Drug-Like Protein-Protein Interaction Modulators: Challenges and Opportunities for Drug Discovery and Chemical Biology

Prediction of transient and permanent protein interactions using AI methods

Protein-protein interactions (PPIs) can be classified as permanent or transient interactions based on their stability or lifetime. Understanding the precise details of such protein interactions will pave the way for the discovery of inhibitors and for understanding the nature and function of PPIs. In the present work, 43 relevant physicochemical, geometrical and structural features were calculated for a curated dataset from the literature, comprising of 402 protein-protein complexes of permanent and transient categories, and 5 different Supervised Machine Learning models were developed with Scikit-learn to predict transient and permanent PPI. Additionally, deep learning method with Artificial Neural Network was also performed using Tensor Flow and Keras. Predicted models achieved accuracy ranging from 76.54% to 82.71% and k-NN has achieved the highest accuracy. Detailed analysis of these methods revealed that Interface areas such as Percent interface accessible area, Interface accessible area and Total interface area and the parameters defining the shape of the PPI interface such as Planarity, Eccentricity and Circularity are the most discriminating factors between these two categories. The present method could serve as an effective tool to understand the mechanism of protein association and to predict the transient and permanent interactions, which could supplement the costly and time-consuming experimental techniques.

Selected Approaches to Disrupting Protein-Protein Interactions within the MAPK/RAS Pathway

Within the MAPK/RAS pathway, there exists a plethora of protein-protein interactions (PPIs). For many years, scientists have focused efforts on drugging KRAS and its effectors in hopes to provide much needed therapies for patients with KRAS-mutant driven cancers. In this review, we focus on recent strategies to inhibit RAS-signaling via disrupting PPIs associated with SOS1, RAF, PDEδ, Grb2, and RAS.

Identification of Four New Chemical Series of Small Drug-Like Natural Products as Potential Neuropilin-1 Inhibitors by Structure-Based Virtual Screening: Pharmacophore-Based Molecular Docking and Dynamics Simulation

Neuropilin-1 (NRP-1), a surface transmembrane glycoprotein, is one of the most important co-receptors of VEGF-A165 (vascular endothelial growth factor) responsible for pathological angiogenesis. In general, NRP-1 overexpression in cancer correlates with poor prognosis and more tumor aggressiveness. NRP-1 role in cancer has been mainly explained by mediating VEGF-A165-induced effects on tumor angiogenesis. NRP-1 was recently identified as a co-receptor and an independent gateway for SARS-CoV-2 through binding subunit S2 of Spike protein in the same way as VEGF-A165. Thus, NRP-1 is of particular value as a target for cancer therapy and other angiogenesis-dependent diseases as well as for SARS-CoV-2 antiviral intervention. Herein, The Super Natural II, the largest available database of natural products (∼0.33 M), pre-filtered with drug-likeness criteria (absorption, distribution, metabolism and excretion/toxicity), was screened against NRP-1. NRP-1/VEGF-A165 interaction is one of protein-protein interfaces (PPIs) known to be challenging when approached in-silico. Thus, a PPI-suited multi-step virtual screening protocol, incorporating a derived pharmacophore with molecular docking and followed by MD (molecular dynamics) simulation, was designed. Two stages of pharmacophorically constrained molecular docking (standard and extra precisions), a mixed Torsional/Low-mode conformational search and MM-GBSA ΔG binding affinities calculation, resulted in the selection of 100 hits. These 100 hits were subjected to 20 ns MD simulation, that was extended to 100 ns for top hits (20) and followed by post-dynamics analysis (atomic ligand-protein contacts, RMSD, RMSF, MM-GBSA ΔG, Rg, SASA and H-bonds). Post-MD analysis showed that 19 small drug-like nonpeptide natural molecules, grouped in four chemical scaffolds (purine, thiazole, tetrahydropyrimidine and dihydroxyphenyl), well verified the derived pharmacophore and formed stable and compact complexes with NRP-1. The discovered molecules are promising and can serve as a base for further development of new NRP-1 inhibitors.

Peptide-based covalent inhibitors of protein-protein interactions

Protein-protein interactions (PPI) are involved in all cellular processes and many represent attractive therapeutic targets. However, the frequently rather flat and large interaction areas render the identification of small molecular PPI inhibitors very challenging. As an alternative, peptide interaction motifs derived from a PPI interface can serve as starting points for the development of inhibitors. However, certain proteins remain challenging targets when applying inhibitors with a competitive mode of action. For that reason, peptide-based ligands with an irreversible binding mode have gained attention in recent years. This review summarizes examples of covalent inhibitors that employ peptidic binders and have been tested in a biological context.

Machine Learning Models to Predict Protein-Protein Interaction Inhibitors

Protein-protein interaction (PPI) inhibitors have an increasing role in drug discovery. It is hypothesized that machine learning (ML) algorithms can classify or identify PPI inhibitors. This work describes the performance of different algorithms and molecular fingerprints used in chemoinformatics to develop a classification model to identify PPI inhibitors making the codes freely available to the community, particularly the medicinal chemistry research groups working with PPI inhibitors. We found that classification algorithms have different performances according to various features employed in the training process. Random forest (RF) models with the extended connectivity fingerprint radius 2 (ECFP4) had the best classification abilities compared to those models trained with ECFP6 o MACCS keys (166-bits). In general, logistic regression (LR) models had lower performance metrics than RF models, but ECFP4 was the representation most appropriate for LR. ECFP4 also generated models with high-performance metrics with support vector machines (SVM). We also constructed ensemble models based on the top-performing models. As part of this work and to help non-computational experts, we developed a pipeline code freely available.

Divergent, C-C Bond Forming Macrocyclizations Using Modular Sulfonylhydrazone and Derived Substrates

A divergent approach to C-C bond forming macrocycle construction is described. Modular sulfonylhydrazone and derived pyridotriazole substrates with three key building blocks have been constructed and cyclized to afford diverse macrocyclic frameworks. Broad substrate scope and functional group tolerance have been demonstrated. In addition, site-selective postfunctionalization allowed for further diversification of macrocyclic cores.

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

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

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

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

In silico derived small molecules targeting the finger-finger interaction between the histone lysine methyltransferase NSD1 and Nizp1 repressor

PHD fingers are small chromatin binding domains, that alone or in tandem work as versatile interaction platforms for diversified activities, ranging from the decoding of the modification status of histone tails to the specific recognition of non-histone proteins. They play a crucial role in their host protein as mutations thereof cause several human malignancies. Thus, PHD fingers are starting to be considered as valuable pharmacological targets. While inhibitors or chemical probes of the histone binding activity of PHD fingers are emerging, their druggability as non-histone interaction platform is still unexplored. In the current study, using a computational and experimental pipeline, we provide proof of concept that the tandem PHD finger of Nuclear receptor-binding SET (Su(var)3–9, Enhancer of zeste, Trithorax) domain protein 1 (PHD_VC5HCH_NSD1) is ligandable. Combining virtual screening of a small subset of the ZINC database (Zinc Drug Database, ZDD, 2924 molecules) to NMR binding assays and ITC measurements, we have identified Mitoxantrone dihydrochloride, Quinacrine dihydrochloride and Chloroquine diphosphate as the first molecules able to bind to PHD_VC5HCH_NSD1 and to reduce its documented interaction with the Zinc finger domain (C2HR_Nizp1) of the transcriptional repressor Nizp1 (NSD1-interacting Zn-finger protein). These results pave the way for the design of small molecules with improved effectiveness in inhibiting this finger-finger interaction.

Polyglutamine spinocerebellar ataxias: emerging therapeutic targets

INTRODUCTION

Six of the most frequent dominantly inherited spinocerebellar ataxias (SCAs) worldwide - SCA1, SCA2, SCA3, SCA6, SCA7, and SCA17 - are caused by an expansion of a polyglutamine (polyQ) tract in the corresponding proteins. While the identification of the causative mutation has advanced knowledge on the pathogenesis of polyQ SCAs, effective therapeutics able to mitigate the severe clinical manifestation of these highly incapacitating disorders are not yet available.

AREAS COVERED

This review provides a comprehensive and critical perspective on well-established and emerging therapeutic targets for polyQ SCAs; it aims to inspire prospective drug discovery efforts.

EXPERT OPINION

The landscape of polyQ SCAs therapeutic targets and strategies includes (1) the mutant genes and proteins themselves, (2) enhancement of endogenous protein quality control responses, (3) abnormal protein-protein interactions of the mutant proteins, (4) disturbed neuronal function, (5) mitochondrial function, energy availability and oxidative stress, and (6) glial dysfunction, growth factor or hormone imbalances. Challenges include gaining a clearer definition of therapeutic targets for the drugs in clinical development, the discovery of novel drug-like molecules for challenging key targets, and the attainment of a stronger translation of preclinical findings to the clinic.

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.

Structure-based drug repositioning over the human TMPRSS2 protease domain: search for chemical probes able to repress SARS-CoV-2 Spike protein cleavages

In December 2019, a new coronavirus was identified in the Hubei province of central china and named SARS-CoV-2. This new virus induces COVID-19, a severe respiratory disease with high death rate. A putative target to interfere with the virus is the host transmembrane serine protease family member II (TMPRSS2). This enzyme is critical for the entry of coronaviruses into human cells by cleaving and activating the spike protein (S) of SARS-CoV-2. Repositioning approved, investigational and experimental drugs on the serine protease domain of TMPRSS2 could thus be valuable. There is no experimental structure for TMPRSS2 but it is possible to develop quality structural models for the serine protease domain using comparative modeling strategies as such domains are highly structurally conserved. Beside the TMPRSS2 catalytic site, we predicted on our structural models a main exosite that could be important for the binding of protein partners and/or substrates. To block the catalytic site or the exosite of TMPRSS2 we used structure-based virtual screening computations and two different collections of approved, investigational and experimental drugs. We propose a list of 156 molecules that could bind to the catalytic site and 100 compounds that may interact with the exosite. These small molecules should now be tested in vitro to gain novel insights over the roles of TMPRSS2 or as starting point for the development of second generation analogs.

Synthesis of a Bcl9 Alpha-Helix Mimetic for Inhibition of PPIs by a Combination of Electrooxidative Phenol Coupling and Pd-Catalyzed Cross Coupling

Teraryl-based alpha-helix mimetics have resulted in efficient inhibitors of protein-protein interactions (PPIs). Extending the concept to even longer oligoarene systems would allow for the mimicking of even larger interaction sites. We present a highly efficient synthetic modular access to quateraryl alpha-helix mimetics, in which, at first, two phenols undergo electrooxidative dehydrogenative cross-coupling. The resulting 4,4′-biphenol is then activated by conversion to nonaflates, which serve as leaving groups for iterative Pd-catalyzed Suzuki-cross-coupling reactions with suitably substituted pyridine boronic acids. This work, for the first time, demonstrates the synthetic efficiency of using both electroorganic as well as transition-metal catalyzed cross-coupling in the assembly of oligoarene structures.

Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace

The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.

Novel, Self-Assembling Dimeric Inhibitors of Human β Tryptase

β-Tryptase, a homotetrameric serine protease, has four identical active sites facing a central pore, presenting an optimized setting for the rational design of bivalent inhibitors that bridge two adjacent sites. Using diol, hydroxymethyl phenols or benzoyl methyl hydroxamates, and boronic acid chemistries to reversibly join two [3-(1-acylpiperidin-4-yl)phenyl]methanamine core ligands, we have successfully produced a series of self-assembling heterodimeric inhibitors. These heterodimeric tryptase inhibitors demonstrate superior activity compared to monomeric modes of inhibition. X-ray crystallography validated the dimeric mechanism of inhibition, and compounds demonstrated high selectivity against related proteases, good target engagement, and tryptase inhibition in HMC1 xenograft models. Screening 3872 possible combinations from 44 boronic acid and 88 diol derivatives revealed several combinations that produced nanomolar inhibition, and seven unique pairs produced greater than 100-fold improvement in potency over monomeric inhibition. These heterodimeric tryptase inhibitors demonstrate the power of target-driven combinatorial chemistry to deliver bivalent drugs in a small molecule form.

A Free Web-Based Protocol to Assist Structure-Based Virtual Screening Experiments

Chemical biology and drug discovery are complex and costly processes. In silico screening approaches play a key role in the identification and optimization of original bioactive molecules and increase the performance of modern chemical biology and drug discovery endeavors. Here, we describe a free web-based protocol dedicated to small-molecule virtual screening that includes three major steps: ADME-Tox filtering (via the web service FAF-Drugs4), docking-based virtual screening (via the web service MTiOpenScreen), and molecular mechanics optimization (via the web service AMMOS2 [Automatic Molecular Mechanics Optimization for in silico Screening]). The online tools FAF-Drugs4, MTiOpenScreen, and AMMOS2 are implemented in the freely accessible RPBS (Ressource Parisienne en Bioinformatique Structurale) platform. The proposed protocol allows users to screen thousands of small molecules and to download the top 1500 docked molecules that can be further processed online. Users can then decide to purchase a small list of compounds for in vitro validation. To demonstrate the potential of this online-based protocol, we performed virtual screening experiments of 4574 approved drugs against three cancer targets. The results were analyzed in the light of published drugs that have already been repositioned on these targets. We show that our protocol is able to identify active drugs within the top-ranked compounds. The web-based protocol is user-friendly and can successfully guide the identification of new promising molecules for chemical biology and drug discovery purposes.

Notch Inhibition Prevents Differentiation of Human Limbal Stem/Progenitor Cells in vitro

Notch signaling has been shown to regulate the homeostasis and wound healing of the corneal epithelium. We investigated the effect of Notch inhibition in the human limbal stem/progenitor cells (LSCs) in vitro by using small molecules. Treatment of the LSCs with DAPT and SAHM1 reduced the proliferation rate and maintained the undifferentiated state of the LSCs in a concentration dependent manner. Stratification and differentiation of the corneal epithelium were not reduced after Notch inhibition, indicating that the function of the corneal basal cells is retained. Our findings suggest that Notch signaling plays a role in the proliferation and maintenance of LSCs.

ProteinsPlus: a web portal for structure analysis of macromolecules

With currently more than 126 000 publicly available structures and an increasing growth rate, the Protein Data Bank constitutes a rich data source for structure-driven research in fields like drug discovery, crop science and biotechnology in general. Typical workflows in these areas involve manifold computational tools for the analysis and prediction of molecular functions. Here, we present the ProteinsPlus web server that offers a unified easy-to-use interface to a broad range of tools for the early phase of structure-based molecular modeling. This includes solutions for commonly required pre-processing tasks like structure quality assessment (EDIA), hydrogen placement (Protoss) and the search for alternative conformations (SIENA). Beyond that, it also addresses frequent problems as the generation of 2D-interaction diagrams (PoseView), protein-protein interface classification (HyPPI) as well as automatic pocket detection and druggablity assessment (DoGSiteScorer). The unified ProteinsPlus interface covering all featured approaches provides various facilities for intuitive input and result visualization, case-specific parameterization and download options for further processing. Moreover, its generalized workflow allows the user a quick familiarization with the different tools. ProteinsPlus also stores the calculated results temporarily for future request and thus facilitates convenient result communication and re-access. The server is freely available at http://proteins.plus.

Modeling ErbB2-p130Cas interaction to design new potential anticancer agents

The ErbB2 receptor tyrosine kinase is overexpressed in approximately 15–20% of breast tumors and associated with aggressive disease and poor clinical outcome. p130Cas represents a nodal scaffold protein regulating cell survival, migration and proliferation in normal and pathological contexts. p130Cas overexpression in ErbB2 human breast cancer correlates with poor prognosis and metastasis formation. Recent data indicate that p130Cas association to ErbB2 protects ErbB2 from degradation, thus enhancing tumorigenesis. Therefore, inhibiting p130Cas/ErbB2 interaction might represent a new therapeutic strategy to target breast cancer. Here we demonstrate by performing Molecular Modeling, Molecular Dynamics, dot blot, ELISA and fluorescence quenching experiments, that p130Cas binds directly to ErbB2. Then, by structure-based virtual screening, we identified two potential inhibitors of p130Cas/ErbB2 interaction. Their experimental validation was performed in vitro and in ErbB2-positive breast cancer cellular models. The results highlight that both compounds interfere with p130Cas/ErbB2 binding and significantly affect cell proliferation and sensitivity to Trastuzumab. Overall, this study identifies p130Cas/ErbB2 complex as a potential breast cancer target revealing new therapeutic perspectives for protein-protein interaction (PPI).

A simple fluorescent assay for the discovery of protein-protein interaction inhibitors

Due to the therapeutic potential of targeting protein-protein interactions (PPIs) there is a need for easily executed assays to perform high throughput screening (HTS) of inhibitors. We have developed and optimized an innovative and robust fluorescence-based assay for detecting PPI inhibitors, called FluorIA (Fluorescence-based protein-protein Interaction Assay). Targeting the PPI of RAD52 with replication protein A (RPA) was used as an example, and the FluorIA protocol design, optimization and successful application to HTS of large chemical libraries are described. Here enhanced green fluorescent protein (EGFP)-tagged RAD52 detected the PPI using full-length RPA heterotrimer coated, black microtiter plates and loss in fluorescence intensity identified small molecule inhibitors (SMIs) that displaced the EGFP-tagged RAD52. The FluorIA design and protocol can be adapted and applied to detect PPIs for other protein systems. This should push forward efforts to develop targeted therapeutics against protein complexes in pathological processes.

3D Modeling of Tumor Necrosis Factor Receptor and Tumor Necrosis Factor-bound Receptor Systems

The interactions between the tumor necrosis factor (TNF) and its receptor molecule are responsible for various signaling networks that are central to the functioning of human immune homeostasis. The present work is a computational study of certain structural aspects of this cell-signaling protein, specifically focusing on the molecular level analyses of the TNF receptor (TNF-R), guided by its crystallographic structure. We also examine the possible binding sites of the TNF onto TNF-R, and the associated interactions. The structural and conformational variations in the TNF-R and TNF bound TNF-R systems are examined in this context using molecular dynamics (MD) simulations. The time dependent variations of the dimeric TNF-R structures are compared with, and shown to be steadier than their isolated monomers. This dimeric stability is favored under acidic conditions. The results are used to further illustrate how 3D modeling and computer simulations can aid the structure-based approach to probing a ligand-receptor system.

Chemical Space Overlap with Critical Protein-Protein Interface Residues in Commercial and Specialized Small-Molecule Libraries

There is growing interest in the use of structure-based virtual screening to identify small molecules that inhibit challenging protein-protein interactions (PPIs). In this study, we investigated how effectively chemical library members docked at the PPI interface mimic the position of critical side-chain residues known as "hot spots". Three compound collections were considered, a commercially available screening collection (ChemDiv), a collection of diversity-oriented synthesis (DOS) compounds that contains natural-product-like small molecules, and a library constructed using established reactions (the "screenable chemical universe based on intuitive data organization", SCUBIDOO). Three different tight PPIs for which hot-spot residues have been identified were selected for analysis: uPAR⋅uPA, TEAD4⋅Yap1, and CaV α⋅CaV β. Analysis of library physicochemical properties was followed by docking to the PPI receptors. A pharmacophore method was used to measure overlap between small-molecule substituents and hot-spot side chains. Fragment-like conformationally restricted small molecules showed better hot-spot overlap for interfaces with well-defined pockets such as uPAR⋅uPA, whereas better overlap was observed for more complex DOS compounds in interfaces lacking a well-defined binding site such as TEAD4⋅Yap1. Virtual screening of conformationally restricted compounds targeting uPAR⋅uPA and TEAD4⋅Yap1 followed by experimental validation reinforce these findings, as the best hits were fragment-like and had few rotatable bonds for the former, while no hits were identified for the latter. Overall, such studies provide a framework for understanding PPIs in the context of additional chemical matter and new PPI definitions.

Inhibitors of DNA Methyltransferases From Natural Sources: A Computational Perspective

Naturally occurring small molecules include a large variety of natural products from different sources that have confirmed activity against epigenetic targets. In this work we review chemoinformatic, molecular modeling, and other computational approaches that have been used to uncover natural products as inhibitors of DNA methyltransferases, a major family of epigenetic targets with therapeutic interest. Examples of computational approaches surveyed in this work are docking, similarity-based virtual screening, and pharmacophore modeling. It is also discussed the chemoinformatic-guided exploration of the chemical space of naturally occurring compounds as epigenetic modulators which may have significant implications in epigenetic drug discovery and nutriepigenetics.

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

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

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

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

Tailored Approaches in Drug Development and Diagnostics: From Molecular Design to Biological Model Systems

Approaches to increase the efficiency in developing drugs and diagnostics tools, including new drug delivery and diagnostic technologies, are needed for improved diagnosis and treatment of major diseases and health problems such as cancer, inflammatory diseases, chronic wounds, and antibiotic resistance. Development within several areas of research ranging from computational sciences, material sciences, bioengineering to biomedical sciences and bioimaging is needed to realize innovative drug development and diagnostic (DDD) approaches. Here, an overview of recent progresses within key areas that can provide customizable solutions to improve processes and the approaches taken within DDD is provided. Due to the broadness of the area, unfortunately all relevant aspects such as pharmacokinetics of bioactive molecules and delivery systems cannot be covered. Tailored approaches within (i) bioinformatics and computer-aided drug design, (ii) nanotechnology, (iii) novel materials and technologies for drug delivery and diagnostic systems, and (iv) disease models to predict safety and efficacy of medicines under development are focused on. Current developments and challenges ahead are discussed. The broad scope reflects the multidisciplinary nature of the field of DDD and aims to highlight the convergence of biological, pharmaceutical, and medical disciplines needed to meet the societal challenges of the 21st century.

Interfering peptides targeting protein-protein interactions: the next generation of drugs?

Protein-protein interactions (PPIs) are well recognized as promising therapeutic targets. Consequently, interfering peptides (IPs) - natural or synthetic peptides capable of interfering with PPIs - are receiving increasing attention. Given their physicochemical characteristics, IPs seem better suited than small molecules to interfere with the large surfaces implicated in PPIs. Progress on peptide administration, stability, biodelivery and safety are also encouraging the interest in peptide drug development. The concept of IPs has been validated for several PPIs, generating great expectations for their therapeutic potential. Here, we describe approaches and methods useful for IPs identification and in silico, physicochemical and biological-based strategies for their design and optimization. Selected promising in-vivo-validated examples are described and advantages, limitations and potential of IPs as therapeutic tools are discussed.

Modulation of ABA Signaling by Altering VxGΦL Motif of PP2Cs in Oryza sativa

The abscisic acid (ABA) signaling pathway is regulated by clade A type 2C protein phosphatases (PP2CAs) in plants. In the presence of ABA, PP2Cs release stress/ABA-activated protein kinases by binding to ABA-bound receptors (PYL/RCARs) for activation. Although the wedging tryptophan in PP2Cs is critical in the interaction with PYL/RCARs in Arabidopsis and rice, it remains elusive as to how other interface regions are involved in the interaction. Here, we report the identification of a conserved region on PP2Cs, termed the VxGΦL motif, which modulates the interaction with PYL/RCARs through its second and fourth residues. The effects of the second and fourth residues on the interaction of OsPP2C50 with several OsPYL/RCAR proteins were investigated by systematic mutagenesis. One OsPP2C50 mutant, VFGML ("FM") mutant, lowered the affinity to OsPYL/RCAR3 by ∼15-fold in comparison with the wild-type. Comparison of the crystal structures of wild-type OsPP2C50:ABA:OsPYL/RCAR3 with those composed of FM mutant revealed local conformational changes near the VxGΦL motif, further supported by hydrogen-deuterium exchange mass spectrometry. In rice protoplasts, ABA signaling was altered by mutations in the VxGΦL motif. Transgenic Arabidopsis plants overexpressing OsPP2C50 and OsPP2C50FM showed altered ABA sensitivity. Taken together, the VxGΦL motif of PP2Cs appears to modulate the affinity of PP2Cs with PYL/RCARs and thus likely to alter the ABA signaling, leading to the differential sensitivity to ABA in planta.

Protein-Protein Interaction Modulators for Epigenetic Therapies

Targeting protein-protein interactions (PPIs) is becoming an attractive approach for drug discovery. This is particularly true for difficult or emerging targets, such as epitargets that may be elusive to drugs that fall into the traditional chemical space. The chemical nature of the PPIs makes attractive the use of peptides or peptidomimetics to selectively modulate such interactions. Despite the fact peptide-based drug discovery has been challenging, the use of peptides as leads compounds for drug discovery is still a valid strategy. This chapter discusses the current status of PPIs in epigenetic drug discovery. A special emphasis is made on peptides and peptide-like compounds as potential drug candidates.

Gly74Ser mutation in protein C causes thrombosis due to a defect in protein S-dependent anticoagulant function

Protein C is a vitamin K-dependent serine protease zymogen in plasma which upon activation by thrombin in complex with thrombomodulin (TM) down-regulates the clotting cascade by a feedback loop inhibition mechanism. Activated protein C (APC) exerts its anticoagulant function through protein S-dependent degradation of factors Va and VIIIa. We recently identified a venous thrombosis patient whose plasma level of protein C antigen is normal, but its anticoagulant activity is only 34 % of the normal level. Genetic analysis revealed that the proband and her younger brother carry a novel heterozygous mutation c.346G>A, p.Gly74Ser (G74S) in PROC. Thrombin generation assay indicated that the TM-dependent anticoagulant activity of the proband's plasma has been significantly impaired. We expressed protein C-G74S in mammalian cells and characterised its properties in established coagulation assays. We demonstrate that the protein C variant can be normally activated by the thrombin-TM complex and the resulting APC mutant also exhibits normal amidolytic and proteolytic activities toward both FVa and FVIIIa. However, it was discovered the protein S-dependent catalytic activity of APC variant toward both procoagulant cofactors has been significantly impaired. Protein S concentration-dependence of FVa degradation revealed that the capacity of APC variant to interact with the cofactor has been markedly impaired. The same results were obtained for inactivation of FVa-Leiden suggesting that the protein S-dependent activity of APC variant toward cleavage of Arg-306 site has been adversely affected. These results provide insight into the mechanism through which G74S substitution in APC causes thrombosis in the proband carrying this mutation.

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.

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

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

Small molecules inhibiting Keap1-Nrf2 protein-protein interactions: a novel approach to activate Nrf2 function

Oxidative stress is well recognized to contribute to the cause of a wide range of diseases, such as cancer, diabetes, Alzheimer's disease, arteriosclerosis, and inflammation. The Keap1-Nrf2-ARE pathway plays a critical regulatory role and can protect cells from oxidative stress through activating Nrf2 to induce its downstream phase II enzymes. Nrf2 activation through the covalent inactivation of Keap1 may cause unpredictable side effects. Non-covalent disruption of the Keap1-Nrf2 protein-protein interactions is an alternative strategy for Nrf2 activation, potentially with reduced risk of toxicity. Efforts have been made in recent years to develop peptide- and small molecule-based Keap1-Nrf2 PPI inhibitors via different approaches, including high-throughput screening, target-based virtual screening, structure-based optimization, and fragment-based drug design. This review aims to highlight the recently discovered small-molecule inhibitors as well as their therapeutic potential.

Underlying mechanisms of cyclic peptide inhibitors interrupting the interaction of CK2α/CK2β: comparative molecular dynamics simulation studies

Protein-protein interactions (PPIs) are fundamental to all biological processes. Recently, the CK2β-derived cyclic peptide Pc has been demonstrated to efficiently antagonize the CK2α/CK2β interaction and strongly affect the phosphorylation of CK2β-dependent CK2 substrate specificity. The binding affinity of Pc to CK2α is destroyed to different extents by two single-point mutations of Tyr188 to Ala (Y188A) and Phe190 to Ala (F190A), which exert negative effects on the inhibitory activity (IC50) of Pc against the CK2α/CK2β interaction from 3.0 μM to 54.0 μM and ≫100 μM, respectively. However, the structural influences of Y188A and F190A mutations on the CK2α-Pc complex remain unclear. In this study, comparative molecular dynamics (MD) simulations, principal component analysis (PCA), domain cross-correlation map (DCCM) analysis and energy calculations were performed on wild type (WT), Y188A mutant, and F190A mutant systems. The results revealed that ordered communications between hydrophobic and polar interactions were essential for CK2α-Pc binding in the WT system. In addition to the loss of the hydrogen bond between Gln36 of CK2α and Gly189 of Pc in the two mutants, the improper recognition mechanisms occurred through different pathways. These pathways included the weakened hydrophobic interactions in the Y188A mutant as well as decreased polar and hydrophobic interactions in the F190A mutant. The energy analysis results qualitatively elucidated the instability of the two mutants and energetic contributions of the key residues. This study not only revealed the structural mechanisms for the decreased binding affinity of Y188A and F190A mutant CK2α-Pc complexes, but also provided valuable clues for the rational design of CK2α/CK2β subunit interaction inhibitors with high affinity and specificity.

Protein-Protein Docking

Protein-protein docking algorithms are powerful computational tools, capable of analyzing the protein-protein interactions at the atomic-level. In this chapter, we will review the theoretical concepts behind different protein-protein docking algorithms, highlighting their strengths as well as their limitations and pointing to important case studies for each method. The methods we intend to cover in this chapter include various search strategies and scoring techniques. This includes exhaustive global search, fast Fourier transform search, spherical Fourier transform-based search, direct search in Cartesian space, local shape feature matching, geometric hashing, genetic algorithm, randomized search, and Monte Carlo search. We will also discuss the different ways that have been used to incorporate protein flexibility within the docking procedure and some other future directions in this field, suggesting possible ways to improve the different methods.

Computational Tools for Allosteric Drug Discovery: Site Identification and Focus Library Design

Allostery is an intrinsic phenomenon of biological macromolecules involving regulation and/or signal transduction induced by a ligand binding to an allosteric site distinct from a molecule's active site. Allosteric drugs are currently receiving increased attention in drug discovery because drugs that target allosteric sites can provide important advantages over the corresponding orthosteric drugs including specific subtype selectivity within receptor families. Consequently, targeting allosteric sites, instead of orthosteric sites, can reduce drug-related side effects and toxicity. On the down side, allosteric drug discovery can be more challenging than traditional orthosteric drug discovery due to difficulties associated with determining the locations of allosteric sites and designing drugs based on these sites and the need for the allosteric effects to propagate through the structure, reach the ligand binding site and elicit a conformational change. In this study, we present computational tools ranging from the identification of potential allosteric sites to the design of "allosteric-like" modulator libraries. These tools may be particularly useful for allosteric drug discovery.

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

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

Global vision of druggability issues: applications and perspectives

During the preliminary stage of a drug discovery project, the lack of druggability information and poor target selection are the main causes of frequent failures. Elaborating on accurate computational druggability prediction methods is a requirement for prioritizing target selection, designing new drugs and avoiding side effects. In this review, we describe a survey of recently reported druggability prediction methods mainly based on networks, statistical pocket druggability predictions and virtual screening. An application for a frequent mutation of p53 tumor suppressor is presented, illustrating the complementarity of druggability prediction approaches, the remaining challenges and potential new drug development perspectives.

In silico identification of essential proteins in Corynebacterium pseudotuberculosis based on protein-protein interaction networks

Background

Corynebacterium pseudotuberculosis (Cp) is a gram-positive bacterium that is classified into equi and ovis serovars. The serovar ovis is the etiological agent of caseous lymphadenitis, a chronic infection affecting sheep and goats, causing economic losses due to carcass condemnation and decreased production of meat, wool, and milk. Current diagnosis or treatment protocols are not fully effective and, thus, require further research of Cp pathogenesis.

Results

Here, we mapped known protein-protein interactions (PPI) from various species to nine Cp strains to reconstruct parts of the potential Cp interactome and to identify potentially essential proteins serving as putative drug targets. On average, we predict 16,669 interactions for each of the nine strains (with 15,495 interactions shared among all strains). An in silico sanity check suggests that the potential networks were not formed by spurious interactions but have a strong biological bias. With the inferred Cp networks we identify 181 essential proteins, among which 41 are non-host homologous.

Conclusions

The list of candidate interactions of the Cp strains lay the basis for developing novel hypotheses and designing according wet-lab studies. The non-host homologous essential proteins are attractive targets for therapeutic and diagnostic proposes. They allow for searching of small molecule inhibitors of binding interactions enabling modern drug discovery. Overall, the predicted Cp PPI networks form a valuable and versatile tool for researchers interested in Corynebacterium pseudotuberculosis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0346-4) contains supplementary material, which is available to authorized users.