Identification of Similar Binding Sites to Detect Distant Polypharmacology

Comprehensive detection and characterization of human druggable pockets through binding site descriptors

Druggable pockets are protein regions that have the ability to bind organic small molecules, and their characterization is essential in target-based drug discovery. However, deriving pocket descriptors is challenging and existing strategies are often limited in applicability. We introduce PocketVec, an approach to generate pocket descriptors via inverse virtual screening of lead-like molecules. PocketVec performs comparably to leading methodologies while addressing key limitations. Additionally, we systematically search for druggable pockets in the human proteome, using experimentally determined structures and AlphaFold2 models, identifying over 32,000 binding sites across 20,000 protein domains. We then generate PocketVec descriptors for each site and conduct an extensive similarity search, exploring over 1.2 billion pairwise comparisons. Our results reveal druggable pocket similarities not detected by structure- or sequence-based methods, uncovering clusters of similar pockets in proteins lacking crystallized inhibitors and opening the door to strategies for prioritizing chemical probe development to explore the druggable space.

Medicinal polypharmacology-a scientific glossary of terminology and concepts

Medicinal polypharmacology is one answer to the complex reality of multifactorial human diseases that are often unresponsive to single-targeted treatment. It is an admittance that intrinsic feedback mechanisms, crosstalk, and disease networks necessitate drugs with broad modes-of-action and multitarget affinities. Medicinal polypharmacology grew to be an independent research field within the last two decades and stretches from basic drug development to clinical research. It has developed its own terminology embedded in general terms of pharmaceutical drug discovery and development at the intersection of medicinal chemistry, chemical biology, and clinical pharmacology. A clear and precise language of critical terms and a thorough understanding of underlying concepts is imperative; however, no comprehensive work exists to this date that could support researchers in this and adjacent research fields. In order to explore novel options, establish interdisciplinary collaborations, and generate high-quality research outputs, the present work provides a first-in-field glossary to clarify the numerous terms that have originated from various individual disciplines.

Computer-aided pattern scoring - A multitarget dataset-driven workflow to predict ligands of orphan targets

The identification of lead molecules and the exploration of novel pharmacological drug targets are major challenges of medical life sciences today. Genome-wide association studies, multi-omics, and systems pharmacology steadily reveal new protein networks, extending the known and relevant disease-modifying proteome. Unfortunately, the vast majority of the disease-modifying proteome consists of 'orphan targets' of which intrinsic ligands/substrates, (patho)physiological roles, and/or modulators are unknown. Undruggability is a major challenge in drug development today, and medicinal chemistry efforts cannot keep up with hit identification and hit-to-lead optimization studies. New 'thinking-outside-the-box' approaches are necessary to identify structurally novel and functionally distinctive ligands for orphan targets. Here we present a unique dataset that includes critical information on the orphan target ABCA1, from which a novel cheminformatic workflow - computer-aided pattern scoring (C@PS) - for the identification of novel ligands was developed. Providing a hit rate of 95.5% and molecules with high potency and molecular-structural diversity, this dataset represents a suitable template for general deorphanization studies.

Medicinal polypharmacology: Exploration and exploitation of the polypharmacolome in modern drug development

At the core of complex and multifactorial human diseases, such as cancer, metabolic syndrome, or neurodegeneration, are multiple players that cross-talk in robust biological networks which are intrinsically resilient to alterations. These multifactorial diseases are characterized by sophisticated feedback mechanisms which manifest cellular imbalance and resistance to drug therapy. By adhering to the specificity paradigm ("one target-one drug concept"), research focused for many years on drugs with very narrow mechanisms of action. This narrow focus promoted therapy ineffectiveness and resistance. However, modern drug discovery has evolved over the last years, increasingly emphasizing integral strategies for the development of clinically effective drugs. These integral strategies include the controlled engagement of multiple targets to overcome therapy resistance. Apart from the additive or even synergistic effects in therapy, multitarget drugs harbor molecular-structural attributes to explore orphan targets of which intrinsic substrates/physiological role(s) and/or modulators are unknown for future therapy purposes. We designated this multidisciplinary and translational research field between medicinal chemistry, chemical biology, and molecular pharmacology as 'medicinal polypharmacology'. Medicinal polypharmacology emerged as alternative approach to common single-targeted pharmacology stretching from basic drug and target identification processes to clinical evaluation of multitarget drugs, and the exploration and exploitation of the 'polypharmacolome' is at the forefront of modern drug development research.

Promiscuity in drug discovery on the verge of the structural revolution: recent advances and future chances

INTRODUCTION

Promiscuity denotes the ability of ligands and targets to specifically interact with multiple binding partners. Despite negative aspects like side effects, promiscuity is receiving increasing attention in drug discovery as it can enhance drug efficacy and provides a molecular basis for drug repositioning. The three-dimensional structure of ligand-target complexes delivers exclusive insights into the molecular mechanisms of promiscuity and structure-based methods enable the identification of promiscuous interactions. With the recent breakthrough in protein structure prediction, novel possibilities open up to reveal unknown connections in ligand-target interaction networks.

AREAS COVERED

This review highlights the significance of structure in the identification and characterization of promiscuity and evaluates the potential of protein structure prediction to advance our knowledge of drug-target interaction networks. It discusses the definition and relevance of promiscuity in drug discovery and explores different approaches to detecting promiscuous ligands and targets.

EXPERT OPINION

Examination of structural data is essential for understanding and quantifying promiscuity. The recent advancements in structure prediction have resulted in an abundance of targets that are well-suited for structure-based methods like docking. In silico approaches may eventually completely transform our understanding of drug-target networks by complementing the millions of predicted protein structures with billions of predicted drug-target interactions.

DTSEA: A network-based drug target set enrichment analysis method for drug repurposing against COVID-19

The Coronavirus Disease 2019 (COVID-19) pandemic is still wreaking havoc worldwide. Therefore, the urgent need for efficient treatments pushes researchers and clinicians into screening effective drugs. Drug repurposing may be a promising and time-saving strategy to identify potential drugs against this disease. Here, we developed a novel computational approach, named Drug Target Set Enrichment Analysis (DTSEA), to identify potent drugs against COVID-19. DTSEA first mapped the disease-related genes into a gene functional interaction network, and then it used a network propagation algorithm to rank all genes in the network by calculating the network proximity of genes to disease-related genes. Finally, an enrichment analysis was performed on drug target sets to prioritize disease-candidate drugs. It was shown that the top three drugs predicted by DTSEA, including Ataluren, Carfilzomib, and Aripiprazole, were significantly enriched in the immune response pathways indicating the potential for use as promising COVID-19 inhibitors. In addition to these drugs, DTSEA also identified several drugs (such as Remdesivir and Olumiant), which have obtained emergency use authorization (EUA) for COVID-19. These results indicated that DTSEA could effectively identify the candidate drugs for COVID-19, which will help to accelerate the development of drugs for COVID-19. We then performed several validations to ensure the reliability and validity of DTSEA, including topological analysis, robustness analysis, and prediction consistency. Collectively, DTSEA successfully predicted candidate drugs against COVID-19 with high accuracy and reliability, thus making it a formidable tool to identify potential drugs for a specific disease and facilitate further investigation.

Activity Models of Key GPCR Families in the Central Nervous System: A Tool for Many Purposes

G protein-coupled receptors (GPCRs) are targets of many drugs, of which ∼25% are indicated for central nervous system (CNS) disorders. Drug promiscuity affects their efficacy and safety profiles. Predicting the polypharmacology profile of compounds against GPCRs can thus provide a basis for producing more precise therapeutics by considering the targets and the anti-targets in that family of closely related proteins. We provide a tool for predicting the polypharmacology of compounds within prominent GPCR families in the CNS: serotonin, dopamine, histamine, muscarinic, opioid, and cannabinoid receptors. Our in-house algorithm, "iterative stochastic elimination" (ISE), produces high-quality ligand-based models for agonism and antagonism at 31 GPCRs. The ISE models correctly predict 68% of CNS drug-GPCR interactions, while the "similarity ensemble approach" predicts only 33%. The activity models correctly predict 56% of reported activities of DrugBank molecules for these CNS receptors. We conclude that the combination of interactions and activity profiles generated by screening through our models form the basis for subsequent designing and discovering novel therapeutics, either single, multitargeting, or repurposed.

TWN-RENCOD: A novel method for protein binding site comparison

Several diverse proteins possess similar binding sites. Protein binding site comparison provides valuable insights for the drug discovery and development. Binding site similarities are useful in understanding polypharmacology, identifying potential off-targets and repurposing of known drugs. Many binding site analysis and comparison methods are available today, however, these methods may not be adequate to explain variation in the activity of a drug or a small molecule against a number of similar proteins. Water molecules surrounding the protein surface contribute to structure and function of proteins. Water molecules form diverse types of hydrogen-bonded cyclic water-ring networks known as topological water networks (TWNs). Analysis of TWNs in binding site of proteins may improve understanding of the characteristics of binding sites. We propose TWN-based residue encoding (TWN-RENCOD), a novel binding site comparison method which compares the aqueous environment in binding sites of similar proteins. As compared to other existing methods, results obtained using our method correlated better with differences in wide range of activity of a known drug (Sunitinib) against nine different protein kinases (KIT, PDGFRA, VEGFR2, PHKG2, ITK, HPK1, MST3, PAK6 and CDK2).

Docking-Based Virtual Screening Enables Prioritizing Protein Kinase Inhibitors With In Vitro Phenotypic Activity Against Schistosoma mansoni

Schistosomiasis is a parasitic neglected disease with praziquantel (PZQ) utilized as the main drug for treatment, despite its low effectiveness against early stages of the worm. To aid in the search for new drugs to tackle schistosomiasis, computer-aided drug design has been proved a helpful tool to enhance the search and initial identification of schistosomicidal compounds, allowing fast and cost-efficient progress in drug discovery. The combination of high-throughput in silico data followed by in vitro phenotypic screening assays allows the assessment of a vast library of compounds with the potential to inhibit a single or even several biological targets in a more time- and cost-saving manner. Here, we describe the molecular docking for in silico screening of predicted homology models of five protein kinases (JNK, p38, ERK1, ERK2, and FES) of Schistosoma mansoni against approximately 85,000 molecules from the Managed Chemical Compounds Collection (MCCC) of the University of Nottingham (UK). We selected 169 molecules predicted to bind to SmERK1, SmERK2, SmFES, SmJNK, and/or Smp38 for in vitro screening assays using schistosomula and adult worms. In total, 89 (52.6%) molecules were considered active in at least one of the assays. This approach shows a much higher efficiency when compared to using only traditional high-throughput in vitro screening assays, where initial positive hits are retrieved from testing thousands of molecules. Additionally, when we focused on compound promiscuity over selectivity, we were able to efficiently detect active compounds that are predicted to target all kinases at the same time. This approach reinforces the concept of polypharmacology aiming for “one drug-multiple targets”. Moreover, at least 17 active compounds presented satisfactory drug-like properties score when compared to PZQ, which allows for optimization before further in vivo screening assays. In conclusion, our data support the use of computer-aided drug design methodologies in conjunction with high-throughput screening approach.

A New Strategy for Multitarget Drug Discovery/Repositioning Through the Identification of Similar 3D Amino Acid Patterns Among Proteins Structures: The Case of Tafluprost and its Effects on Cardiac Ion Channels

The identification of similar three-dimensional (3D) amino acid patterns among different proteins might be helpful to explain the polypharmacological profile of many currently used drugs. Also, it would be a reasonable first step for the design of novel multitarget compounds. Most of the current computational tools employed for this aim are limited to the comparisons among known binding sites, and do not consider several additional important 3D patterns such as allosteric sites or other conserved motifs. In the present work, we introduce Geomfinder2.0, which is a new and improved version of our previously described algorithm for the deep exploration and discovery of similar and druggable 3D patterns. As compared with the original version, substantial improvements that have been incorporated to our software allow: (i) to compare quaternary structures, (ii) to deal with a list of pairs of structures, (iii) to know how druggable is the zone where similar 3D patterns are detected and (iv) to significantly reduce the execution time. Thus, the new algorithm achieves up to 353x speedup as compared to the previous sequential version, allowing the exploration of a significant number of quaternary structures in a reasonable time. In order to illustrate the potential of the updated Geomfinder version, we show a case of use in which similar 3D patterns were detected in the cardiac ions channels NaV1.5 and TASK-1. These channels are quite different in terms of structure, sequence and function and both have been regarded as important targets for drugs aimed at treating atrial fibrillation. Finally, we describe the in vitro effects of tafluprost (a drug currently used to treat glaucoma, which was identified as a novel putative ligand of NaV1.5 and TASK-1) upon both ion channels’ activity and discuss its possible repositioning as a novel antiarrhythmic drug.

An up-to-date overview of computational polypharmacology in modern drug discovery

INTRODUCTION

In recent years, computational polypharmacology has gained significant attention to study the promiscuous nature of drugs. Despite tremendous challenges, community-wide efforts have led to a variety of novel approaches for predicting drug polypharmacology. In particular, some rapid advances using machine learning and artificial intelligence have been reported with great success.

AREAS COVERED

In this article, the authors provide a comprehensive update on the current state-of-the-art polypharmacology approaches and their applications, focusing on those reports published after our 2017 review article. The authors particularly discuss some novel, groundbreaking concepts, and methods that have been developed recently and applied to drug polypharmacology studies.

EXPERT OPINION

Polypharmacology is evolving and novel concepts are being introduced to counter the current challenges in the field. However, major hurdles remain including incompleteness of high-quality experimental data, lack of in vitro and in vivo assays to characterize multi-targeting agents, shortage of robust computational methods, and challenges to identify the best target combinations and design effective multi-targeting agents. Fortunately, numerous national/international efforts including multi-omics and artificial intelligence initiatives as well as most recent collaborations on addressing the COVID-19 pandemic have shown significant promise to propel the field of polypharmacology forward.

PatchSearch: a web server for off-target protein identification

The large number of proteins found in the human body implies that a drug may interact with many proteins, called off-target proteins, besides its intended target. The PatchSearch web server provides an automated workflow that allows users to identify structurally conserved binding sites at the protein surfaces in a set of user-supplied protein structures. Thus, this web server may help to detect potential off-target protein. It takes as input a protein complexed with a ligand and identifies within user-defined or predefined collections of protein structures, those having a binding site compatible with this ligand in terms of geometry and physicochemical properties. It is based on a non-sequential local alignment of the patch over the entire protein surface. Then the PatchSearch web server proposes a ligand binding mode for the potential off-target, as well as an estimated affinity calculated by the Vinardo scoring function. This novel tool is able to efficiently detects potential interactions of ligands with distant off-target proteins. Furthermore, by facilitating the discovery of unexpected off-targets, PatchSearch could contribute to the repurposing of existing drugs. The server is freely available at http://bioserv.rpbs.univ-paris-diderot.fr/services/PatchSearch.

A Point of Inflection and Reflection on Systems Chemical Biology

For the past several decades, chemical biologists have been leveraging chemical principles for understanding biology, tackling disease, and biomanufacturing, while systems biologists have holistically applied computation and genome-scale experimental tools to the same problems. About a decade ago, the benefit of combining the philosophies of chemical biology with systems biology into systems chemical biology was advocated, with the potential to systematically understand the way small molecules affect biological systems. Recently, there has been an explosion in new technologies that permit massive expansion in the scale of biological experimentation, increase access to more diverse chemical space, and enable powerful computational interpretation of large datasets. Fueled by these rapidly increasing capabilities, systems chemical biology is now at an inflection point, poised to enter a new era of more holistic and integrated scientific discovery. Systems chemical biology is primed to reveal an integrated understanding of fundamental biology and to discover new chemical probes to comprehensively dissect and systematically understand that biology, thereby providing a path to novel strategies for discovering therapeutics, designing drug combinations, avoiding toxicity, and harnessing beneficial polypharmacology. In this Review, we examine the emergence of new capabilities driving us to this inflection point in systems chemical biology, and highlight holistic approaches and opportunities that are arising from integrating chemical biology with a systems-level understanding of the intersection of biology and chemistry.

An Integrated System Biology Approach Yields Drug Repositioning Candidates for the Treatment of Heart Failure

Identifying effective pharmacological treatments for heart failure (HF) patients remains critically important. Given that the development of drugs de novo is expensive and time consuming, drug repositioning has become an increasingly important branch. In the present study, we propose a two-step drug repositioning pipeline and investigate the novel therapeutic effects of existing drugs approved by the US Food and Drug Administration to discover potential therapeutic drugs for HF. In the first step, we compared the gene expression pattern of HF patients with drug-induced gene expression profiles to obtain preliminary candidates. In the second step, we performed a systems biology approach based on the known protein–protein interaction information and targets of drugs to narrow down preliminary candidates to obtain final candidates. Drug set enrichment analysis and literature search were applied to assess the performance of our repositioning approach. We also constructed a mode of action network for each candidate and performed pathway analysis for each candidate using genes contained in their mode of action network to uncover pathways that potentially reflect the mechanisms of candidates’ therapeutic efficacy to HF. We discovered numerous preliminary candidates, some of which are used in clinical practice and supported by the literature. The final candidates contained nearly all of the preliminary candidates supported by previous studies. Drug set enrichment analysis and literature search support the validity of our repositioning approach. The mode of action network for each candidate not only displayed the underlying mechanisms of drug efficacy but also uncovered potential biomarkers and therapeutic targets for HF. Our two-step drug repositioning approach is efficient to find candidates with potential therapeutic efficiency to HF supported by the literature and might be of particular use in the discovery of novel effective pharmacological therapies for HF.

Insights into Computational Drug Repurposing for Neurodegenerative Disease

Computational drug repurposing has the ability to remarkably reduce drug development time and cost in an era where these factors are prohibitively high. Several examples of successful repurposed drugs exist in fields such as oncology, diabetes, leprosy, inflammatory bowel disease, among others, however computational drug repurposing in neurodegenerative disease has presented several unique challenges stemming from the lack of validation methods and difficulty in studying heterogenous diseases of aging. Here, we examine existing approaches to computational drug repurposing, including molecular, clinical, and biophysical methods, and propose data sources and methods to advance computational drug repurposing in neurodegenerative disease using Alzheimer's disease as an example.

High Impact: The Role of Promiscuous Binding Sites in Polypharmacology

The literature focuses on drug promiscuity, which is a drug’s ability to bind to several targets, because it plays an essential role in polypharmacology. However, little work has been completed regarding binding site promiscuity, even though its properties are now recognized among the key factors that impact drug promiscuity. Here, we quantified and characterized the promiscuity of druggable binding sites from protein-ligand complexes in the high quality Mother Of All Databases while using statistical methods. Most of the sites (80%) exhibited promiscuity, irrespective of the protein class. Nearly half were highly promiscuous and able to interact with various types of ligands. The corresponding pockets were rather large and hydrophobic, with high sulfur atom and aliphatic residue frequencies, but few side chain atoms. Consequently, their interacting ligands can be large, rigid, and weakly hydrophilic. The selective sites that interacted with one ligand type presented less favorable pocket properties for establishing ligand contacts. Thus, their ligands were highly adaptable, small, and hydrophilic. In the dataset, the promiscuity of the site rather than the drug mainly explains the multiple interactions between the drug and target, as most ligand types are dedicated to one site. This underlines the essential contribution of binding site promiscuity to drug promiscuity between different protein classes.

Binding site characterization - similarity, promiscuity, and druggability

The elucidation of non-obvious binding site similarities has provided useful indications for the establishment of polypharmacology, the identification of potential off-targets, or the repurposing of known drugs. The concept underlying all of these approaches is promiscuous binding which can be analyzed from a ligand-based or a binding site-based perspective. Herein, we applied methods for the automated analysis and comparison of protein binding sites to study promiscuous binding on a novel dataset of sites in complex with ligands sharing common shape and physicochemical properties. We show the suitability of this dataset for the benchmarking of novel binding site comparison methods. Our investigations also reveal promising directions for further in-depth analyses of promiscuity and druggability in a pocket-centered manner. Drawbacks concerning binding site similarity assessment and druggability prediction are outlined, enabling researchers to avoid the typical pitfalls of binding site analyses.

Updates to Binding MOAD (Mother of All Databases): Polypharmacology Tools and Their Utility in Drug Repurposing

The goal of Binding MOAD is to provide users with a data set focused on high-quality x-ray crystal structures that have been solved with biologically relevant ligands bound. Where available, experimental binding affinities (K_a, K_d, K_i, IC₅₀) are provided from the primary literature of the crystal structure. The database has been updated regularly since 2005, and this most recent update has added nearly 7000 new structures (growth of 21%). MOAD currently contains 32,747 structures, composed of 9117 protein families and 16,044 unique ligands. The data are freely available on www.BindingMOAD.org. This paper outlines updates to the data in Binding MOAD as well as improvements made to both the website and its contents. The NGL viewer has been added to improve visualization of the ligands and protein structures. MarvinJS has been implemented, over the outdated MarvinView, to work with JChem for small molecule searching in the database. To add tools for predicting polypharmacology, we have added information about sequence, binding-site, and ligand similarity between entries in the database. A main premise behind polypharmacology is that similar binding sites will bind similar ligands. The large amount of protein-ligand information available in Binding MOAD allows us to compute pairwise ligand and binding-site similarities. Lists of similar ligands and similar binding sites have been added to allow users to identify potential polypharmacology pairs. To show the utility of the polypharmacology data, we detail a few examples from Binding MOAD of drug repurposing targets with their respective similarities.

Repurposing Drugs Based on Evolutionary Relationships Between Targets of Approved Drugs and Proteins of Interest

Drug repurposing has garnered much interest as an effective method for drug development among biopharmaceutical companies. The availability of information on complete sequences of genomes and their associated biological data, genotype-phenotype-disease relationships, and properties of small molecules offers opportunities to explore the repurpose-able potential of existing pharmacopoeia. This method gains further importance, especially, in the context of development of drugs against infectious diseases, some of which pose serious complications due to emergence of drug-resistant pathogens. In this article, we describe computational means to achieve potential repurpose-able drug candidates that may be used against infectious diseases by exploring evolutionary relationships between established targets of FDA-approved drugs and proteins of pathogen of interest.

A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs)

The automated comparison of protein-ligand binding sites provides useful insights into yet unexplored site similarities. Various stages of computational and chemical biology research can benefit from this knowledge. The search for putative off-targets and the establishment of polypharmacological effects by comparing binding sites led to promising results for numerous projects. Although many cavity comparison methods are available, a comprehensive analysis to guide the choice of a tool for a specific application is wanting. Moreover, the broad variety of binding site modeling approaches, comparison algorithms, and scoring metrics impedes this choice. Herein, we aim to elucidate strengths and weaknesses of binding site comparison methodologies. A detailed benchmark study is the only possibility to rationalize the selection of appropriate tools for different scenarios. Specific evaluation data sets were developed to shed light on multiple aspects of binding site comparison. An assembly of all applied benchmark sets (ProSPECCTs–Protein Site Pairs for the Evaluation of Cavity Comparison Tools) is made available for the evaluation and optimization of further and still emerging methods. The results indicate the importance of such analyses to facilitate the choice of a methodology that complies with the requirements of a specific scientific challenge.

Binding site similarities are useful in the context of promiscuity prediction, drug repurposing, the analysis of protein-ligand and protein-protein complexes, function prediction, and further fields of general interest in chemical biology and biochemistry. Many years of research have led to the development of a multitude of methods for binding site analysis and comparison. On the one hand, their availability supports research. On the other hand, the huge number of methods hampers the efficient selection of a specific tool. Our research is dedicated to the analysis of different cavity comparison tools. We use several binding site data sets to establish guidelines which can be applied to ensure a successful application of comparison methods by circumventing potential pitfalls.

Selection of protein conformations for structure-based polypharmacology studies

Several drugs exert their therapeutic effect through the modulation of multiple targets. Structure-based approaches hold great promise for identifying compounds with the desired polypharmacological profiles. These methods use knowledge of the protein binding sites to identify stereoelectronically complementary ligands. The selection of the most suitable protein conformations to be used in the design process is vital, especially for multitarget drug design in which the same ligand has to be accommodated in multiple binding pockets. Herein, we focus on currently available techniques for the selection of the most suitable protein conformations for multitarget drug design, compare the potential advantages and limitations of each method, and comment on how their combination could help in polypharmacology drug design.

3D similarities between the binding sites of monoaminergic target proteins

The study of binding site similarities can be relevant to understand the interaction of different drugs at several molecular targets. The increasing availability of protein crystal structures and the development of novel algorithms designed to evaluate three-dimensional similarities, represent a great opportunity to explore the existence of electronic and shape features shared by clinically relevant proteins, which could assist drug design and discovery. Proteins involved in the recognition of monoaminergic neurotransmitters, such as monoamine transporters or monoamine oxidases (MAO) have been related to several psychiatric and neurological disorders such as depression or Parkinson’s disease. In this work, we evaluated the possible existence of similarities among the binding sites of the serotonin transporter (SERT), the dopamine transporter (DAT), MAO-A and MAO-B. This study was carried out using molecular simulation methodologies linked to the statistical algorithm PocketMatch, which was modified in order to obtain similarities profiles. Our results show that DAT and SERT exhibit a high degree of 3-D similarities all along the pathway that is presumably involved in the substrate transport process. Distinct differences, on the other hand, were found both at the extracellular and the intracellular ends of the transporters, which might be involved in the selective initial recognition of the corresponding substrate. Similarities were also found between the active (catalytic) site of MAO-A and the extracellular vestibule of SERT (the S2 binding site). These results suggest some degree of structural convergence for these proteins, which have different functions, tissue distribution and genetic origin, but which share the same endogenous ligand (serotonin). Beyond the functional implications, these findings are valuable for the design of both selective and non-selective ligands.

Strategies of Virtual Screening in Medicinal Chemistry

Virtual screening represents an effective computational strategy to rise-up the chances of finding new bioactive compounds by accelerating the time needed to move from an initial intuition to market. Classically, the most pursued approaches rely on ligand- and structure-based studies, the former employed when structural data information about the target is missing while the latter employed when X-ray/NMR solved or homology models are instead available for the target. The authors will focus on the most advanced techniques applied in this area. In particular, they will survey the key concepts of virtual screening by discussing how to properly select chemical libraries, how to make database curation, how to applying and- and structure-based techniques, how to wisely use post-processing methods. Emphasis will be also given to the most meaningful databases used in VS protocols. For the ease of discussion several examples will be presented.

Statistical Profiling of One Promiscuous Protein Binding Site: Illustrated by Urokinase Catalytic Domain

While recent literature focuses on drug promiscuity, the characterization of promiscuous binding sites (ability to bind several ligands) remains to be explored. Here, we present a proteochemometric modeling approach to analyze diverse ligands and corresponding multiple binding sub-pockets associated with one promiscuous binding site to characterize protein-ligand recognition. We analyze both geometrical and physicochemical profile correspondences. This approach was applied to examine the well-studied druggable urokinase catalytic domain inhibitor binding site, which results in a large number of complex structures bound to various ligands. This approach emphasizes the importance of jointly characterizing pocket and ligand spaces to explore the impact of ligand diversity on sub-pocket properties and to establish their main profile correspondences. This work supports an interest in mining available 3D holo structures associated with a promiscuous binding site to explore its main protein-ligand recognition tendency.

Detecting similar binding pockets to enable systems polypharmacology

In the era of systems biology, multi-target pharmacological strategies hold promise for tackling disease-related networks. In this regard, drug promiscuity may be leveraged to interfere with multiple receptors: the so-called polypharmacology of drugs can be anticipated by analyzing the similarity of binding sites across the proteome. Here, we perform a pairwise comparison of 90,000 putative binding pockets detected in 3,700 proteins, and find that 23,000 pairs of proteins have at least one similar cavity that could, in principle, accommodate similar ligands. By inspecting these pairs, we demonstrate how the detection of similar binding sites expands the space of opportunities for the rational design of drug polypharmacology. Finally, we illustrate how to leverage these opportunities in protein-protein interaction networks related to several therapeutic classes and tumor types, and in a genome-scale metabolic model of leukemia.

Traditionally, the fact that most drugs are promiscuous binders has been a major concern in pharmacology, due to the occurrence of undesired off-target clinical events. In the recent years, however, the realization that many diseases are the result of complex biological processes has encouraged rethinking of drug promiscuity as a promising feature, since it is sometimes necessary to interfere with multiple receptors in order to overcome the robustness of disease-related networks. One way to identify groups of proteins that could be targeted simultaneously is to look for similar binding sites. We have massively done so for all human proteins with a known high-resolution three-dimensional structure, unveiling a vast space of ‘polypharmacology’ opportunities. Of these, we know, a great majority is not of therapeutic interest. To pinpoint promising multi-target combinations, we advocate for the use of computational tools that are able to rapidly simulate the effect of drug-target interactions on biological networks.

On the Integration of In Silico Drug Design Methods for Drug Repurposing

Drug repurposing has become an important branch of drug discovery. Several computational approaches that help to uncover new repurposing opportunities and aid the discovery process have been put forward, or adapted from previous applications. A number of successful examples are now available. Overall, future developments will greatly benefit from integration of different methods, approaches and disciplines. Steps forward in this direction are expected to help to clarify, and therefore to rationally predict, new drug–target, target–disease, and ultimately drug–disease associations.

Large-scale detection of drug off-targets: hypotheses for drug repurposing and understanding side-effects

Background

Promiscuity in molecular interactions between small-molecules, including drugs, and proteins is widespread. Such unintended interactions can be exploited to suggest drug repurposing possibilities as well as to identify potential molecular mechanisms responsible for observed side-effects.

Methods

We perform a large-scale analysis to detect binding-site molecular interaction field similarities between the binding-sites of the primary target of 400 drugs against a dataset of 14082 cavities within 7895 different proteins representing a non-redundant dataset of all proteins with known structure. Statistically-significant cases with high levels of similarities represent potential cases where the drugs that bind the original target may in principle bind the suggested off-target. Such cases are further analysed with docking simulations to verify if indeed the drug could, in principle, bind the off-target. Diverse sources of data are integrated to associated potential cross-reactivity targets with side-effects.

Results

We observe that promiscuous binding-sites tend to display higher levels of hydrophobic and aromatic similarities. Focusing on the most statistically significant similarities (Z-score ≥ 3.0) and corroborating docking results (RMSD < 2.0 Å), we find 2923 cases involving 140 unique drugs and 1216 unique potential cross-reactivity protein targets. We highlight a few cases with a potential for drug repurposing (acetazolamide as a chorismate pyruvate lyase inhibitor, raloxifene as a bacterial quorum sensing inhibitor) as well as to explain the side-effects of zanamivir and captopril. A web-interface permits to explore the detected similarities for each of the 400 binding-sites of the primary drug targets and visualise them for the most statistically significant cases.

Conclusions

The detection of molecular interaction field similarities provide the opportunity to suggest drug repurposing opportunities as well as to identify potential molecular mechanisms responsible for side-effects. All methods utilized are freely available and can be readily applied to new query binding-sites. All data is freely available and represents an invaluable source to identify further candidates for repurposing and suggest potential mechanisms responsible for side-effects.

Electronic supplementary material

The online version of this article (doi:10.1186/s40360-017-0128-7) contains supplementary material, which is available to authorized users.

PatchSearch: A Fast Computational Method for Off-Target Detection

Many therapeutic molecules are known to bind several proteins, which can be different from the initially targeted one. Such unexpected interactions with proteins called off-targets can lead to adverse effects. Potential off-target identification is important to predict to avoid drug side effects or to discover new targets for existing drugs. We propose a new program named PatchSearch that implements local nonsequential searching for similar binding sites on protein surfaces with a controlled amount of flexibility. It is based on detection of quasi-cliques in product graphs representing all the possible matchings between two compared structures. This method has been benchmarked on a large diversity of ligands and on five data sets ranging from 12 to more than 7000 protein structures. The experiments conducted in this study show that the PatchSearch method could be useful in the early identification of off-targets. The program and the benchmarks presented in this paper are available as an R package at https://github.com/MTiPatchSearch .

Mapping the 3D structures of small molecule binding sites

Background

Analysis of the 3D structures of protein–ligand binding sites can provide valuable insights for drug discovery. Binding site comparison (BSC) studies can be employed to elucidate the function of orphan proteins or to predict the potential for polypharmacology. Many previous binding site analyses only consider binding sites surrounding an experimentally observed bound ligand.

Results

To encompass potential protein–ligand binding sites that do not have ligands known to bind, we have incorporated fpocket cavity detection software and assessed the impact of this inclusion on BSC performance. Using fpocket, we generated a database of ligand-independent potential binding sites and applied the BSC tool, SiteHopper, to analyze similarity relationships between protein binding sites. We developed a method for clustering potential binding sites using a curated dataset of structures for six therapeutically relevant proteins from diverse protein classes in the protein data bank. Two clustering methods were explored; hierarchical clustering and a density-based method adept at excluding noise and outliers from a dataset. We introduce circular plots to visualize binding site structure space. From the datasets analyzed in this study, we highlight a structural relationship between binding sites of cationic trypsin and prothrombin, protein targets known to bind structurally similar small molecules, exemplifying the potential utility of objectively and holistically mapping binding site space from the structural proteome.

Conclusions

We present a workflow for the objective mapping of potential protein–ligand binding sites derived from the currently available structural proteome. We show that ligand-independent binding site detection tools can be introduced without excessive penalty on BSC performance. Clustering combined with intuitive visualization tools can be applied to map relationships between the 3D structures of protein binding sites.Graphical abstract

Identification of gefitinib off-targets using a structure-based systems biology approach; their validation with reverse docking and retrospective data mining

Gefitinib, an EGFR tyrosine kinase inhibitor, is used as FDA approved drug in breast cancer and non-small cell lung cancer treatment. However, this drug has certain side effects and complications for which the underlying molecular mechanisms are not well understood. By systems biology based in silico analysis, we identified off-targets of gefitinib that might explain side effects of this drugs. The crystal structure of EGFR-gefitinib complex was used for binding pocket similarity searches on a druggable proteome database (Sc-PDB) by using IsoMIF Finder. The top 128 hits of putative off-targets were validated by reverse docking approach. The results showed that identified off-targets have efficient binding with gefitinib. The identified human specific off-targets were confirmed and further analyzed for their links with biological process and clinical disease pathways using retrospective studies and literature mining, respectively. Noticeably, many of the identified off-targets in this study were reported in previous high-throughput screenings. Interestingly, the present study reveals that gefitinib may have positive effects in reducing brain and bone metastasis, and may be useful in defining novel gefitinib based treatment regime. We propose that a system wide approach could be useful during new drug development and to minimize side effect of the prospective drug.

Identification of protein-ligand binding sites by the level-set variational implicit-solvent approach

Protein–ligand binding is a key biological process at the molecular level. The identification and characterization of small-molecule binding sites on therapeutically relevant proteins have tremendous implications for target evaluation and rational drug design. In this work, we used the recently developed level-set variational implicit-solvent model (VISM) with the Coulomb field approximation (CFA) to locate and characterize potential protein–small-molecule binding sites. We applied our method to a data set of 515 protein–ligand complexes and found that 96.9% of the cocrystallized ligands bind to the VISM-CFA-identified pockets and that 71.8% of the identified pockets are occupied by cocrystallized ligands. For 228 tight-binding protein–ligand complexes (i.e, complexes with experimental pK_d values larger than 6), 99.1% of the cocrystallized ligands are in the VISM-CFA-identified pockets. In addition, it was found that the ligand binding orientations are consistent with the hydrophilic and hydrophobic descriptions provided by VISM. Quantitative characterization of binding pockets with topological and physicochemical parameters was used to assess the “ligandability” of the pockets. The results illustrate the key interactions between ligands and receptors and can be very informative for rational drug design.

Geomfinder: a multi-feature identifier of similar three-dimensional protein patterns: a ligand-independent approach

Background

Since the structure of proteins is more conserved than the sequence, the identification of conserved three-dimensional (3D) patterns among a set of proteins, can be important for protein function prediction, protein clustering, drug discovery and the establishment of evolutionary relationships. Thus, several computational applications to identify, describe and compare 3D patterns (or motifs) have been developed. Often, these tools consider a 3D pattern as that described by the residues surrounding co-crystallized/docked ligands available from X-ray crystal structures or homology models. Nevertheless, many of the protein structures stored in public databases do not provide information about the location and characteristics of ligand binding sites and/or other important 3D patterns such as allosteric sites, enzyme-cofactor interaction motifs, etc. This makes necessary the development of new ligand-independent methods to search and compare 3D patterns in all available protein structures.

Results

Here we introduce Geomfinder, an intuitive, flexible, alignment-free and ligand-independent web server for detailed estimation of similarities between all pairs of 3D patterns detected in any two given protein structures. We used around 1100 protein structures to form pairs of proteins which were assessed with Geomfinder. In these analyses each protein was considered in only one pair (e.g. in a subset of 100 different proteins, 50 pairs of proteins can be defined). Thus: (a) Geomfinder detected identical pairs of 3D patterns in a series of monoamine oxidase-B structures, which corresponded to the effectively similar ligand binding sites at these proteins; (b) we identified structural similarities among pairs of protein structures which are targets of compounds such as acarbose, benzamidine, adenosine triphosphate and pyridoxal phosphate; these similar 3D patterns are not detected using sequence-based methods; (c) the detailed evaluation of three specific cases showed the versatility of Geomfinder, which was able to discriminate between similar and different 3D patterns related to binding sites of common substrates in a range of diverse proteins.

Conclusions

Geomfinder allows detecting similar 3D patterns between any two pair of protein structures, regardless of the divergency among their amino acids sequences. Although the software is not intended for simultaneous multiple comparisons in a large number of proteins, it can be particularly useful in cases such as the structure-based design of multitarget drugs, where a detailed analysis of 3D patterns similarities between a few selected protein targets is essential.

Electronic supplementary material

The online version of this article (doi:10.1186/s13321-016-0131-9) contains supplementary material, which is available to authorized users.

In silico methods to address polypharmacology: current status, applications and future perspectives

Polypharmacology, a new paradigm in drug discovery that focuses on multi-target drugs (MTDs), has potential application for drug repurposing, the process of finding new uses for existing approved drugs, prediction of off-target toxicities and rational design of MTDs. In this scenario, computational strategies have demonstrated great potential in predicting polypharmacology and in facilitating drug repurposing. Here, we provide a comprehensive overview of various computational approaches that enable the prediction and analysis of in vitro and in vivo drug-response phenotypes and outline their potential for drug discovery. We give an outlook on the latest advances in rational design of MTDs and discuss possible future directions of algorithm development in this field.

Considerations of Protein Subpockets in Fragment-Based Drug Design

While the fragment-based drug design approach continues to gain importance, gaps in the tools and methods available in the identification and accurate utilization of protein subpockets have limited the scope. The importance of these features of small molecule-protein recognition is highlighted with several examples. A generalized solution for the identification of subpockets and corresponding chemical fragments remains elusive, but there are numerous advancements in methods that can be used in combination to address subpockets. Finally, additional examples of approaches that consider the relative importance of small-molecule co-dependence of protein conformations are highlighted to emphasize an increased significance of subpockets, especially at protein interfaces.

Extraction of protein binding pockets in close neighborhood of bound ligands makes comparisons simple due to inherent shape similarity

Methods for comparing protein binding sites are frequently validated on data sets of pockets that were obtained simply by extracting the protein area next to the bound ligands. With this strategy, any unoccupied pocket will remain unconsidered. Furthermore, a large amount of ligand-biased intrinsic shape information is predefined, inclining the subsequent comparisons as rather trivial even in data sets that hardly contain redundancies in sequence information. In this study, we present the results of a very simplistic and shape-biased comparison approach, which stress that unrestricted cavity extraction is essential to enable unexpected cross-reactivity predictions among proteins and function annotations of orphan proteins.