Modulating protein-protein interactions with small molecules: the importance of binding hotspots

Role of two modules controlling the interaction between SKAP1 and SRC kinases comparison with SKAP2 architecture and consequences for evolution

SRC kinase associated phosphoprotein 1 (SKAP1), an adaptor for protein assembly, plays an important role in the immune system such as stabilizing immune synapses. Understanding how these functions are controlled at the level of the protein-protein interactions is necessary to describe these processes and to develop therapeutics. Here, we dissected the SKAP1 modular organization to recognize SRC kinases and compared it to that of its paralog SRC kinase associated phosphoprotein 2 (SKAP2). Different conserved motifs common to either both proteins or specific to SKAP2 were found using this comparison. Two modules harboring different binding properties between SKAP1 and SKAP2 were identified: one composed of two conserved motifs located in the second interdomain interacting at least with the SH2 domain of SRC kinases and a second one composed of the DIM domain modulated by the SH3 domain and the activation of SRC kinases. This work suggests a convergent evolution of the binding properties of some SRC kinases interacting specifically with either SKAP1 or SKAP2.

Develop a PD-1-blockade peptide to reinvigorate T-cell activity and inhibit tumor progress

Immune checkpoint inhibitors, particularly monoclonal antibodies blocking the programmed cell death 1 (PD-1)/programmed cell death ligand-1 (PD-L1) pathway, have been successfully utilized in the clinic. However, certain drawbacks associated with antibodies, such as high immunogenicity and poor tissue penetration, need to be addressed for their broader clinical application. Peptides, as low molecular weight alternatives, have garnered increasing interest in this field. In this study, we employed bacterial surface display technology to identify a PD-1-binding peptide, PBP. The PBP peptide exhibited moderate affinity for human PD-1 (hPD-1) and displayed cross-reactivity with mouse PD-1 (mPD-1). Molecular docking analysis revealed that the interaction residues of the PBP peptide with PD-1 played crucial roles in the formation of the PD-1/PD-L1 complex. A competing binding assay demonstrated that the peptide could interfere the interaction of PD-1 and PD-L1. Moreover, in vitro experiments showed that the PBP peptide could reinvigorate T cells inhibited by PD-L1. In an in vivo mouse model of CT26, the PBP peptide effectively suppressed tumor growth by enhancing T cell function. In conclusion, our results suggest that the PBP peptide exerts an anti-tumor effect by impeding the interplay between PD-1 and PD-L1, highlighting its potential as an alternative for tumor immunotherapy.

Structural modulation of insulin by hydrophobic and hydrophilic molecules

In the bloodstream, insulin interacts with various kinds of molecules, which can alter its structure and modulate its function. In this work, we have synthesized two molecules having extremely hydrophilic and hydrophobic side chains. The effects of hydrophilic and hydrophobic molecules on the binding with insulin have been investigated through a multi-spectroscopic approach. We found that hydrophilic molecules have a slightly higher binding affinity towards insulin. Insulin can bind with the hydrophilic molecules as it binds glucose. The high insulin binding affinity of a hydrophobic molecule indicates its dual nature. The hydrophobic molecule binds at the hydrophobic pocket of the insulin surface, where hydrophilic molecules interact at the polar surface of the insulin. Such binding with the hydrophobic molecule perturbs strongly the secondary structure of the insulin much more in comparison to hydrophilic molecules. Therefore, the stability of insulin decreases in the presence of hydrophobic molecules.

QuoteTarget: A sequence-based transformer protein language model to identify potentially druggable protein targets

The development of efficient computational methods for drug target protein identification can compensate for the high cost of experiments and is therefore of great significance for drug development. However, existing structure-based drug target protein-identification algorithms are limited by the insufficient number of proteins with experimentally resolved structures. Moreover, sequence-based algorithms cannot effectively extract information from protein sequences and thus display insufficient accuracy. Here, we combined the sequence-based self-supervised pretraining protein language model ESM1b with a graph convolutional neural network classifier to develop an improved, sequence-based drug target protein identification method. This complete model, named QuoteTarget, efficiently encodes proteins based on sequence information alone and achieves an accuracy of 95% with the nonredundant drug target and nondrug target datasets constructed for this study. When applied to all proteins from Homo sapiens, QuoteTarget identified 1213 potential undeveloped drug target proteins. We further inferred residue-binding weights from the well-trained network using the gradient-weighted class activation mapping (Grad-Cam) algorithm. Notably, we found that without any binding site information input, significant residues inferred by the model closely match the experimentally confirmed drug molecule-binding sites. Thus, our work provides a highly effective sequence-based identifier for drug target proteins, as well to yield new insights into recognizing drug molecule-binding sites. The entire model is available at https://github.com/Chenjxjx/drug-target-prediction.

Pathogen-driven cancers from a structural perspective: Targeting host-pathogen protein-protein interactions

Host-pathogen interactions (HPIs) affect and involve multiple mechanisms in both the pathogen and the host. Pathogen interactions disrupt homeostasis in host cells, with their toxins interfering with host mechanisms, resulting in infections, diseases, and disorders, extending from AIDS and COVID-19, to cancer. Studies of the three-dimensional (3D) structures of host-pathogen complexes aim to understand how pathogens interact with their hosts. They also aim to contribute to the development of rational therapeutics, as well as preventive measures. However, structural studies are fraught with challenges toward these aims. This review describes the state-of-the-art in protein-protein interactions (PPIs) between the host and pathogens from the structural standpoint. It discusses computational aspects of predicting these PPIs, including machine learning (ML) and artificial intelligence (AI)-driven, and overviews available computational methods and their challenges. It concludes with examples of how theoretical computational approaches can result in a therapeutic agent with a potential of being used in the clinics, as well as future directions.

Synaptic Secretion and Beyond: Targeting Synapse and Neurotransmitters to Treat Neurodegenerative Diseases

The nervous system is important, because it regulates the physiological function of the body. Neurons are the most basic structural and functional unit of the nervous system. The synapse is an asymmetric structure that is important for neuronal function. The chemical transmission mode of the synapse is realized through neurotransmitters and electrical processes. Based on vesicle transport, the abnormal information transmission process in the synapse can lead to a series of neurorelated diseases. Numerous proteins and complexes that regulate the process of vesicle transport, such as SNARE proteins, Munc18-1, and Synaptotagmin-1, have been identified. Their regulation of synaptic vesicle secretion is complicated and delicate, and their defects can lead to a series of neurodegenerative diseases. This review will discuss the structure and functions of vesicle-based synapses and their roles in neurons. Furthermore, we will analyze neurotransmitter and synaptic functions in neurodegenerative diseases and discuss the potential of using related drugs in their treatment.

Leveraging the multivalent p53 peptide-MdmX interaction to guide the improvement of small molecule inhibitors

Overexpressed Mdm2 and its 7homolog MdmX impair p53 activity in many cancers. Small molecules mimicking a p53 peptide can effectively inhibit Mdm2 but not MdmX. Here, we show a strategy for improving lead compounds for Mdm2 and MdmX inhibition based on the multivalency of the p53 peptide. Crystal structures of MdmX complexed with nutlin-3a, a strong Mdm2 inhibitor but a weak one for MdmX, reveal that nutlin-3a fits into the ligand binding pocket of MdmX mimicking the p53 peptide. However, due to distinct flexibility around the MdmX ligand binding pocket, the structures are missing many important intermolecular interactions that exist in the MdmX/p53 peptide and Mdm2/nultin-3a complexes. By targeting these flexible regions, we identify allosteric and additive fragments that enhance the binding affinity of nutlin-3a for MdmX, leading to potent Mdm2/MdmX inhibitors with anticancer activity. Our work provides a practical approach to drug design for signal transduction therapy.

Peptide fragments derived from the interfaces of protein-protein interactions (PPIs) provide useful templates for designing small molecule PPI inhibitors. Here, the authors utilize the multivalency of an MdmX-binding p53 peptide to develop a weak inhibitor of MdmX into potent Mdm2/MdmX inhibitors.

Characterization and Validation of Arg286 Residue of IL-1RAcP as a Potential Drug Target for Osteoarthritis

Osteoarthritis (OA) is the most common form of arthritis and the fastest growing cause of chronic disability in the world. Formation of the ternary IL-1β /IL-1R1/IL-1RAcP protein complex and its downstream signaling has been implicated in osteoarthritis pathology. Current OA therapeutic approaches target either the cytokine IL-1β or the primary receptor IL-1RI but do not exploit the potential of the secondary receptor IL-1RAcP. Our previous work implicated the Arg286 residue of IL-1RAcP as a key mediator of complex formation. Molecular modeling confirmed Arg286 as a high-energy mediator of the ternary IL-1β complex architecture and interaction network. Anti-IL-1RAcP monoclonal antibodies (mAb) targeting the Arg286 residue were created and were shown to effectively reduce the influx of inflammatory cells to damaged joints in a mouse model of osteoarthritis. Inhibitory peptides based on the native sequence of IL-1RAcP were prepared and examined for efficacy at disrupting the complex formation. The most potent peptide inhibitor had an IC50 value of 304 pM in a pull-down model of complex formation, and reduced IL-1β signaling in a cell model by 90% at 2 μM. Overall, therapies that target the Arg286 region surface of IL-1RAcP, and disrupt subsequent interactions with subunits, have the potential to serve as next generation treatments for osteoarthritis.

Computational analysis of binding free energies, hotspots and the binding mechanism of Bcl-xL/Bcl-2 binding to Bad/Bax

Hierarchical clustering tree of residues providing contributions to system binding based on the binding free energy of specific residues for (A) Bcl-xL systems (B) Bcl-2 systems.

Recent advances in the development of protein-protein interactions modulators: mechanisms and clinical trials

Protein-protein interactions (PPIs) have pivotal roles in life processes. The studies showed that aberrant PPIs are associated with various diseases, including cancer, infectious diseases, and neurodegenerative diseases. Therefore, targeting PPIs is a direction in treating diseases and an essential strategy for the development of new drugs. In the past few decades, the modulation of PPIs has been recognized as one of the most challenging drug discovery tasks. In recent years, some PPIs modulators have entered clinical studies, some of which been approved for marketing, indicating that the modulators targeting PPIs have broad prospects. Here, we summarize the recent advances in PPIs modulators, including small molecules, peptides, and antibodies, hoping to provide some guidance to the design of novel drugs targeting PPIs in the future.

Allosteric Binding Sites On Nuclear Receptors: Focus On Drug Efficacy and Selectivity

Nuclear receptors (NRs) are highly relevant drug targets in major indications such as oncologic, metabolic, reproductive, and immunologic diseases. However, currently, marketed drugs designed towards the orthosteric binding site of NRs often suffer from resistance mechanisms and poor selectivity. The identification of two superficial allosteric sites, activation function-2 (AF-2) and binding function-3 (BF-3), as novel drug targets sparked the development of inhibitors, while selectivity concerns due to a high conservation degree remained. To determine important pharmacophores and hydration sites among AF-2 and BF-3 of eight hormonal NRs, we systematically analyzed over 10 μ s of molecular dynamics simulations including simulations in explicit water and solvent mixtures. In addition, a library of over 300 allosteric inhibitors was evaluated by molecular docking. Based on our results, we suggest the BF-3 site to offer a higher potential for drug selectivity as opposed to the AF-2 site that is more conserved among the selected receptors. Detected similarities among the AF-2 sites of various NRs urge for a broader selectivity assessment in future studies. In combination with the Supplementary Material, this work provides a foundation to improve both selectivity and potency of allosteric inhibitors in a rational manner and increase the therapeutic applicability of this promising compound class.

Investigation of protein-protein interactions and hot spot region between PD-1 and PD-L1 by fragment molecular orbital method

Inhibitors to interfere protein-protein interactions (PPI) between programmed cell death 1 (PD-1) and programmed death ligand-1 (PD-L1) block evasion of cancers from immune surveillance. Analyzing hot spot residues in PPI is important for small-molecule drug development. In order to find out hot spots on PPI interface in PD-1/PD-L1 complex, we analyzed PPI in PD-1/PD-L1 with a new analysis method, 3-dimensional scattered pair interactions energies (3D-SPIEs), which assorts significant interactions with fragment molecular orbital (FMO) method. By additionally analyzing PPI in PD-1/antibody and PD-L1/antibody complexes, and small-ligand interactions in PD-L1/peptide and PD-L1/small-molecule complexes, we narrowed down the hot spot region with 3D-SPIEs-based interaction map, which integrates PPI and small-ligand interactions. Based on the map, there are two hot spot regions in PPI of PD-1/PD-L1 and the first hot spot region is important for inhibitors. In particular, _LY56, _LE58, and _LN66 in the first hot spot of PD-L1 are important for PD-L1-antibodies and small-inhibitors in common, while _LM115 is important for small-inhibitors. Therefore, the 3D-SPIEs-based map would provide valuable information for designing new small-molecule inhibitors to inhibit PPI of PD-1/PD-L1 and the FMO/3D-SPIEs method provides an effectual tool to understand PPI and integrate PPI and small-ligand interactions at a quantum mechanical level.

RMut: R package for a Boolean sensitivity analysis against various types of mutations

There have been many in silico studies based on a Boolean network model to investigate network sensitivity against gene or interaction mutations. However, there are no proper tools to examine the network sensitivity against many different types of mutations, including user-defined ones. To address this issue, we developed RMut, which is an R package to analyze the Boolean network-based sensitivity by efficiently employing not only many well-known node-based and edgetic mutations but also novel user-defined mutations. In addition, RMut can specify the mutation area and the duration time for more precise analysis. RMut can be used to analyze large-scale networks because it is implemented in a parallel algorithm using the OpenCL library. In the first case study, we observed that the real biological networks were most sensitive to overexpression/state-flip and edge-addition/-reverse mutations among node-based and edgetic mutations, respectively. In the second case study, we showed that edgetic mutations can predict drug-targets better than node-based mutations. Finally, we examined the network sensitivity to double edge-removal mutations and found an interesting synergistic effect. Taken together, these findings indicate that RMut is a flexible R package to efficiently analyze network sensitivity to various types of mutations. RMut is available at https://github.com/csclab/RMut.

Functional expression and impact of testis-specific gene antigen 10 in breast cancer: a combined in vitro and in silico analysis

Introduction: Testis-specific gene antigen 10 (TSGA10) is a less-known gene, which is involved in the vague biological paths of different cancers. Here, we investigated the TSGA10 expression using different concentrations of glucose under hypoxia and also its interaction with the hypoxia-inducible factor 1 (HIF-1). Methods: The breast cancer MDA-MB-231 and MCF-7 cells were cultured with different concentrations of glucose (5.5, 11.0 and 25.0 mM) under normoxia/hypoxia for 24, 48, and 72 hours and examined for the HIF-1α expression and cell migration by Western blotting and scratch assays. The qPCR was employed to analyze the expression of TSGA10. Three-dimensional (3D) structure and the energy minimization of the interacting domain of TSGA10 were performed by MODELLER v9.17 and Swiss-PDB viewer v4.1.0/UCSF Chimera v1.11. The UCSF Chimera v1.13.1 and Hex 6.0 were used for the molecular docking simulation. The Cytoscape v3.7.1 and STRING v11.0 were used for protein-protein interaction (PPI) network analysis. The HIF-1a related hypoxia pathways were obtained from BioModels database and reconstructed in CellDesigner v4.4.2. Results: The increased expression of TSGA10 was found to be significantly associated with the reduced metastasis in the MDA-MB-231 cells, while an inverse relationship was seen between the TSGA10 mRNA level and cellular migration but not in the MCF-7 cells. The C-terminal domain of TSGA10 interacted with HIF-1α with high affinity, resulting in PPI network with 10 key nodes (HIF-1α, VEGFA, HSP90AA1, AKT1, ARNT, TP53, TSGA10, VHL, JUN, and EGFR). Conclusions: Collectively, TSGA10 functional expression alters under the hyper-/hypo-glycemia and hypoxia, which indicates its importance as a candidate bio-target for the cancer therapy.

Methods for Discovering and Targeting Druggable Protein-Protein Interfaces and Their Application to Repurposing

Drug repurposing is a creative and resourceful approach to increase the number of therapies by exploiting available and approved drugs. However, identifying new protein targets for previously approved drugs is challenging. Although new strategies have been developed for drug repurposing, there is broad agreement that there is room for further improvements. In this chapter, we review protein-protein interaction (PPI) interface-targeting strategies for drug repurposing applications. We discuss certain features, such as hot spot residue and hot region prediction and their importance in drug repurposing, and illustrate common methods used in PPI networks to identify drug off-targets. We also collect available online resources for hot spot prediction, binding pocket identification, and interface clustering which are effective resources in polypharmacology. Finally, we provide case studies showing the significance of protein interfaces and hot spots in drug repurposing.

Mimicry of a β-Hairpin Turn by a Nonpeptidic Laterally Flexible Foldamer

The design and characterization of a proteomimetic foldamer that displays lateral flexibility endowed by intramolecular bifurcated hydrogen bonds is reported. The MAMBA scaffold, derived from meta-aminomethylbenzoic acid, adopts a serpentine conformation that mimics the side chain projection of all four residues in a β-hairpin turn.

The design of high affinity human PD-1 mutants by using molecular dynamics simulations (MD)

BACKGROUND

Programmed cell death protein 1 (PD-1), a negative co-stimulatory molecule, plays crucial roles in immune escape. Blockade of the interaction between PD-1 and PD-L1 shows exciting clinical responses in a fraction of cancer patients and the success makes PD-1 as a valuable target in immune checkpoint therapy. For the rational design of PD-1 targeting modulators, the ligand binding mechanism of PD-1 should be well understood in prior.

METHODS

In this study, we applied 50 ns molecular dynamics simulations to observe the structural properties of PD-1 molecule in both apo and ligand bound states, and we studied the structural features of PD-1 in human and mouse respectively.

RESULTS

The results showed that the apo hPD-1 was more flexible than that in PD-L1 bound state. We unexpectedly found that K135 was important for binding energy although it was not at the binding interface. Moreover, the residues which stabilized the interactions with PD-L1 were distinguished. Taking the dynamic features of these residues into account, we identified several residual sites where mutations may gain the function of ligand binding. The in vitro binding experiments revealed the mutants M70I, S87 W, A129L, A132L, and K135 M were better in ligand binding than the wild type PD-1.

CONCLUSIONS

The structural information from MD simulation combined with in silico mutagenesis provides guidance to design engineered PD-1 mutants to modulate the PD-1/PD-L1 pathway.

Yeast-based assays for detecting protein-protein/drug interactions and their inhibitors

Understanding cellular processes at molecular levels in health and disease requires the knowledge of protein-protein interactions (PPIs). In line with this, identification of PPIs at genome-wide scale is highly valuable to understand how different cellular pathways are interconnected, and it eventually facilitates designing effective drugs against certain PPIs. Furthermore, investigating PPIs at a small laboratory scale for deciphering certain biochemical pathways has been demanded for years. In this regard, yeast two hybrid system (Y2HS) has proven an extremely useful tool to discover novel PPIs, while Y2HS derivatives and novel yeast-based assays are contributing significantly to identification of protein-drug/inhibitor interaction at both large- and small-scale set-ups. These methods have been evolving over time to provide more accurate, reproducible and quantitative results. Here we briefly describe different yeast-based assays for identification of various protein-protein/drug/inhibitor interactions and their specific applications, advantages, shortcomings, and improvements. The broad range of yeast-based assays facilitates application of the most suitable method(s) for each specific need.

Application of the fragment molecular orbital method analysis to fragment-based drug discovery of BET (bromodomain and extra-terminal proteins) inhibitors

The molecular interactions of inhibitors of bromodomains (BRDs) were investigated. BRDs are protein interaction modules that recognizing ε-N-acetyl-lysine (εAc-Lys) motifs found in histone tails and are promising protein-protein interaction (PPI) targets. First, we analyzed a peptide ligand containing εAc-Lys to evaluate native PPIs. We then analyzed tetrahydroquinazoline-6-yl-benzensulfonamide derivatives found by fragment-based drug design (FBDD) and examined their interactions with the protein compared with the peptide ligand in terms of the inter-fragment interaction energy. In addition, we analyzed benzodiazepine derivatives that are high-affinity ligands for BRDs and examined differences in the CH/π interactions of the amino acid residues. We further surveyed changes in the charges of the amino acid residues among individual ligands, performed pair interaction energy decomposition analysis and estimated the water profile within the ligand binding site. Thus, useful insights for drug design were provided. Through these analyses and considerations, we show that the FMO method is a useful drug design tool to evaluate the process of FBDD and to explore PPI inhibitors.

A diversity oriented synthesis of natural product inspired molecular libraries

Diversity oriented synthesis of natural product inspired compounds from S-tryptophan methyl ester.

Exploring Protein-Protein Interactions as Drug Targets for Anti-cancer Therapy with In Silico Workflows

We describe a computational protocol to aid the design of small molecule and peptide drugs that target protein-protein interactions, particularly for anti-cancer therapy. To achieve this goal, we explore multiple strategies, including finding binding hot spots, incorporating chemical similarity and bioactivity data, and sampling similar binding sites from homologous protein complexes. We demonstrate how to combine existing interdisciplinary resources with examples of semi-automated workflows. Finally, we discuss several major problems, including the occurrence of drug-resistant mutations, drug promiscuity, and the design of dual-effect inhibitors.

InteractoMIX: a suite of computational tools to exploit interactomes in biological and clinical research

Virtually all the biological processes that occur inside or outside cells are mediated by protein–protein interactions (PPIs). Hence, the charting and description of the PPI network, initially in organisms, the interactome, but more recently in specific tissues, is essential to fully understand cellular processes both in health and disease. The study of PPIs is also at the heart of renewed efforts in the medical and biotechnological arena in the quest of new therapeutic targets and drugs. Here, we present a mini review of 11 computational tools and resources tools developed by us to address different aspects of PPIs: from interactome level to their atomic 3D structural details. We provided details on each specific resource, aims and purpose and compare with equivalent tools in the literature. All the tools are presented in a centralized, one-stop, web site: InteractoMIX (http://interactomix.com).

Developments in SPR Fragment Screening

INTRODUCTION

Fragment-based approaches have played an increasing role alongside high-throughput screening in drug discovery for 15 years. The label-free biosensor technology based on surface plasmon resonance (SPR) is now sensitive and informative enough to serve during primary screens and validation steps.

AREAS COVERED

In this review, the authors discuss the role of SPR in fragment screening. After a brief description of the underlying principles of the technique and main device developments, they evaluate the advantages and adaptations of SPR for fragment-based drug discovery. SPR can also be applied to challenging targets such as membrane receptors and enzymes.

EXPERT OPINION

The high-level of immobilization of the protein target and its stability are key points for a relevant screening that can be optimized using oriented immobilized proteins and regenerable sensors. Furthermore, to decrease the rate of false negatives, a selectivity test may be performed in parallel on the main target bearing the binding site mutated or blocked with a low-off-rate ligand. Fragment-based drug design, integrated in a rational workflow led by SPR, will thus have a predominant role for the next wave of drug discovery which could be greatly enhanced by new improvements in SPR devices.

DrugMiner: comparative analysis of machine learning algorithms for prediction of potential druggable proteins

Application of computational methods in drug discovery has received increased attention in recent years as a way to accelerate drug target prediction. Based on 443 sequence-derived protein features, we applied the most commonly used machine learning methods to predict whether a protein is druggable as well as to opt for superior algorithm in this task. In addition, feature selection procedures were used to provide the best performance of each classifier according to the optimum number of features. When run on all features, Neural Network was the best classifier, with 89.98% accuracy, based on a k-fold cross-validation test. Among all the algorithms applied, the optimum number of most-relevant features was 130, according to the Support Vector Machine-Feature Selection (SVM-FS) algorithm. This study resulted in the discovery of new drug target which potentially can be employed in cell signaling pathways, gene expression, and signal transduction. The DrugMiner web tool was developed based on the findings of this study to provide researchers with the ability to predict druggable proteins. DrugMiner is freely available at www.DrugMiner.org.

Progress in discovery of small-molecule modulators of protein-protein interactions via fragment screening

Protein-protein interactions (PPIs) present a formidable challenge to medicinal chemistry. The extended and open nature of many binding sites at protein interfaces has made it difficult to find useful chemical matter by traditional screening methods using standard screening libraries. This Digest focuses on the progress that has been made in discovering small-molecule modulators for a diverse selection of PPI targets using fragment screening and highlights the utility of this strategy in this context.

In silico design of low molecular weight protein-protein interaction inhibitors: Overall concept and recent advances

Protein-protein interactions (PPIs) are carrying out diverse functions in living systems and are playing a major role in the health and disease states. Low molecular weight (LMW) "drug-like" inhibitors of PPIs would be very valuable not only to enhance our understanding over physiological processes but also for drug discovery endeavors. However, PPIs were deemed intractable by LMW chemicals during many years. But today, with the new experimental and in silico technologies that have been developed, about 50 PPIs have already been inhibited by LMW molecules. Here, we first focus on general concepts about protein-protein interactions, present a consensual view about ligandable pockets at the protein interfaces and the possibilities of using fast and cost effective structure-based virtual screening methods to identify PPI hits. We then discuss the design of compound collections dedicated to PPIs. Recent financial analyses of the field suggest that LMW PPI modulators could be gaining momentum over biologics in the coming years supporting further research in this area.

NMR approaches in structure-based lead discovery: recent developments and new frontiers for targeting multi-protein complexes

Nuclear magnetic resonance (NMR) spectroscopy is a pivotal method for structure-based and fragment-based lead discovery because it is one of the most robust techniques to provide information on protein structure, dynamics and interaction at an atomic level in solution. Nowadays, in most ligand screening cascades, NMR-based methods are applied to identify and structurally validate small molecule binding. These can be high-throughput and are often used synergistically with other biophysical assays. Here, we describe current state-of-the-art in the portfolio of available NMR-based experiments that are used to aid early-stage lead discovery. We then focus on multi-protein complexes as targets and how NMR spectroscopy allows studying of interactions within the high molecular weight assemblies that make up a vast fraction of the yet untargeted proteome. Finally, we give our perspective on how currently available methods could build an improved strategy for drug discovery against such challenging targets.

Linkage, mobility, and selfishness in the MazF family of bacterial toxins: a snapshot of bacterial evolution

Prokaryotic MazF family toxins cooccur with cognate antitoxins having divergent DNA-binding folds and can be of chromosomal or plasmid origin. Sequence similarity search was carried out to identify the Toxin–Antitoxin (TA) operons of MazF family followed by sequence analysis and phylogenetic studies. The genomic DNA upstream of the TA operons was searched for the presence of regulatory motifs. The MazF family toxins showed a conserved hydrophobic pocket in a multibinding site and are present in pathogenic bacteria. The toxins of the MazF family are associated with four main types of cognate antitoxin partners and cluster as a subfamily on the branches of the phylogenetic tree. This indicates that transmission of the entire operon is the dominant mode of inheritance. The plasmid borne TA modules were interspersed between the chromosomal TA modules of the same subfamily, compatible with a frequent interchange of TA genes between the chromosome and the plasmid akin to that observed for antibiotic resistance gens. The split network of the MazF family toxins showed the AbrB-linked toxins as a hub of horizontal gene transfer. Distinct motifs are present in the upstream region of each subfamily. The presence of MazF family TA modules in pathogenic bacteria and identification of a conserved binding pocket are significant for the development of novel antibacterials to disrupt the TA interaction. However, the role of TAs in stress resistance needs to be established. Phylogenetic studies provide insight into the evolution of MazF family TAs and effect on the bacterial genome.

An overview of recent advances in structural bioinformatics of protein-protein interactions and a guide to their principles

Rich data bearing on the structural and evolutionary principles of protein-protein interactions are paving the way to a better understanding of the regulation of function in the cell. This is particularly the case when these interactions are considered in the framework of key pathways. Knowledge of the interactions may provide insights into the mechanisms of crucial 'driver' mutations in oncogenesis. They also provide the foundation toward the design of protein-protein interfaces and inhibitors that can abrogate their formation or enhance them. The main features to learn from known 3-D structures of protein-protein complexes and the extensive literature which analyzes them computationally and experimentally include the interaction details which permit undertaking structure-based drug discovery, the evolution of complexes and their interactions, the consequences of alterations such as post-translational modifications, ligand binding, disease causing mutations, host pathogen interactions, oligomerization, aggregation and the roles of disorder, dynamics, allostery and more to the protein and the cell. This review highlights some of the recent advances in these areas, including design, inhibition and prediction of protein-protein complexes. The field is broad, and much work has been carried out in these areas, making it challenging to cover it in its entirety. Much of this is due to the fast increase in the number of molecules whose structures have been determined experimentally and the vast increase in computational power. Here we provide a concise overview.

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

[Formula: see text] Fundamental processes in living cells are largely controlled by macromolecular interactions and among them, protein-protein interactions (PPIs) have a critical role while their dysregulations can contribute to the pathogenesis of numerous diseases. Although PPIs were considered as attractive pharmaceutical targets already some years ago, they have been thus far largely unexploited for therapeutic interventions with low molecular weight compounds. Several limiting factors, from technological hurdles to conceptual barriers, are known, which, taken together, explain why research in this area has been relatively slow. However, this last decade, the scientific community has challenged the dogma and became more enthusiastic about the modulation of PPIs with small drug-like molecules. In fact, several success stories were reported both, at the preclinical and clinical stages. In this review article, written for the 2014 International Summer School in Chemoinformatics (Strasbourg, France), we discuss in silico tools (essentially post 2012) and databases that can assist the design of low molecular weight PPI modulators (these tools can be found at www.vls3d.com). We first introduce the field of protein-protein interaction research, discuss key challenges and comment recently reported in silico packages, protocols and databases dedicated to PPIs. Then, we illustrate how in silico methods can be used and combined with experimental work to identify PPI modulators.

2P2I HUNTER: a tool for filtering orthosteric protein-protein interaction modulators via a dedicated support vector machine

Over the last 10 years, protein-protein interactions (PPIs) have shown increasing potential as new therapeutic targets. As a consequence, PPIs are today the most screened target class in high-throughput screening (HTS). The development of broad chemical libraries dedicated to these particular targets is essential; however, the chemical space associated with this 'high-hanging fruit' is still under debate. Here, we analyse the properties of 40 non-redundant small molecules present in the 2P2I database (http://2p2idb.cnrs-mrs.fr/) to define a general profile of orthosteric inhibitors and propose an original protocol to filter general screening libraries using a support vector machine (SVM) with 11 standard Dragon molecular descriptors. The filtering protocol has been validated using external datasets from PubChem BioAssay and results from in-house screening campaigns. This external blind validation demonstrated the ability of the SVM model to reduce the size of the filtered chemical library by eliminating up to 96% of the compounds as well as enhancing the proportion of active compounds by up to a factor of 8. We believe that the resulting chemical space identified in this paper will provide the scientific community with a concrete support to search for PPI inhibitors during HTS campaigns.

Druggable protein-protein interactions--from hot spots to hot segments

Protein-Protein Interactions (PPIs) mediate numerous biological functions. As such, the inhibition of specific PPIs has tremendous therapeutic value. The notion that these interactions are 'undruggable' has petered out with the emergence of more and more successful examples of PPI inhibitors, expanding considerably the scope of potential drug targets. The accumulated data on successes in the inhibition of PPIs allow us to analyze the features that are required for such inhibition. Whereas it has been suggested and shown that targeting hot spots at PPI interfaces is a good strategy to achieve inhibition, in this review we focus on the notion that the most amenable interactions for inhibition are those that are mediated by a 'hot segment', a continuous epitope that contributes the majority of the binding energy. This criterion is both useful in guiding future target selection efforts, and in suggesting immediate inhibitory candidates--the dominant peptidic segment that mediates the targeted interaction.

Development of a fluorescent monoclonal antibody-based assay to measure the allosteric effects of synthetic peptides on self-oligomerization of AGR2 protein

Many regulatory proteins are homo-oligomeric and designing assays that measure self-assembly will provide novel approaches to study protein allostery and screen for novel small molecule modulators of protein interactions. We present an assay to begin to define the biochemical determinants that regulate dimerization of the cancer-associated oncoprotein AGR2. A two site-sandwich microtiter assay ((2S) MTA) was designed using a DyLight800-labeled monoclonal antibody that binds to an epitope in AGR2 to screen for synthetic self-peptides that might regulate dimer stability. Peptides derived from the intrinsically disordered N-terminal region of AGR2 increase in trans oligomer stability as defined using the (2S) MTA assay. A DSS-crosslinking assay that traps the AGR2 dimer through K95-K95 adducts confirmed that Δ45-AGR2 was a more stable dimer using denaturing gel electrophoresis. A titration of wt-AGR2, Δ45-AGR2 (more stable dimer), and monomeric AGR2(E60A) revealed that Δ45-AGR2 was more active in binding to Reptin than either wt-AGR2 or the AGR2(E60A) mutant. Our data have defined a functional role for the AGR2 dimer in the binding to its most well characterized interacting protein, Reptin. The ability to regulate AGR2 oligomerization in trans opens the possibility for developing small molecules that regulate its' biochemical activity as potential cancer therapeutics. The data also highlight the utility of this oligomerization assay to screen chemical libraries for ligands that could regulate AGR2 dimer stability and its' oncogenic potential.

Plucking the high hanging fruit: a systematic approach for targeting protein-protein interactions

Development of specific ligands for protein targets that help decode the complexities of protein-protein interaction networks is a key goal for the field of chemical biology. Despite the emergence of powerful in silico and experimental high-throughput screening strategies, the discovery of synthetic ligands that selectively modulate protein-protein interactions remains a challenge for bioorganic and medicinal chemists. This Perspective discusses emerging principles for the rational design of PPI inhibitors. Fundamentally, the approach seeks to adapt nature's protein recognition principles for the design of suitable secondary structure mimetics.

Probing the dimerization interface of Leishmania infantum trypanothione reductase with site-directed mutagenesis and short peptides

Binding at the interface: We tested the inhibitory activity of a set of peptide sequences derived from an α-helix of the dimeric trypanothione reductase from Leishmania infantum. Replacement of a glutamic acid residue with a lysine promoted monomer dissociation and enzyme inhibition.

Fluorescence Observables and Enzyme Kinetics in the Investigation of PPI Modulation by Small Molecules: Detection, Mechanistic Insight, and Functional Consequences

The potential of fluorescence-based methods and kinetic analysis in the screening and molecular-scale mechanistic investigation of PPI modulation by small molecules is discussed through several representative examples collected and commented. These experimental approaches take advantage of a variety of observables. Changes in the protein aggregation pattern have been monitored through fluorescence properties such as spectra, intensities (related to quantum yields), time-decays, and anisotropies of intrinsic protein fluorophores, of extrinsic fluorescent tags and, even, of the same small molecules added to modulate PPIs, as well as through bimolecular excited-state processes such as static and collisional quenching, including electron and excitation-energy transfer, or exciton interaction, whose efficiencies are crucially structure dependent. Besides allowing for qualitative and quantitative information on the small-molecule induced PPI modulation, these approaches can take advantage from the sensitivity of fluorescence observables on fine structural details to shed light on the molecular-scale mechanisms of action and their functional consequences. Direct investigation of the latter by kinetic inhibition analysis represents a useful change in perspective whenever PPI are relevant for enzyme activity. Dissociative inhibition, that is, the ability of some small molecules to inhibit enzymes by disrupting their active oligomeric assembly is shortly reviewed.

Features of protein-protein interactions that translate into potent inhibitors: topology, surface area and affinity

Protein-protein interactions (PPIs) control the assembly of multi-protein complexes and, thus, these contacts have enormous potential as drug targets. However, the field has produced a mix of both exciting success stories and frustrating challenges. Here, we review known examples and explore how the physical features of a PPI, such as its affinity, hotspots, off-rates, buried surface area and topology, might influence the chances of success in finding inhibitors. This analysis suggests that concise, tight binding PPIs are most amenable to inhibition. However, it is also clear that emerging technical methods are expanding the repertoire of 'druggable' protein contacts and increasing the odds against difficult targets. In particular, natural product-like compound libraries, high throughput screens specifically designed for PPIs and approaches that favour discovery of allosteric inhibitors appear to be attractive routes. The first group of PPI inhibitors has entered clinical trials, further motivating the need to understand the challenges and opportunities in pursuing these types of targets.