iPPI-DB: a manually curated and interactive database of small non-peptide inhibitors of protein-protein interactions

Recent advances in targeting the "undruggable" proteins: from drug discovery to clinical trials

Undruggable proteins are a class of proteins that are often characterized by large, complex structures or functions that are difficult to interfere with using conventional drug design strategies. Targeting such undruggable targets has been considered also a great opportunity for treatment of human diseases and has attracted substantial efforts in the field of medicine. Therefore, in this review, we focus on the recent development of drug discovery targeting "undruggable" proteins and their application in clinic. To make this review well organized, we discuss the design strategies targeting the undruggable proteins, including covalent regulation, allosteric inhibition, protein-protein/DNA interaction inhibition, targeted proteins regulation, nucleic acid-based approach, immunotherapy and others.

Type II bacterial toxin-antitoxins: hypotheses, facts, and the newfound plethora of the PezAT system

Toxin-antitoxin (TA) systems are entities found in the prokaryotic genomes, with eight reported types. Type II, the best characterized, is comprised of two genes organized as an operon. Whereas toxins impair growth, the cognate antitoxin neutralizes its activity. TAs appeared to be involved in plasmid maintenance, persistence, virulence, and defence against bacteriophages. Most Type II toxins target the bacterial translational machinery. They seem to be antecessors of Higher Eukaryotes and Prokaryotes Nucleotide-binding (HEPN) RNases, minimal nucleotidyltransferase domains, or CRISPR-Cas systems. A total of four TAs encoded by Streptococcus pneumoniae, RelBE, YefMYoeB, Phd-Doc, and HicAB, belong to HEPN-RNases. The fifth is represented by PezAT/Epsilon-Zeta. PezT/Zeta toxins phosphorylate the peptidoglycan precursors, thereby blocking cell wall synthesis. We explore the body of knowledge (facts) and hypotheses procured for Type II TAs and analyse the data accumulated on the PezAT family. Bioinformatics analyses showed that homologues of PezT/Zeta toxin are abundantly distributed among 14 bacterial phyla mostly in Proteobacteria (48%), Firmicutes (27%), and Actinobacteria (18%), showing the widespread distribution of this TA. The pezAT locus was found to be mainly chromosomally encoded whereas its homologue, the tripartite omega-epsilon-zeta locus, was found mostly on plasmids. We found several orphan pezT/zeta toxins, unaccompanied by a cognate antitoxin.

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

BACKGROUND

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

AREAS COVERED

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

EXPERT OPINION

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

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).

Protein-protein interactions: Methods, databases, and applications in virus-host study

Almost all the cellular processes in a living system are controlled by proteins: They regulate gene expression, catalyze chemical reactions, transport small molecules across membranes, and transmit signal across membranes. Even, a viral infection is often initiated through virus-host protein interactions. Protein-protein interactions (PPIs) are the physical contacts between two or more proteins and they represent complex biological functions. Nowadays, PPIs have been used to construct PPI networks to study complex pathways for revealing the functions of unknown proteins. Scientists have used PPIs to find the molecular basis of certain diseases and also some potential drug targets. In this review, we will discuss how PPI networks are essential to understand the molecular basis of virus-host relationships and several databases which are dedicated to virus-host interaction studies. Here, we present a short but comprehensive review on PPIs, including the experimental and computational methods of finding PPIs, the databases dedicated to virus-host PPIs, and the associated various applications in protein interaction networks of some lethal viruses with their hosts.

Targeting the Integrated Stress Response Kinase GCN2 to Modulate Retroviral Integration

Multiple viral targets are now available in the clinic to fight HIV infection. Even if this targeted therapy is highly effective at suppressing viral replication, caregivers are facing growing therapeutic failures in patients due to resistance, with or without treatment-adherence glitches. Accordingly, it is important to better understand how HIV and other retroviruses replicate in order to propose alternative antiviral strategies. Recent studies have shown that multiple cellular factors are implicated during the integration step and, more specifically, that integrase can be regulated through post-translational modifications. We have shown that integrase is phosphorylated by GCN2, a cellular protein kinase of the integrated stress response, leading to a restriction of HIV replication. In addition, we found that this mechanism is conserved among other retroviruses. Accordingly, we developed an in vitro interaction assay, based on the AlphaLISA technology, to monitor the integrase-GCN2 interaction. From an initial library of 133 FDA-approved molecules, we identified nine compounds that either inhibited or stimulated the interaction between GCN2 and HIV integrase. In vitro characterization of these nine hits validated this pilot screen and demonstrated that the GCN2-integrase interaction could be a viable solution for targeting integrase out of its active site.

Ensemble completeness in conformer sampling: the case of small macrocycles

In this study we compare the three algorithms for the generation of conformer ensembles Biovia BEST, Schrödinger Prime macrocycle sampling (PMM) and Conformator (CONF) form the University of Hamburg, with ensembles derived for exhaustive molecular dynamics simulations applied to a dataset of 7 small macrocycles in two charge states and three solvents. Ensemble completeness is a prerequisite to allow for the selection of relevant diverse conformers for many applications in computational chemistry. We apply conformation maps using principal component analysis based on ring torsions. Our major finding critical for all applications of conformer ensembles in any computational study is that maps derived from MD with explicit solvent are significantly distinct between macrocycles, charge states and solvents, whereas the maps for post-optimized conformers using implicit solvent models from all generator algorithms are very similar independent of the solvent. We apply three metrics for the quantification of the relative covered ensemble space, namely cluster overlap, variance statistics, and a novel metric, Mahalanobis distance, showing that post-optimized MD ensembles cover a significantly larger conformational space than the generator ensembles, with the ranking PMM > BEST >> CONF. Furthermore, we find that the distributions of 3D polar surface areas are very similar for all macrocycles independent of charge state and solvent, except for the smaller and more strained compound 7, and that there is also no obvious correlation between 3D PSA and intramolecular hydrogen bond count distributions.

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.

Targeting SARS-CoV-2 Spike Protein/ACE2 Protein-Protein Interactions: a Computational Study

The spike glycoprotein (S) of the SARS-CoV-2 virus surface plays a key role in receptor binding and virus entry. The S protein uses the angiotensin converting enzyme (ACE2) for entry into the host cell and binding to ACE2 occurs at the receptor binding domain (RBD) of the S protein. Therefore, the protein-protein interactions (PPIs) between the SARS-CoV-2 RBD and human ACE2, could be attractive therapeutic targets for drug discovery approaches designed to inhibit the entry of SARS-CoV-2 into the host cells. Herein, with the support of machine learning approaches, we report structure-based virtual screening as an effective strategy to discover PPIs inhibitors from ZINC database. The proposed computational protocol led to the identification of a promising scaffold which was selected for subsequent binding mode analysis and that can represent a useful starting point for the development of new treatments of the SARS-CoV-2 infection.

Alteraciones Hepaticas en pacientes con infección por VIH en un centro de investigacion en Bogotá Colombia 2009 – 2019

Introducción: Las alteraciones de la bioquímica hepática son frecuentes en los pacientes con infección por VIH, la etiología es variada, la esteatosis hepática es frecuente con una prevalencia estimada del 60% Objetivos: Caracterizar las alteraciones hepáticas en una serie de pacientes con infección por VIH en un centro de investigación de Bogotá Colombia durante el periodo 2009 – 2019. Materiales y Métodos: Estudio descriptivo, retrospectivo, observacional de pacientes con infección por VIH que asistieron a un centro de investigación durante los años 2009-2019. Resultados: 94% fueron hombres y 6% mujeres con edad promedio de 44 años, 92,5% de los pacientes presentaba uso de terapia antiretroviral. Las principales hepatopatías fueron la coinfección VIH-Hepatitis C y el hígado graso en iguales porcentajes, 31,3%. El promedio del indice HOMA fue de 2,58. Discusión: Las enfermedades hepáticas son una causa importante de morbimortalidad en pacientes con infección por VIH, las coinfecciones virales y el hígado graso pueden ser muy frecuentes en nuestro medio a diferencia de otros estudios Conclusiones: Este es el primer estudio a nivel local en describir las alteraciones hepáticas en pacientes con VIH, las comorbilidades no SIDA, juegan un papel importante dentro de la enfermedad. La hepatitis C continúa siendo una coinfección frecuente en la población VIH.

Substrate-induced modulation of protein-protein interactions within human mitochondrial cytochrome P450-dependent system

Steroidogenesis is strictly regulated at multiple levels, as produced steroid hormones are crucial to maintain physiological functions. Cytochrome P450 enzymes are key players in adrenal steroid hormone biosynthesis and function within short redox-chains in mitochondria and endoplasmic reticulum. However, mechanisms regulating supply of reducing equivalents in the mitochondrial CYP-dependent system are not fully understood. In the present work, we aimed to estimate how the specific steroids, substrates, intermediates and products of multistep reactions modulate protein-protein interactions between adrenodoxin (Adx) and mitochondrial CYP11 s. Using the SPR technology we determined that steroid substrates affect affinity and stability of CYP11s-Adx complexes in an isoform-specific mode. In particular, cholesterol induces a 4-fold increase in the rate of CYP11A1 - Adx complex formation without significant effect on dissociation (koff decreased ∼1.5-fold), overall increasing complex affinity. At the same time steroid substrates decrease the affinity of both CYP11B1 - Adx and CYP11B2 - Adx complexes, predominantly reducing their stability (4-7 fold). This finding reveals differentiation of protein-protein interactions within the mitochondrial pool of CYPs, which have the same electron donor. The regulation of electron supply by the substrates might affect the overall steroid hormones production. Our experimental data provide further insight into protein-protein interactions within CYP-dependent redox chains involved in steroidogenesis.

Survey of Similarity-Based Prediction of Drug-Protein Interactions

Therapeutic activity of a significant majority of drugs is determined by their interactions with proteins. Databases of drug-protein interactions (DPIs) primarily focus on the therapeutic protein targets while the knowledge of the off-targets is fragmented and partial. One way to bridge this knowledge gap is to employ computational methods to predict protein targets for a given drug molecule, or interacting drugs for given protein targets. We survey a comprehensive set of 35 methods that were published in high-impact venues and that predict DPIs based on similarity between drugs and similarity between protein targets. We analyze the internal databases of known PDIs that these methods utilize to compute similarities, and investigate how they are linked to the 12 publicly available source databases. We discuss contents, impact and relationships between these internal and source databases, and well as the timeline of their releases and publications. The 35 predictors exploit and often combine three types of similarities that consider drug structures, drug profiles, and target sequences. We review the predictive architectures of these methods, their impact, and we explain how their internal DPIs databases are linked to the source databases. We also include a detailed timeline of the development of these predictors and discuss the underlying limitations of the current resources and predictive tools. Finally, we provide several recommendations concerning the future development of the related databases and methods.

The iPPI-DB initiative: A Community-centered database of Protein-Protein Interaction modulators

MOTIVATION

One avenue to address the paucity of clinically testable targets is to reinvestigate the druggable genome by tackling complicated types of targets such as Protein-Protein Interactions (PPIs). Given the challenge to target those interfaces with small chemical compounds, it has become clear that learning from successful examples of PPI modulation is a powerful strategy. Freely-accessible databases of PPI modulators that provide the community with tractable chemical and pharmacological data, as well as powerful tools to query them, are therefore essential to stimulate new drug discovery projects on PPI targets.

RESULTS

Here, we present the new version iPPI-DB, our manually curated database of PPI modulators. In this completely redesigned version of the database, we introduce a new web interface relying on crowdsourcing for the maintenance of the database. This interface was created to enable community contributions, whereby external experts can suggest new database entries. Moreover, the data model, the graphical interface, and the tools to query the database have been completely modernized and improved. We added new PPI modulators, new PPI targets, and extended our focus to stabilizers of PPIs as well.

AVAILABILITY AND IMPLEMENTATION

The iPPI-DB server is available at https://ippidb.pasteur.fr The source code for this server is available at https://gitlab.pasteur.fr/ippidb/ippidb-web/ and is distributed under GPL licence (http://www.gnu.org/licences/gpl). Queries can be shared through persistent links according to the FAIR data standards. Data can be downloaded from the website as csv files.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Decoding Protein-protein Interactions: An Overview

Drug discovery has focused on the paradigm "one drug, one target" for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

Diameter Is a Key 3D Characteristic for Assessments of Efficient Inhibitors of Protein-Protein Interactions

Three-dimensional (3D) molecular descriptors, including physicochemical and shape properties, for protein-protein interaction (PPI) interface inhibitors have become a topic of discussion. However, the relationships between such properties and binding free energy have not been adequately investigated. In this study, we focused on identifying key 3D molecular descriptors related to the binding free energy and/or the ligand efficiency (LE) of PPI interface inhibitors. A positive correlation was found between the binding free energy and the diameter (D) of cylindrical 3D molecules, in addition to a correlation between LE and D/heavy atom count (HAC). In addition, we showed a correlation between LE and D/HAC for macrocyclic compounds, suggesting that the present findings could be applied during assessments of the potential of macrocyclic PPI interface inhibitors in drug discovery processes.

De-risking Drug Discovery of Intracellular Targeting Peptides: Screening Strategies to Eliminate False-Positive Hits

Nonspecific promiscuous compounds can mislead researchers and waste significant resources. This phenomenon, though well-documented for small molecules, has not been widely explored for the peptide modality. Here we demonstrate that two purported peptide-based KRas inhibitors, SAH-SOS1 _A and cyclorasin 9A5, exemplify false-positive molecules-in terms of both their binding affinities and cellular activities. Through multiple gold-standard biophysical techniques, we unambiguously show that both peptides lack specific binding to KRas and instead induce protein unfolding. Although these peptides inhibited cellular proliferation, the activities appeared to be off-target on the basis of a counterscreen with KRas-independent cell lines. We further demonstrate that their cellular activities are derived from membrane disruption. Accordingly, we propose that to de-risk false-positive molecules, orthogonal binding assays and cellular counterscreens are indispensable.

The design and development of covalent protein-protein interaction inhibitors for cancer treatment

Protein-protein interactions (PPIs) are central to a variety of biological processes, and their dysfunction is implicated in the pathogenesis of a range of human diseases, including cancer. Hence, the inhibition of PPIs has attracted significant attention in drug discovery. Covalent inhibitors have been reported to achieve high efficiency through forming covalent bonds with cysteine or other nucleophilic residues in the target protein. Evidence suggests that there is a reduced risk for the development of drug resistance against covalent drugs, which is a major challenge in areas such as oncology and infectious diseases. Recent improvements in structural biology and chemical reactivity have enabled the design and development of potent and selective covalent PPI inhibitors. In this review, we will highlight the design and development of therapeutic agents targeting PPIs for cancer therapy.

Protein-protein interactions: a structural view of inhibition strategies and the IL-23/IL-17 axis

Protein-protein interactions are central to biology and provide opportunities to modulate disease with small-molecule or protein therapeutics. Recent developments in the understanding of the tractability of protein-protein interactions are discussed with a focus on the ligandable nature of protein-protein interaction surfaces. General principles of inhibiting protein-protein interactions are illustrated with structural biology examples from six members of the IL-23/IL-17 signaling family (IL-1, IL-6, IL-17, IL-23 RORγT and TNFα). These examples illustrate the different approaches to discover protein-protein interaction inhibitors on a target-specific basis that has proven fruitful in terms of discovering both small molecule and biologic based protein-protein interaction inhibitors.

Development of a Novel Cell-Permeable Protein-Protein Interaction Inhibitor for the Polo-box Domain of Polo-like Kinase 1

Polo-like kinase 1 (PLK1) is a key regulator of mitosis and a recognized drug target for cancer therapy. Inhibiting the polo-box domain of PLK1 offers potential advantages of increased selectivity and subsequently reduced toxicity compared with targeting the kinase domain. However, many if not all existing polo-box domain inhibitors have been shown to be unsuitable for further development. In this paper, we describe a novel compound series, which inhibits the protein-protein interactions of PLK1 via the polo-box domain. We combine high throughput screening with molecular modeling and computer-aided design, synthetic chemistry, and cell biology to address some of the common problems with protein-protein interaction inhibitors, such as solubility and potency. We use molecular modeling to improve the solubility of a hit series with initially poor physicochemical properties, enabling biophysical and biochemical characterization. We isolate and characterize enantiomers to improve potency and demonstrate on-target activity in both cell-free and cell-based assays, entirely consistent with the proposed binding model. The resulting compound series represents a promising starting point for further progression along the drug discovery pipeline and a new tool compound to study kinase-independent PLK functions.

Prediction and targeting of GPCR oligomer interfaces

GPCR oligomerization has emerged as a hot topic in the GPCR field in the last years. Receptors that are part of these oligomers can influence each other's function, although it is not yet entirely understood how these interactions work. The existence of such a highly complex network of interactions between GPCRs generates the possibility of alternative targets for new therapeutic approaches. However, challenges still exist in the characterization of these complexes, especially at the interface level. Different experimental approaches, such as FRET or BRET, are usually combined to study GPCR oligomer interactions. Computational methods have been applied as a useful tool for retrieving information from GPCR sequences and the few X-ray-resolved oligomeric structures that are accessible, as well as for predicting new and trustworthy GPCR oligomeric interfaces. Machine-learning (ML) approaches have recently helped with some hindrances of other methods. By joining and evaluating multiple structure-, sequence- and co-evolution-based features on the same algorithm, it is possible to dilute the issues of particular structures and residues that arise from the experimental methodology into all-encompassing algorithms capable of accurately predict GPCR-GPCR interfaces. All these methods used as a single or a combined approach provide useful information about GPCR oligomerization and its role in GPCR function and dynamics. Altogether, we present experimental, computational and machine-learning methods used to study oligomers interfaces, as well as strategies that have been used to target these dynamic complexes.

A Fluorescence Anisotropy-Based Comprehensive Method for the In Vitro Screening of COI1-JAZs Agonists and Antagonists

The phytohormone (+)-7-iso-jasmonoyl-L-isoleucine (JA-Ile) causes protein-protein interactions (PPI) between F-box Protein CORONATINE INSENSITIVE 1 (COI1) and JASMONATE ZIM DOMAIN (JAZ) transcriptional repressor. A total of 13 JAZ subtypes are encoded in the genome of Arabidopsis thaliana; however, their genetic redundancy obfuscates the individual function of each JAZ. One approach to decipher this redundant signaling network is chemical genetics, using small molecules specific to individual JAZ subtype, for which a reliable and high-throughput screening system of the ligands for all combinations of COI1-JAZs would be indispensable. In this chapter, we describe a fluorescence anisotropy-based quantitative screening system for the ligands of COI1-JAZ co-receptors. Our method is applicable to agonists and antagonists of the COI1-JAZs.

Tranilast directly targets NLRP3 to treat inflammasome-driven diseases

The dysregulation of NLRP3 inflammasome can cause uncontrolled inflammation and drive the development of a wide variety of human diseases, but the medications targeting NLRP3 inflammasome are not available in clinic. Here, we show that tranilast (TR), an old anti‐allergic clinical drug, is a direct NLRP3 inhibitor. TR inhibits NLRP3 inflammasome activation in macrophages, but has no effects on AIM2 or NLRC4 inflammasome activation. Mechanismly, TR directly binds to the NACHT domain of NLRP3 and suppresses the assembly of NLRP3 inflammasome by blocking NLRP3 oligomerization. In vivo experiments show that TR has remarkable preventive or therapeutic effects on the mouse models of NLRP3 inflammasome‐related human diseases, including gouty arthritis, cryopyrin‐associated autoinflammatory syndromes, and type 2 diabetes. Furthermore, TR is active ex vivo for synovial fluid mononuclear cells from patients with gout. Thus, our study identifies the old drug TR as a direct NLRP3 inhibitor and provides a potentially practical pharmacological approach for treating NLRP3‐driven diseases.

A comprehensive in vitro fluorescence anisotropy assay system for screening ligands of the jasmonate COI1-JAZ co-receptor in plants

The phytohormone (+)-7-iso-jasmonoyl-l-isoleucine regulates many developmental and stress responses in plants and induces protein-protein interactions between COI1, the F-box component of E3 ubiquitin ligase, and jasmonate ZIM domain (JAZ) repressors. These interactions cause JAZ degradation and activate jasmonate (JA), leading to plant defense responses, growth inhibition, and senescence. Thirteen JAZ subtypes are encoded in the Arabidopsis thaliana genome, but a detailed understanding of the physiological functions of these JAZ subtypes remains unclear, partially because of the genetic redundancy of JAZ genes. One strategy to elucidate the complex JA signaling pathways is to develop a reliable and comprehensive binding assay system of the ligands with all combinations of the co-receptors. Herein, we report the development of a fluorescence anisotropy-based in vitro binding assay system to screen for the ligands of the COI1-JAZ co-receptors. Our assay enabled the first quantitative analysis of the affinity values and JAZ-subtype selectivity of various endogenous JA derivatives, such as coronatine, jasmonic acid, and 12-hydroxyjasmonoyl-l-isoleucine. Because of its high signal-to-noise ratio and convenient mix-and-read assay system, our screening approach can be used in plate reader-based assays of both agonists and antagonists of COI1-JAZ co-receptors.

When Humans Met Superbugs: Strategies to Tackle Bacterial Resistances to Antibiotics

Bacterial resistance to antibiotics poses enormous health and economic burdens to our society, and it is of the essence to explore old and new ways to deal with these problems. Here we review the current status of multi-resistance genes and how they spread among bacteria. We discuss strategies to deal with resistant bacteria, namely the search for new targets and the use of inhibitors of protein-protein interactions, fragment-based methods, or modified antisense RNAs. Finally, we discuss integrated approaches that consider bacterial populations and their niches, as well as the role of global regulators that activate and/or repress the expression of multiple genes in fluctuating environments and, therefore, enable resistant bacteria to colonize new niches. Understanding how the global regulatory circuits work is, probably, the best way to tackle bacterial resistance.

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

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

Toward the Discovery of a Novel Class of YAP⁻TEAD Interaction Inhibitors by Virtual Screening Approach Targeting YAP⁻TEAD Protein⁻Protein Interface

Intrinsically disordered protein YAP (yes-associated protein) interacts with TEADs transcriptional factors family (transcriptional enhancer associated domain) creating three interfaces. Interface 3, between the Ω-loop of YAP and a shallow pocket of TEAD was identified as the most important TEAD zone for YAP-TEAD interaction. Using the first X-ray structure of the hYAP_50–71-hTEAD1_209–426 complex (PDB 3KYS) published in 2010, a protein-protein interaction inhibitors-enriched library (175,000 chemical compounds) was screened against this hydrophobic pocket of TEAD. Four different chemical families have been identified and evaluated using biophysical techniques (thermal shift assay, microscale thermophoresis) and in cellulo assays (luciferase activity in transfected HEK293 cells, RTqPCR in MDA-MB231 cells). A first promising hit with micromolar inhibition in the luciferase gene reporter assay was discovered. This hit also decreased mRNA levels of TEAD target genes.

Assessment of a Single Decoupling Alchemical Approach for the Calculation of the Absolute Binding Free Energies of Protein-Peptide Complexes

The computational modeling of peptide inhibitors to target protein-protein binding interfaces is growing in interest as these are often too large, too shallow, and too feature-less for conventional small molecule compounds. Here, we present a rare successful application of an alchemical binding free energy method for the calculation of converged absolute binding free energies of a series of protein-peptide complexes. Specifically, we report the binding free energies of a series of cyclic peptides derived from the LEDGF/p75 protein to the integrase receptor of the HIV1 virus. The simulations recapitulate the effect of mutations relative to the wild-type binding motif of LEDGF/p75, providing structural, energetic and dynamical interpretations of the observed trends. The equilibration and convergence of the calculations are carefully analyzed. Convergence is aided by the adoption of a single-decoupling alchemical approach with implicit solvation, which circumvents the convergence difficulties of conventional double-decoupling protocols. We hereby present the single-decoupling methodology and critically evaluate its advantages and limitations. We also discuss some of the challenges and potential pitfalls of binding free energy calculations for complex molecular systems which have generally limited their applicability to the quantitative study of protein-peptide binding equilibria.

Exploring the chemical space of peptides for drug discovery: a focus on linear and cyclic penta-peptides

Peptide and peptide-like structures are regaining attention in drug discovery. Previous studies suggest that bioactive peptides have diverse structures and may have physicochemical properties attractive to become hit and lead compounds. However, chemoinformatic studies that characterize such diversity are limited. Herein, we report the physicochemical property profile and chemical space of four synthetic linear and cyclic combinatorial peptide libraries. As a case study, the analysis was focused on penta-peptides. The chemical space of the peptide and N-methylated peptides libraries was compared to compound data sets of pharmaceutical relevance. Results indicated that there is a major overlap in the chemical space of N-methylated cyclic peptides with inhibitors of protein-protein interactions and macrocyclic natural products available for screening. Also, there is an overlap between the chemical space of the synthetic peptides with peptides approved for clinical use (or in clinical trials), and to other approved drugs that are outside the traditional chemical space. Results further support that synthetic penta-peptides are suitable compounds to be used in drug discovery projects.

Hot-spot analysis for drug discovery targeting protein-protein interactions

INTRODUCTION

Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions. Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions. Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.

Functional details of the Mycobacterium tuberculosis VapBC26 toxin-antitoxin system based on a structural study: insights into unique binding and antibiotic peptides

Toxin-antitoxin (TA) systems are essential for bacterial persistence under stressful conditions. In particular, Mycobacterium tuberculosis express VapBC TA genes that encode the stable VapC toxin and the labile VapB antitoxin. Under normal conditions, these proteins interact to form a non-toxic TA complex, but the toxin is activated by release from the antitoxin in response to unfavorable conditions. Here, we present the crystal structure of the M. tuberculosis VapBC26 complex and show that the VapC26 toxin contains a pilus retraction protein (PilT) N-terminal (PIN) domain that is essential for ribonuclease activity and that, the VapB26 antitoxin folds into a ribbon-helix-helix DNA-binding motif at the N-terminus. The active site of VapC26 is sterically blocked by the flexible C-terminal region of VapB26. The C-terminal region of free VapB26 adopts an unfolded conformation but forms a helix upon binding to VapC26. The results of RNase activity assays show that Mg²⁺ and Mn²⁺ are essential for the ribonuclease activity of VapC26. As shown in the nuclear magnetic resonance spectra, several residues of VapB26 participate in the specific binding to the promoter region of the VapBC26 operon. In addition, toxin-mimicking peptides were designed that inhibit TA complex formation and thereby increase toxin activity, providing a novel approach to the development of new antibiotics.

Privileged Substructures to Modulate Protein-Protein Interactions

Given the difficulties to identify chemical probes that can modulate protein-protein interactions (PPIs), actors in the field have started to agree on the necessity to use PPI-tailored screening chemical collections. However, which type of scaffolds may promote the binding of compounds to PPI targets remains unclear. In this big data analysis, we have identified a list of privileged chemical substructures that are most often observed within inhibitors of PPIs. Using molecular frameworks as a way to perceive chemical substructures with the combination of an experimental and a machine-learning based predicted data set of iPPI compounds, we propose a list of privileged substructures in the form of scaffolds and chemical moieties that can be substantially chemically functionalized and do not present any toxicophore nor pan-assay interference (PAINS) alerts. We think that such chemical guidance will be valuable for medicinal chemists in their attempt to identify initial quality chemical probes on PPI targets.

Group-Based Optimization of Potent and Cell-Active Inhibitors of the von Hippel-Lindau (VHL) E3 Ubiquitin Ligase: Structure-Activity Relationships Leading to the Chemical Probe (2S,4R)-1-((S)-2-(1-Cyanocyclopropanecarboxamido)-3,3-dimethylbutanoyl)-4-hydroxy-N-(4-(4-methylthiazol-5-yl)benzyl)pyrrolidine-2-carboxamide (VH298)

The von Hippel–Lindau tumor suppressor protein is the substrate binding subunit of the VHL E3 ubiquitin ligase, which targets hydroxylated α subunit of hypoxia inducible factors (HIFs) for ubiquitination and subsequent proteasomal degradation. VHL is a potential target for treating anemia and ischemic diseases, motivating the development of inhibitors of the VHL:HIF-α protein–protein interaction. Additionally, bifunctional proteolysis targeting chimeras (PROTACs) containing a VHL ligand can hijack the E3 ligase activity to induce degradation of target proteins. We report the structure-guided design and group-based optimization of a series of VHL inhibitors with low nanomolar potencies and improved cellular permeability. Structure–activity relationships led to the discovery of potent inhibitors 10 and chemical probe VH298, with dissociation constants <100 nM, which induced marked HIF-1α intracellular stabilization. Our study provides new chemical tools to probe the VHL-HIF pathways and new VHL ligands for next-generation PROTACs.

Intrinsic Disorder, Protein-Protein Interactions, and Disease

It is recognized now that biologically active proteins without stable tertiary structure (known as intrinsically disordered proteins, IDPs) and hybrid proteins containing ordered domains and intrinsically disordered protein regions (IDPRs) are important players found in any given proteome. These IDPs/IDPRs possess functions that complement functional repertoire of their ordered counterparts, being commonly related to recognition, as well as control and regulation of various signaling pathways. They are interaction masters, being able to utilize a wide spectrum of interaction mechanisms, ranging from induced folding to formation of fuzzy complexes where significant levels of disorder are preserved, to polyvalent stochastic interactions playing crucial roles in the liquid-liquid phase transitions leading to the formation of proteinaceous membrane-less organelles. IDPs/IDPRs are tightly controlled themselves via various means, including alternative splicing, precisely controlled expression and degradation, binding to specific partners, and posttranslational modifications. Distortions in the regulation and control of IDPs/IDPRs, as well as their aberrant interactivity are commonly associated with various human diseases. This review presents some aspects of the intrinsic disorder-based functionality and dysfunctionality, paying special attention to the normal and pathological protein-protein interactions.

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

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

Synthesis of the Src SH2 domain and its application in bioassays for mirror-image screening

Mirror-image screening systems for Src SH2 domain inhibitors were established using a synthetic Src SH2 domain.

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

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

Recent Advances in Computational Models for the Study of Protein-Peptide Interactions

We review computational models and software tools in current use for the study of protein-peptide interactions. Peptides and peptide derivatives are growing in interest as therapeutic agents to target protein-protein interactions. Protein-protein interactions are pervasive in biological systems and are responsible for the regulation of critical functions within the cell. Mutations or dysregulation of expression can alter the network of interactions among proteins and cause diseases such as cancer. Protein-protein binding interfaces, which are often large, shallow, and relatively feature-less, are difficult to target with small-molecule inhibitors. Peptide derivatives based on the binding motifs present in the target protein complex are increasingly drawing interest as superior alternatives to conventional small-molecule inhibitors. However, the design of peptide-based inhibitors also presents novel challenges. Peptides are more complex and more flexible than standard medicinal compounds. They also tend to form more extended and more complex interactions with their protein targets. Computational modeling is increasingly being employed to supplement synthetic and biochemical work to offer guidance and energetic and structural insights. In this review, we discuss recent in silico structure-based and physics-based approaches currently employed to model protein-peptide interactions with a few examples of their applications.

Recent progress in the development of protein-protein interaction inhibitors targeting androgen receptor-coactivator binding in prostate cancer

The androgen receptor (AR) is a key regulator for the growth, differentiation and survival of prostate cancer cells. Identified as a primary target for the treatment of prostate cancer, many therapeutic strategies have been developed to attenuate AR signaling in prostate cancer cells. While frontline androgen-deprivation therapies targeting either the production or action of androgens usually yield favorable responses in prostate cancer patients, a significant number acquire treatment resistance. Known as the castration-resistant prostate cancer (CRPC), the treatment options are limited for this advanced stage. It has been shown that AR signaling is restored in CRPC due to many aberrant mechanisms such as AR mutations, amplification or expression of constitutively active splice-variants. Coregulator recruitment is a crucial regulatory step in AR signaling and the direct blockade of coactivator binding to AR offers the opportunity to develop therapeutic agents that would remain effective in prostate cancer cells resistant to conventional endocrine therapies. Structural analyses of the AR have identified key surfaces involved in protein-protein interaction with coregulators that have been recently used to design and develop promising AR-coactivator binding inhibitors. In this review we will discuss the design and development of small-molecule inhibitors targeting the AR-coactivator interactions for the treatment of prostate cancer.

Structure-guided design of small-molecule therapeutics against RSV disease

INTRODUCTION

In the United States, respiratory syncytial virus (RSV) is responsible for the majority of infant hospitalizations resulting from viral infections, as well as a leading source of pneumonia and bronchiolitis in young children and the elderly. In the absence of vaccine prophylaxis or an effective antiviral for improved disease management, the development of novel anti-RSV therapeutics is critical. Several advanced drug development campaigns of the past decade have focused on blocking viral infection. These efforts have returned a chemically distinct panel of small-molecule RSV entry inhibitors, but binding sites and molecular mechanism of action appeared to share a common mechanism, resulting in comprehensive cross-resistance and calling for alternative druggable targets such as viral RNA-dependent RNA-polymerase complex. Areas Covered: In this review, the authors discuss the current status of the mechanism of action of RSV entry inhibitors. They also provide the recent structural insight into the organization of the polymerase complex that have revealed novel drug targets sites, and outline a path towards the discovery of next-generation RSV therapeutics. Expert opinion: Considering the tremendous progress experienced in our structural understanding of RSV biology in recent years and encouraging early results of a nucleoside analog inhibitor in clinical trials, there is high prospect that new generations of much needed effective anti-RSV therapeutics will become available for clinical use in the foreseeable future.

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

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

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

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

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

An Intriguing Correlation Based on the Superimposition of Residue Pairs with Inhibitors that Target Protein-Protein Interfaces

Druggable sites on protein-protein interfaces are difficult to predict. To survey inhibitor-binding sites onto which residues are superimposed at protein-protein interfaces, we analyzed publicly available information for 39 inhibitors that target the protein-protein interfaces of 8 drug targets. By focusing on the differences between residues that were superimposed with inhibitors and non-superimposed residues, we observed clear differences in the distances and changes in the solvent-accessible surface areas (∆SASA). Based on the observation that two or more residues were superimposed onto inhibitors in 37 (95%) of 39 protein-inhibitor complexes, we focused on the two-residue relationships. Application of a cross-validation procedure confirmed a linear negative correlation between the absolute value of the dihedral angle and the sum of the ∆SASAs of the residues. Finally, we applied the regression equation of this correlation to four inhibitors that bind to new sites not bound by the 39 inhibitors as well as additional inhibitors of different targets. Our results shed light on the two-residue correlation between the absolute value of the dihedral angle and the sum of the ∆SASA, which may be a useful relationship for identifying the key two-residues as potential targets of protein-protein interfaces.

Stabilization of protein-protein interaction complexes through small molecules

Most of the small molecules that have been identified thus far to modulate protein-protein interactions (PPIs) are inhibitors. Another promising way to interfere with PPI-associated biological processes is to promote PPI stabilization. Even though PPI stabilizers are still scarce, stabilization of PPIs by small molecules is gaining momentum and offers new pharmacological options. Therefore, we have performed a literature survey of PPI stabilization using small molecules. From this, we propose a classification of PPI stabilizers based on their binding mode and the architecture of the complex to facilitate the structure-based design of stabilizers.

iPPI-DB: an online database of modulators of protein-protein interactions

In order to boost the identification of low-molecular-weight drugs on protein–protein interactions (PPI), it is essential to properly collect and annotate experimental data about successful examples. This provides the scientific community with the necessary information to derive trends about privileged physicochemical properties and chemotypes that maximize the likelihood of promoting a given chemical probe to the most advanced stages of development. To this end we have developed iPPI-DB (freely accessible at http://www.ippidb.cdithem.fr), a database that contains the structure, some physicochemical characteristics, the pharmacological data and the profile of the PPI targets of several hundreds modulators of protein–protein interactions. iPPI-DB is accessible through a web application and can be queried according to two general approaches: using physicochemical/pharmacological criteria; or by chemical similarity to a user-defined structure input. In both cases the results are displayed as a sortable and exportable datasheet with links to external databases such as Uniprot, PubMed. Furthermore each compound in the table has a link to an individual ID card that contains its physicochemical and pharmacological profile derived from iPPI-DB data. This includes information about its binding data, ligand and lipophilic efficiencies, location in the PPI chemical space, and importantly similarity with known drugs, and links to external databases like PubChem, and ChEMBL.

Computational probing protein-protein interactions targeting small molecules

MOTIVATION

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

RESULTS

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

CONCLUSION

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

AVAILABILITY AND IMPLEMENTATION

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

CONTACT

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

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.