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

Novel Antiviral Molecules against Ebola Virus Infection

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

Discovery of a PDZ Domain Inhibitor Targeting the Syndecan/Syntenin Protein-Protein Interaction: A Semi-Automated "Hit Identification-to-Optimization" Approach

The rapid identification of early hits by fragment-based approaches and subsequent hit-to-lead optimization represents a challenge for drug discovery. To address this challenge, we created a strategy called "DOTS" that combines molecular dynamic simulations, computer-based library design (chemoDOTS) with encoded medicinal chemistry reactions, constrained docking, and automated compound evaluation. To validate its utility, we applied our DOTS strategy to the challenging target syntenin, a PDZ domain containing protein and oncology target. Herein, we describe the creation of a "best-in-class" sub-micromolar small molecule inhibitor for the second PDZ domain of syntenin validated in cancer cell assays. Key to the success of our DOTS approach was the integration of protein conformational sampling during hit identification stage and the synthetic feasibility ranking of the designed compounds throughout the optimization process. This approach can be broadly applied to other protein targets with known 3D structures to rapidly identify and optimize compounds as chemical probes and therapeutic candidates.

DLiP-PPI library: An integrated chemical database of small-to-medium-sized molecules targeting protein-protein interactions

Protein-protein interactions (PPIs) are recognized as important targets in drug discovery. The characteristics of molecules that inhibit PPIs differ from those of small-molecule compounds. We developed a novel chemical library database system (DLiP) to design PPI inhibitors. A total of 32,647 PPI-related compounds are registered in the DLiP. It contains 15,214 newly synthesized compounds, with molecular weight ranging from 450 to 650, and 17,433 active and inactive compounds registered by extracting and integrating known compound data related to 105 PPI targets from public databases and published literature. Our analysis revealed that the compounds in this database contain unique chemical structures and have physicochemical properties suitable for binding to the protein-protein interface. In addition, advanced functions have been integrated with the web interface, which allows users to search for potential PPI inhibitor compounds based on types of protein-protein interfaces, filter results by drug-likeness indicators important for PPI targeting such as rule-of-4, and display known active and inactive compounds for each PPI target. The DLiP aids the search for new candidate molecules for PPI drug discovery and is available online (https://skb-insilico.com/dlip).

Structure and Sequence Requirements for RNA Capping at the Venezuelan Equine Encephalitis Virus RNA 5' End

Venezuelan equine encephalitis virus (VEEV) is a reemerging arthropod-borne virus causing encephalitis in humans and domesticated animals. VEEV possesses a positive single-stranded RNA genome capped at its 5' end. The capping process is performed by the nonstructural protein nsP1, which bears methyl and guanylyltransferase activities. The capping reaction starts with the methylation of GTP. The generated m⁷GTP is complexed to the enzyme to form an m⁷GMP-nsP1 covalent intermediate. The m⁷GMP is then transferred onto the 5'-diphosphate end of the viral RNA. Here, we explore the specificities of the acceptor substrate in terms of length, RNA secondary structure, and/or sequence. Any diphosphate nucleosides but GDP can serve as acceptors of the m⁷GMP to yield m⁷GpppA, m⁷GpppC, or m⁷GpppU. We show that capping is more efficient on small RNA molecules, whereas RNAs longer than 130 nucleotides are barely capped by the enzyme. The structure and sequence of the short, conserved stem-loop, downstream to the cap, is an essential regulatory element for the capping process. IMPORTANCE The emergence, reemergence, and expansion of alphaviruses (genus of the family Togaviridae) are a serious public health and epizootic threat. Venezuelan equine encephalitis virus (VEEV) causes encephalitis in human and domesticated animals, with a mortality rate reaching 80% in horses. To date, no efficient vaccine or safe antivirals are available for human use. VEEV nonstructural protein 1 (nsP1) is the viral capping enzyme characteristic of the Alphavirus genus. nsP1 catalyzes methyltransferase and guanylyltransferase reactions, representing a good therapeutic target. In the present report, we provide insights into the molecular features and specificities of the cap acceptor substrate for the guanylylation reaction.

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

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

Fragment-based drug design targeting syntenin PDZ2 domain involved in exosomal release and tumour spread

Syntenin stimulates exosome production and its expression is upregulated in many cancers and implicated in the spread of metastatic tumor. These effects are supported by syntenin PDZ domains interacting with syndecans. We therefore aimed to develop, through a fragment-based drug design approach, novel inhibitors targeting syntenin-syndecan interactions. We describe here the optimization of a fragment, 'hit' C58, identified by in vitro screening of a PDZ-focused fragment library, which binds specifically to the syntenin-PDZ2 domain at the same binding site as the syndecan-2 peptide. X-ray crystallographic structures and computational docking were used to guide our optimization process and lead to compounds 45 and 57 (IC₅₀ = 33 μM and 47 μM; respectively), two representatives of syntenin-syndecan interactions inhibitors, that selectively affect the syntenin-exosome release. These findings demonstrate that it is possible to identify small molecules inhibiting syntenin-syndecan interaction and exosome release that may be useful for cancer therapy.

Scanning Protein Surfaces with DNA-Encoded Libraries

Understanding the ligandability of a target protein, defined as the capability of a protein to bind drug-like compounds on any site, can give important stimuli to drug-development projects. For instance, inhibition of protein-protein interactions usually depends on the identification of protein surface binders. DNA-encoded chemical libraries (DELs) allow scanning of protein surfaces with large chemical space. Encoded library selection screens uncovered several protein-protein interaction inhibitors and compounds binding to the surface of G protein-coupled receptors (GPCRs) and kinases. The protein surface-binding chemotypes from DELs are predominantly chemically modified and cyclized peptides, and functional small-molecule peptidomimetics. Peptoid libraries and structural peptidomimetics have been less studied in the DEL field, hinting at hitherto less populated chemical space and suggesting alternative library designs. Roughly a third of bioactive molecules evolved from smaller, target-focused libraries. They showcase the potential of encoded libraries to identify more potent molecules from weak, for example, fragment-like, starting points.

Identification of novel inhibitors of Keap1/Nrf2 by a promising method combining protein-protein interaction-oriented library and machine learning

Protein–protein interactions (PPIs) are prospective but challenging targets for drug discovery, because screening using traditional small-molecule libraries often fails to identify hits. Recently, we developed a PPI-oriented library comprising 12,593 small-to-medium-sized newly synthesized molecules. This study validates a promising combined method using PPI-oriented library and ligand-based virtual screening (LBVS) to discover novel PPI inhibitory compounds for Kelch-like ECH-associated protein 1 (Keap1) and nuclear factor erythroid 2-related factor 2 (Nrf2). We performed LBVS with two random forest models against our PPI library and the following time-resolved fluorescence resonance energy transfer (TR-FRET) assays of 620 compounds identified 15 specific hit compounds. The high hit rates for the entire PPI library (estimated 0.56–1.3%) and the LBVS (maximum 5.4%) compared to a conventional screening library showed the utility of the library and the efficiency of LBVS. All the hit compounds possessed novel structures with Tanimoto similarity ≤ 0.26 to known Keap1/Nrf2 inhibitors and aqueous solubility (AlogP < 5). Reasonable binding modes were predicted using 3D alignment of five hit compounds and a Keap1/Nrf2 peptide crystal structure. Our results represent a new, efficient method combining the PPI library and LBVS to identify novel PPI inhibitory ligands with expanded chemical space.

Pharmacological inhibition of syntenin PDZ2 domain impairs breast cancer cell activities and exosome loading with syndecan and EpCAM cargo

Exosomes support cell-to-cell communication in physiology and disease, including cancer. We currently lack tools, such as small chemicals, capable of modifying exosome composition and activity in a specific manner. Building on our previous understanding of how syntenin, and its PDZ partner syndecan (SDC), impact on exosome composition we optimized a small chemical compound targeting the PDZ2 domain of syntenin. In vitro , in tests on MCF-7 breast carcinoma cells, this compound is non-toxic and impairs cell proliferation, migration and primary sphere formation. It does not affect the size or the number of secreted particles, yet it decreases the amounts of exosomal syntenin, ALIX and SDC4 while leaving other exosomal markers unaffected. Interestingly, it also blocks the sorting of EpCAM, a bona fide target used for carcinoma exosome immunocapture. Our study highlights the first characterization of a small pharmacological inhibitor of the syntenin-exosomal pathway, of potential interest for exosome research and oncology.

The selenium-containing drug ebselen potently disrupts LEDGF/p75-HIV-1 integrase interaction by targeting LEDGF/p75

Lens-epithelium-derived growth-factor (LEDGF/p75)-binding site on HIV-1 integrase (IN), is an attractive target for antiviral chemotherapy. Small-molecule compounds binding to this site are referred as LEDGF-IN inhibitors (LEDGINs). In this study, compound libraries were screened to identify new inhibitors of LEDGF/p75-IN interaction. Ebselen (2-phenyl-1,2-benzisoselenazol-3-one), a reported anti-HIV-1 agent, was identified as a moderate micromolar inhibitor of LEDGF/p75-IN interaction. Ebselen inhibited the interaction by binding to LEDGF/p75 and the ability of ebselen to inhibit the interaction could be reversed by dithiothreitol (DTT). BLI experiment showed that ebselen probably formed selenium-sulphur bonds with reduced thiols in LEDGF/p75. To the best of our knowledge, we showed for the first time that small-molecule compound, ebselen inhibited LEDGF/p75-IN interaction by directly binding to LEDGF/p75. The compound discovered here could be used as probe compounds to design and develop new disrupter of LEDGF/p75-IN interaction.

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

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

Fast Rescoring Protocols to Improve the Performance of Structure-Based Virtual Screening Performed on Protein-Protein Interfaces

Protein-protein interactions (PPIs) are attractive targets for drug design because of their essential role in numerous cellular processes and disease pathways. However, in general, PPIs display exposed binding pockets at the interface, and as such, have been largely unexploited for therapeutic interventions with low-molecular weight compounds. Here, we used docking and various rescoring strategies in an attempt to recover PPI inhibitors from a set of active and inactive molecules for 11 targets collected in ChEMBL and PubChem. Our focus is on the screening power of the various developed protocols and on using fast approaches so as to be able to apply such a strategy to the screening of ultralarge libraries in the future. First, we docked compounds into each target using the fast "pscreen" mode of the structure-based virtual screening (VS) package Surflex. Subsequently, the docking poses were postprocessed to derive a set of 3D topological descriptors: (i) shape similarity and (ii) interaction fingerprint similarity with a co-crystallized inhibitor, (iii) solvent-accessible surface area, and (iv) extent of deviation from the geometric center of a reference inhibitor. The derivatized descriptors, together with descriptor-scaled scoring functions, were utilized to investigate possible impacts on VS performance metrics. Moreover, four standalone scoring functions, RF-Score-VS (machine-learning), DLIGAND2 (knowledge-based), Vinardo (empirical), and X-SCORE (empirical), were employed to rescore the PPI compounds. Collectively, the results indicate that the topological scoring algorithms could be valuable both at a global level, with up to 79% increase in areas under the receiver operating characteristic curve for some targets, and in early stages, with up to a 4-fold increase in enrichment factors at 1% of the screened collections. Outstandingly, DLIGAND2 emerged as the best scoring function on this data set, outperforming all rescoring techniques in terms of VS metrics. The described methodology could help in the rational design of small-molecule PPI inhibitors and has direct applications in many therapeutic areas, including cancer, CNS, and infectious diseases such as COVID-19.

Mutations on VEEV nsP1 relate RNA capping efficiency to ribavirin susceptibility

Alphaviruses are arthropod-borne viruses of public health concern. To date no efficient vaccine nor antivirals are available for safe human use. During viral replication the nonstructural protein 1 (nsP1) catalyzes capping of genomic and subgenomic RNAs. The capping reaction is unique to the Alphavirus genus. The whole three-step process follows a particular order: (i) transfer of a methyl group from S-adenosyl methionine (SAM) onto a GTP forming ^m7GTP; (ii) guanylylation of the enzyme to form a ^m7GMP-nsP1adduct; (iii) transfer of ^m7GMP onto 5'-diphosphate RNA to yield capped RNA. Specificities of these reactions designate nsP1 as a promising target for antiviral drug development. In the current study we performed a mutational analysis on two nsP1 positions associated with Sindbis virus (SINV) ribavirin resistance in the Venezuelan equine encephalitis virus (VEEV) context through reverse genetics correlated to enzyme assays using purified recombinant VEEV nsP1 proteins. The results demonstrate that the targeted positions are strongly associated to the regulation of the capping reaction by increasing the affinity between GTP and nsP1. Data also show that in VEEV the S21A substitution, naturally occurring in Chikungunya virus (CHIKV), is a hallmark of ribavirin susceptibility. These findings uncover the specific mechanistic contributions of these residues to nsp1-mediated methyl-transfer and guanylylation reactions.

Fsp3: A new parameter for drug-likeness

The drug-likeness of a compound is a key factor during the initial phases of drug discovery. It can be defined as the similarity between compounds and drugs. Here, we collate research related to the fraction of sp³ carbon atoms (Fsp³), including related high-throughput screening (HTS) cases, structural modifications based on Fsp³, and strategies to improve it. We also introduce new synthetic methods for spirocyclic scaffolds. It is likely that the reasonable rigidity of spirocyclic scaffolds will provide a new generation of drug candidates.

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

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

Förster Resonance Energy Transfer Based Biosensor for Targeting the hNTH1-YB1 Interface as a Potential Anticancer Drug Target

The Y-box binding protein 1 (YB1) is an established metastatic marker: high expression and nuclear localization of YB1 correlate with tumor aggressiveness, drug resistance, and poor patient survival in various tumors. In the nucleus, YB1 interacts with and regulates the activities of several nuclear proteins, including the DNA glycosylase, human endonuclease III (hNTH1). In the present study, we used Förster resonance energy transfer (FRET) and AlphaLISA technologies to further characterize this interaction and define the minimal regions of hNTH1 and YB1 required for complex formation. This work led us to design an original and cost-effective FRET-based biosensor for the rapid in vitro high-throughput screening for potential inhibitors of the hNTH1-YB1 complex. Two pilot screens were carried out, allowing the selection of several promising compounds exhibiting IC₅₀ values in the low micromolar range. Interestingly, two of these compounds bind to YB1 and sensitize drug-resistant breast tumor cells to the chemotherapeutic agent, cisplatin. Taken together, these findings demonstrate that the hNTH1-YB1 interface is a druggable target for the development of new therapeutic strategies for the treatment of drug-resistant tumors. Moreover, beyond this study, the simple design of our biosensor defines an innovative and efficient strategy for the screening of inhibitors of therapeutically relevant protein-protein interfaces.

Chimeric Protein-Protein Interface Inhibitors Allow Efficient Inhibition of Type III Secretion Machinery and Pseudomonas aeruginosa Virulence

Pseudomonas aeruginosa (P. aeruginosa) is an opportunistic pathogen naturally resistant to many common antibiotics and acquires new resistance traits at an alarming pace. Targeting the bacterial virulence factors by an antivirulence strategy, therefore, represents a promising alternative approach besides antibiotic therapy. The Type III secretion system (T3SS) of P. aeruginosa is one of its main virulence factors. It consists of more than 20 proteins building a complex syringe-like machinery enabling the injection of toxin into host cells. Previous works showed that disrupting interactions between components of this machinery efficiently lowers the bacterial virulence. Using automated target-based screening of commercial and in-house libraries of small molecules, we identified compounds inhibiting the protein-protein interaction between PscE and PscG, the two cognate chaperones of the needle subunit PscF of P. aeruginosa T3SS. Two hits were selected and assembled using Split/Mix/Click chemistry to build larger hybrid analogues. Their efficacy and toxicity were evaluated using phenotypic analysis including automated microscopy and image analysis. Two nontoxic hybrid leads specifically inhibited the T3SS and reduced the ex vivo cytotoxicity of bacteria and their virulence in Galleria mellonella.

Exhaustive Repertoire of Druggable Cavities at Protein-Protein Interfaces of Known Three-Dimensional Structure

Protein-protein interactions (PPIs) offer the unique opportunity to tailor ligands aimed at specifically stabilizing or disrupting the corresponding interfaces and providing a safer alternative to conventional ligands targeting monomeric macromolecules. Selecting biologically relevant protein-protein interfaces for either stabilization or disruption by small molecules is usually biology-driven on a case-by-case basis and does not follow a structural rationale that could be applied to an entire interactome. We herewith provide a first step to the latter goal by using a fully automated and structure-based workflow, applicable to any PPI of known three-dimensional (3D) structure, to identify and prioritize druggable cavities at and nearby PPIs of pharmacological interest. When applied to the entire Protein Data Bank, 164 514 druggable cavities were identified and classified in four groups (interfacial, rim, allosteric, orthosteric) according to their properties and spatial locations. Systematic comparison of PPI cavities with pockets deduced from druggable protein-ligand complexes shows almost no overlap in property space, suggesting that even the most druggable PPI cavities are unlikely to be addressed with conventional drug-like compound libraries. The archive is freely accessible at http://drugdesign.unistra.fr/ppiome .

Quantitative high-throughput screening assays for the discovery and development of SIRPα-CD47 interaction inhibitors

CD47 is an immune checkpoint molecule that downregulates key aspects of both the innate and adaptive anti-tumor immune response via its counter receptor SIRPα, and it is expressed at high levels in a wide variety of tumor types. This has led to the development of biologics that inhibit SIRPα engagement including humanized CD47 antibodies and a soluble SIRPα decoy receptor that are currently undergoing clinical trials. Unfortunately, toxicological issues, including anemia related to on-target mechanisms, are barriers to their clinical advancement. Another potential issue with large biologics that bind CD47 is perturbation of CD47 signaling through its high-affinity interaction with the matricellular protein thrombospondin-1 (TSP1). One approach to avoid these shortcomings is to identify and develop small molecule molecular probes and pretherapeutic agents that would (1) selectively target SIRPα or TSP1 interactions with CD47, (2) provide a route to optimize pharmacokinetics, reduce on-target toxicity and maximize tissue penetration, and (3) allow more flexible routes of administration. As the first step toward this goal, we report the development of an automated quantitative high-throughput screening (qHTS) assay platform capable of screening large diverse drug-like chemical libraries to discover novel small molecules that inhibit CD47-SIRPα interaction. Using time-resolved Förster resonance energy transfer (TR-FRET) and bead-based luminescent oxygen channeling assay formats (AlphaScreen), we developed biochemical assays, optimized their performance, and individually tested them in small-molecule library screening. Based on performance and low false positive rate, the LANCE TR-FRET assay was employed in a ~90,000 compound library qHTS, while the AlphaScreen oxygen channeling assay served as a cross-validation orthogonal assay for follow-up characterization. With this multi-assay strategy, we successfully eliminated compounds that interfered with the assays and identified five compounds that inhibit the CD47-SIRPα interaction; these compounds will be further characterized and later disclosed. Importantly, our results validate the large library qHTS for antagonists of CD47-SIRPα interaction and suggest broad applicability of this approach to screen chemical libraries for other protein-protein interaction modulators.

In Silico Peptide Ligation: Iterative Residue Docking and Linking as a New Approach to Predict Protein-Peptide Interactions

Peptide⁻protein interactions are corner-stones of living functions involved in essential mechanisms, such as cell signaling. Given the difficulty of obtaining direct experimental structural biology data, prediction of those interactions is of crucial interest for the rational development of new drugs, notably to fight diseases, such as cancer or Alzheimer's disease. Because of the high flexibility of natural unconstrained linear peptides, prediction of their binding mode in a protein cavity remains challenging. Several theoretical approaches have been developed in the last decade to address this issue. Nevertheless, improvements are needed, such as the conformation prediction of peptide side-chains, which are dependent on peptide length and flexibility. Here, we present a novel in silico method, Iterative Residue Docking and Linking (IRDL), to efficiently predict peptide⁻protein interactions. In order to reduce the conformational space, this innovative method splits peptides into several short segments. Then, it uses the performance of intramolecular covalent docking to rebuild, sequentially, the complete peptide in the active site of its protein target. Once the peptide is constructed, a rescoring step is applied in order to correctly rank all IRDL solutions. Applied on a set of 11 crystallized peptide⁻protein complexes, the IRDL method shows promising results, since it is able to retrieve experimental binding conformations with a Root Mean Square Deviation (RMSD) below 2 Å in the top five ranked solutions. For some complexes, IRDL method outperforms two other docking protocols evaluated in this study. Hence, IRDL is a new tool that could be used in drug design projects to predict peptide⁻protein interactions.

Design criteria for minimalist mimics of protein-protein interface segments

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

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

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

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.

Discovery of a Small-Molecule Bromodomain-Containing Protein 4 (BRD4) Inhibitor That Induces AMP-Activated Protein Kinase-Modulated Autophagy-Associated Cell Death in Breast Cancer

Upon the basis of The Cancer Genome Atlas (TCGA) data set, we identified that several autophagy-related proteins such as AMP-activated protein kinase (AMPK) were remarkably downregulated in breast cancer. Combined with coimmunoprecipitation assay, we demonstrated that BRD4 might interact with AMPK. After analyses of the pharmacophore and WPF interaction optimization, we designed a small-molecule inhibitor of BRD4, 9f (FL-411) which was validated by cocrystal structure with BD1 of BRD4. Subsequently, 9f was discovered to induce ATG5-dependent autophagy-associated cell death (ACD) by blocking BRD4-AMPK interaction and thus activating AMPK-mTOR-ULK1-modulated autophagic pathway in breast cancer cells. Interestingly, the iTRAQ-based proteomics analyses revealed that 9f induced ACD pathways involved in HMGB1, VDAC1/2, and eEF2. Moreover, 9f displayed a therapeutic potential on both breast cancer xenograft mouse and zebrafish models. Together, these results demonstrate that a novel small-molecule inhibitor of BRD4 induces BRD4-AMPK-modulated ACD in breast cancer, which may provide a candidate drug for future cancer therapy.

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

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

Toward the identification of viral cap-methyltransferase inhibitors by fluorescence screening assay

Two highly pathogenic human coronaviruses associated with severe respiratory syndromes emerged since the beginning of the century. The severe acute respiratory syndrome SARS-coronavirus (CoV) spread first in southern China in 2003 with about 8000 infected cases in few months. Then in 2012, the Middle East respiratory syndrome (MERS-CoV) emerged from the Arabian Peninsula giving a still on-going epidemic associated to a high fatality rate. CoVs are thus considered a major health threat. This is especially true as no vaccine nor specific therapeutic are available against either SARS- or MERS-CoV. Therefore, new drugs need to be identified in order to develop antiviral treatments limiting CoV replication. In this study, we focus on the nsp14 protein, which plays a key role in virus replication as it methylates the RNA cap structure at the N7 position of the guanine. We developed a high-throughput N7-MTase assay based on Homogenous Time Resolved Fluorescence (HTRF^®) and screened chemical libraries (2000 compounds) on the SARS-CoV nsp14. 20 compounds inhibiting the SARS-CoV nsp14 were further evaluated by IC₅₀ determination and their specificity was assessed toward flavivirus- and human cap N7-MTases. Our results reveal three classes of compounds: 1) molecules inhibiting several MTases as well as the dengue virus polymerase activity unspecifically, 2) pan MTases inhibitors targeting both viral and cellular MTases, and 3) inhibitors targeting one viral MTase more specifically showing however activity against the human cap N7-MTase. These compounds provide a first basis towards the development of more specific inhibitors of viral methyltransferases.