Structure-based systems biology for analyzing off-target binding

Individuality, phenotypic differentiation, dormancy and 'persistence' in culturable bacterial systems: commonalities shared by environmental, laboratory, and clinical microbiology

For bacteria, replication mainly involves growth by binary fission. However, in a very great many natural environments there are examples of phenotypically dormant, non-growing cells that do not replicate immediately and that are phenotypically 'nonculturable' on media that normally admit their growth. They thereby evade detection by conventional culture-based methods. Such dormant cells may also be observed in laboratory cultures and in clinical microbiology. They are usually more tolerant to stresses such as antibiotics, and in clinical microbiology they are typically referred to as 'persisters'. Bacterial cultures necessarily share a great deal of relatedness, and inclusive fitness theory implies that there are conceptual evolutionary advantages in trading a variation in growth rate against its mean, equivalent to hedging one's bets. There is much evidence that bacteria exploit this strategy widely. We here bring together data that show the commonality of these phenomena across environmental, laboratory and clinical microbiology. Considerable evidence, using methods similar to those common in environmental microbiology, now suggests that many supposedly non-communicable, chronic and inflammatory diseases are exacerbated (if not indeed largely caused) by the presence of dormant or persistent bacteria (the ability of whose components to cause inflammation is well known). This dormancy (and resuscitation therefrom) often reflects the extent of the availability of free iron. Together, these phenomena can provide a ready explanation for the continuing inflammation common to such chronic diseases and its correlation with iron dysregulation. This implies that measures designed to assess and to inhibit or remove such organisms (or their access to iron) might be of much therapeutic benefit.

Individuality, phenotypic differentiation, dormancy and 'persistence' in culturable bacterial systems: commonalities shared by environmental, laboratory, and clinical microbiology

For bacteria, replication mainly involves growth by binary fission. However, in a very great many natural environments there are examples of phenotypically dormant, non-growing cells that do not replicate immediately and that are phenotypically 'nonculturable' on media that normally admit their growth. They thereby evade detection by conventional culture-based methods. Such dormant cells may also be observed in laboratory cultures and in clinical microbiology. They are usually more tolerant to stresses such as antibiotics, and in clinical microbiology they are typically referred to as 'persisters'. Bacterial cultures necessarily share a great deal of relatedness, and inclusive fitness theory implies that there are conceptual evolutionary advantages in trading a variation in growth rate against its mean, equivalent to hedging one's bets. There is much evidence that bacteria exploit this strategy widely. We here bring together data that show the commonality of these phenomena across environmental, laboratory and clinical microbiology. Considerable evidence, using methods similar to those common in environmental microbiology, now suggests that many supposedly non-communicable, chronic and inflammatory diseases are exacerbated (if not indeed largely caused) by the presence of dormant or persistent bacteria (the ability of whose components to cause inflammation is well known). This dormancy (and resuscitation therefrom) often reflects the extent of the availability of free iron. Together, these phenomena can provide a ready explanation for the continuing inflammation common to such chronic diseases and its correlation with iron dysregulation. This implies that measures designed to assess and to inhibit or remove such organisms (or their access to iron) might be of much therapeutic benefit.

SInCRe-structural interactome computational resource for Mycobacterium tuberculosis

We have developed an integrated database for Mycobacterium tuberculosis H37Rv (Mtb) that collates information on protein sequences, domain assignments, functional annotation and 3D structural information along with protein–protein and protein–small molecule interactions. SInCRe (Structural Interactome Computational Resource) is developed out of CamBan (Cambridge and Bangalore) collaboration. The motivation for development of this database is to provide an integrated platform to allow easily access and interpretation of data and results obtained by all the groups in CamBan in the field of Mtb informatics. In-house algorithms and databases developed independently by various academic groups in CamBan are used to generate Mtb-specific datasets and are integrated in this database to provide a structural dimension to studies on tuberculosis. The SInCRe database readily provides information on identification of functional domains, genome-scale modelling of structures of Mtb proteins and characterization of the small-molecule binding sites within Mtb. The resource also provides structure-based function annotation, information on small-molecule binders including FDA (Food and Drug Administration)-approved drugs, protein–protein interactions (PPIs) and natural compounds that bind to pathogen proteins potentially and result in weakening or elimination of host–pathogen protein–protein interactions. Together they provide prerequisites for identification of off-target binding.

Database URL:http://proline.biochem.iisc.ernet.in/sincre

Improving protein-ligand binding site prediction accuracy by classification of inner pocket points using local features

BACKGROUND

Protein-ligand binding site prediction from a 3D protein structure plays a pivotal role in rational drug design and can be helpful in drug side-effects prediction or elucidation of protein function. Embedded within the binding site detection problem is the problem of pocket ranking - how to score and sort candidate pockets so that the best scored predictions correspond to true ligand binding sites. Although there exist multiple pocket detection algorithms, they mostly employ a fairly simple ranking function leading to sub-optimal prediction results.

RESULTS

We have developed a new pocket scoring approach (named PRANK) that prioritizes putative pockets according to their probability to bind a ligand. The method first carefully selects pocket points and labels them by physico-chemical characteristics of their local neighborhood. Random Forests classifier is subsequently applied to assign a ligandability score to each of the selected pocket point. The ligandability scores are finally merged into the resulting pocket score to be used for prioritization of the putative pockets. With the used of multiple datasets the experimental results demonstrate that the application of our method as a post-processing step greatly increases the quality of the prediction of Fpocket and ConCavity, two state of the art protein-ligand binding site prediction algorithms.

CONCLUSIONS

The positive experimental results show that our method can be used to improve the success rate, validity and applicability of existing protein-ligand binding site prediction tools. The method was implemented as a stand-alone program that currently contains support for Fpocket and Concavity out of the box, but is easily extendible to support other tools. PRANK is made freely available at http://siret.ms.mff.cuni.cz/prank.

Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently

The amino acid sequence of a protein affects both its structure and its function. Thus, the ability to modify the sequence, and hence the structure and activity, of individual proteins in a systematic way, opens up many opportunities, both scientifically and (as we focus on here) for exploitation in biocatalysis. Modern methods of synthetic biology, whereby increasingly large sequences of DNA can be synthesised de novo, allow an unprecedented ability to engineer proteins with novel functions. However, the number of possible proteins is far too large to test individually, so we need means for navigating the 'search space' of possible protein sequences efficiently and reliably in order to find desirable activities and other properties. Enzymologists distinguish binding (Kd) and catalytic (kcat) steps. In a similar way, judicious strategies have blended design (for binding, specificity and active site modelling) with the more empirical methods of classical directed evolution (DE) for improving kcat (where natural evolution rarely seeks the highest values), especially with regard to residues distant from the active site and where the functional linkages underpinning enzyme dynamics are both unknown and hard to predict. Epistasis (where the 'best' amino acid at one site depends on that or those at others) is a notable feature of directed evolution. The aim of this review is to highlight some of the approaches that are being developed to allow us to use directed evolution to improve enzyme properties, often dramatically. We note that directed evolution differs in a number of ways from natural evolution, including in particular the available mechanisms and the likely selection pressures. Thus, we stress the opportunities afforded by techniques that enable one to map sequence to (structure and) activity in silico, as an effective means of modelling and exploring protein landscapes. Because known landscapes may be assessed and reasoned about as a whole, simultaneously, this offers opportunities for protein improvement not readily available to natural evolution on rapid timescales. Intelligent landscape navigation, informed by sequence-activity relationships and coupled to the emerging methods of synthetic biology, offers scope for the development of novel biocatalysts that are both highly active and robust.

Algorithmic approaches to protein-protein interaction site prediction

Interaction sites on protein surfaces mediate virtually all biological activities, and their identification holds promise for disease treatment and drug design. Novel algorithmic approaches for the prediction of these sites have been produced at a rapid rate, and the field has seen significant advancement over the past decade. However, the most current methods have not yet been reviewed in a systematic and comprehensive fashion. Herein, we describe the intricacies of the biological theory, datasets, and features required for modern protein-protein interaction site (PPIS) prediction, and present an integrative analysis of the state-of-the-art algorithms and their performance. First, the major sources of data used by predictors are reviewed, including training sets, evaluation sets, and methods for their procurement. Then, the features employed and their importance in the biological characterization of PPISs are explored. This is followed by a discussion of the methodologies adopted in contemporary prediction programs, as well as their relative performance on the datasets most recently used for evaluation. In addition, the potential utility that PPIS identification holds for rational drug design, hotspot prediction, and computational molecular docking is described. Finally, an analysis of the most promising areas for future development of the field is presented.

Computational discovery and experimental verification of tyrosine kinase inhibitor pazopanib for the reversal of memory and cognitive deficits in rat model neurodegeneration

Pazopanib, a tyrosine kinase inhibitor marketed for cancer treatment, abrogates the course of neurodegeneration.

Cognition and memory impairment are hallmarks of the pathological cascade of various neurodegenerative disorders. Herein, we developed a novel computational strategy with two-dimensional virtual screening for not only affinity but also specificity. We integrated the two-dimensional virtual screening with ligand screening for 3D shape, electrostatic similarity and local binding site similarity to find existing drugs that may reduce the signs of cognitive deficits. For the first time, we found that pazopanib, a tyrosine kinase inhibitor marketed for cancer treatment, inhibits acetylcholinesterase (AchE) activities at sub-micromolar concentration. We evaluated and compared the effects of intragastrically-administered pazopanib with donepezil, a marketed AchE inhibitor, in cognitive and behavioral assays including the novel object recognition test, Y maze and Morris water maze test. Surprisingly, we found that pazopanib can restore memory loss and cognitive dysfunction to a similar extent as donepezil in a dosage of 15 mg kg^–1, only one fifth of the equivalent clinical dosage for cancer treatment. Furthermore, we demonstrated that pazopanib dramatically enhances the hippocampal Ach levels and increases the expression of the synaptic marker SYP. These findings suggest that pazopanib may become a viable treatment option for memory and cognitive deficits with a good safety profile in humans.

Nonclinical Safety and Toxicology

Nonclinical safety pharmacology and toxicology testing of drug candidates assess the potential adverse effects caused by the drug in relation to its intended use in humans. Hazards related to a drug have to be identified and the potential risks at the intended exposure have to be evaluated in comparison to the potential benefit of the drug. Preclinical safety is thus an integral part of drug discovery and drug development. It still causes significant attrition during drug development.Therefore, there is a need for smart selection of drug candidates in drug discovery including screening of important safety endpoints. In the recent years,there was significant progress in computational and in vitro technology allowing in silico assessment as well as high-throughput screening of some endpoints at very early stages of discovery. Despite all this progress, in vivo evaluation of drug candidates is still an important part to safety testing. The chapter provides an overview on the most important areas of nonclinical safety screening during drug discovery of small molecules.

Human structural proteome-wide characterization of Cyclosporine A targets

MOTIVATION

Off-target interactions of a popular immunosuppressant Cyclosporine A (CSA) with several proteins besides its molecular target, cyclophilin A, are implicated in the activation of signaling pathways that lead to numerous side effects of this drug.

RESULTS

Using structural human proteome and a novel algorithm for inverse ligand binding prediction, ILbind, we determined a comprehensive set of 100+ putative partners of CSA. We empirically show that predictive quality of ILbind is better compared with other available predictors for this compound. We linked the putative target proteins, which include many new partners of CSA, with cellular functions, canonical pathways and toxicities that are typical for patients who take this drug. We used complementary approaches (molecular docking, molecular dynamics, surface plasmon resonance binding analysis and enzymatic assays) to validate and characterize three novel CSA targets: calpain 2, caspase 3 and p38 MAP kinase 14. The three targets are involved in the apoptotic pathways, are interconnected and are implicated in nephrotoxicity.

Achievements and challenges in structural bioinformatics and computational biophysics

Motivation: The field of structural bioinformatics and computational biophysics has undergone a revolution in the last 10 years. Developments that are captured annually through the 3DSIG meeting, upon which this article reflects.

Results: An increase in the accessible data, computational resources and methodology has resulted in an increase in the size and resolution of studied systems and the complexity of the questions amenable to research. Concomitantly, the parameterization and efficiency of the methods have markedly improved along with their cross-validation with other computational and experimental results.

Conclusion: The field exhibits an ever-increasing integration with biochemistry, biophysics and other disciplines. In this article, we discuss recent achievements along with current challenges within the field.

Contact:Rafael.Najmanovich@USherbrooke.ca

Essential gene identification and drug target prioritization in Leishmania species

Leishmaniasis is one of the neglected tropical diseases (NTDs), mainly affecting impoverished communities and having varied ranges of pathogenicity according to the diverse spectrum of clinical manifestations. It is endemic in many countries and poses major challenges to healthcare systems in developing countries. Despite the fact that most of the current mono and combination therapies are found to be failures, clear perception of gene essentiality for parasite survival are now desideratum to identify potential biochemical targets through selection. Here we used the metabolic network of L. major, to perform a comprehensive set of in silico deletion mutants and have systematically recognized a clearly defined set of essential proteins by combining several essential criteria. In this paper we summarize the efforts to prioritize potential drug targets up to a five-fold enrichment compared with a random selection.

Heterogeneity of glycolysis in cancers and therapeutic opportunities

Upregulated glycolysis, both in normoxic and hypoxic environments, is a nearly universal trait of cancer cells. The enormous difference in glucose metabolism offers a target for therapeutic intervention with a potentially low toxicity profile. The past decade has seen a steep rise in the development and clinical assessment of small molecules that target glycolysis. The enzymes in glycolysis have a highly heterogeneous nature that allows for the different bioenergetic, biosynthetic, and signaling demands needed for various tissue functions. In cancers, these properties enable them to respond to the variable requirements of cell survival, proliferation and adaptation to nutrient availability. Heterogeneity in glycolysis occurs through the expression of different isoforms, posttranslational modifications that affect the kinetic and regulatory properties of the enzyme. In this review, we will explore this vast heterogeneity of glycolysis and discuss how this information might be exploited to better target glucose metabolism and offer possibilities for biomarker development.

A curated C. difficile strain 630 metabolic network: prediction of essential targets and inhibitors

BACKGROUND

Clostridium difficile is the leading cause of hospital-borne infections occurring when the natural intestinal flora is depleted following antibiotic treatment. Current treatments for Clostridium difficile infections present high relapse rates and new hyper-virulent and multi-resistant strains are emerging, making the study of this nosocomial pathogen necessary to find novel therapeutic targets.

RESULTS

We present iMLTC806cdf, an extensively curated reconstructed metabolic network for the C. difficile pathogenic strain 630. iMLTC806cdf contains 806 genes, 703 metabolites and 769 metabolic, 117 exchange and 145 transport reactions. iMLTC806cdf is the most complete and accurate metabolic reconstruction of a gram-positive anaerobic bacteria to date. We validate the model with simulated growth assays in different media and carbon sources and use it to predict essential genes. We obtain 89.2% accuracy in the prediction of gene essentiality when compared to experimental data for B. subtilis homologs (the closest organism for which such data exists). We predict the existence of 76 essential genes and 39 essential gene pairs, a number of which are unique to C. difficile and have non-existing or predicted non-essential human homologs. For 29 of these potential therapeutic targets, we find 125 inhibitors of homologous proteins including approved drugs with the potential for drug repositioning, that when validated experimentally could serve as starting points in the development of new antibiotics.

CONCLUSIONS

We created a highly curated metabolic network model of C. difficile strain 630 and used it to predict essential genes as potential new therapeutic targets in the fight against Clostridium difficile infections.

Structural bioinformatics of the interactome

The past decade has seen a dramatic expansion in the number and range of techniques available to obtain genome-wide information and to analyze this information so as to infer both the functions of individual molecules and how they interact to modulate the behavior of biological systems. Here, we review these techniques, focusing on the construction of physical protein-protein interaction networks, and highlighting approaches that incorporate protein structure, which is becoming an increasingly important component of systems-level computational techniques. We also discuss how network analyses are being applied to enhance our basic understanding of biological systems and their disregulation, as well as how these networks are being used in drug development.

Structural bioinformatics-based identification of EGFR inhibitor gefitinib as a putative lead compound for BACE

β-secretase (BACE-1) is a potential target for the treatment of Alzheimer's disease (AD). Despite its potential, only few compounds targeting BACE have entered the clinical trials. Herein, we describe the identification of Gefitinib as a potential lead compound for BACE through an integrated approach of structural bioinformatics analysis, experimental assessment and computational analysis. In particular, we performed ELISA and western analysis to assess the effect of Gefitinib using N2a human APP695 cells. In addition, we investigated the binding mechanism of Gefitinib with BACE through molecular docking coupled with molecular dynamics simulations. The computational analyses revealed that hydrophobic contact is a major contributing factor to the binding of Gefitinib with BACE. The results obtained in the study have rendered Gefitinib as a putative lead compound for BACE. Further optimization studies are warranted to improve its potency and pharmacological properties against BACE for potential AD treatment.

Towards structural systems pharmacology to study complex diseases and personalized medicine

Genome-Wide Association Studies (GWAS), whole genome sequencing, and high-throughput omics techniques have generated vast amounts of genotypic and molecular phenotypic data. However, these data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery, which continues along a one-drug-one-target-one-disease paradigm. As a partial consequence, both the cost to launch a new drug and the attrition rate are increasing. Systems pharmacology and pharmacogenomics are emerging to exploit the available data and potentially reverse this trend, but, as we argue here, more is needed. To understand the impact of genetic, epigenetic, and environmental factors on drug action, we must study the structural energetics and dynamics of molecular interactions in the context of the whole human genome and interactome. Such an approach requires an integrative modeling framework for drug action that leverages advances in data-driven statistical modeling and mechanism-based multiscale modeling and transforms heterogeneous data from GWAS, high-throughput sequencing, structural genomics, functional genomics, and chemical genomics into unified knowledge. This is not a small task, but, as reviewed here, progress is being made towards the final goal of personalized medicines for the treatment of complex diseases.

Elucidating common structural features of human pathogenic variations using large-scale atomic-resolution protein networks

With the rapid growth of structural genomics, numerous protein crystal structures have become available. However, the parallel increase in knowledge of the functional principles underlying biological processes, and more specifically the underlying molecular mechanisms of disease, has been less dramatic. This notwithstanding, the study of complex cellular networks has made possible the inference of protein functions on a large scale. Here, we combine the scale of network systems biology with the resolution of traditional structural biology to generate a large-scale atomic-resolution interactome-network comprising 3,398 interactions between 2,890 proteins with a well-defined interaction interface and interface residues for each interaction. Within the framework of this atomic-resolution network, we have explored the structural principles underlying variations causing human-inherited disease. We find that in-frame pathogenic variations are enriched at both the interface and in the interacting domain, suggesting that variations not only at interface "hot-spots," but in the entire interacting domain can result in alterations of interactions. Further, the sites of pathogenic variations are closely related to the biophysical strength of the interactions they perturb. Finally, we show that biochemical alterations consequent to these variations are considerably more disruptive than evolutionary changes, with the most significant alterations at the protein interaction interface.

eFindSite: Enhanced Fingerprint-Based Virtual Screening Against Predicted Ligand Binding Sites in Protein Models

A standard practice for lead identification in drug discovery is ligand virtual screening, which utilizes computing technologies to detect small compounds that likely bind to target proteins prior to experimental screens. A high accuracy is often achieved when the target protein has a resolved crystal structure; however, using protein models still renders significant challenges. Towards this goal, we recently developed eFindSite that predicts ligand binding sites using a collection of effective algorithms, including meta-threading, machine learning and reliable confidence estimation systems. Here, we incorporate fingerprint-based virtual screening capabilities in eFindSite in addition to its flagship role as a ligand binding pocket predictor. Virtual screening benchmarks using the enhanced Directory of Useful Decoys demonstrate that eFindSite significantly outperforms AutoDock Vina as assessed by several evaluation metrics. Importantly, this holds true regardless of the quality of target protein structures. As a first genome-wide application of eFindSite, we conduct large-scale virtual screening of the entire proteome of Escherichia coli with encouraging results. In the new approach to fingerprint-based virtual screening using remote protein homology, eFindSite demonstrates its compelling proficiency offering a high ranking accuracy and low susceptibility to target structure deformations. The enhanced version of eFindSite is freely available to the academic community at http://www.brylinski.org/efindsite.

Network pharmacology strategies toward multi-target anticancer therapies: from computational models to experimental design principles

Polypharmacology has emerged as novel means in drug discovery for improving treatment response in clinical use. However, to really capitalize on the polypharmacological effects of drugs, there is a critical need to better model and understand how the complex interactions between drugs and their cellular targets contribute to drug efficacy and possible side effects. Network graphs provide a convenient modeling framework for dealing with the fact that most drugs act on cellular systems through targeting multiple proteins both through on-target and off-target binding. Network pharmacology models aim at addressing questions such as how and where in the disease network should one target to inhibit disease phenotypes, such as cancer growth, ideally leading to therapies that are less vulnerable to drug resistance and side effects by means of attacking the disease network at the systems level through synergistic and synthetic lethal interactions. Since the exponentially increasing number of potential drug target combinations makes pure experimental approach quickly unfeasible, this review depicts a number of computational models and algorithms that can effectively reduce the search space for determining the most promising combinations for experimental evaluation. Such computational-experimental strategies are geared toward realizing the full potential of multi-target treatments in different disease phenotypes. Our specific focus is on system-level network approaches to polypharmacology designs in anticancer drug discovery, where we give representative examples of how network-centric modeling may offer systematic strategies toward better understanding and even predicting the phenotypic responses to multi-target therapies.

Anti-infectious drug repurposing using an integrated chemical genomics and structural systems biology approach

The emergence of multi-drug and extensive drug resistance of microbes to antibiotics poses a great threat to human health. Although drug repurposing is a promising solution for accelerating the drug development process, its application to anti-infectious drug discovery is limited by the scope of existing phenotype-, ligand-, or target-based methods. In this paper we introduce a new computational strategy to determine the genome-wide molecular targets of bioactive compounds in both human and bacterial genomes. Our method is based on the use of a novel algorithm, ligand Enrichment of Network Topological Similarity (ligENTS), to map the chemical universe to its global pharmacological space. ligENTS outperforms the state-of-the-art algorithms in identifying novel drug-target relationships. Furthermore, we integrate ligENTS with our structural systems biology platform to identify drug repurposing opportunities via target similarity profiling. Using this integrated strategy, we have identified novel P. falciparum targets of drug-like active compounds from the Malaria Box, and suggest that a number of approved drugs may be active against malaria. This study demonstrates the potential of an integrative chemical genomics and structural systems biology approach to drug repurposing.

A comprehensive survey of small-molecule binding pockets in proteins

Many biological activities originate from interactions between small-molecule ligands and their protein targets. A detailed structural and physico-chemical characterization of these interactions could significantly deepen our understanding of protein function and facilitate drug design. Here, we present a large-scale study on a non-redundant set of about 20,000 known ligand-binding sites, or pockets, of proteins. We find that the structural space of protein pockets is crowded, likely complete, and may be represented by about 1,000 pocket shapes. Correspondingly, the growth rate of novel pockets deposited in the Protein Data Bank has been decreasing steadily over the recent years. Moreover, many protein pockets are promiscuous and interact with ligands of diverse scaffolds. Conversely, many ligands are promiscuous and interact with structurally different pockets. Through a physico-chemical and structural analysis, we provide insights into understanding both pocket promiscuity and ligand promiscuity. Finally, we discuss the implications of our study for the prediction of protein-ligand interactions based on pocket comparison.

The life of a living cell relies on many distinct proteins to carry out their functions. Most of these functions are rooted in interactions between the proteins and metabolites, small-molecules essential for life. By targeting specific proteins relevant to a disease, drug molecules may provide a cure. A deep understanding of the nature of interactions between proteins and small-molecules (or ligands) through analyzing their structures may help predict protein function or improve drug design. In this contribution, we present a large-scale analysis of a non-redundant set of over 20,000 experimental protein-ligand complex structures available in the current Protein Data Bank. We seek answers to several fundamental questions: How many representative pockets are there that serve as ligand-binding sites in proteins? To what extent can we infer a similar protein-ligand interaction by matching the structures of protein pockets? How different are the ligands found in the same pocket? For a promiscuous protein pocket, how does a pocket maintain favorable interactions with very different ligands? Conversely, how different are those pockets that interact with the same ligand? We find the structural space of protein pocket is small and that both protein promiscuity and ligand promiscuity are very common in Nature.

In silico mechanistic profiling to probe small molecule binding to sulfotransferases

Drug metabolizing enzymes play a key role in the metabolism, elimination and detoxification of xenobiotics, drugs and endogenous molecules. While their principal role is to detoxify organisms by modifying compounds, such as pollutants or drugs, for a rapid excretion, in some cases they render their substrates more toxic thereby inducing severe side effects and adverse drug reactions, or their inhibition can lead to drug–drug interactions. We focus on sulfotransferases (SULTs), a family of phase II metabolizing enzymes, acting on a large number of drugs and hormones and showing important structural flexibility. Here we report a novel in silico structure-based approach to probe ligand binding to SULTs. We explored the flexibility of SULTs by molecular dynamics (MD) simulations in order to identify the most suitable multiple receptor conformations for ligand binding prediction. Then, we employed structure-based docking-scoring approach to predict ligand binding and finally we combined the predicted interaction energies by using a QSAR methodology. The results showed that our protocol successfully prioritizes potent binders for the studied here SULT1 isoforms, and give new insights on specific molecular mechanisms for diverse ligands’ binding related to their binding sites plasticity. Our best QSAR models, introducing predicted protein-ligand interaction energy by using docking, showed accuracy of 67.28%, 78.00% and 75.46%, for the isoforms SULT1A1, SULT1A3 and SULT1E1, respectively. To the best of our knowledge our protocol is the first in silico structure-based approach consisting of a protein-ligand interaction analysis at atomic level that considers both ligand and enzyme flexibility, along with a QSAR approach, to identify small molecules that can interact with II phase dug metabolizing enzymes.

Proteome-scale docking: myth and reality

Docking is the computational method of choice to quickly predict how a low molecular-weight ligand binds to its macromolecular target. Despite persistent problems in predicting binding free energies, docking has undergone significant advances in numerous topics (throughput, target flexibility). The ever increasing availability of high-resolution X-ray structures and the development of more reliable comparative models for proteins of pharmacological interest paved the way to apply protein–ligand docking to multiple targets to predict main and off-targets for bioactive compounds and even to repurpose existing drugs. Applying docking to multiple targets brings an additional level of complexity in scoring numerous and heterogeneous docking poses. Despite undeniable successes, proteomewide docking should, however, be considered with caution with regard to recall and precision of the predictions.

Accidental interaction between PDZ domains and diclofenac revealed by NMR-assisted virtual screening

In silico approaches have become indispensable for drug discovery as well as drug repositioning and adverse effect prediction. We have developed the eF-seek program to predict protein–ligand interactions based on the surface structure of proteins using a clique search algorithm. We have also developed a special protein structure prediction pipeline and accumulated predicted 3D models in the Structural Atlas of the Human Genome (SAHG) database. Using this database, genome-wide prediction of non-peptide ligands for proteins in the human genome was performed, and a subset of predicted interactions including 14 PDZ domains was then confirmed by NMR titration. Surprisingly, diclofenac, a non-steroidal anti-inflammatory drug, was found to be a non-peptide PDZ domain ligand, which bound to 5 of 15 tested PDZ domains. The critical residues for the PDZ–diclofenac interaction were also determined. Pharmacological implications of the accidental PDZ–diclofenac interaction are further discussed.

One hundred thousand mouse clicks down the road: selected online resources supporting drug discovery collected over a decade

Online resources enabling and supporting drug discovery have blossomed during the past ten years. However, drug hunters commonly find themselves overwhelmed by the proliferation of these computer-based resources. Ten years ago, we, the authors of this review, felt that a comprehensive list of in silico resources relating to drug discovery was needed. Especially because the internet provides a wealth of inspiring tools that, if fully exploited, could greatly assist the process. We present here a compilation of online tools and databases collected over the past decade. The tools were essentially found through literature and internet searches and, currently, our list contains over 1500 URLs. We also briefly highlight some recently reported services and comment about ongoing and future efforts in the field.

Insights into an original pocket-ligand pair classification: a promising tool for ligand profile prediction

Pockets are today at the cornerstones of modern drug discovery projects and at the crossroad of several research fields, from structural biology to mathematical modeling. Being able to predict if a small molecule could bind to one or more protein targets or if a protein could bind to some given ligands is very useful for drug discovery endeavors, anticipation of binding to off- and anti-targets. To date, several studies explore such questions from chemogenomic approach to reverse docking methods. Most of these studies have been performed either from the viewpoint of ligands or targets. However it seems valuable to use information from both ligands and target binding pockets. Hence, we present a multivariate approach relating ligand properties with protein pocket properties from the analysis of known ligand-protein interactions. We explored and optimized the pocket-ligand pair space by combining pocket and ligand descriptors using Principal Component Analysis and developed a classification engine on this paired space, revealing five main clusters of pocket-ligand pairs sharing specific and similar structural or physico-chemical properties. These pocket-ligand pair clusters highlight correspondences between pocket and ligand topological and physico-chemical properties and capture relevant information with respect to protein-ligand interactions. Based on these pocket-ligand correspondences, a protocol of prediction of clusters sharing similarity in terms of recognition characteristics is developed for a given pocket-ligand complex and gives high performances. It is then extended to cluster prediction for a given pocket in order to acquire knowledge about its expected ligand profile or to cluster prediction for a given ligand in order to acquire knowledge about its expected pocket profile. This prediction approach shows promising results and could contribute to predict some ligand properties critical for binding to a given pocket, and conversely, some key pocket properties for ligand binding.

Profiling 976 ToxCast chemicals across 331 enzymatic and receptor signaling assays

Understanding potential health risks is a significant challenge due to the large numbers of diverse chemicals with poorly characterized exposures and mechanisms of toxicities. The present study analyzes 976 chemicals (including failed pharmaceuticals, alternative plasticizers, food additives, and pesticides) in Phases I and II of the U.S. EPA's ToxCast project across 331 cell-free enzymatic and ligand-binding high-throughput screening (HTS) assays. Half-maximal activity concentrations (AC50) were identified for 729 chemicals in 256 assays (7,135 chemical-assay pairs). Some of the most commonly affected assays were CYPs (CYP2C9 and CYP2C19), transporters (mitochondrial TSPO, norepinephrine, and dopaminergic), and GPCRs (aminergic). Heavy metals, surfactants, and dithiocarbamate fungicides showed promiscuous but distinctly different patterns of activity, whereas many of the pharmaceutical compounds showed promiscuous activity across GPCRs. Literature analysis confirmed >50% of the activities for the most potent chemical-assay pairs (54) but also revealed 10 missed interactions. Twenty-two chemicals with known estrogenic activity were correctly identified for the majority (77%), missing only the weaker interactions. In many cases, novel findings for previously unreported chemical-target combinations clustered with known chemical-target interactions. Results from this large inventory of chemical-biological interactions can inform read-across methods as well as link potential targets to molecular initiating events in adverse outcome pathways for diverse toxicities.

Profiling 976 ToxCast chemicals across 331 enzymatic and receptor signaling assays

Understanding potential health risks is a significant challenge due to the large numbers of diverse chemicals with poorly characterized exposures and mechanisms of toxicities. The present study analyzes 976 chemicals (including failed pharmaceuticals, alternative plasticizers, food additives, and pesticides) in Phases I and II of the U.S. EPA's ToxCast project across 331 cell-free enzymatic and ligand-binding high-throughput screening (HTS) assays. Half-maximal activity concentrations (AC50) were identified for 729 chemicals in 256 assays (7,135 chemical-assay pairs). Some of the most commonly affected assays were CYPs (CYP2C9 and CYP2C19), transporters (mitochondrial TSPO, norepinephrine, and dopaminergic), and GPCRs (aminergic). Heavy metals, surfactants, and dithiocarbamate fungicides showed promiscuous but distinctly different patterns of activity, whereas many of the pharmaceutical compounds showed promiscuous activity across GPCRs. Literature analysis confirmed >50% of the activities for the most potent chemical-assay pairs (54) but also revealed 10 missed interactions. Twenty-two chemicals with known estrogenic activity were correctly identified for the majority (77%), missing only the weaker interactions. In many cases, novel findings for previously unreported chemical-target combinations clustered with known chemical-target interactions. Results from this large inventory of chemical-biological interactions can inform read-across methods as well as link potential targets to molecular initiating events in adverse outcome pathways for diverse toxicities.

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

Catalytic site identification--a web server to identify catalytic site structural matches throughout PDB

The catalytic site identification web server provides the innovative capability to find structural matches to a user-specified catalytic site among all Protein Data Bank proteins rapidly (in less than a minute). The server also can examine a user-specified protein structure or model to identify structural matches to a library of catalytic sites. Finally, the server provides a database of pre-calculated matches between all Protein Data Bank proteins and the library of catalytic sites. The database has been used to derive a set of hypothesized novel enzymatic function annotations. In all cases, matches and putative binding sites (protein structure and surfaces) can be visualized interactively online. The website can be accessed at http://catsid.llnl.gov.

Finding novel pharmaceuticals in the systems biology era using multiple effective drug targets, phenotypic screening and knowledge of transporters: where drug discovery went wrong and how to fix it

Despite the sequencing of the human genome, the rate of innovative and successful drug discovery in the pharmaceutical industry has continued to decrease. Leaving aside regulatory matters, the fundamental and interlinked intellectual issues proposed to be largely responsible for this are: (a) the move from 'function-first' to 'target-first' methods of screening and drug discovery; (b) the belief that successful drugs should and do interact solely with single, individual targets, despite natural evolution's selection for biochemical networks that are robust to individual parameter changes; (c) an over-reliance on the rule-of-5 to constrain biophysical and chemical properties of drug libraries; (d) the general abandoning of natural products that do not obey the rule-of-5; (e) an incorrect belief that drugs diffuse passively into (and presumably out of) cells across the bilayers portions of membranes, according to their lipophilicity; (f) a widespread failure to recognize the overwhelmingly important role of proteinaceous transporters, as well as their expression profiles, in determining drug distribution in and between different tissues and individual patients; and (g) the general failure to use engineering principles to model biology in parallel with performing 'wet' experiments, such that 'what if?' experiments can be performed in silico to assess the likely success of any strategy. These facts/ideas are illustrated with a reasonably extensive literature review. Success in turning round drug discovery consequently requires: (a) decent systems biology models of human biochemical networks; (b) the use of these (iteratively with experiments) to model how drugs need to interact with multiple targets to have substantive effects on the phenotype; (c) the adoption of polypharmacology and/or cocktails of drugs as a desirable goal in itself; (d) the incorporation of drug transporters into systems biology models, en route to full and multiscale systems biology models that incorporate drug absorption, distribution, metabolism and excretion; (e) a return to 'function-first' or phenotypic screening; and (f) novel methods for inferring modes of action by measuring the properties on system variables at all levels of the 'omes. Such a strategy offers the opportunity of achieving a state where we can hope to predict biological processes and the effect of pharmaceutical agents upon them. Consequently, this should both lower attrition rates and raise the rates of discovery of effective drugs substantially.

Target identification and mechanism of action in chemical biology and drug discovery

Target-identification and mechanism-of-action studies have important roles in small-molecule probe and drug discovery. Biological and technological advances have resulted in the increasing use of cell-based assays to discover new biologically active small molecules. Such studies allow small-molecule action to be tested in a more disease-relevant setting at the outset, but they require follow-up studies to determine the precise protein target or targets responsible for the observed phenotype. Target identification can be approached by direct biochemical methods, genetic interactions or computational inference. In many cases, however, combinations of approaches may be required to fully characterize on-target and off-target effects and to understand mechanisms of small-molecule action.

Are predicted protein structures of any value for binding site prediction and virtual ligand screening?

The recently developed field of ligand homology modeling (LHM) that extends the ideas of protein homology modeling to the prediction of ligand binding sites and for use in virtual ligand screening has emerged as a powerful new approach. Unlike traditional docking methodologies, LHM can be applied to low-to-moderate resolution predicted as well as experimental structures with little if any diminution in performance; thereby enabling ≈ 75% of an average proteome to have potentially significant virtual screening predictions. In large scale benchmarking, LHM is able to predict off-target ligand binding. Thus, despite the widespread belief to the contrary, low-to-moderate resolution predicted structures have considerable utility for biochemical function prediction.

Fast protein binding site comparison via an index-based screening technology

We present TrixP, a new index-based method for fast protein binding site comparison and function prediction. TrixP determines binding site similarities based on the comparison of descriptors that encode pharmacophoric and spatial features. Therefore, it adopts the efficient core components of TrixX, a structure-based virtual screening technology for large compound libraries. TrixP expands this technology by new components in order to allow a screening of protein libraries. TrixP accounts for the inherent flexibility of proteins employing a partial shape matching routine. After the identification of structures with matching pharmacophoric features and geometric shape, TrixP superimposes the binding sites and, finally, assesses their similarity according to the fit of pharmacophoric properties. TrixP is able to find analogies between closely and distantly related binding sites. Recovery rates of 81.8% for similar binding site pairs, assisted by rejecting rates of 99.5% for dissimilar pairs on a test data set containing 1331 pairs, confirm this ability. TrixP exclusively identifies members of the same protein family on top ranking positions out of a library consisting of 9802 binding sites. Furthermore, 30 predicted kinase binding sites can almost perfectly be classified into their known subfamilies.

Finding protein targets for small biologically relevant ligands across fold space using inverse ligand binding predictions

Inverse ligand binding prediction utilizes a few protein-ligand (drug) complexes to predict other secondary therapeutic and off-targets of a given drug molecule on a proteomic scale. We adapt two binding site predictors, FINDSITE and SMAP, to perform the inverse predictions and evaluate them on over 30 representative ligands. Use of just one complex allows the identification of other protein targets; the availability of additional complexes improves the results. Both methods offer comparable quality when using three complexes with diverse proteins. SMAP is better when fewer complexes are available, while FINDSITE provides stronger predictions for smaller ligands. We propose a consensus that combines (and outperforms) the two complementary approaches implemented by FINDSITE and SMAP. Most importantly, we demonstrate that these methods successfully find distant targets that belong to structurally different folds compared to the proteins in the input complexes.

Toward a three-dimensional view of protein networks between species

General principles governing biomolecular interactions between species are expected to differ significantly from known principles governing the interactions within species, yet these principles remain poorly understood at the systems level. A key reason for this knowledge gap is the lack of a detailed three-dimensional (3D), atomistic view of biomolecular interaction networks between species. Recent progress in structural biology, systems biology, and computational biology has enabled accurate and large-scale construction of 3D structural models of nodes and edges for protein–protein interaction networks within and between species. The resulting within- and between-species structural interaction networks have provided new biophysical, functional, and evolutionary insights into species interactions and infectious disease. Here, we review the nascent field of between-species structural systems biology, focusing on interactions between host and pathogens such as viruses.

Clarifying off-target effects for torcetrapib using network pharmacology and reverse docking approach

Background

Torcetrapib, a cholesteryl ester transfer protein (CETP) inhibitor which raises high-density lipoprotein (HDL) cholesterol and reduces low-density lipoprotein (LDL) cholesterol level, has been documented to increase mortality and cardiac events associated with adverse effects. However, it is still unclear the underlying mechanisms of the off-target effects of torcetrapib.

Results

In the present study, we developed a systems biology approach by combining a human reassembled signaling network with the publicly available microarray gene expression data to provide unique insights into the off-target adverse effects for torcetrapib. Cytoscape with three plugins including BisoGenet, NetworkAnalyzer and ClusterONE was utilized to establish a context-specific drug-gene interaction network. The DAVID functional annotation tool was applied for gene ontology (GO) analysis, while pathway enrichment analysis was clustered by ToppFun. Furthermore, potential off-targets of torcetrapib were predicted by a reverse docking approach. In general, 10503 nodes were retrieved from the integrative signaling network and 47660 inter-connected relations were obtained from the BisoGenet plugin. In addition, 388 significantly up-regulated genes were detected by Significance Analysis of Microarray (SAM) in adrenal carcinoma cells treated with torcetrapib. After constructing the human signaling network, the over-expressed microarray genes were mapped to illustrate the context-specific network. Subsequently, three conspicuous gene regulatory networks (GRNs) modules were unearthed, which contributed to the off-target effects of torcetrapib. GO analysis reflected dramatically over-represented biological processes associated with torcetrapib including activation of cell death, apoptosis and regulation of RNA metabolic process. Enriched signaling pathways uncovered that IL-2 Receptor Beta Chain in T cell Activation, Platelet-Derived Growth Factor Receptor (PDGFR) beta signaling pathway, IL2-mediated signaling events, ErbB signaling pathway and signaling events mediated by Hepatocyte Growth Factor Receptor (HGFR, c-Met) might play decisive characters in the adverse cardiovascular effects associated with torcetrapib. Finally, a reverse docking algorithm in silico between torcetrapib and transmembrane receptors was conducted to identify the potential off-targets. This screening was carried out based on the enriched signaling network analysis.

Conclusions

Our study provided unique insights into the biological processes of torcetrapib-associated off-target adverse effects in a systems biology visual angle. In particular, we highlighted the importance of PDGFR, HGFR, IL-2 Receptor and ErbB1tyrosine kinase might be direct off-targets, which were highly related to the unfavorable adverse effects of torcetrapib and worthy of further experimental validation.

The promiscuous binding of pharmaceutical drugs and their transporter-mediated uptake into cells: what we (need to) know and how we can do so

A recent paper in this journal sought to counter evidence for the role of transport proteins in effecting drug uptake into cells, and questions that transporters can recognize drug molecules in addition to their endogenous substrates. However, there is abundant evidence that both drugs and proteins are highly promiscuous. Most proteins bind to many drugs and most drugs bind to multiple proteins (on average more than six), including transporters (mutations in these can determine resistance); most drugs are known to recognise at least one transporter. In this response, we alert readers to the relevant evidence that exists or is required. This needs to be acquired in cells that contain the relevant proteins, and we highlight an experimental system for simultaneous genome-wide assessment of carrier-mediated uptake in a eukaryotic cell (yeast).

Merging systems biology with pharmacodynamics

The emerging discipline of systems pharmacology aims to combine analysis and computational modeling of cellular regulatory networks with quantitative pharmacology approaches to drive the drug discovery processes, predict rare adverse events, and catalyze the practice of personalized precision medicine. Here, we introduce the concept of enhanced pharmacodynamic (ePD) models, which synergistically combine the desirable features of systems biology and current PD models within the framework of ordinary or partial differential equations. ePD models that analyze regulatory networks involved in drug action can account for a drug's multiple targets and for the effects of genomic, epigenomic, and posttranslational changes on the drug efficacy. This new knowledge can drive drug discovery and shape precision medicine.

Investigating drug repositioning opportunities in FDA drug labels through topic modeling

Background

Drug repositioning offers an opportunity to revitalize the slowing drug discovery pipeline by finding new uses for currently existing drugs. Our hypothesis is that drugs sharing similar side effect profiles are likely to be effective for the same disease, and thus repositioning opportunities can be identified by finding drug pairs with similar side effects documented in U.S. Food and Drug Administration (FDA) approved drug labels. The safety information in the drug labels is usually obtained in the clinical trial and augmented with the observations in the post-market use of the drug. Therefore, our drug repositioning approach can take the advantage of more comprehensive safety information comparing with conventional de novo approach.

Method

A probabilistic topic model was constructed based on the terms in the Medical Dictionary for Regulatory Activities (MedDRA) that appeared in the Boxed Warning, Warnings and Precautions, and Adverse Reactions sections of the labels of 870 drugs. Fifty-two unique topics, each containing a set of terms, were identified by using topic modeling. The resulting probabilistic topic associations were used to measure the distance (similarity) between drugs. The success of the proposed model was evaluated by comparing a drug and its nearest neighbor (i.e., a drug pair) for common indications found in the Indications and Usage Section of the drug labels.

Results

Given a drug with more than three indications, the model yielded a 75% recall, meaning 75% of drug pairs shared one or more common indications. This is significantly higher than the 22% recall rate achieved by random selection. Additionally, the recall rate grows rapidly as the number of drug indications increases and reaches 84% for drugs with 11 indications. The analysis also demonstrated that 65 drugs with a Boxed Warning, which indicates significant risk of serious and possibly life-threatening adverse effects, might be replaced with safer alternatives that do not have a Boxed Warning. In addition, we identified two therapeutic groups of drugs (Musculo-skeletal system and Anti-infective for systemic use) where over 80% of the drugs have a potential replacement with high significance.

Conclusion

Topic modeling can be a powerful tool for the identification of repositioning opportunities by examining the adverse event terms in FDA approved drug labels. The proposed framework not only suggests drugs that can be repurposed, but also provides insight into the safety of repositioned drugs.

Targeting the off-targets: a computational bioinformatics approach to understanding the polypharmacology of nelfinavir

In recent years, the identification of new pharmacological effects of already established or abandoned drugs has become a valuable tool for drug repositioning purposes. The HIV drug nelfinavir belongs to those drugs for which empirical data indicate additional pharmacological applications for various diseases, including cancer. To identify and confirm binding partners of nelfinavir other than HIV-1 protease, Xie et al. performed a systematic computational analysis to identify possible structural similarities between the nelfinavir-binding pocket of HIV-1 protease and 5985 protein database entries. Of 126 possible binding partners to nelfinavir, a remarkably high percentage of protein kinases were identified. Further in-depth computational ligand-binding studies indicated the EGF receptor and cytosolic protein kinase B as the most likely off-targets of nelfinavir. Astonishingly, these in silico data are in accordance with previous data obtained by experimental in vitro analysis, indicating a high predictive value of the computer-based approach developed and applied by Xie et al. The computational approach and the authors' results, with respect to their integration in systems biology, are presented and discussed.

A strategy based on protein-protein interface motifs may help in identifying drug off-targets

Networks are increasingly used to study the impact of drugs at the systems level. From the algorithmic standpoint, a drug can "attack" nodes or edges of a protein-protein interaction network. In this work, we propose a new network strategy, "The Interface Attack", based on protein-protein interfaces. Similar interface architectures can occur between unrelated proteins. Consequently, in principle, a drug that binds to one has a certain probability of binding to others. The interface attack strategy simultaneously removes from the network all interactions that consist of similar interface motifs. This strategy is inspired by network pharmacology and allows inferring potential off-targets. We introduce a network model that we call "Protein Interface and Interaction Network (P2IN)", which is the integration of protein-protein interface structures and protein interaction networks. This interface-based network organization clarifies which protein pairs have structurally similar interfaces and which proteins may compete to bind the same surface region. We built the P2IN with the p53 signaling network and performed network robustness analysis. We show that (1) "hitting" frequent interfaces (a set of edges distributed around the network) might be as destructive as eleminating high degree proteins (hub nodes), (2) frequent interfaces are not always topologically critical elements in the network, and (3) interface attack may reveal functional changes in the system better than the attack of single proteins. In the off-target detection case study, we found that drugs blocking the interface between CDK6 and CDKN2D may also affect the interaction between CDK4 and CDKN2D.