Context-Based Identification of Protein-Protein Interfaces and “Hot-Spot” Residues

Deep learning for advancing peptide drug development: Tools and methods in structure prediction and design

Peptides can bind challenging disease targets with high affinity and specificity, offering enormous opportunities for addressing unmet medical needs. However, peptides' unique features, including smaller size, increased structural flexibility, and limited data availability, pose additional challenges to the design process compared to proteins. This review explores the dynamic field of peptide therapeutics, leveraging deep learning to enhance structure prediction and design. Our exploration encompasses various facets of peptide research, ranging from dataset curation handling to model development. As deep learning technologies become more refined, we channel our efforts into peptide structure prediction and design, aligning with the fundamental principles of structure-activity relationships in drug development. To guide researchers in harnessing the potential of deep learning to advance peptide drug development, our insights comprehensively explore current challenges and future directions of peptide therapeutics.

Conformation state-specific monobodies regulate the functions of flexible proteins through conformation trapping

Synthetic binding proteins have emerged as modulators of protein functions through protein-protein interactions (PPIs). Because PPIs are influenced by the structural dynamics of targeted proteins, investigating whether the synthetic-binders-based strategy is applicable for proteins with large conformational changes is important. This study demonstrates the applicability of monobodies (fibronectin type-III domain-based synthetic binding proteins) in regulating the functions of proteins that undergo tens-of-angstroms-scale conformational changes, using an example of the A55C/C77S/V169C triple mutant (Adktm ; a phosphoryl transfer-catalyzing enzyme with a conformational change between OPEN/CLOSED forms). Phage display successfully developed monobodies that recognize the OPEN form (substrate-unbound form), but not the CLOSED form of Adktm . Two OPEN form-specific clones (OP-2 and OP-4) inhibited Adktm kinase activity. Epitope mapping with a yeast-surface display/flow cytometry indicated that OP-2 binds to the substrate-entry side of Adktm , whereas OP-4 binding occurs at another site. Small angle X-ray scattering  coupled with size-exclusion chromatography (SEC-SAXS) indicated that OP-4 binds to the hinge side opposite to the substrate-binding site of Adktm , retaining the whole OPEN-form structure of Adktm . Titration of the OP-4-Adktm complex with Ap5 A, a transition-state analog of Adktm , showed that the conformational shift to the CLOSED form was suppressed although Adktm retained the OPEN-form (i.e., substrate-binding ready form). These results show that OP-4 captures and stabilizes the OPEN-form state, thereby affecting the hinge motion. These experimental results indicate that monobody-based modulators can regulate the functions of proteins that show tens-of-angstroms-scale conformational changes, by trapping specific conformational states generated during large conformational change process that is essential for function exertion.

Drug discovery by targeting the protein-protein interactions involved in autophagy

Autophagy is a cellular process in which proteins and organelles are engulfed in autophagosomal vesicles and transported to the lysosome/vacuole for degradation. Protein-protein interactions (PPIs) play a crucial role at many stages of autophagy, which present formidable but attainable targets for autophagy regulation. Moreover, selective regulation of PPIs tends to have a lower risk in causing undesired off-target effects in the context of a complicated biological network. Thus, small-molecule regulators, including peptides and peptidomimetics, targeting the critical PPIs involved in autophagy provide a new opportunity for innovative drug discovery. This article provides general background knowledge of the critical PPIs involved in autophagy and reviews a range of successful attempts on discovering regulators targeting those PPIs. Successful strategies and existing limitations in this field are also discussed.

Engineering a Formate Dehydrogenase for NADPH Regeneration

Nicotinamide adenine dinucleotide (NADH) and nicotinamide adenine dinucleotide phosphate (NADPH) constitute major hydrogen donors for oxidative/reductive bio-transformations. NAD(P)H regeneration systems coupled with formate dehydrogenases (FDHs) represent a dreamful method. However, most of the native FDHs are NAD+ -dependent and suffer from insufficient reactivity compared to other enzymatic tools, such as glucose dehydrogenase. An efficient and competitive NADP+ -utilizing FDH necessitates the availability and robustness of NADPH regeneration systems. Herein, we report the engineering of a new FDH from Candida dubliniensis (CdFDH), which showed no strict NAD+ preference by a structure-guided rational/semi-rational design. A combinatorial mutant CdFDH-M4 (D197Q/Y198R/Q199N/A372S/K371T/▵Q375/K167R/H16L/K159R) exhibited 75-fold intensification of catalytic efficiency (kcat /Km ). Moreover, CdFDH-M4 has been successfully employed in diverse asymmetric oxidative/reductive processes with cofactor total turnover numbers (TTNs) ranging from 135 to 986, making it potentially useful for NADPH-required biocatalytic transformations.

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

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

Identification of a short ACE2-derived stapled peptide targeting the SARS-CoV-2 spike protein

The design and synthesis of a series of peptide derivatives based on a short ACE2 α-helix 1 epitope and subsequent [i - i+4] stapling of the secondary structure resulted in the identification of a 9-mer peptide capable to compete with recombinant ACE2 towards Spike RBD in the micromolar range. Specifically, SARS-CoV-2 Spike inhibitor screening based on colorimetric ELISA assay and structural studies by circular dichroism showed the ring-closing metathesis cyclization being capable to stabilize the helical structure of the 9-mer ³⁴HEAEDLFYQ⁴² epitope better than the triazole stapling via click chemistry. MD simulations showed the stapled peptide being able not only to bind the Spike RBD, sterically interfering with ACE2, but also showing higher affinity to the target as compared to parent epitope.

Targeting virulence regulation to disarm Acinetobacter baumannii pathogenesis

The development of anti-virulence drug therapy against Acinetobacter baumannii infections would provide an alternative to traditional antibacterial therapy that are increasingly failing. Here, we demonstrate that the OmpR transcriptional regulator plays a pivotal role in the pathogenesis of diverse A. baumannii clinical strains in multiple murine and G. mellonella invertebrate infection models. We identified OmpR-regulated genes using RNA sequencing and further validated two genes whose expression can be used as robust biomarker to quantify OmpR inhibition in A. baumannii. Moreover, the determination of the structure of the OmpR DNA binding domain of A. baumannii and the development of in vitro protein-DNA binding assays enabled the identification of an OmpR small molecule inhibitor. We conclude that OmpR is a valid and unexplored target to fight A. baumannii infections and we believe that the described platform combining in silico methods, in vitro OmpR inhibitory assays and in vivo G. mellonella surrogate infection model will facilitate future drug discovery programs.

Bovine pancreatic trypsin inhibitor and soybean Kunitz trypsin inhibitor: Differential effects on proteases and larval development of the soybean pest Anticarsia gemmatalis (Lepidoptera: Noctuidae)

Pest management is challenged with resistant herbivores and problems regarding human health and environmental issues. Indeed, the greatest challenge to modern agriculture is to protect crops from pests and still maintain environmental quality. This study aimed to analyze by in silico, in vitro, and in vivo approaches to the feasibility of using the inhibitory protein extracted from mammals - Bovine Pancreatic Trypsin Inhibitor (BPTI) as a potential inhibitor of digestive trypsins from the pest Anticarsia gemmatalis and comparing the results with the host-plant inhibitor - Soybean Kunitz Trypsin Inhibitor (SKTI). BPTI and SKTI interacts with A. gemmatalis trypsin-like enzyme competitively, through hydrogen and hydrophobic bonds. A. gemmatalis larvae exposed to BPTI did not show two common adaptative mechanisms i.e., proteolytic degradation and overproduction of proteases, presenting highly reduced trypsin-like activity. On the other hand, SKTI-fed larvae did not show reduced trypsin-like activity, presenting overproduction of proteases and SKTI digestion. In addition, the larval survival was reduced by BPTI similarly to SKTI, and additionally caused a decrease in pupal weight. The non-plant protease inhibitor BPTI presents intriguing element to compose biopesticide formulations to help decrease the use of conventional refractory pesticides into integrated pest management programs.

Therapeutic peptides: current applications and future directions

Peptide drug development has made great progress in the last decade thanks to new production, modification, and analytic technologies. Peptides have been produced and modified using both chemical and biological methods, together with novel design and delivery strategies, which have helped to overcome the inherent drawbacks of peptides and have allowed the continued advancement of this field. A wide variety of natural and modified peptides have been obtained and studied, covering multiple therapeutic areas. This review summarizes the efforts and achievements in peptide drug discovery, production, and modification, and their current applications. We also discuss the value and challenges associated with future developments in therapeutic peptides.

Targeting protein-protein interaction interfaces in COVID-19 drug discovery

To date, the COVID-19 pandemic has claimed over 1 million human lives, infected another 50 million individuals and wreaked havoc on the global economy. The crisis has spurred the ongoing development of drugs targeting its etiological agent, the SARS-CoV-2. Targeting relevant protein-protein interaction interfaces (PPIIs) is a viable paradigm for the design of antiviral drugs and enriches the targetable chemical space by providing alternative targets for drug discovery. In this review, we will provide a comprehensive overview of the theory, methods and applications of PPII-targeted drug development towards COVID-19 based on recent literature. We will also highlight novel developments, such as the successful use of non-native protein-protein interactions as targets for antiviral drug screening. We hope that this review may serve as an entry point for those interested in applying PPIIs towards COVID-19 drug discovery and speed up drug development against the pandemic.

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.

A novel FRET peptide assay reveals efficient Helicobacter pylori HtrA inhibition through zinc and copper binding

Helicobacter pylori (H. pylori) secretes the chaperone and serine protease high temperature requirement A (HtrA) that cleaves gastric epithelial cell surface proteins to disrupt the epithelial integrity and barrier function. First inhibitory lead structures have demonstrated the essential role of HtrA in H. pylori physiology and pathogenesis. Comprehensive drug discovery techniques allowing high-throughput screening are now required to develop effective compounds. Here, we designed a novel fluorescence resonance energy transfer (FRET) peptide derived from a gel-based label-free proteomic approach (direct in-gel profiling of protease specificity) as a valuable substrate for H. pylori HtrA. Since serine proteases are often sensitive to metal ions, we investigated the influence of different divalent ions on the activity of HtrA. We identified Zn⁺⁺ and Cu⁺⁺ ions as inhibitors of H. pylori HtrA activity, as monitored by in vitro cleavage experiments using casein or E-cadherin as substrates and in the FRET peptide assay. Putative binding sites for Zn⁺⁺ and Cu⁺⁺ were then analyzed in thermal shift and microscale thermophoresis assays. The findings of this study will contribute to the development of novel metal ion-dependent protease inhibitors, which might help to fight bacterial infections.

Construction of piperidine-2,4-dione-type azaheterocycles and their application in modern drug development and natural product synthesis

The review surveys the existing routes to piperidine-2,4-dione-type heterocycles including derivatives with the most vital types of biological activity. This heterocyclic platform is ideal for the construction of modern drugs and natural products.

Activity and Functional Importance of Helicobacter pylori Virulence Factors

Helicobacter pylori is a very successful Gram-negative pathogen colonizing the stomach of humans worldwide. Infections with this bacterium can generate pathologies ranging from chronic gastritis and peptic ulceration to gastric cancer. The best characterized H. pylori virulence factors that cause direct cell damage include an effector protein encoded by the cytotoxin-associated gene A (CagA), a type IV secretion system (T4SS) encoded in the cag-pathogenicity island (cag PAI), vacuolating cytotoxin A (VacA), γ-glutamyl transpeptidase (GGT), high temperature requirement A (HtrA, a serine protease) and cholesterol glycosyl-transferase (CGT). Since these H. pylori factors are either surface-exposed, secreted or translocated, they can directly interact with host cell molecules and are able to hijack cellular functions. Studies on these bacterial factors have progressed substantially in recent years. Here, we review the current status in the characterization of signaling cascades by these factors in vivo and in vitro, which comprise the disruption of cell-to-cell junctions, induction of membrane rearrangements, cytoskeletal dynamics, proliferative, pro-inflammatory, as well as, pro-apoptotic and anti-apoptotic responses or immune evasion. The impact of these signal transduction modules in the pathogenesis of H. pylori infections is discussed.

Applications of in Silico Methods for Design and Development of Drugs Targeting Protein-Protein Interactions

BACKGROUND

Identification of Protein-Protein Interactions (PPIs) is a major challenge in modern molecular biology and biochemistry research, due to the unquestionable role of proteins in cells, biological process and pathological states. Over the past decade, the PPIs have evolved from being considered a highly challenging field of research to being investigated and examined as targets for pharmacological intervention.

OBJECTIVE

Comprehension of protein interactions is crucial to known how proteins come together to build signalling pathways, to carry out their functions, or to cause diseases, when deregulated. Multiplicity and great amount of PPIs structures offer a huge number of new and potential targets for the treatment of different diseases.

METHODS

Computational techniques are becoming predominant in PPIs studies for their effectiveness, flexibility, accuracy and cost. As a matter of fact, there are effective in silico approaches which are able to identify PPIs and PPI site. Such methods for computational target prediction have been developed through molecular descriptors and data-mining procedures.

RESULTS

In this review, we present different types of interactions between protein-protein and the application of in silico methods for design and development of drugs targeting PPIs. We described computational approaches for the identification of possible targets on protein surface and to detect of stimulator/ inhibitor molecules.

CONCLUSION

A deeper study of the most recent bioinformatics methodologies for PPIs studies is vital for a better understanding of protein complexes and for discover new potential PPI modulators in therapeutic intervention.

H. pylori isolates with amino acid sequence polymorphisms as presence of both HtrA-L171 & CagL-Y58/E59 increase the risk of gastric cancer

Background

H. pylori CagL-Y58/E59 increase gastric cancer risk by stronger binding with integrin to faciliate type IV secretory system (T4SS). H. pylori can secrete high temperature requirement A (HtrA) to mediate E-Cadherin cleavage for gastric epithelial junction disruption, so H. pylori CagL can adhere to integrin located on basolateral side of epithelium. The study test whether H. pylori HtrA amino acid polymorphisms can increase gastric cancer risk synergistically with CagL-Y58/E59.

Methods

One-hundred and sixty-four H. pylori-positive patients, including 71 with non-ulcer dyspepsia (NUD), 63 with peptic ulcers (PU), and 30 with gastric cancers (GC), were enrolled to receive upper gastrointestinal endoscopy to obtain gastric biopsies for H. pylori culture and histology by the updated Sydney system. Each isolate was screened for htrA & cagL genotype by polymerase chain reaction and HtrA & CagL-Y58/E59 amino acid sequence polymorphisms by sequencing.

Results

The prevalence rates of htrA & cagL gene were both 100%. The HtrA amino acid sequence polymorphisms were not different between NUD and PU. The H. pylori isolates of GC had higher rates of HtrA residue 171 as leucine than those of NUD (73.3% vs. 50.7%, P = 0.036, OR[95%CI] = 2.7[1.1–6.8]). The risk of the H. pylori-infected subjects to get gastric cancer was increased up to 15.4-fold, if the infected isolates had presence of both HtrA-L171 and CagL-Y58/E59 (P < 0.001).

Conclusions

The H. pylori isolates of gastric cancer subjects had a higher rate of HtrA-L171. H. pylori isolates with presence of both HtrA-171 & CagL-Y58/E59 can synergistically increase the risk of gastric cancer.

Electronic supplementary material

The online version of this article (10.1186/s12929-019-0498-9) contains supplementary material, which is available to authorized users.

Computational analysis of hot spots and binding mechanism in the PD-1/PD-L1 interaction

Programmed cell death protein-1 (PD-1) is an important immunological checkpoint and plays a vital role in maintaining the peripheral tolerance of the human body by interacting with its ligand PD-L1. The overexpression of PD-L1 in tumor cells induces local immune suppression and helps the tumor cells to evade the endogenous anti-tumor immunity. Developing monoclonal antibodies against the PD-1/PD-L1 protein–protein interaction to block the PD-1/PD-L1 signaling pathway has demonstrated superior anti-tumor efficacy in a variety of solid tumors and has made a profound impact on the field of cancer immunotherapy in recent years. Although the X-ray crystal structure of the PD-1/PD-L1 complex has been solved, the detailed binding mechanism of the PD-1/PD-L1 interaction is not fully understood from a theoretical point of view. In this study, we performed computational alanine scanning on the PD-1/PD-L1 complex to quantitatively identify the hot spots in the PD-1/PD-L1 interaction and characterize its binding mechanisms at the atomic level. To the best of our knowledge, this is the first time that theoretical calculations have been used to systematically and quantitatively predict the hot spots in the PD-1/PD-L1 interaction. We hope that the predicted hot spots and the energy profile of the PD-1/PD-L1 interaction presented in this work can provide guidance for the design of peptide and small molecule drugs targeting PD-1 or PD-L1.

The hot spots quantitatively predicted by the recently developed MM/GBSA/IE method reveal a hydrophobic core in the PD-1/PD-L1 interaction.

Prediction of Hot Regions in PPIs Based on Improved Local Community Structure Detecting

The hot regions in PPIs are some assembly regions which are composed of the tightly packed HotSpots. The discovery of hot regions helps to understand life activities and has very important value for biological applications. The identification of hot regions is the basis for protein design and cancer prevention. The existing algorithms of predicting hot regions often have some defects, such as low accuracy and unstability. This paper proposes a novel hot region prediction method based on diverse biological characteristics. First, feature evaluation is employed by using an impoved mRMR method. Then, SVM is adopted to create cassification model based on the features selected. In addition, a new clustering algorithm, namely LCSD (Local community structure detecting), is developed to detect and analyze the conformation of hot regions. In the clustering process, the link similarity of protein residues is introduced to handle the boundary nodes. This algorithm can effectively deal with the missing residue nodes and control the local community boundaries. The results indicate that the spatial structure of hot regions can be obtained more effectively, and that our method is more effective than previous methods for precise identification of hot regions.

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

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

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

INTRODUCTION

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

Bacterial serine protease HtrA as a promising new target for antimicrobial therapy?

Recent studies have demonstrated that the bacterial chaperone and serine protease high temperature requirement A (HtrA) is closely associated with the establishment and progression of several infectious diseases. HtrA activity enhances bacterial survival under stress conditions, but also has direct effects on functions of the cell adhesion protein E-cadherin and extracellular matrix proteins, including fibronectin and proteoglycans. Although HtrA cannot be considered as a pathogenic factor per se, it exhibits favorable characteristics making HtrA a potentially attractive drug target to combat various bacterial infections.

Explaining RANKL inhibition by OPG through quantum biochemistry computations and insights into peptide-design for the treatment of osteoporosis

Quantum biochemistry computations are applied to precisely describe important protein–protein interactions, providing a basis for the design of inhibitory peptides against osteoporosis.

Modulating protein-protein interactions: the potential of peptides

Protein-protein interactions (PPIs) have emerged as important and challenging targets in chemical biology and medicinal chemistry. The main difficulty encountered in the discovery of small molecule modulators derives from the large contact surfaces involved in PPIs when compared with those that participate in protein-small molecule interactions. Because of their intrinsic features, peptides can explore larger surfaces and therefore represent a useful alternative to modulate PPIs. The use of peptides as therapeutics has been held back by their instability in vivo and poor cell internalization. However, more than 200 peptide drugs and homologous compounds (proteins or antibodies) containing peptide bonds are (or have been) on the market, and many alternatives are now available to tackle these limitations. This review will focus on the latest progress in the field, spanning from "lead" identification methods to binding evaluation techniques, through an update of the most successful examples described in the literature.

Structure-Based Design of Inhibitors of Protein-Protein Interactions: Mimicking Peptide Binding Epitopes

Protein-protein interactions (PPIs) are involved at all levels of cellular organization, thus making the development of PPI inhibitors extremely valuable. The identification of selective inhibitors is challenging because of the shallow and extended nature of PPI interfaces. Inhibitors can be obtained by mimicking peptide binding epitopes in their bioactive conformation. For this purpose, several strategies have been evolved to enable a projection of side chain functionalities in analogy to peptide secondary structures, thereby yielding molecules that are generally referred to as peptidomimetics. Herein, we introduce a new classification of peptidomimetics (classes A-D) that enables a clear assignment of available approaches. Based on this classification, the Review summarizes strategies that have been applied for the structure-based design of PPI inhibitors through stabilizing or mimicking turns, β-sheets, and helices.

Surfing the Protein-Protein Interaction Surface Using Docking Methods: Application to the Design of PPI Inhibitors

Blocking protein-protein interactions (PPI) using small molecules or peptides modulates biochemical pathways and has therapeutic significance. PPI inhibition for designing drug-like molecules is a new area that has been explored extensively during the last decade. Considering the number of available PPI inhibitor databases and the limited number of 3D structures available for proteins, docking and scoring methods play a major role in designing PPI inhibitors as well as stabilizers. Docking methods are used in the design of PPI inhibitors at several stages of finding a lead compound, including modeling the protein complex, screening for hot spots on the protein-protein interaction interface and screening small molecules or peptides that bind to the PPI interface. There are three major challenges to the use of docking on the relatively flat surfaces of PPI. In this review we will provide some examples of the use of docking in PPI inhibitor design as well as its limitations. The combination of experimental and docking methods with improved scoring function has thus far resulted in few success stories of PPI inhibitors for therapeutic purposes. Docking algorithms used for PPI are in the early stages, however, and as more data are available docking will become a highly promising area in the design of PPI inhibitors or stabilizers.

A new scoring function for protein-protein docking that identifies native structures with unprecedented accuracy

Protein-protein (P-P) 3D structures are fundamental to structural biology and drug discovery. However, most of them have never been determined. Many docking algorithms were developed for that purpose, but they have a very limited accuracy in generating native-like structures and identifying the most correct one, in particular when a single answer is asked for. With such a low success rate it is difficult to point out one docked structure as being native-like. Here we present a new, high accuracy, scoring method to identify the 3D structure of P-P complexes among a set of trial poses. It incorporates alanine scanning mutagenesis experimental data that need to be obtained a priori. The scoring scheme works by matching the computational and the experimental alanine scanning mutagenesis results. The size of the trial P-P interface area is also taken into account. We show that the method ranks the trial structures and identifies the native-like structures with unprecedented accuracy (∼94%), providing the correct P-P 3D structures that biochemists and molecular biologists need to pursue their studies. With such a success rate, the bottleneck of protein-protein docking moves from the scoring to searching algorithms.

Fractal Dimensions of Macromolecular Structures

Quantifying the properties of macromolecules is a prerequisite for understanding their roles in biochemical processes. One of the less-explored geometric features of macromolecules is molecular surface irregularity, or 'roughness', which can be measured in terms of fractal dimension (D). In this study, we demonstrate that surface roughness correlates with ligand binding potential. We quantified the surface roughnesses of biological macromolecules in a large-scale survey that revealed D values between 2.0 and 2.4. The results of our study imply that surface patches involved in molecular interactions, such as ligand-binding pockets and protein-protein interfaces, exhibit greater local fluctuations in their fractal dimensions than 'inert' surface areas. We expect approximately 22 % of a protein's surface outside of the crystallographically known ligand binding sites to be ligandable. These findings provide a fresh perspective on macromolecular structure and have considerable implications for drug design as well as chemical and systems biology.

Inhibiting Helicobacter pylori HtrA protease by addressing a computationally predicted allosteric ligand binding site

Helicobacter pylori is associated with inflammatory diseases and can cause gastric cancer and mucosa-associated lymphoma. One of the bacterium's key proteins is high temperature requirement A (HpHtrA) protein, an extracellular serine protease that cleaves E-cadherin of gastric epithelial cells, which leads to loss of cell-cell adhesion. Inhibition of HpHtrA may constitute an intervention strategy against H. pylori infection. Guided by the computational prediction of hypothetical ligand binding sites on the surface of HpHtrA, we performed residue mutation experiments that confirmed the functional relevance of an allosteric region. We virtually screened for potential ligands addressing this surface cleft located between the catalytic and PDZ1 domains. Our receptor-based computational method represents protein surface pockets in terms of graph frameworks and retrieves small molecules that satisfy the constraints given by the pocket framework. A new chemical entity was identified that blocked E-cadherin cleavage in vitro by direct binding to HpHtrA, and efficiently blocked pathogen transmigration across the gastric epithelial barrier. A preliminary crystal structure of HpHtrA confirms the validity of a comparative "homology" model of the enzyme, which we used for the computational study. The results of this study demonstrate that addressing orphan protein surface cavities of target macromolecules can lead to new bioactive ligands.

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.

Computational method to identify druggable binding sites that target protein-protein interactions

Protein-protein interactions are implicated in the pathogenesis of many diseases and are therefore attractive but challenging targets for drug design. One of the challenges in development is the identification of potential druggable binding sites in protein interacting interfaces. Identification of interface surfaces can greatly aid rational drug design of small molecules inhibiting protein-protein interactions. In this work, starting from the structure of a free monomer, we have developed a ligand docking based method, called "FindBindSite" (FBS), to locate protein-protein interacting interface regions and potential druggable sites in this interface. FindBindSite utilizes the results from docking a small and diverse library of small molecules to the entire protein structure. By clustering regions with the highest docked ligand density from FBS, we have shown that these high ligand density regions strongly correlate with the known protein-protein interacting surfaces. We have further predicted potential druggable binding sites on the protein surface using FBS, with druggability being defined as the site with high density of ligands docked. FBS shows a hit rate of 71% with high confidence and 93% with lower confidence for the 41 proteins used for predicting druggable binding sites on the protein-protein interface. Mining the regions of lower ligand density that are contiguous with the high scoring high ligand density regions from FBS, we were able to map 70% of the protein-protein interacting surface in 24 out of 41 structures tested. We also observed that FBS has limited sensitivity to the size and nature of the small molecule library used for docking. The experimentally determined hotspot residues for each protein-protein complex cluster near the best scoring druggable binding sites identified by FBS. These results validate the ability of our technique to identify druggable sites within protein-protein interface regions that have the maximal possibility of interface disruption.

Structure-based design of small-molecule protein–protein interaction modulators: the story so far

As the pivotal role of protein–protein interactions in cell growth, transcriptional activity, intracellular trafficking, signal transduction and pathological conditions has been assessed, experimental and in silico strategies have been developed to design protein–protein interaction modulators. State-of-the-art structure-based design methods, mainly pharmacophore modeling and docking, which have succeeded in the identification of enzyme inhibitors, receptor agonists and antagonists, and new tools specifically conceived to target protein–protein interfaces (e.g., hot-spot and druggable pocket prediction methods) have been applied in the search for small-molecule protein–protein interaction modulators. Many successful applications of structure-based design approaches that, despite the challenge of targeting protein–protein interfaces with small molecules, have led to the identification of micromolar and submicromolar hits are reviewed here.

Inhibitors of protein-protein interactions: new methodologies to tackle this challenge

Several advances in the fields of crystallography, molecular modeling, biophysical assays and chemistry are converging to making protein-protein interaction targets more amenable to drug design. These include steps towards improving crystallization of protein-protein complexes, identifying the clusters of residues that constitute putative small molecule binding 'hot spots', generating new methods for detecting the binding of small molecules to target proteins, and generating custom libraries via diversity oriented synthesis to enable the identification of natural-product-like hits.

De-peptidising protein-protein interactions - big jobs for small molecules

Virtually all biological processes rely on protein-protein interactions (PPIs) for signal propagation, therefore representing a vast array of potentially viable therapeutic intervention points. Targeting PPIs is a relatively novel drug development strategy so computational approaches towards analysing the interface between protein partners and predicting the likelihood of developing a small molecule inhibitor are still progressing. This review provides an overview of recent successful examples of computational methodologies used to predict druggable PPIs and small molecules designed to inhibit them.

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.

Peptide-based inhibitors of Plk1 polo-box domain containing mono-anionic phosphothreonine esters and their pivaloyloxymethyl prodrugs

Binding of polo-like kinase 1 (Plk1) polo-box domains (PBDs) to phosphothreonine (pThr)/phosphoserine (pSer)-containing sequences is critical for the proper function of Plk1. Although high-affinity synthetic pThr-containing peptides may be used to disrupt PBD function, the efficacy of such peptides in whole cell assays has been poor. This potentially reflects limited cell membrane permeability arising in part from the di-anionic nature of the phosphoryl group. We report five-mer peptides containing mono-anionic pThr phosphoryl esters that exhibit single-digit nanomolar PBD binding affinities in extracellular assays and improved antimitotic efficacies in whole cell assays. The cellular efficacies of these peptides have been further enhanced by the application of bio-reversible pivaloyloxymethyl (POM) phosphoryl protection to a pThr-containing polypeptide. Our findings may redefine structural parameters for the development of PBD-binding peptides and peptide mimetics.

Identification of Similar Binding Sites to Detect Distant Polypharmacology

The ability of small molecules to interact with multiple proteins is referred to as polypharmacology. This property is often linked to the therapeutic action of drugs but it is known also to be responsible for many of their side effects. Because of its importance, the development of computational methods that can predict drug polypharmacology has become an important line of research that led recently to the identification of many novel targets for known drugs. Nowadays, the majority of these methods are based on measuring the similarity of a query molecule against the hundreds of thousands of molecules for which pharmacological data on thousands of proteins are available in public sources. However, similarity-based methods are inherently biased by the chemical coverage offered by the active molecules present in those public repositories, which limits significantly their capacity to predict interactions with proteins structurally and functionally unrelated to any of the already known targets for drugs. It is in this respect that structure-based methods aiming at identifying similar binding sites may offer an alternative complementary means to ligand-based methods for detecting distant polypharmacology. The different existing approaches to binding site detection, representation, comparison, and fragmentation are reviewed and recent successful applications presented.

Effects on polo-like kinase 1 polo-box domain binding affinities of peptides incurred by structural variation at the phosphoamino acid position

Protein-protein interactions (PPIs) mediated by the polo-box domain (PBD) of polo-like kinase 1 (Plk1) serve important roles in cell proliferation. Critical elements in the high affinity recognition of peptides and proteins by PBD are derived from pThr/pSer-residues in the binding ligands. However, there has been little examination of pThr/pSer mimetics within a PBD context. Our current paper compares the abilities of a variety of amino acid residues and derivatives to serve as pThr/pSer replacements by exploring the role of methyl functionality at the pThr β-position and by replacing the phosphoryl group by phosphonic acid, sulfonic acid and carboxylic acids. This work sheds new light on structure activity relationships for PBD recognition of phosphoamino acid mimetics.

A compound-based computational approach for the accurate determination of hot spots

A plethora of both experimental and computational methods have been proposed in the past 20 years for the identification of hot spots at a protein-protein interface. The experimental determination of a protein-protein complex followed by alanine scanning mutagenesis, though able to determine hot spots with much precision, is expensive and has no guarantee of success while the accuracy of the current computational methods for hot-spot identification remains low. Here, we present a novel structure-based computational approach that accurately determines hot spots through docking into a set of proteins homologous to only one of the two interacting partners of a compound capable of disrupting the protein-protein interaction (PPI). This approach has been applied to identify the hot spots of human activin receptor type II (ActRII) critical for its binding toward Cripto-I. The subsequent experimental confirmation of the computationally identified hot spots portends a potentially accurate method for hot-spot determination in silico given a compound capable of disrupting the PPI in question. The hot spots of human ActRII first reported here may well become the focal points for the design of small molecule drugs that target the PPI. The determination of their interface may have significant biological implications in that it suggests that Cripto-I plays an important role in both activin and nodal signal pathways.

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.

A unifying motif of intermolecular cooperativity in protein associations

At the molecular level, most biological processes entail protein associations which in turn rely on a small fraction of interfacial residues called hot spots. Our theoretical analysis shows that hot spots share a unifying molecular attribute: they provide a third-body contribution to intermolecular cooperativity. Such motif, based on the wrapping of interfacial electrostatic interactions, is essential to maintain the integrity of the interface. Thus, our main result is to unravel the molecular nature of the protein association problem by revealing its underlying physics and thus by casting it in simple physical grounds. Such knowledge could then be exploited in rational drug design since the regions here indicated may serve as blueprints to engineer small molecules disruptive of protein-protein interfaces.