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

In Silico Exploration of the Trypanothione Reductase (TryR) of L. mexicana

Human leishmaniasis is a neglected tropical disease which affects nearly 1.5 million people every year, with Mexico being an important endemic region. One of the major defense mechanisms of these parasites is based in the polyamine metabolic pathway, as it provides the necessary compounds for its survival. Among the enzymes in this route, trypanothione reductase (TryR), an oxidoreductase enzyme, is crucial for the Leishmania genus' survival against oxidative stress. Thus, it poses as an attractive drug target, yet due to the size and features of its catalytic pocket, modeling techniques such as molecular docking focusing on that region is not convenient. Herein, we present a computational study using several structure-based approaches to assess the druggability of TryR from L. mexicana, the predominant Leishmania species in Mexico, beyond its catalytic site. Using this consensus methodology, three relevant pockets were found, of which the one we call σ-site promises to be the most favorable one. These findings may help the design of new drugs of trypanothione-related diseases.

Unraveling allostery within the angiotensin II type 1 receptor for Gαq and β-arrestin coupling

G protein-coupled receptors engage both G proteins and β-arrestins, and their coupling can be biased by ligands and mutations. Here, to resolve structural elements and mechanisms underlying effector coupling to the angiotensin II (AngII) type 1 receptor (AT1R), we combined alanine scanning mutagenesis of the entire sequence of the receptor with pharmacological profiling of Gαq and β-arrestin engagement to mutant receptors and molecular dynamics simulations. We showed that Gαq coupling to AT1R involved a large number of residues spread across the receptor, whereas fewer structural regions of the receptor contributed to β-arrestin coupling regulation. Residue stretches in transmembrane domain 4 conferred β-arrestin bias and represented an important structural element in AT1R for functional selectivity. Furthermore, we identified allosteric small-molecule binding sites that were enclosed by communities of residues that produced biased signaling when mutated. Last, we showed that allosteric communication within AT1R emanating from the Gαq coupling site spread beyond the orthosteric AngII-binding site and across different regions of the receptor, including currently unresolved structural regions. Our findings reveal structural elements and mechanisms within AT1R that bias Gαq and β-arrestin coupling and that could be harnessed to design biased receptors for research purposes and to develop allosteric modulators.

SiteRadar: Utilizing Graph Machine Learning for Precise Mapping of Protein-Ligand-Binding Sites

Identifying ligand-binding sites on the protein surface is a crucial step in the structure-based drug design. Although multiple techniques have been proposed, including those using machine learning algorithms, the existing solutions do not provide significant advantages over nonmachine learning approaches and there is still a big room for improvement. The low ability to identify protein-ligand-binding sites makes available approaches inapplicable to automated drug design. Here, we present SiteRadar, a new algorithm for mapping cavities that are likely to bind a small-molecule ligand. SiteRadar shows higher accuracy in binding site identification compared with FPocket and PUResNet. SiteRadar demonstrates an ability to detect up to 74% of true ligand-binding sites according to the top N + 2 metric and usually covers approximately 80% of ligand atoms. Therefore, SiteRadar can be regarded as a promising solution for implementation into algorithms for automated drug design.

In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets

Target identification is an important step in drug discovery, and computer-aided drug target identification methods are attracting more attention compared with traditional drug target identification methods, which are time-consuming and costly. Computer-aided drug target identification methods can greatly reduce the searching scope of experimental targets and associated costs by identifying the diseases-related targets and their binding sites and evaluating the druggability of the predicted active sites for clinical trials. In this review, we introduce the principles of computer-based active site identification methods, including the identification of binding sites and assessment of druggability. We provide some guidelines for selecting methods for the identification of binding sites and assessment of druggability. In addition, we list the databases and tools commonly used with these methods, present examples of individual and combined applications, and compare the methods and tools. Finally, we discuss the challenges and limitations of binding site identification and druggability assessment at the current stage and provide some recommendations and future perspectives.

Kinase inhibitors allosterically disrupt a regulatory interaction to enhance PKCα membrane translocation

The eukaryotic kinase domain has multiple intrinsically disordered regions whose conformation dictates kinase activity. Small molecule kinase inhibitors (SMKIs) rely on disrupting the active conformations of these disordered regions to inactivate the kinase. While SMKIs are selected for their ability to cause this disruption, the allosteric effects of conformational changes in disordered regions is limited by a lack of dynamic information provided by traditional structural techniques. In this study, we integrated multiscale molecular dynamics simulations with FRET sensors to characterize a novel allosteric mechanism that is selectively triggered by SMKI binding to the protein kinase Cα domain. The indole maleimide inhibitors BimI and sotrastaurin were found to displace the Gly-rich loop (G-loop) that normally shields the ATP-binding site. Displacement of the G-loop interferes with a newly identified, structurally conserved binding pocket for the C1a domain on the N lobe of the kinase domain. This binding pocket, in conjunction with the N-terminal regulatory sequence, masks a diacylglycerol (DAG) binding site on the C1a domain. SMKI-mediated displacement of the G-loop released C1a and exposed the DAG binding site, enhancing protein kinase Cα translocation both to synthetic lipid bilayers and to live cell membranes in the presence of DAG. Inhibitor chemotype determined the extent of the observed allosteric effects on the kinase domain and correlated with the extent of membrane recruitment. Our findings demonstrate the allosteric effects of SMKIs beyond the confines of kinase catalytic conformation and provide an integrated computational-experimental paradigm to investigate parallel mechanisms in other kinases.

Modulating Protein-Protein Interactions by Cyclic and Macrocyclic Peptides. Prominent Strategies and Examples

Cyclic and macrocyclic peptides constitute advanced molecules for modulating protein-protein interactions (PPIs). Although still peptide derivatives, they are metabolically more stable than linear counterparts, and should have a lower degree of flexibility, with more defined secondary structure conformations that can be adapted to imitate protein interfaces. In this review, we analyze recent progress on the main methods to access cyclic/macrocyclic peptide derivatives, with emphasis in a few selected examples designed to interfere within PPIs. These types of peptides can be from natural origin, or prepared by biochemical or synthetic methodologies, and their design could be aided by computational approaches. Some advances to facilitate the permeability of these quite big molecules by conjugation with cell penetrating peptides, and the incorporation of β-amino acid and peptoid structures to improve metabolic stability, are also commented. It is predicted that this field of research could have an important future mission, running in parallel to the discovery of new, relevant PPIs involved in pathological processes.

Recent Advances in Computational Protocols Addressing Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDP) are abundant in the human genome and have recently emerged as major therapeutic targets for various diseases. Unlike traditional proteins that adopt a definitive structure, IDPs in free solution are disordered and exist as an ensemble of conformations. This enables the IDPs to signal through multiple signaling pathways and serve as scaffolds for multi-protein complexes. The challenge in studying IDPs experimentally stems from their disordered nature. Nuclear magnetic resonance (NMR), circular dichroism, small angle X-ray scattering, and single molecule Förster resonance energy transfer (FRET) can give the local structural information and overall dimension of IDPs, but seldom provide a unified picture of the whole protein. To understand the conformational dynamics of IDPs and how their structural ensembles recognize multiple binding partners and small molecule inhibitors, knowledge-based and physics-based sampling techniques are utilized in-silico, guided by experimental structural data. However, efficient sampling of the IDP conformational ensemble requires traversing the numerous degrees of freedom in the IDP energy landscape, as well as force-fields that accurately model the protein and solvent interactions. In this review, we have provided an overview of the current state of computational methods for studying IDP structure and dynamics and discussed the major challenges faced in this field.

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.

Molecular Dynamics Simulation and Prediction of Druggable Binding Sites

Binding site identification and druggability evaluation are two essential steps in structure-based drug design. A druggable binding site tends to have high binding affinity to drug-like molecules. Predicting such sites can have a significant impact on a drug design campaign. This chapter focuses on summarizing the different methods that are used to predict druggable binding sites. The chapter also discusses the importance of including protein flexibility in the search process and the use of molecular dynamics simulations to address this aspect. Case studies from the literature are also summarized and discussed. We hope that this chapter would provide an overview on the different methods employed in binding site identification evaluation.

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

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

Computer prediction of paratope on antithrombotic antibody 10B12 and epitope on platelet glycoprotein VI via molecular dynamics simulation

BACKGROUND

Interaction between immunoglobulin-like receptor glycoprotein VI (GPVI) and collagen plays a central role in platelet activation and sequent firm adhesion. Of various antithrombotic agents targeting GPVI, antibody 10B12 is of great potential to block the GPVI-collagen interaction, but less is known about 10B12 paratope and GPVI epitope.

METHODS

Along the pathway in the computer strategy presented in our previous work, the 10B12/GPVI complex model was constructed through homology modeling and rigid-body docking, and the molecular dynamics (MD) simulation was used to detect the paratope residues on 10B12 and their partners on GPVI. Quantified by free and steered MD simulations, the stabilities of hydrogen bonds and salt bridges were used to rank the contributions of interface residues to binding of 10B12 and GPVI.

RESULTS

We predicted 12 key and seven dispensable residues in interaction of 10B12 to GPVI with present computational procedure. Besides of the 12 key residues, two are epitope residues (LYS⁴¹ and LYS⁵⁹) which had been identified by previous mutation experiments, and others, including four epitope residues (ARG³⁸, SER⁴⁴, ARG⁴⁶ and TYR⁴⁷ on GPVI) and six paratope residues (GLU¹, ASP⁹⁸, GLU¹⁰², ASP¹⁰⁷, ASP¹⁰⁸ and ASP¹¹¹ on 10B12), were newly found and also might be important for the 10B12-GPVI binding. The seven predicted dispensable residues on GPVI were had been illustrated in previous mutation experiments.

CONCLUSIONS

The present computer strategy combining homology modeling, rigid body docking and MD simulation was illustrated to be effective in mapping paratope on antithrombotic antibody 10B12 to epitope on GPVI, and have large potential in drug discovery and antibody research.

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

Background

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

Results

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

Conclusions

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

Electronic supplementary material

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

Identification of Covalent Binding Sites Targeting Cysteines Based on Computational Approaches

Covalent drugs have attracted increasing attention in recent years due to good inhibitory activity and selectivity. Targeting noncatalytic cysteines with irreversible inhibitors is a powerful approach for enhancing pharmacological potency and selectivity because cysteines can form covalent bonds with inhibitors through their nucleophilic thiol groups. However, most human kinases have multiple noncatalytic cysteines within the active site; to accurately predict which cysteine is most likely to form covalent bonds is of great importance but remains a challenge when designing irreversible inhibitors. In this work, FTMap was first applied to check its ability in predicting covalent binding site defined as the region where covalent bonds are formed between cysteines and irreversible inhibitors. Results show that it has excellent performance in detecting the hot spots within the binding pocket, and its hydrogen bond interaction frequency analysis could give us some interesting instructions for identification of covalent binding cysteines. Furthermore, we proposed a simple but useful covalent fragment probing approach and showed that it successfully predicted the covalent binding site of seven targets. By adopting a distance-based method, we observed that the closer the nucleophiles of covalent warheads are to the thiol group of a cysteine, the higher the possibility that a cysteine is prone to form a covalent bond. We believe that the combination of FTMap and our distance-based covalent fragment probing method can become a useful tool in detecting the covalent binding site of these targets.

AlphaSpace: Fragment-Centric Topographical Mapping To Target Protein-Protein Interaction Interfaces

Inhibition of protein–protein interactions (PPIs) is emerging as a promising therapeutic strategy despite the difficulty in targeting such interfaces with drug-like small molecules. PPIs generally feature large and flat binding surfaces as compared to typical drug targets. These features pose a challenge for structural characterization of the surface using geometry-based pocket-detection methods. An attractive mapping strategy—that builds on the principles of fragment-based drug discovery (FBDD)—is to detect the fragment-centric modularity at the protein surface and then characterize the large PPI interface as a set of localized, fragment-targetable interaction regions. Here, we introduce AlphaSpace, a computational analysis tool designed for fragment-centric topographical mapping (FCTM) of PPI interfaces. Our approach uses the alpha sphere construct, a geometric feature of a protein’s Voronoi diagram, to map out concave interaction space at the protein surface. We introduce two new features—alpha-atom and alpha-space—and the concept of the alpha-atom/alpha-space pair to rank pockets for fragment-targetability and to facilitate the evaluation of pocket/fragment complementarity. The resulting high-resolution interfacial map of targetable pocket space can be used to guide the rational design and optimization of small molecule or biomimetic PPI inhibitors.

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.

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.

An improved interolog mapping-based computational prediction of protein–protein interactions with increased network coverage

Automated and efficient methods that map ortholog interactions from several organisms and public databases (pDB) are needed to identify new interactions in an organism of interest (interolog mapping).