High-throughput docking as a source of novel drug leads

Improving protein-ligand docking results using the Semiempirical quantum mechanics: testing on the PDBbind 2016 core set

Molecular docking techniques are routinely employed for predicting ligand binding conformations and affinities in the in silico phase of the drug design and development process. In this study, a reliable semiempirical quantum mechanics (SQM) method, PM7, was employed for geometry optimization of top-ranked poses obtained from two widely used docking programs, AutoDock4 and AutoDock Vina. The PDBbind core set (version 2016), which contains high-quality crystal protein - ligand complexes with their corresponding experimental binding affinities, was used as an initial dataset in this research. It was shown that docking pose optimization improves the accuracy of pose predictions and is very useful for the refinement of docked complexes via removing clashes between ligands and proteins. It was also demonstrated that AutoDock Vina achieves a higher sampling power than AutoDock4 in generating accurate ligand poses (RMSD ≤ 2.0 Å), while AutoDock4 exhibits a better ranking power than AutoDock Vina. Finally, a new protocol based on a combination of the results obtained from the two docking programs was proposed for structure-based virtual screening studies, which benefits from the robust sampling abilities of AutoDock Vina and the reliable ranking performance of AutoDock4.Communicated by Ramaswamy H. Sarma.

Cordyceps militaris: A Comprehensive Study on Laboratory Cultivation and Anticancer Potential in Dalton's Ascites Lymphoma Tumor Model

BACKGROUND

Cancer, a predominant cause of mortality, poses a formidable challenge in our pursuit of elevating life expectancy. Throughout history, individuals have sought natural remedies with minimal side effects as an appealing substitute for chemotherapeutic drugs. One such remedy is Cordyceps militaris, a renowned medicinal mushroom deeply entrenched in Asian ethnomedicine. Revered for its rejuvenating and curative attributes, it relied upon for ages.

OBJECTIVE

The mushroom's soaring demand outpaced natural availability, necessitating controlled laboratory cultivation as the core focus and exploring the potential of methanolic extracts from harvested Cordyceps militaris fruiting bodies against Dalton's Lymphoma Ascites (DLA) cells in vitro, with a specific emphasis on its anticancer traits.

METHODS

For cultivation, we employed a diverse range of rice substrates, among which bora rice showed promising growth of C. militaris fruiting bodies. To assess DLA cell cytotoxicity, several assays, including trypan blue exclusion assay, MTT assay, and LDH assay, were employed at different time points (24-96 h), which provided valuable insights on DLA cell viability and proliferation, shedding light on its therapeutic potential against cancer.

RESULTS

Our studies unveiled that methanolic extract prompts apoptosis in DLA cells via AO/EB dual staining, manifesting consistent apoptosis indicators such as membrane blebbing, chromatin condensation, nuclei fragmentation, and cellular shrinkage at 48-96 h of treatment. Furthermore, these striking repercussions of apoptosis were comprehended by an in silico approach having molecular docking simulation against antiapoptotic proteins like BCL-2, BCL-XL, MCL-1, BFL-1 & HSP100.

CONCLUSION

Methanolic C. militaris extracts exhibited cytotoxicity and apoptotic alterations in DLA cells.

Kawsar SM. Marine-Derived Compounds as Potential Inhibitors of Hsp90 for Anticancer and Antimicrobial Drug Development: A Comprehensive In Silico Study

Marine compounds constitute a diverse and invaluable resource for the discovery of bioactive substances with promising applications in the pharmaceutical development of anti-inflammatory and antibacterial agents. In this study, a comprehensive methodology was employed, encompassing pharmacophore modeling, virtual screening, in silico ADMET assessment (encompassing aspects of absorption, distribution, metabolism, excretion, and toxicity), and molecular dynamics simulations. These methods were applied to identify new inhibitors targeting the Hsp90 protein (heat shock protein 90), commencing with a diverse assembly of compounds sourced from marine origins. During the virtual screening phase, an extensive exploration was conducted on a dataset comprising 31,488 compounds sourced from the CMNPD database, characterized by a wide array of molecular structures. The principal objective was the development of structure-based pharmacophore models, a valuable approach when the pool of known ligands is limited. The pharmacophore model DDRRR was successfully constructed within the active sites of the Hsp90 crystal structure. Subsequent docking studies led to the identification of six compounds (CMNPD 22591, 9335, 10015, 360799, 15115, and 20988) demonstrating substantial binding affinities, each with values below -8.3 kcal/mol. In the realm of in silico ADMET predictions, five of these compounds exhibited favorable pharmacokinetic properties. Furthermore, molecular dynamics simulations and total binding energy calculations using MM-PBSA indicated that these marine-derived compounds formed exceptionally stable complexes with the Hsp90 receptor over a 100-nanosecond simulation period. These findings underscore the considerable potential of these novel marine compounds as promising candidates for anticancer and antimicrobial drug development.

The Impact of Supervised Learning Methods in Ultralarge High-Throughput Docking

Structure-based virtual screening methods are, nowadays, one of the key pillars of computational drug discovery. In recent years, a series of studies have reported docking-based virtual screening campaigns of large databases ranging from hundreds to thousands of millions compounds, further identifying novel hits after experimental validation. As these larg-scale efforts are not generally accessible, machine learning-based protocols have emerged to accelerate the identification of virtual hits within an ultralarge chemical space, reaching impressive reductions in computational time. Herein, we illustrate the motivation and the problem behind the screening of large databases, providing an overview of key concepts and essential applications of machine learning-accelerated protocols, specifically concerning supervised learning methods. We also discuss where the field stands with these novel developments, highlighting possible insights for future studies.

Identification of Lignan Compounds as New 6-Phosphogluconate Dehydrogenase Inhibitors for Lung Cancer

Targeting pentose phosphate pathway (PPP) enzymes has emerged as a promising strategy to combat cancer. 6-Phosphogluconate dehydrogenase (6-PGD), the third critical enzyme of the PPP, catalyzes oxidative decarboxylation of 6-phosphogluconate (6-PG) to produce ribulose-5-phosphate (Ru-5-P) and CO2. Overexpression of 6-PGD has been reported in multiple cancers and is recognized as a potential anticancer drug target. The current study is focused on the utilization of indispensable virtual screening tools for structure-based drug discovery. During the study, 17,000 natural compounds were screened against the 3-phosphoglycerate (3-PG) binding site of 6-PGD through a molecular operating environment (MOE), which revealed 115 inhibitors with higher selectivity and binding affinity. Out of the 115 best-fit compounds within the 6-PGD binding cavity, 15 compounds were selected and optimized through stringent in silico ADMET assessment models that justified the desirable pharmacokinetic, pharmacodynamic and physicochemical profiles of 5 ligands. Further protein−ligand stability assessment through molecular dynamics (MD) simulation illustrated three potential hits, secoisolariciresinol, syringaresinol and cleomiscosin A, with stable confirmation. Moreover, 6-PGD inhibitor validation was performed by an in vitro enzymatic assay using human erythrocytes purified 6-PGD protein and A549 cell lysate protein. The results of the in vitro assays supported the in silico findings. In order to gain insight into the anticancer activity of the aforementioned compounds, they were subjected to CLC-Pred, an in silico cytotoxicity browsing tool, which proved their anticancer activity against several cancer cell lines at Pa > 0.5. Additionally, a confirmation for in silico cytotoxicity was made by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay for commercially available hits syringaresinol and cleomiscosin A against lung cancer (A549) cells. The results demonstrated that syringaresinol has an IC50 value of 36.9 μg/mL, while cleomiscosin A has an IC50 value of 133 μg/mL. After MTT, flow cytometry analysis confirmed that compounds induced apoptosis in A549 cells in a dose-dependent manner. This study suggested that the respective lignan compounds can serve as lead candidates for lung cancer therapy via 6-PGD inhibition. Furthermore, in vivo experiments need to be conducted to confirm their efficacy.

Biologically relevant and energetically significant cooperative ternary (π–π)2/(π–π)1/(π–π)2 assemblies and fascinating discrete (H2O)21 clusters in isostructural 2,5-pyridine dicarboxylato Co(ii) and Zn(ii) phenanthroline compounds: antiproliferative evaluation and theoretical studies

Cytotoxicity in cancer cells with structure activity relationship has been explored in isostructural Co(ii) and Zn(ii) compounds involving energetically significant cooperative (π–π)₂/(π–π)₁/(π–π)₂ assemblies and fascinating (H₂O)₂₁ clusters.

Augmenting Hit Identification by Virtual Screening Techniques in Small Molecule Drug Discovery

Two orthogonal approaches for hit identification in drug discovery are large-scale in vitro and in silico screening. In recent years, due to the emergence of new targets and a rapid increase in the size of the readily synthesizable chemical space, there is a growing emphasis on the integration of the two techniques to improve the hit finding efficiency. Here, we highlight three examples of drug discovery projects at Merck & Co., Inc., Kenilworth, NJ, USA in which different virtual screening (VS) techniques, each specifically tailored to leverage knowledge available for the target, were utilized to augment the selection of high-quality chemical matter for in vitro assays and to enhance the diversity and tractability of hits. Central to success is a fully integrated workflow combining in silico and experimental expertise at every stage of the hit identification process. We advocate that workflows encompassing VS as part of an integrated hit finding plan should be widely adopted to accelerate hit identification and foster cross-functional collaborations in modern drug discovery.

Oxalato bridged coordination polymer of manganese(iii) involving unconventional O⋯π-hole(nitrile) and antiparallel nitrile⋯nitrile contacts: antiproliferative evaluation and theoretical studies

Unconventional O⋯π-hole(nitrile) and antiparallel nitrile⋯nitrile contacts have been theoretically investigated for a Mn(iii) coordination polymer considering cytotoxicity, apoptosis, ROS generation, molecular docking and pharmacophore features.

Adipato bridged novel hexanuclear Cu(ii) and polymeric Co(ii) coordination compounds involving cooperative supramolecular assemblies and encapsulated guest water clusters in a square grid host: antiproliferative evaluation and theoretical studies

Novel adipato bridged Cu(ii) and Co(ii) complexes synthesized by considering cytotoxicity, apoptosis, ROS generation, molecular docking and pharmacophore features.

Rational Drug Design of Antineoplastic Agents Using 3D-QSAR, Cheminformatic, and Virtual Screening Approaches

Background:

Computer-Aided Drug Design has strongly accelerated the development of novel antineoplastic agents by helping in the hit identification, optimization, and evaluation.

Results:

Computational approaches such as cheminformatic search, virtual screening, pharmacophore modeling, molecular docking and dynamics have been developed and applied to explain the activity of bioactive molecules, design novel agents, increase the success rate of drug research, and decrease the total costs of drug discovery. Similarity, searches and virtual screening are used to identify molecules with an increased probability to interact with drug targets of interest, while the other computational approaches are applied for the design and evaluation of molecules with enhanced activity and improved safety profile.

Conclusion:

In this review are described the main in silico techniques used in rational drug design of antineoplastic agents and presented optimal combinations of computational methods for design of more efficient antineoplastic drugs.

Computer-aided identification of protein targets of four polyphenols in Alzheimer's disease (AD) and validation in a mouse AD model

Natural polyphenols are a large class of phytochemicals with neuroprotective effects. Four polyphenolic compounds: hesperidin, icariin, dihydromyricetin and baicalin were selected to evaluate their effects on Alzheimer’s disease (AD). We analyzed by an inverse docking procedure (INVDOCK) the potential protein targets of these polyphenols within the KEGG AD pathway. Consequently, their therapeutic effects were evaluated and compared in a transgenic APP/PS1 mouse model of AD. These polyphenols were docked to several targets, including APP, BACE, PSEN, IDE, CASP, calpain and TNF-α, suggesting potential in vivo activities. Five month old transgenic mice were treated with these polyphenols. Icariin and hesperidin restored behavioral deficits and ameliorated Aβ deposits in both the cortex and hippocampus while baicalin and dihydromyricetin showed no substantial effects. Our findings suggest that hesperidin and icariin could be considered potential therapeutic candidates of human AD.

Integration of target discovery, drug discovery and drug delivery: A review on computational strategies

Most of the computational tools involved in drug discovery developed during the 1980s were largely based on computational chemistry, quantitative structure-activity relationship (QSAR) and cheminformatics. Subsequently, the advent of genomics in the 2000s gave rise to a huge number of databases and computational tools developed to analyze large quantities of data, through bioinformatics, to obtain valuable information about the genomic regulation of different organisms. Target identification and validation is a long process during which evidence for and against a target is accumulated in the pursuit of developing new drugs. Finally, the drug delivery system appears as a novel approach to improve drug targeting and releasing into the cells, leading to new opportunities to improve drug efficiency and avoid potential secondary effects. In each area: target discovery, drug discovery and drug delivery, different computational strategies are being developed to accelerate the process of selection and discovery of new tools to be applied to different scientific fields. Research on these three topics is growing rapidly, but still requires a global view of this landscape to detect the most challenging bottleneck and how computational tools could be integrated in each topic. This review describes the current state of the art in computational strategies for target discovery, drug discovery and drug delivery and how these fields could be integrated. Finally, we will discuss about the current needs in these fields and how the continuous development of databases and computational tools will impact on the improvement of those areas. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Therapeutic Approaches and Drug Discovery > Nanomedicine for Infectious Disease Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.

Inverse Molecular Docking as a Novel Approach to Study Anticarcinogenic and Anti-Neuroinflammatory Effects of Curcumin

Research efforts are placing an ever increasing emphasis on identifying signal transduction pathways related to the chemopreventive activity of curcumin. Its anticarcinogenic effects are presumably mediated by the regulation of signaling cascades, including nuclear factor κB (NF-κB), activator protein 1 (AP-1), and mitogen-activated protein kinases (MAPK). By modulating signal transduction pathways, curcumin induces apoptosis in malignant cells, thus inhibiting cancer development and progression. Due to the lack of mechanistic insight in the scientific literature, we developed a novel inverse molecular docking protocol based on the CANDOCK algorithm. For the first time, we performed inverse molecular docking of curcumin into a collection of 13,553 available human protein structures from the Protein Data Bank resulting in prioritized target proteins of curcumin. Our predictions were in agreement with the scientific literature and confirmed that curcumin binds to folate receptor β, DNA (cytosine-5)-methyltransferase 3A, metalloproteinase-2, mitogen-activated protein kinase 9, epidermal growth factor receptor and apoptosis-inducing factor 1. We also identified new potential protein targets of curcumin, namely deoxycytidine kinase, NAD-dependent protein deacetylase sirtuin-1 and -2, ecto-5'-nucleotidase, core histone macro-H2A.1, tyrosine-protein phosphatase non-receptor type 11, macrophage colony-stimulating factor 1 receptor, GTPase HRas, aflatoxin B1 aldehyde reductase member 3, aldo-keto reductase family 1 member C3, amiloride-sensitive amine oxidase, death-associated protein kinase 2 and tryptophan-tRNA ligase, that may all play a crucial role in its observed anticancer effects. Moreover, our inverse docking results showed that curcumin potentially binds also to the proteins cAMP-specific 3',5'-cyclic phosphodiesterase 4D and 17-β-hydroxysteroid dehydrogenase type 10, which provides a new explanation for its efficiency in the treatment of Alzheimer's disease. We firmly believe that our computational results will complement and direct future experimental studies on curcumin's anticancer activity as well as on its therapeutic effects against Alzheimer's disease.

Integration of Lead Discovery Tactics and the Evolution of the Lead Discovery Toolbox

There has been much debate around the success rates of various screening strategies to identify starting points for drug discovery. Although high-throughput target-based and phenotypic screening has been the focus of this debate, techniques such as fragment screening, virtual screening, and DNA-encoded library screening are also increasingly reported as a source of new chemical equity. Here, we provide examples in which integration of more than one screening approach has improved the campaign outcome and discuss how strengths and weaknesses of various methods can be used to build a complementary toolbox of approaches, giving researchers the greatest probability of successfully identifying leads. Among others, we highlight case studies for receptor-interacting serine/threonine-protein kinase 1 and the bromo- and extra-terminal domain family of bromodomains. In each example, the unique insight or chemistries individual approaches provided are described, emphasizing the synergy of information obtained from the various tactics employed and the particular question each tactic was employed to answer. We conclude with a short prospective discussing how screening strategies are evolving, what this screening toolbox might look like in the future, how to maximize success through integration of multiple tactics, and scenarios that drive selection of one combination of tactics over another.

Structure-activity relationships and docking studies of synthetic 2-arylindole derivatives determined with aromatase and quinone reductase 1

In our ongoing effort of discovering anticancer and chemopreventive agents, a series of 2-arylindole derivatives were synthesized and evaluated toward aromatase and quinone reductase 1 (QR1). Biological evaluation revealed that several compounds (e.g., 2d, IC50 = 1.61 μM; 21, IC50 = 3.05 μM; and 27, IC50 = 3.34 μM) showed aromatase inhibitory activity with half maximal inhibitory concentration (IC50) values in the low micromolar concentrations. With regard to the QR1 induction activity, 11 exhibited the highest QR1 induction ratio (IR) with a low concentration to double activity (CD) value (IR = 8.34, CD = 2.75 μM), while 7 showed the most potent CD value of 1.12 μM. A dual acting compound 24 showed aromatase inhibition (IC50 = 9.00 μM) as well as QR1 induction (CD = 5.76 μM) activities. Computational docking studies using CDOCKER (Discovery Studio 3.5) provided insight in regard to the potential binding modes of 2-arylindoles within the aromatase active site. Predominantly, the 2-arylindoles preferred binding with the 2-aryl group toward a small hydrophobic pocket within the active site. The C-5 electron withdrawing group on indole was predicted to have an important role and formed a hydrogen bond with Ser478 (OH). Alternatively, meta-pyridyl analogs may orient with the pyridyl 3'-nitrogen coordinating with the heme group.

Screening Explorer-An Interactive Tool for the Analysis of Screening Results

Screening Explorer is a web-based application that allows for an intuitive evaluation of the results of screening experiments using complementary metrics in the field. The usual evaluation of screening results implies the separate generation and apprehension of the ROC, predictiveness, and enrichment curves and their global metrics. Similarly, partial metrics need to be calculated repeatedly for different fractions of a data set and there exists no handy tool that allows reading partial metrics simultaneously on different charts. For a deeper understanding of the results of screening experiments, we rendered their analysis straightforward by linking these metrics interactively in an interactive usable web-based application. We also implemented simple consensus scoring methods based on scores normalization, standardization (z-scores), and compounds ranking to evaluate the enrichments that can be expected through methods combination. Two demonstration data sets allow the users to easily apprehend the functions of this tool that can be applied to the analysis of virtual and experimental screening results. Screening Explorer is freely accessible at http://stats.drugdesign.fr .

Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies

HIGHLIGHTS

Many CNS targets are being explored for multi-target drug design

New databases and cheminformatic methods enable prediction of primary pharmaceutical target and off-targets of compounds

QSAR, virtual screening and docking methods increase the potential of rational drug design

The diverse cerebral mechanisms implicated in Central Nervous System (CNS) diseases together with the heterogeneous and overlapping nature of phenotypes indicated that multitarget strategies may be appropriate for the improved treatment of complex brain diseases. Understanding how the neurotransmitter systems interact is also important in optimizing therapeutic strategies. Pharmacological intervention on one target will often influence another one, such as the well-established serotonin-dopamine interaction or the dopamine-glutamate interaction. It is now accepted that drug action can involve plural targets and that polypharmacological interaction with multiple targets, to address disease in more subtle and effective ways, is a key concept for development of novel drug candidates against complex CNS diseases. A multi-target therapeutic strategy for Alzheimer‘s disease resulted in the development of very effective Multi-Target Designed Ligands (MTDL) that act on both the cholinergic and monoaminergic systems, and also retard the progression of neurodegeneration by inhibiting amyloid aggregation. Many compounds already in databases have been investigated as ligands for multiple targets in drug-discovery programs. A probabilistic method, the Parzen-Rosenblatt Window approach, was used to build a “predictor” model using data collected from the ChEMBL database. The model can be used to predict both the primary pharmaceutical target and off-targets of a compound based on its structure. Several multi-target ligands were selected for further study, as compounds with possible additional beneficial pharmacological activities. Based on all these findings, it is concluded that multipotent ligands targeting AChE/MAO-A/MAO-B and also D₁-R/D₂-R/5-HT_2A-R/H₃-R are promising novel drug candidates with improved efficacy and beneficial neuroleptic and procognitive activities in treatment of Alzheimer's and related neurodegenerative diseases. Structural information for drug targets permits docking and virtual screening and exploration of the molecular determinants of binding, hence facilitating the design of multi-targeted drugs. The crystal structures and models of enzymes of the monoaminergic and cholinergic systems have been used to investigate the structural origins of target selectivity and to identify molecular determinants, in order to design MTDLs.

Potential Broad Spectrum Inhibitors of the Coronavirus 3CLpro: A Virtual Screening and Structure-Based Drug Design Study

Human coronaviruses represent a significant disease burden; however, there is currently no antiviral strategy to combat infection. The outbreak of severe acute respiratory syndrome (SARS) in 2003 and Middle East respiratory syndrome (MERS) less than 10 years later demonstrates the potential of coronaviruses to cross species boundaries and further highlights the importance of identifying novel lead compounds with broad spectrum activity. The coronavirus 3CL(pro) provides a highly validated drug target and as there is a high degree of sequence homology and conservation in main chain architecture the design of broad spectrum inhibitors is viable. The ZINC drugs-now library was screened in a consensus high-throughput pharmacophore modeling and molecular docking approach by Vina, Glide, GOLD and MM-GBSA. Molecular dynamics further confirmed results obtained from structure-based techniques. A highly defined hit-list of 19 compounds was identified by the structure-based drug design methodologies. As these compounds were extensively validated by a consensus approach and by molecular dynamics, the likelihood that at least one of these compounds is bioactive is excellent. Additionally, the compounds segregate into 15 significantly dissimilar (p < 0.05) clusters based on shape and features, which represent valuable scaffolds that can be used as a basis for future anti-coronaviral inhibitor discovery experiments. Importantly though, the enriched subset of 19 compounds identified from the larger library has to be validated experimentally.

Predictiveness curves in virtual screening

Background

In the present work, we aim to transfer to the field of virtual screening the predictiveness curve, a metric that has been advocated in clinical epidemiology. The literature describes the use of predictiveness curves to evaluate the performances of biological markers to formulate diagnoses, prognoses and assess disease risks, assess the fit of risk models, and estimate the clinical utility of a model when applied to a population. Similarly, we use logistic regression models to calculate activity probabilities related to the scores that the compounds obtained in virtual screening experiments. The predictiveness curve can provide an intuitive and graphical tool to compare the predictive power of virtual screening methods.

Results

Similarly to ROC curves, predictiveness curves are functions of the distribution of the scores and provide a common scale for the evaluation of virtual screening methods. Contrarily to ROC curves, the dispersion of the scores is well described by predictiveness curves. This property allows the quantification of the predictive performance of virtual screening methods on a fraction of a given molecular dataset and makes the predictiveness curve an efficient tool to address the early recognition problem. To this last end, we introduce the use of the total gain and partial total gain to quantify recognition and early recognition of active compounds attributed to the variations of the scores obtained with virtual screening methods. Additionally to its usefulness in the evaluation of virtual screening methods, predictiveness curves can be used to define optimal score thresholds for the selection of compounds to be tested experimentally in a drug discovery program. We illustrate the use of predictiveness curves as a complement to ROC on the results of a virtual screening of the Directory of Useful Decoys datasets using three different methods (Surflex-dock, ICM, Autodock Vina).

Conclusion

The predictiveness curves cover different aspects of the predictive power of the scores, allowing a detailed evaluation of the performance of virtual screening methods. We believe predictiveness curves efficiently complete the set of tools available for the analysis of virtual screening results.

Electronic supplementary material

The online version of this article (doi:10.1186/s13321-015-0100-8) contains supplementary material, which is available to authorized users.

Optimal ligand descriptor for pocket recognition based on the Beta-shape

Structure-based virtual screening is one of the most important and common computational methods for the identification of predicted hit at the beginning of drug discovery. Pocket recognition and definition is frequently a prerequisite of structure-based virtual screening, reducing the search space of the predicted protein-ligand complex. In this paper, we present an optimal ligand shape descriptor for a pocket recognition algorithm based on the beta-shape, which is a derivative structure of the Voronoi diagram of atoms. We investigate six candidates for a shape descriptor for a ligand using statistical analysis: the minimum enclosing sphere, three measures from the principal component analysis of atoms, the van der Waals volume, and the beta-shape volume. Among them, the van der Waals volume of a ligand is the optimal shape descriptor for pocket recognition and best tunes the pocket recognition algorithm based on the beta-shape for efficient virtual screening. The performance of the proposed algorithm is verified by a benchmark test.

Practical Considerations in Virtual Screening and Molecular Docking

Molecular docking has become an important common component of the drug discovery toolbox, and its relative low-cost implications and perceived simplicity of use has stimulated an everincreasing popularity within academic communities. The inherent “garbage-in-garbage-out” defect of molecular docking, however, leads a lot of researchers to dedicate countless hours to the identification of hit compounds that later prove to be inactive. Several considerations that can greatly improve the success and enrichment of true bioactive hit compounds are commonly overlooked at the initial stages of a molecular docking study. This chapter will cover several of these considerations, including protonation states, active site waters, separating actives from decoys, consensus docking and molecular mechanics generalized-Born/surface area (MM-GBSA) rescoring, and incorporation of pharmacophoric constraints, in an attempt to clarify what is, in fact, very complicated and inherent difficulties of a structure-based drug design study.

Statistical analysis, optimization, and prioritization of virtual screening parameters for zinc enzymes including the anthrax toxin lethal factor

The anthrax toxin lethal factor (LF) and matrix metalloproteinase-3 (MMP-3, stromelysin-1) are popular zinc metalloenzyme drug targets, with LF primarily responsible for anthrax-related toxicity and host death, while MMP-3 is involved in cancer- and rheumatic disease-related tissue remodeling. A number of in silico screening techniques, most notably docking and scoring, have proven useful for identifying new potential drug scaffolds targeting LF and MMP-3, as well as for optimizing lead compounds and investigating mechanisms of action. However, virtual screening outcomes can vary significantly depending on the specific docking parameters chosen, and systematic statistical significance analyses are needed to prioritize key parameters for screening small molecules against these zinc systems. In the current work, we present a series of chi-square statistical analyses of virtual screening outcomes for cocrystallized LF and MMP-3 inhibitors docked into their respective targets, evaluated by predicted enzyme-inhibitor dissociation constant and root-mean-square deviation (RMSD) between predicted and experimental bound configurations, and we present a series of preferred parameters for use with these systems in the industry-standard Surflex-Dock screening program, for use by researchers utilizing in silico techniques to discover and optimize new scaffolds.

KAISO inhibition: an atomic insight

In today's world, the pursuit of a novel anti-cancer agent remains top priority because of the fact that the global burden of this malady is continuously increasing. Our work is no different from others in searching for new therapeutic solutions. To achieve this, we are looking into Epigenetics, the phenomenon governed by hypermethylation and hypomethylation of tumor suppressor genes and oncogenes, respectively. Our target for this study is an important intermediary methyl-CpG binding protein named kaiso. In our study, we have used the X-ray crystallographic structure of Kaiso for virtual screening and molecular dynamics simulations to study the binding modes of possible inhibitors. The C2H2 domain comprising LYS539 was used for screening the inter bio screen Database having 48,531 natural compounds. Our approach of using computer-aided drug designing methods helped us to remove the execrable compounds and narrowed our focus on a selected few for molecular simulation studies. The top ranked compound (chem. ID 28127) exhibited the highest binding affinity and was also found to be stable throughout the 20 ns timeframe. This compound is therefore a good starting point for developing strong inhibitors.

SEABED: Small molEcule activity scanner weB servicE baseD

Motivation: The SEABED web server integrates a variety of docking and QSAR techniques in a user-friendly environment. SEABED goes beyond the basic docking and QSAR web tools and implements extended functionalities like receptor preparation, library editing, flexible ensemble docking, hybrid docking/QSAR experiments or virtual screening on protein mutants. SEABED is not a monolithic workflow tool but Software as a Service platform.

Availability and implementation: SEABED is a free web server available athttp://www.bsc.es/SEABED. No registration is required.

Contact:ramon.goni@bsc.es

Supplementary information:Supplementary data are available atBioinformatics online.

Biomimetic design of platelet adhesion inhibitors to block integrin α2β1-collagen interactions: II. Inhibitor library, screening, and experimental validation

Platelet adhesion on collagen mediated by integrin α2β1 has been proven important in arterial thrombus formation, leading to an exigent demand on development of potent inhibitors for the integrin α2β1-collagen binding. In the present study, a biomimetic design strategy of platelet adhesion inhibitors was established, based on the affinity binding model of integrin proposed in part I. First, a heptapeptide library containing 8000 candidates was designed to functionally mimic the binding motif of integrin α2β1. Then, each heptapeptide in the library was docked onto a collagen molecule for the assessment of its affinity, followed by a screening based on its structure similarity to the original structure in the affinity binding model. Eight candidates were then selected for further screening by molecular dynamics (MD) simulations. Thereafter, three candidates chosen from MD simulations were separately added into the physiological saline containing separated integrin and collagen, to check their abilities for blocking the integrin-collagen interaction using MD simulations. Of these three candidates, significant inhibition was observed in the presence of LWWNSYY. Finally, the binding affinity of LWWNSYY for collagen was demonstrated by isothermal titration calorimetry. Moreover, significant inhibition of platelet adhesion in the presence of LWWNSYY has been experimentally validated. This work has thus developed an effective strategy for the biomimetic design of peptide-based platelet adhesion inhibitors.

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

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

The impact of introducing a histidine into an apolar cavity site on docking and ligand recognition

Simplified model binding sites allow one to isolate entangled terms in molecular energy functions. Here, we investigate the effects on ligand recognition of the introduction of a histidine into a hydrophobic cavity in lysozyme. We docked 656040 molecules and tested 26 highly and nine poorly ranked. Twenty-one highly ranked molecules bound and five were false positives, while three poorly ranked molecules were false negatives. In the 16 X-ray complexes now known, the docking predictions overlaid well with the crystallographic results. Although ligand enrichment was high, the false negatives, the false positives, and the inability to rank order illuminated weaknesses in our scoring, particularly overweighed apolar and underweighted polar terms. Adjusting these led to new problems, reflecting the entangled nature of docking scoring functions. Changes in ligand affinity relative to other lysozyme cavities speak to the subtleties of molecular recognition even in these simple sites and to their relevance for testing different models of recognition.

Multiple structures for virtual ligand screening: defining binding site properties-based criteria to optimize the selection of the query

Structure based virtual ligand screening (SBVLS) methods are widely used in drug discovery programs. When several structures of the target are available, protocols based either on single structure docking or on ensemble docking can be used. The performance of the methods depends on the structure(s) used as a reference, whose choice requires retrospective enrichment studies on benchmarking databases which consume additional resources. In the present study, we have identified several trends in the properties of the binding sites of the structures that led to the optimal performance in retrospective SBVLS tests whatever the docking program used (Surflex-dock or ICM). By assessing their hydrophobicity and comparing their volume and opening, we show that the selection of optimal structures should be possible with no requirement of prior retrospective enrichment studies. If the mean binding site volume is lower than 350 A(3), the structure with the smaller volume should be preferred. In the other cases, the structure with the largest binding site should be preferred. These optimal structures may be either selected for a single structure docking strategy or an ensemble docking strategy. When constructing an ensemble, the opening of the site might be an interesting criterion additionaly to its volume as the most closed structures should not be preferred in the large systems. These "binding site properties-based" guidelines could be helpful to optimize future prospective drug discovery protocols when several structures of the target are available.

MM-GB/SA rescoring of docking poses

The critical issues in docking include the prediction of the correct binding pose and the accurate estimation of the corresponding binding affinity. Different docking methodologies have all been successful in reproducing the crystallographic binding modes, but struggle when predicting the corresponding binding affinities. The rescoring of docking poses using the MM-GB/SA technique has emerged as an important computational approach in structure-based lead optimization as it provides for congeneric molecules, clearly superior correlations with experimental data to those obtained with typical docking scoring functions. Although the technique has been collectively referred as MM-GB/SA, there are in fact many flavors in the literature. Here we describe the details of our MM-GB/SA scoring protocol, highlighting not only its strengths but also the limitations.

Rapid context-dependent ligand desolvation in molecular docking

In structure-based screens for new ligands, a molecular docking algorithm must rapidly score many molecules in multiple configurations, accounting for both the ligand's interactions with receptor and its competing interactions with solvent. Here we explore a context-dependent ligand desolvation scoring term for molecular docking. We relate the Generalized-Born effective Born radii for every ligand atom to a fractional desolvation and then use this fraction to scale an atom-by-atom decomposition of the full transfer free energy. The fractional desolvation is precomputed on a scoring grid by numerically integrating over the volume of receptor proximal to a ligand atom, weighted by distance. To test this method's performance, we dock ligands versus property-matched decoys over 40 DUD targets. Context-dependent desolvation better enriches ligands compared to both the raw full transfer free energy penalty and compared to ignoring desolvation altogether, though the improvement is modest. More compellingly, the new method improves docking performance across receptor types. Thus, whereas entirely ignoring desolvation works best for charged sites and overpenalizing with full desolvation works well for neutral sites, the physically more correct context-dependent ligand desolvation is competitive across both types of targets. The method also reliably discriminates ligands from highly charged molecules, where ignoring desolvation performs poorly. Since this context-dependent ligand desolvation may be precalculated, it improves docking reliability with minimal cost to calculation time and may be readily incorporated into any physics-based docking program.

Comparative evaluation of 3D virtual ligand screening methods: impact of the molecular alignment on enrichment

In the early stage of drug discovery programs, when the structure of a complex involving a target and a small molecule is available, structure-based virtual ligand screening methods are generally preferred. However, ligand-based strategies like shape-similarity search methods can also be applied. Shape-similarity search methods consist in exploring a pseudo-binding-site derived from the known small molecule used as a reference. Several of these methods use conformational sampling algorithms which are also shared by corresponding docking methods: for example Surflex-dock/Surflex-sim, FlexX/FlexS, ICM, and OMEGA-FRED/OMEGA-ROCS. Using 11 systems issued from the challenging "own" subsets of the Directory of Useful Decoys (DUD-own), we evaluated and compared the performance of the above-cited programs in terms of molecular alignment accuracy, enrichment in active compounds, and enrichment in different chemotypes (scaffold-hopping). Since molecular alignment is a crucial aspect of performance for the different methods, we have assessed its impact on enrichment. We have also illustrated the paradox of retrieving active compounds with good scores even if they are inaccurately positioned. Finally, we have highlighted possible positive aspects of using shape-based approaches in drug-discovery protocols when the structure of the target in complex with a small molecule is known.

An enriched structural kinase database to enable kinome-wide structure-based analyses and drug discovery

The development of a kinase structural database, the kinase knowledge base (KKB), is described. It covers all human kinase domain structures that have been deposited in the Protein Data Bank. All structures are renumbered using a common scheme, which enables efficient cross-comparisons and multiple queries of interest to the kinase field. The common numbering scheme is also used to automatically annotate conserved residues and motifs, and conformationally classify the structures based on the DFG-loop and Helix C. Analyses of residue conservation in the ATP binding site using the full human-kinome-sequence alignment lead to the identification of a conserved hydrogen bond between the hinge region backbone and a glycine in the specificity surface. Furthermore, 90% of kinases are found to have at least one stabilizing interaction for the hinge region, which has not been described before.

Crystal structure of Schistosoma purine nucleoside phosphorylase complexed with a novel monocyclic inhibitor

A novel inhibitor of Schistosoma PNP was identified using an "in silico" approach allied to enzyme inhibition assays. The compound has a monocyclic structure which has not been previously described for PNP inhibitors. The crystallographic structure of the complex was determined and used to elucidate the binding mode within the active site. Furthermore, the predicted pose was very similar to that determined crystallographically, validating the methodology. The compound Sm_VS1, despite its low molecular weight, possesses an IC(50) of 1.3 microM, surprisingly low when compared with purine analogues. This is presumably due to the formation of eight hydrogen bonds with key residues in the active site E203, N245 and T244. The results of this study highlight the importance of the use of multiple conformations for the target during virtual screening. Indeed the Sm_VS1 compound was only identified after flipping the N245 side chain. It is expected that the structure will be of use in the development of new highly active non-purine based compounds against the Schistosoma enzyme.

DG-AMMOS: a new tool to generate 3d conformation of small molecules using distance geometry and automated molecular mechanics optimization for in silico screening

BACKGROUND

Discovery of new bioactive molecules that could enter drug discovery programs or that could serve as chemical probes is a very complex and costly endeavor. Structure-based and ligand-based in silico screening approaches are nowadays extensively used to complement experimental screening approaches in order to increase the effectiveness of the process and facilitating the screening of thousands or millions of small molecules against a biomolecular target. Both in silico screening methods require as input a suitable chemical compound collection and most often the 3D structure of the small molecules has to be generated since compounds are usually delivered in 1D SMILES, CANSMILES or in 2D SDF formats.

RESULTS

Here, we describe the new open source program DG-AMMOS which allows the generation of the 3D conformation of small molecules using Distance Geometry and their energy minimization via Automated Molecular Mechanics Optimization. The program is validated on the Astex dataset, the ChemBridge Diversity database and on a number of small molecules with known crystal structures extracted from the Cambridge Structural Database. A comparison with the free program Balloon and the well-known commercial program Omega generating the 3D of small molecules is carried out. The results show that the new free program DG-AMMOS is a very efficient 3D structure generator engine.

CONCLUSION

DG-AMMOS provides fast, automated and reliable access to the generation of 3D conformation of small molecules and facilitates the preparation of a compound collection prior to high-throughput virtual screening computations. The validation of DG-AMMOS on several different datasets proves that generated structures are generally of equal quality or sometimes better than structures obtained by other tested methods.

Genomes2Drugs: identifies target proteins and lead drugs from proteome data

BACKGROUND

Genome sequencing and bioinformatics have provided the full hypothetical proteome of many pathogenic organisms. Advances in microarray and mass spectrometry have also yielded large output datasets of possible target proteins/genes. However, the challenge remains to identify new targets for drug discovery from this wealth of information. Further analysis includes bioinformatics and/or molecular biology tools to validate the findings. This is time consuming and expensive, and could fail to yield novel drugs if protein purification and crystallography is impossible. To pre-empt this, a researcher may want to rapidly filter the output datasets for proteins that show good homology to proteins that have already been structurally characterised or proteins that are already targets for known drugs. Critically, those researchers developing novel antibiotics need to select out the proteins that show close homology to any human proteins, as future inhibitors are likely to cross-react with the host protein, causing off-target toxicity effects later in clinical trials.

METHODOLOGY/PRINCIPAL FINDINGS

To solve many of these issues, we have developed a free online resource called Genomes2Drugs which ranks sequences to identify proteins that are (i) homologous to previously crystallized proteins or (ii) targets of known drugs, but are (iii) not homologous to human proteins. When tested using the Plasmodium falciparum malarial genome the program correctly enriched the ranked list of proteins with known drug target proteins.

CONCLUSIONS/SIGNIFICANCE

Genomes2Drugs rapidly identifies proteins that are likely to succeed in drug discovery pipelines. This free online resource helps in the identification of potential drug targets. Importantly, the program further highlights proteins that are likely to be inhibited by FDA-approved drugs. These drugs can then be rapidly moved into Phase IV clinical studies under 'change-of-application' patents.

An effective docking strategy for virtual screening based on multi-objective optimization algorithm

Background

Development of a fast and accurate scoring function in virtual screening remains a hot issue in current computer-aided drug research. Different scoring functions focus on diverse aspects of ligand binding, and no single scoring can satisfy the peculiarities of each target system. Therefore, the idea of a consensus score strategy was put forward. Integrating several scoring functions, consensus score re-assesses the docked conformations using a primary scoring function. However, it is not really robust and efficient from the perspective of optimization. Furthermore, to date, the majority of available methods are still based on single objective optimization design.

Results

In this paper, two multi-objective optimization methods, called MOSFOM, were developed for virtual screening, which simultaneously consider both the energy score and the contact score. Results suggest that MOSFOM can effectively enhance enrichment and performance compared with a single score. For three different kinds of binding sites, MOSFOM displays an excellent ability to differentiate active compounds through energy and shape complementarity. EFMOGA performed particularly well in the top 2% of database for all three cases, whereas MOEA_Nrg and MOEA_Cnt performed better than the corresponding individual scoring functions if the appropriate type of binding site was selected.

Conclusion

The multi-objective optimization method was successfully applied in virtual screening with two different scoring functions that can yield reasonable binding poses and can furthermore, be ranked with the potentially compromised conformations of each compound, abandoning those conformations that can not satisfy overall objective functions.