Increasing Nrf2 activity has been proposed as a therapeutic intervention in a range of chronic neurodegenerative conditions and cancer chemoprevention. One of the main mechanisms by which Nrf2 is negatively regulated involves an interaction with the ubiquitination facilitator protein, Kelch-like ECH-associated protein 1 (Keap1) that facilitates degradation of Nrf2. Inhibition of this process underlies the mode of action of a broad group of compounds that increase Nrf2 activity. A number of natural products, including the isothiocyanate sulforaphane, up-regulate Nrf2 by interacting with Keap1 in a covalent manner to stall its activity. Recently, a number of peptide and small molecule inhibitors of the protein-protein interaction (PPI) between Keap1 and Nrf2 have been described. These classes of compound have contrasting modes of action at the molecular level and there is emerging evidence that their biological activities have similarities and differences. This review describes the various classes of PPI inhibitor that have been described in the literature and the biological evaluations that have been performed.

DARC: Mapping Surface Topography by Ray-Casting for Effective Virtual Screening at Protein Interaction Sites

Protein-protein interactions represent an exciting and challenging target class for therapeutic intervention using small molecules. Protein interaction sites are often devoid of the deep surface pockets presented by "traditional" drug targets, and crystal structures reveal that inhibitors typically engage these sites using very shallow binding modes. As a consequence, modern virtual screening tools developed to identify inhibitors of traditional drug targets do not perform as well when they are instead deployed at protein interaction sites. To address the need for novel inhibitors of important protein interactions, here we introduce an alternate docking strategy specifically designed for this regime. Our method, termed DARC (Docking Approach using Ray-Casting), matches the topography of a surface pocket "observed" from within the protein to the topography "observed" when viewing a potential ligand from the same vantage point. We applied DARC to carry out a virtual screen against the protein interaction site of human antiapoptotic protein Mcl-1 and found that four of the top-scoring 21 compounds showed clear inhibition in a biochemical assay. The Ki values for these compounds ranged from 1.2 to 21 μM, and each had ligand efficiency comparable to promising small-molecule inhibitors of other protein-protein interactions. These hit compounds do not resemble the natural (protein) binding partner of Mcl-1, nor do they resemble any known inhibitors of Mcl-1. Our results thus demonstrate the utility of DARC for identifying novel inhibitors of protein-protein interactions.

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.

Overcoming Chemical, Biological, and Computational Challenges in the Development of Inhibitors Targeting Protein-Protein Interactions

Protein-protein interactions (PPIs) underlie the majority of biological processes, signaling, and disease. Approaches to modulate PPIs with small molecules have therefore attracted increasing interest over the past decade. However, there are a number of challenges inherent in developing small-molecule PPI inhibitors that have prevented these approaches from reaching their full potential. From target validation to small-molecule screening and lead optimization, identifying therapeutically relevant PPIs that can be successfully modulated by small molecules is not a simple task. Following the recent review by Arkin et al., which summarized the lessons learnt from prior successes, we focus in this article on the specific challenges of developing PPI inhibitors and detail the recent advances in chemistry, biology, and computation that facilitate overcoming them. We conclude by providing a perspective on the field and outlining four innovations that we see as key enabling steps for successful development of small-molecule inhibitors targeting PPIs.

Structure- and ligand-based virtual screening identifies new scaffolds for inhibitors of the oncoprotein MDM2

A major challenge in the field of ligand discovery is to identify chemically useful fragments that can be developed into inhibitors of specific protein-protein interactions. Low molecular weight fragments (with molecular weight less than 250 Da) are likely to bind weakly to a protein’s surface. Here we use a new virtual screening procedure which uses a combination of similarity searching and docking to identify chemically tractable scaffolds that bind to the p53-interaction site of MDM2. The binding has been verified using capillary electrophoresis which has proven to be an excellent screening method for such small, weakly binding ligands.

The re-emergence of natural products for drug discovery in the genomics era

Natural products have been a rich source of compounds for drug discovery. However, their use has diminished in the past two decades, in part because of technical barriers to screening natural products in high-throughput assays against molecular targets. Here, we review strategies for natural product screening that harness the recent technical advances that have reduced these barriers. We also assess the use of genomic and metabolomic approaches to augment traditional methods of studying natural products, and highlight recent examples of natural products in antimicrobial drug discovery and as inhibitors of protein-protein interactions. The growing appreciation of functional assays and phenotypic screens may further contribute to a revival of interest in natural products for drug discovery.

Targeting protein-protein interactions for drug discovery

Protein-protein interactions are associated with key activities and pathways in the cell, and in that regard are promising targets for drug discovery. However, in terms of small molecule drugs, this promise has not been realized. The physical nature of many protein-protein interaction surfaces renders them unable to support binding of small drug-like molecules. In addition, there are other unique hurdles presented by this class that make the drug development process difficult and risky. Nevertheless, success stories have begun to steadily appear in this field. These experiences are starting to provide general strategies and tools to help overcome the problems inherent in pursuing protein-protein interaction targets. These lessons should improve the rate of success as these systems are pursued in the future.

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.

De novo designed library of linear helical peptides: an exploratory tool in the discovery of protein-protein interaction modulators

Protein-protein interactions (PPIs) have emerged as important targets for pharmaceutical intervention because of their essential role in numerous physiological and pathological processes, but screening efforts using small-molecules have led to very low hit rates. Linear peptides could represent a quick and effective approach to discover initial PPI hits, particularly if they have inherent ability to adopt specific peptide secondary structures. Here, we address this hypothesis through a linear helical peptide library, composed of four sublibraries, which was designed by theoretical predictions of helicity (Agadir software). The 13-mer peptides of this collection fixes either a combination of three aromatic or two aromatic and one aliphatic residues on one face of the helix (Ac-SSEEX(5)ARNX(9)AAX(12)N-NH2), since these are structural features quite common at PPIs interfaces. The 81 designed peptides were conveniently synthesized by parallel solid-phase methodologies, and the tendency of some representative library components to adopt the intended secondary structure was corroborated through CD and NMR experiments. As proof of concept in the search for PPI modulators, the usefulness of this library was verified on the widely studied p53-MDM2 interaction and on the communication between VEGF and its receptor Flt-1, two PPIs for which a hydrophobic α-helix is essential for the interaction. We have demonstrated here that, in both cases, selected peptides from the library, containing the right hydrophobic sequence of the hot-spot in one of the protein partners, are able to interact with the complementary protein. Moreover, we have discover some new, quite potent inhibitors of the VEGF-Flt-1 interaction, just by replacing one of the aromatic residues of the initial F(5)Y(9)Y(12) peptide by W, in agreement with previous results on related antiangiogenic peptides. Finally, the HTS evaluation of the full collection on thermoTRPs has led to a few antagonists of TRPV1 and TRPA1 channels, which open new avenues on the way to innovative modulators of these channels.

Discovery of Small-Molecule Glucokinase Regulatory Protein Modulators That Restore Glucokinase Activity

In the nuclei of hepatocytes, glucokinase regulatory protein (GKRP) modulates the activity of glucokinase (GK), a key regulator of glucose homeostasis. Currently, direct activators of GK (GKAs) are in development for the treatment of type 2 diabetes. However, this approach is generally associated with a risk of hypoglycemia. To mitigate such risk, we target the GKRP regulation, which indirectly restores GK activity. Here we describe a screening strategy to look specifically for GKRP modulators, in addition to traditional GKAs. Two high-throughput screening campaigns were performed with our compound libraries using a luminescence assay format, one with GK alone and the other with a GK/GKRP complex in the presence of sorbitol-6-phosphate (S6P). By a subtraction method in the hit triage process of these campaigns, we discovered two close analogs that bind GKRP specifically with sub-µM potency to a site distinct from where fructose-1-phosphate binds. These small molecules are first-in-class allosteric modulators of the GK/GKRP interaction and are fully active even in the presence of S6P. Activation of GK by this particular mechanism, without altering the enzymatic profile, represents a novel pharmacologic modality of intervention in the GK/GKRP pathway.

Synthesis of complex and diverse compounds through ring distortion of abietic acid

Many compound screening collections are populated by members that possess a low degree of structural complexity. In an effort to generate compounds that are both complex and diverse, we have developed a strategy that uses natural products as a starting point for complex molecule synthesis. Herein we apply this complexity-to-diversity approach to abietic acid, an abundant natural product used commercially in paints, varnishes, and lacquers. From abietic acid we synthesize a collection of complex (as assessed by fraction of sp(3) -hybridized carbons and number of stereogenic centers) and diverse (as assessed by Tanimoto analysis) small molecules. The 84 compounds constructed herein, and those created through similar efforts, should find utility in a variety of biological screens.

Transient protein states in designing inhibitors of the MDM2-p53 interaction

Reactivation of p53 by release of the functional protein from its inhibition by MDM2 provides an efficient, nongenotoxic approach to a wide variety of cancers. We present the cocrystal structures of two complexes of MDM2 with inhibitors based on 6-chloroindole scaffolds. Both molecules bound to a distinct conformational state of MDM2 with nM-μM affinities. In contrast to other structurally characterized antagonists, which mimic three amino acids of p53 (Phe19, Trp23, and Leu26), the compounds induced an additional hydrophobic pocket on the MDM2 surface and unveiled a four-point binding mode. The enlarged interaction interface of the inhibitors resulted in extension of small molecules binding toward the "lid" segment of MDM2 (residues 19-23)--a nascent element that interferes with p53 binding. As supported by protein engineering and molecular dynamics studies, employing these unstable elements of MDM2 provides an efficient and yet unexplored alternative in development of MDM2-p53 association inhibitors.

Benzimidazole-2-one: a novel anchoring principle for antagonizing p53-Mdm2

Herein we propose the benzimidazole-2-one substructure as a suitable tryptophan mimic and thus a reasonable starting point for the design of p53 Mdm2 antagonists. We devise a short multicomponent reaction route to hitherto unknown 2-(2-oxo-2,3-dihydro-1H-benzo[d]imidazol-1-yl)acetamides by reacting mono N-carbamate protected phenylenediamine in a Ugi-3CR followed by base induced cyclisation. Our preliminary synthesis and screening results are presented here. The finding of the benzimidazolone moiety as a tryptophan replacement in mdm2 is significant as it offers access to novel scaffolds with potentially higher selectivity and potency and improved biological activities. Observing low μM affinities to mdm2 by NMR and fluorescence polarization we conclude that the 2-(2-oxo-2,3-dihydro-1H-benzo[d]imidazol-1-yl)acetamide scaffold might be a good starting point to further optimize the affinities to Mdm2.

[Small compounds libraries: a research tool for chemical biology]

Obtaining and screening collections of small molecules remain a challenge for biologists. Recent advances in analytical techniques and instrumentation now make screening possible in academia. The history of the creation of such public or commercial collections and their accessibility is related. It shows that there is interest for an academic laboratory involved in medicinal chemistry, chemogenomics or "chemical biology" to organize its own collection and make it available through existing networks such as the French National chimiothèque or the European partner network "European Infrastructure of open screening platforms for Chemical Biology" EU-OpenScreen under construction.

Comprehensive experimental and computational analysis of binding energy hot spots at the NF-κB essential modulator/IKKβ protein-protein interface

We report a comprehensive analysis of binding energy hot spots at the protein-protein interaction (PPI) interface between nuclear factor kappa B (NF-κB) essential modulator (NEMO) and IκB kinase subunit β (IKKβ), an interaction that is critical for NF-κB pathway signaling, using experimental alanine scanning mutagenesis and also the FTMap method for computational fragment screening. The experimental results confirm that the previously identified NEMO binding domain (NBD) region of IKKβ contains the highest concentration of hot-spot residues, the strongest of which are W739, W741, and L742 (ΔΔG = 4.3, 3.5, and 3.2 kcal/mol, respectively). The region occupied by these residues defines a potentially druggable binding site on NEMO that extends for ~16 Å to additionally include the regions that bind IKKβ L737 and F734. NBD residues D738 and S740 are also important for binding but do not make direct contact with NEMO, instead likely acting to stabilize the active conformation of surrounding residues. We additionally found two previously unknown hot-spot regions centered on IKKβ residues L708/V709 and L719/I723. The computational approach successfully identified all three hot-spot regions on IKKβ. Moreover, the method was able to accurately quantify the energetic importance of all hot-spot residues involving direct contact with NEMO. Our results provide new information to guide the discovery of small-molecule inhibitors that target the NEMO/IKKβ interaction. They additionally clarify the structural and energetic complementarity between "pocket-forming" and "pocket-occupying" hot-spot residues, and further validate computational fragment mapping as a method for identifying hot spots at PPI interfaces.

Druggable protein interaction sites are more predisposed to surface pocket formation than the rest of the protein surface

Despite intense interest and considerable effort via high-throughput screening, there are few examples of small molecules that directly inhibit protein-protein interactions. This suggests that many protein interaction surfaces may not be intrinsically “druggable” by small molecules, and elevates in importance the few successful examples as model systems for improving our fundamental understanding of druggability. Here we describe an approach for exploring protein fluctuations enriched in conformations containing surface pockets suitable for small molecule binding. Starting from a set of seven unbound protein structures, we find that the presence of low-energy pocket-containing conformations is indeed a signature of druggable protein interaction sites and that analogous surface pockets are not formed elsewhere on the protein. We further find that ensembles of conformations generated with this biased approach structurally resemble known inhibitor-bound structures more closely than equivalent ensembles of unbiased conformations. Collectively these results suggest that “druggability” is a property encoded on a protein surface through its propensity to form pockets, and inspire a model in which the crude features of the predisposed pocket(s) restrict the range of complementary ligands; additional smaller conformational changes then respond to details of a particular ligand. We anticipate that the insights described here will prove useful in selecting protein targets for therapeutic intervention.

Identifying small-molecule inhibitors of protein interactions has traditionally presented a challenge for modern screening methods, despite interest stemming from the fact that such interactions comprise the underlying mechanisms for cell proliferation, differentiation, and survival. This suggests that many protein interaction surfaces may not be intrinsically “druggable” by small molecules, and elevates in importance the few successful examples as model systems for improving our understanding of factors contributing to druggability. Here we describe a new approach for exploring protein fluctuations leading to surface pockets suitable for small molecule binding. We find that the presence of such pockets is indeed a signature of druggable protein interaction sites, suggesting that “druggability” is a property encoded on a protein surface through its propensity to form pockets. We anticipate that the insights described here will prove useful in selecting protein targets for therapeutic intervention.

Learning a peptide-protein binding affinity predictor with kernel ridge regression

BACKGROUND

The cellular function of a vast majority of proteins is performed through physical interactions with other biomolecules, which, most of the time, are other proteins. Peptides represent templates of choice for mimicking a secondary structure in order to modulate protein-protein interaction. They are thus an interesting class of therapeutics since they also display strong activity, high selectivity, low toxicity and few drug-drug interactions. Furthermore, predicting peptides that would bind to a specific MHC alleles would be of tremendous benefit to improve vaccine based therapy and possibly generate antibodies with greater affinity. Modern computational methods have the potential to accelerate and lower the cost of drug and vaccine discovery by selecting potential compounds for testing in silico prior to biological validation.

RESULTS

We propose a specialized string kernel for small bio-molecules, peptides and pseudo-sequences of binding interfaces. The kernel incorporates physico-chemical properties of amino acids and elegantly generalizes eight kernels, comprised of the Oligo, the Weighted Degree, the Blended Spectrum, and the Radial Basis Function. We provide a low complexity dynamic programming algorithm for the exact computation of the kernel and a linear time algorithm for it's approximation. Combined with kernel ridge regression and SupCK, a novel binding pocket kernel, the proposed kernel yields biologically relevant and good prediction accuracy on the PepX database. For the first time, a machine learning predictor is capable of predicting the binding affinity of any peptide to any protein with reasonable accuracy. The method was also applied to both single-target and pan-specific Major Histocompatibility Complex class II benchmark datasets and three Quantitative Structure Affinity Model benchmark datasets.

CONCLUSION

On all benchmarks, our method significantly (p-value ≤ 0.057) outperforms the current state-of-the-art methods at predicting peptide-protein binding affinities. The proposed approach is flexible and can be applied to predict any quantitative biological activity. Moreover, generating reliable peptide-protein binding affinities will also improve system biology modelling of interaction pathways. Lastly, the method should be of value to a large segment of the research community with the potential to accelerate the discovery of peptide-based drugs and facilitate vaccine development. The proposed kernel is freely available at http://graal.ift.ulaval.ca/downloads/gs-kernel/.

Mdm2 and MdmX inhibitors for the treatment of cancer: a patent review (2011-present)

INTRODUCTION

One of the hallmarks of cancer cells is the inactivation of the p53 pathway either due to mutations in the p53 gene or over-expression of negative regulators, Mdm2 and/or MdmX. Pharmacological disruption of the Mdm2/X-p53 interaction to restore p53 activity is an attractive concept, aiming at a targeted and non-toxic cancer treatment.

AREAS COVERED

The introduction covers the biological role of p53 pathway and its regulation by Mdm2 and MdmX in normal and cancer cells and the current repertoire and development status of inhibitors of the Mdm2/X-p53 interaction for the treatment of cancer. The main part of the article covers patents and patent applications describing small molecule inhibitors of the Mdm2/X-p53 interaction published from 2011 until 2012.

EXPERT OPINION

The area of small molecule Mdm2/X-p53 interaction inhibitor development is progressing fast. Several Phase I clinical studies and preclinical programs are now in progress, however, the clinical proof concept has yet to be demonstrated. Multiple available compounds inhibit Mdm2-p53 interaction with nanomolar affinities, but MdmX is still missing such potent binders. Since research points to a complementary mode of Mdm2 and MdmX action, the future compound classes will possibly want to include dual actions versus Mdm2 and MdmX.

A ring-distortion strategy to construct stereochemically complex and structurally diverse compounds from natural products

High-throughput screening is the dominant method used to identify lead compounds in drug discovery. As such, the makeup of screening libraries largely dictates the biological targets that can be modulated and the therapeutics that can be developed. Unfortunately, most compound-screening collections consist principally of planar molecules with little structural or stereochemical complexity, compounds that do not offer the arrangement of chemical functionality necessary for the modulation of many drug targets. Here we describe a novel, general and facile strategy for the creation of diverse compounds with high structural and stereochemical complexity using readily available natural products as synthetic starting points. We show through the evaluation of chemical properties (which include fraction of sp(3) carbons, ClogP and the number of stereogenic centres) that these compounds are significantly more complex and diverse than those in standard screening collections, and we give guidelines for the application of this strategy to any suitable natural product.

Computational study and peptide inhibitors design for the CDK9 - cyclin T1 complex

Cyclin dependent kinase 9 (CDK9) is a protein that belongs to the cyclin-dependent kinases family, and its main role is in the regulation of the cell transcription processes. Since the increased activity of CDK9 is connected with the development of pathological processes such as tumor growth and survival and HIV-1 replication, inhibition of the CDK9 could be of particular interest for treating such diseases. The activation of CDK9 is initiated by the formation of CDK9/cyclin T1 complex, therefore disruption of its formation could be a promising strategy for the design of CDK9 inhibitors. In order to assist in the design of potential inhibitors of CDK9/cyclin T1 complex formation, a computational study of the CDK9/cyclin T1 interface was conducted. Ten peptides were designed using the information from the analysis of the complex, hot spot residues and fragment based design. The designed peptides were docked to CDK9 structures obtained by molecular dynamics simulations of CDK9/cyclin T1 complex and the CDK9 alone and their binding affinities were evaluated using molecular mechanics Poisson Boltzman surface area (MM-PBSA) method and steered molecular dynamics (SMD). Designed peptide sequences LQTLGF and ESIILQ, both derived from the surface of cyclin T1, as well as the peptide sequence PRWPE, derived from fragment based design, showed the most favorable binding properties and were selected for our further studies.

On the binding affinity of macromolecular interactions: daring to ask why proteins interact

Interactions between proteins are orchestrated in a precise and time-dependent manner, underlying cellular function. The binding affinity, defined as the strength of these interactions, is translated into physico-chemical terms in the dissociation constant (K_d), the latter being an experimental measure that determines whether an interaction will be formed in solution or not. Predicting binding affinity from structural models has been a matter of active research for more than 40 years because of its fundamental role in drug development. However, all available approaches are incapable of predicting the binding affinity of protein–protein complexes from coordinates alone. Here, we examine both theoretical and experimental limitations that complicate the derivation of structure–affinity relationships. Most work so far has concentrated on binary interactions. Systems of increased complexity are far from being understood. The main physico-chemical measure that relates to binding affinity is the buried surface area, but it does not hold for flexible complexes. For the latter, there must be a significant entropic contribution that will have to be approximated in the future. We foresee that any theoretical modelling of these interactions will have to follow an integrative approach considering the biology, chemistry and physics that underlie protein–protein recognition.