Comparative virtual and experimental high-throughput screening for glycogen synthase kinase-3beta inhibitors

GENERA: A Combined Genetic/Deep-Learning Algorithm for Multiobjective Target-Oriented De Novo Design

This study introduces a new de novo design algorithm called GENERA that combines the capabilities of a deep-learning algorithm for automated drug-like analogue design, called DeLA-Drug, with a genetic algorithm for generating molecules with desired target-oriented properties. Specifically, GENERA was applied to the angiotensin-converting enzyme 2 (ACE2) target, which is implicated in many pathological conditions, including COVID-19. The ability of GENERA to de novo design promising candidates for a specific target was assessed using two docking programs, PLANTS and GLIDE. A fitness function based on the Pareto dominance resulting from computed PLANTS and GLIDE scores was applied to demonstrate the algorithm's ability to perform multiobjective optimizations effectively. GENERA can quickly generate focused libraries that produce better scores compared to a starting set of known ACE-2 binders. This study is the first to utilize a DL-based algorithm designed for analogue generation as a mutational operator within a GA framework, representing an innovative approach to target-oriented de novo design.

Rapid Identification of Potential Drug Candidates from Multi-Million Compounds' Repositories. Combination of 2D Similarity Search with 3D Ligand/Structure Based Methods and In Vitro Screening

Rapid in silico selection of target focused libraries from commercial repositories is an attractive and cost-effective approach in early drug discovery. If structures of active compounds are available, rapid 2D similarity search can be performed on multimillion compounds' databases. This approach can be combined with physico-chemical parameter and diversity filtering, bioisosteric replacements, and fragment-based approaches for performing a first round biological screening. Our objectives were to investigate the combination of 2D similarity search with various 3D ligand and structure-based methods for hit expansion and validation, in order to increase the hit rate and novelty. In the present account, six case studies are described and the efficiency of mixing is evaluated. While sequentially combined 2D/3D similarity approach increases the hit rate significantly, sequential combination of 2D similarity with pharmacophore model or 3D docking enriched the resulting focused library with novel chemotypes. Parallel integrated approaches allowed the comparison of the various 2D and 3D methods and revealed that 2D similarity-based and 3D ligand and structure-based techniques are often complementary, and their combinations represent a powerful synergy. Finally, the lessons we learnt including the advantages and pitfalls of the described approaches are discussed.

Application of In Silico and HTS Approaches to Identify Nuclear Import Inhibitors for Venezuelan Equine Encephalitis Virus Capsid Protein: A Case Study

The development of new drugs is costly and time-consuming, with estimates of over $US1 billion and 15 years for a product to reach the market. As understanding of the molecular basis of disease improves, various approaches have been used to target specific molecular interactions in the search for effective drugs. These include high-throughput screening (HTS) for novel drug identification and computer-aided drug design (CADD) to assess the properties of putative drugs before experimental work begins. We have applied conventional HTS and CADD approaches to the problem of identifying antiviral compounds to limit infection by Venezuelan equine encephalitis virus (VEEV). Nuclear targeting of the VEEV capsid (CP) protein through interaction with the host nuclear import machinery has been shown to be essential for viral pathogenicity, with viruses incapable of this interaction being greatly attenuated. Our previous conventional HTS and in silico structure-based drug design (SBDD) screens were successful in identifying novel inhibitors of CP interaction with the host nuclear import machinery, thus providing a unique opportunity to assess the relative value of the two screening approaches directly. This focused review compares and contrasts the two screening approaches, together with the properties of the inhibitors identified, as a case study for parallel use of the two approaches to identify antivirals. The utility of SBDD screens, especially when used in parallel with traditional HTS, in identifying agents of interest to target the host-pathogen interface is highlighted.

Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction

Natural products comprise a rich reservoir for innovative drug leads and are a constant source of bioactive compounds. To find pharmacological targets for new or already known natural products using modern computer-aided methods is a current endeavor in drug discovery. Nature's treasures, however, could be used more effectively. Yet, reliable pipelines for the large-scale target prediction of natural products are still rare. We developed an in silico workflow consisting of four independent, stand-alone target prediction tools and evaluated its performance on dihydrochalcones (DHCs)-a well-known class of natural products. Thereby, we revealed four previously unreported protein targets for DHCs, namely 5-lipoxygenase, cyclooxygenase-1, 17β-hydroxysteroid dehydrogenase 3, and aldo-keto reductase 1C3. Moreover, we provide a thorough strategy on how to perform computational target predictions and guidance on using the respective tools.

Design and Selection of Novel C1s Inhibitors by In Silico and In Vitro Approaches

The complement system is associated with various diseases such as inflammation or auto-immune diseases. Complement-targeted drugs could provide novel therapeutic intervention against the above diseases. C1s, a serine protease, plays an important role in the CS and could be an attractive target since it blocks the system at an early stage of the complement cascade. Designing C1 inhibitors is particularly challenging since known inhibitors are restricted to a narrow bioactive chemical space in addition selectivity over other serine proteases is an important requirement. The typical architecture of a small molecule inhibitor of C1s contains an amidine (or guanidine) residue, however, the discovery of non-amidine inhibitors might have high value, particularly if novel chemotypes and/or compounds displaying improved selectivity are identified. We applied various virtual screening approaches to identify C1s focused libraries that lack the amidine/guanidine functionalities, then the in silico generated libraries were evaluated by in vitro biological assays. While 3D structure-based methods were not suitable for virtual screening of C1s inhibitors, and a 2D similarity search did not lead to novel chemotypes, pharmacophore model generation allowed us to identify two novel chemotypes with submicromolar activities. In three screening rounds we tested altogether 89 compounds and identified 20 hit compounds (<10 μM activities; overall hit rate: 22.5%). The highest activity determined was 12 nM (1,2,4-triazole), while for the newly identified chemotypes (1,3-benzoxazin-4-one and thieno[2,3-d][1,3]oxazin-4-one) it was 241 nM and 549 nM, respectively.

Energy windows for computed compound conformers: covering artefacts or truly large reorganization energies?

The generation of 3D conformers of small molecules underpins most computational drug discovery. Thus, the conformer quality is critical and depends on their energetics. A key parameter is the empirical conformational energy window (ΔEw), since only conformers within ΔEw are retained. However, ΔEw values in use appear unrealistically large. We analyze the factors pertaining to the conformer energetics and ΔEw. We argue that more attention must be focused on the problem of collapsed low-energy conformers. That is due to artificial intramolecular stabilization and occurs even with continuum solvation. Consequently, the conformational energy of extended bioactive structures is artefactually increased, which inflates ΔEw. Thus, this Perspective highlights the issues arising from low-energy conformers and suggests improvements via empirical or physics-based strategies.

In Silico Screening of Compound Libraries Using a Consensus of Orthogonal Methodologies

A number of diverse approaches for efficient screening of compound collections in silico are nowadays available, each with their own methodological background, successes and limitations. Implementation of such virtual screening methods has enabled an impressive acceleration in the search toward the most biologically relevant regions of chemical space and has greatly facilitated the discovery of novel biologically active molecules. It is noteworthy that the range of principles on which the available virtual screening methodologies are based is wide enough for several of these methods to be considered as orthogonal to a good extent. We hereby propose a simple and extensible protocol aiming at integrating the diverse information derived by such virtual screening methods in a consensus manner that can achieve an improvement of the hit rate obtained by individual use of those methods. The protocol can be performed in its basic version as described in this work, but it can also be extended manually by integrating a number of different screening tools and their case-specific variations to further increase the performance of virtual screening in prioritizing the most promising compounds for in vitro evaluations.

Changing Paradigm from one Target one Ligand Towards Multi-target Directed Ligand Design for Key Drug Targets of Alzheimer Disease: An Important Role of In Silico Methods in Multi-target Directed Ligands Design

Alzheimer disease (AD) is now considered as a multifactorial neurodegenerative disorder and rapidly increasing to an alarming situation and causing higher death rate. One target one ligand hypothesis does not provide complete solution of AD due to multifactorial nature of the disease and one target one drug fails to provide better treatment against AD. Moreover, currently available treatments are limited and most of the upcoming treatments under clinical trials are based on modulating single target. So, the current AD drug discovery research is shifting towards a new approach for a better solution that simultaneously modulates more than one targets in the neurodegenerative cascade. This can be achieved by network pharmacology, multi-modal therapies, multifaceted, and/or the more recently proposed term "multi-targeted designed drugs". Drug discovery project is a tedious, costly and long-term project. Moreover, multi-target AD drug discovery added extra challenges such as the good binding affinity of ligands for multiple targets, optimal ADME/T properties, no/less off-target side effect and crossing of the blood-brain barrier. These hurdles may be addressed by insilico methods for an efficient solution in less time and cost as computational methods successfully applied to single target drug discovery project. Here, we are summarizing some of the most prominent and computationally explored single targets against AD and further, we discussed a successful example of dual or multiple inhibitors for same targets. Moreover, we focused on ligand and structure-based computational approach to design MTDL against AD. However, it is not an easy task to balance dual activity in a single molecule but computational approach such as virtual screening docking, QSAR, simulation and free energy is useful in future MTDLs drug discovery alone or in combination with a fragment-based method. However, rational and logical implementations of computational drug designing methods are capable of assisting AD drug discovery and play an important role in optimizing multi-target drug discovery.

Oxazolo[4,5-b]pyridine-Based Piperazinamides as GSK-3β Inhibitors with Potential for Attenuating Inflammation and Suppression of Pro-Inflammatory Mediators

Recent studies reveal that glycogen synthase kinase-3β (GSK-3β) acts as a pro-inflammatory enzyme, and by inhibiting this kinase, inflammation can be controlled. In this regard, a series of 17 piperazine-linked oxazolo[4,5-b]pyridine-based derivatives was synthesized and evaluated for in vitro GSK-3β inhibitory and in vivo anti-inflammatory activity. The compounds 7d, 7e, 7g, and 7c displayed the best GSK-3β inhibitory activity among all the synthesized compounds, with corresponding IC₅₀ values of 0.34, 0.39, 0.47, and 0.53 µM. Among the compounds 7d, 7e, 7g, and 7c examined for in vivo anti-inflammatory activity in the rat paw edema model, compound 7d exhibited maximum inhibition, reducing the paw volume by 62.79 and 65.91% at 3 and 5 h post-carrageenan administration, respectively, in comparison to indomethacin (76.74% at 3 h and 79.54% at 5 h after carrageenan administration). Furthermore, these compounds (7d, 7e, 7g, and 7c) were also found to substantially inhibit pro-inflammatory mediators, i.e., TNF-α, IL-1β, and IL-6, ex vivo in comparison to indomethacin and did not pose any gastric ulceration risk, indicating the potential of this oxazolopyridine scaffold for the development of GSK-3β inhibitors and their application as anti-inflammatory agents.

(Z)-2-(3-Chlorobenzylidene)-3,4-dihydro-N-(2-methoxyethyl)-3-oxo-2H-benzo[b][1,4]oxazine-6-carboxamide as GSK-3β inhibitor: Identification by virtual screening and its validation in enzyme- and cell-based assay

Glycogen synthase kinase 3β (GSK-3β) is a widely investigated molecular target for numerous diseases including Alzheimer's disease, cancer, and diabetes mellitus. The present study was aimed to discover new scaffolds for GSK-3β inhibition, through protein structure-guided virtual screening approach. With the availability of large number of GSK-3β crystal structures with varying degree of RMSD in protein backbone and RMSF in side chain geometry, herein appropriate crystal structures were selected based on the characteristic ROC curve and percentage enrichment of actives. The validated docking protocol was employed to screen a library of 50,000 small molecules using molecular docking and binding affinity calculations. Based on the GLIDE docking score, Prime MMGB/SA binding affinity, and interaction pattern analysis, the top 50 ligands were selected for GSK-3β inhibition. (Z)-2-(3-chlorobenzylidene)-3,4-dihydro-N-(2-methoxyethyl)-3-oxo-2H-benzo[b][1,4]oxazine-6-carboxamide (F389-0663, 7) was identified as a potent inhibitor of GSK-3β with an IC₅₀ value of 1.6 μm. Further, GSK-3β inhibition activity was then investigated in cell-based assay. The treatment of neuroblastoma N2a cells with 12.5 μm of F389-0663 resulted in the significant increase in GSK-3β Ser9 levels, which is indicative of the GSK-3β inhibitory activity of a compound. The molecular dynamic simulations were carried out to understand the interactions of F389-0663 with GSK-3β protein.

Pharmacophore Models and Pharmacophore-Based Virtual Screening: Concepts and Applications Exemplified on Hydroxysteroid Dehydrogenases

Computational methods are well-established tools in the drug discovery process and can be employed for a variety of tasks. Common applications include lead identification and scaffold hopping, as well as lead optimization by structure-activity relationship analysis and selectivity profiling. In addition, compound-target interactions associated with potentially harmful effects can be identified and investigated. This review focuses on pharmacophore-based virtual screening campaigns specifically addressing the target class of hydroxysteroid dehydrogenases. Many members of this enzyme family are associated with specific pathological conditions, and pharmacological modulation of their activity may represent promising therapeutic strategies. On the other hand, unintended interference with their biological functions, e.g., upon inhibition by xenobiotics, can disrupt steroid hormone-mediated effects, thereby contributing to the development and progression of major diseases. Besides a general introduction to pharmacophore modeling and pharmacophore-based virtual screening, exemplary case studies from the field of short-chain dehydrogenase/reductase (SDR) research are presented. These success stories highlight the suitability of pharmacophore modeling for the various application fields and suggest its application also in futures studies.

Combining Metabolite-Based Pharmacophores with Bayesian Machine Learning Models for Mycobacterium tuberculosis Drug Discovery

Integrated computational approaches for Mycobacterium tuberculosis (Mtb) are useful to identify new molecules that could lead to future tuberculosis (TB) drugs. Our approach uses information derived from the TBCyc pathway and genome database, the Collaborative Drug Discovery TB database combined with 3D pharmacophores and dual event Bayesian models of whole-cell activity and lack of cytotoxicity. We have prioritized a large number of molecules that may act as mimics of substrates and metabolites in the TB metabolome. We computationally searched over 200,000 commercial molecules using 66 pharmacophores based on substrates and metabolites from Mtb and further filtering with Bayesian models. We ultimately tested 110 compounds in vitro that resulted in two compounds of interest, BAS 04912643 and BAS 00623753 (MIC of 2.5 and 5 μg/mL, respectively). These molecules were used as a starting point for hit-to-lead optimization. The most promising class proved to be the quinoxaline di-N-oxides, evidenced by transcriptional profiling to induce mRNA level perturbations most closely resembling known protonophores. One of these, SRI58 exhibited an MIC = 1.25 μg/mL versus Mtb and a CC₅₀ in Vero cells of >40 μg/mL, while featuring fair Caco-2 A-B permeability (2.3 x 10⁻⁶ cm/s), kinetic solubility (125 μM at pH 7.4 in PBS) and mouse metabolic stability (63.6% remaining after 1 h incubation with mouse liver microsomes). Despite demonstration of how a combined bioinformatics/cheminformatics approach afforded a small molecule with promising in vitro profiles, we found that SRI58 did not exhibit quantifiable blood levels in mice.

Role of computer-aided drug design in modern drug discovery

Drug discovery utilizes chemical biology and computational drug design approaches for the efficient identification and optimization of lead compounds. Chemical biology is mostly involved in the elucidation of the biological function of a target and the mechanism of action of a chemical modulator. On the other hand, computer-aided drug design makes use of the structural knowledge of either the target (structure-based) or known ligands with bioactivity (ligand-based) to facilitate the determination of promising candidate drugs. Various virtual screening techniques are now being used by both pharmaceutical companies and academic research groups to reduce the cost and time required for the discovery of a potent drug. Despite the rapid advances in these methods, continuous improvements are critical for future drug discovery tools. Advantages presented by structure-based and ligand-based drug design suggest that their complementary use, as well as their integration with experimental routines, has a powerful impact on rational drug design. In this article, we give an overview of the current computational drug design and their application in integrated rational drug development to aid in the progress of drug discovery research.

Biology-oriented development of novel lipophilic antioxidants with neuroprotective activity

Discovery of hydroxycinnamic acid derivatives with enhanced in lipophilicity, blood brain barrier permeability and neuroprotective potential.

Experimental validation of FINDSITE(comb) virtual ligand screening results for eight proteins yields novel nanomolar and micromolar binders

Background

Identification of ligand-protein binding interactions is a critical step in drug discovery. Experimental screening of large chemical libraries, in spite of their specific role and importance in drug discovery, suffer from the disadvantages of being random, time-consuming and expensive. To accelerate the process, traditional structure- or ligand-based VLS approaches are combined with experimental high-throughput screening, HTS. Often a single protein or, at most, a protein family is considered. Large scale VLS benchmarking across diverse protein families is rarely done, and the reported success rate is very low. Here, we demonstrate the experimental HTS validation of a novel VLS approach, FINDSITE^comb, across a diverse set of medically-relevant proteins.

Results

For eight different proteins belonging to different fold-classes and from diverse organisms, the top 1% of FINDSITE^comb’s VLS predictions were tested, and depending on the protein target, 4%-47% of the predicted ligands were shown to bind with μM or better affinities. In total, 47 small molecule binders were identified. Low nanomolar (nM) binders for dihydrofolate reductase and protein tyrosine phosphatases (PTPs) and micromolar binders for the other proteins were identified. Six novel molecules had cytotoxic activity (<10 μg/ml) against the HCT-116 colon carcinoma cell line and one novel molecule had potent antibacterial activity.

Conclusions

We show that FINDSITE^comb is a promising new VLS approach that can assist drug discovery.

Crosstalk between phosphodiesterase 7 and glycogen synthase kinase-3: two relevant therapeutic targets for neurological disorders

Chronic neuroinflammation has been increasingly recognized as a primary mechanism underlying acute brain injury and neurodegenerative diseases. Enhanced expression of diverse pro-inflammatory agents in glial cells has been shown to contribute to the cell death that takes place in these disorders. Previous data from our group have shown that different inhibitors of the cyclic adenosine monophosphate (cAMP) specific phosphodiesterase 7 (PDE7) and glycogen synthase kinase-3 (GSK-3) enzymes are potent anti-inflammatory agents in different models of brain injury. In this study, we investigated cross-talk between PDE7 and GSK-3, two relevant therapeutic targets for neurological disorders, using a chemical approach. To this end, we compared specific inhibitors of GSK-3 and PDE7 with dual inhibitors of both enzymes with regard to anti-inflammatory effects in primary cultures of glial cells treated with lipopolysaccharide. Our results show that the GSK-3 inhibitors act exclusively by inhibition of this enzyme. By contrast, PDE7 inhibitors exert their effects via inhibition of PDE7 to increase intracellular cAMP levels but also through indirect inhibition of GSK-3. Activation of protein kinase A by cAMP results in phosphorylation of Ser9 of GSK-3 and subsequent inhibition. Our results indicate that the indirect inhibition of GSK-3 by PDE7 inhibitors is an important mechanism that should be considered in the future development of pharmacological treatments.

Structure-based design and synthesis of novel dual-target inhibitors against cyanobacterial fructose-1,6-bisphosphate aldolase and fructose-1,6-bisphosphatase

Cyanobacteria class II fructose-1,6-bisphoshate aldolase (Cy-FBA-II) and cyanobacteria fructose-1,6-bisphosphatase (Cy-FBPase) are two neighboring key regulatory enzymes in the Calvin cycle of the cyanobacteria photosynthesis system. Each of them might be taken as a potential target for designing novel inhibitors to chemically control harmful algal blooms (HABs). In the present paper, a series of novel inhibitors were rationally designed, synthesized, and optimized based upon the structural and interactional information of both Cy-FBA-II and Cy-FBPase, and their inhibitory activities were examined in vitro and in vivo. The experimental results showed that compounds L19e-L19g exhibited moderate inhibitory activities (IC50 = 28.1-103.2 μM) against both Cy-FBA-II and Cy-FBPase; compounds L19a-L19d, L19h, L20a-L20d exhibited high Cy-FBA-II inhibitory activities (IC50 = 2.3-16.9 μM) and moderate Cy-FBPase inhibitory activities (IC50 = 31.5-141.2 μM); however, compounds L20e-L20h could potently inhibit both Cy-FBA-II and Cy-FBPase with IC50 values less than 30 μM, which demonstrated more or less dual-target inhibitor's feature. Moreover, most of them exhibited potent algicide activity (EC50 = 0.8-22.3 ppm) against cyanobacteria Synechocystis sp. PCC 6803.

High-throughput and in silico screenings in drug discovery

BACKGROUND

In the current situation of weak drug pipelines, impending patent expiration of several blockbuster drugs, industry consolidation and changing business models that target special diseases like cancer, diabetes, Alzheimer's and obesity, the pharmaceutical industry is under intense pressure to generate a strong drug pipeline distinguished by better productivity, diversity and cost effectiveness. The goal is discovering high-quality leads in the initial stages of the development cycle, to minimize the costs associated with failures at later ones.

OBJECTIVE

Thus, there is a great amount of interest in further developing and optimizing high-throughput screening and in silico screening, the two methods responsible for generating most of the lead compounds. Although high-throughput screening is the predominant starting point for discovery programs, in silico methods have gradually made inroads by their more rational approach, to expedite the drug discovery and development process.

CONCLUSION

Modern drug discovery strategies include both methods in tandem or in an iterative way. This review primarily provides a succinct overview and comparison of experimental and in silico screening techniques, selected case studies where both methods were used in concert to investigate their performance and complementary nature and a statement on the developments in experimental and in silico approaches in the near future.

The holistic integration of virtual screening in drug discovery

During the past decade, virtual screening (VS) has come of age. In this review, we document the evolution and maturation of VS from a rather exotic, stand-alone method toward a versatile hit and lead identification technology. VS campaigns have become fully integrated into drug discovery campaigns, evenly matched and complementary to high-throughput screening (HTS) methods. Here, we propose a novel classification of VS applications to help to monitor the advances in VS and to support future improvement of computational hit and lead identification methods. Several relevant VS studies from recent publications, in both academic and industrial settings, were selected to demonstrate the progress in this area. Furthermore, we identify challenges that lie ahead for the development of integrated VS campaigns.

Computational models for tuberculosis drug discovery

The search for small molecules with activity against Mycobacterium tuberculosis increasingly uses -high-throughput screening and computational methods. Previously, we have analyzed recent studies in which computational tools were used for cheminformatics. We have now updated this analysis to illustrate how they may assist in finding desirable leads for tuberculosis drug discovery. We provide our thoughts on strategies for drug discovery efforts for neglected diseases.

Virtual and experimental high-throughput screening (HTS) in search of novel inosine 5'-monophosphate dehydrogenase II (IMPDH II) inhibitors

IMPDH (Inosine 5'-monophosphate dehydrogenase) catalyzes a rate-limiting step in the de novo biosynthesis of guanine nucleotides. IMPDH inhibition in sensitive cell types (e.g., lymphocytes) blocks proliferation (by blocking RNA and DNA synthesis as a result of decreased cellular levels of guanine nucleotides). This makes it an interesting target for cancer and autoimmune disorders. Currently available IMPDH inhibitors such as mycophenolic acid (MPA, uncompetitive inhibitor) and nucleoside analogs (e.g., ribavirin, competitive inhibitor after intracellular activation by phosphorylation) have unfavorable tolerability profiles which limit their use. Hence, the quest for novel IMPDH inhibitors continues. In the present study, a ligand-based virtual screening using IMPDH inhibitor pharmacophore models was performed on in-house compound collection. A total of 50,000 virtual hits were selected for primary screen using in vitro IMPDH II inhibition up to 10 μM. The list of 2,500 hits (with >70 % inhibition) was further subjected to hit confirmation for the determination of IC(50) values. The hits obtained were further clustered using maximum common substructure based formalism resulting in 90 classes and 7 singletons. A thorough inspection of these yielded 7 interesting classes in terms of mini-SAR with IC(50) values ranging from 0.163 μM to little over 25 μM. The average ligand efficiency was found to be 0.3 for the best class. The classes thus discovered represent structurally novel chemotypes which can be taken up for further development.

Identification of novel scaffold of benzothiazepinones as non-ATP competitive glycogen synthase kinase-3β inhibitors through virtual screening

Glycogen synthase kinase-3β (GSK-3β) is an important serine/threonine kinase that has been proved as a key target for neurodegenerative diseases and diabetes. Up to date, most of known inhibitors are bound to the ATP-binding pocket of GSK-3β, which might lead widespread effects due to the high homology between kinases. Recently, some of its non-ATP competitive inhibitors had been confirmed having therapeutical effects owing to their high selectivity. This finding opens a new pathway to study hopeful drugs for treatment of these diseases. However, it is still a challenge nowadays on how to efficiently find non-ATP competitors. Here, we successfully discovered a novel scaffold of benzothiazepinones (BTZs) as selective non-ATP competitive GSK-3β inhibitors through virtual screening approach. A 3D receptor model of substrate binding site of GSK-3β was constructed and applied to screen against drug-like Maybridge database through Autodock program. BTZ compounds were top ranked as efficient hits and were then synthesized for further screening. Among them, the representative compound 4j showed activity to GSK-3β (IC(50): 25 μM) in non-ATP competitive mechanism, and nearly no inhibitory effect on other 10 related protein kinases. Overall, the results point out that BTZ compounds might be useful in treatment of Alzheimer's disease and diabetes mellitus as novel GSK-3β inhibitors. It also suggests, on the other hand, that virtual screening would provide a valuable tool in combination with in vitro assays for the identification of novel selective and potent inhibitors.

IspE inhibitors identified by a combination of in silico and in vitro high-throughput screening

CDP-ME kinase (IspE) contributes to the non-mevalonate or deoxy-xylulose phosphate (DOXP) pathway for isoprenoid precursor biosynthesis found in many species of bacteria and apicomplexan parasites. IspE has been shown to be essential by genetic methods and since it is absent from humans it constitutes a promising target for antimicrobial drug development. Using in silico screening directed against the substrate binding site and in vitro high-throughput screening directed against both, the substrate and co-factor binding sites, non-substrate-like IspE inhibitors have been discovered and structure-activity relationships were derived. The best inhibitors in each series have high ligand efficiencies and favourable physico-chemical properties rendering them promising starting points for drug discovery. Putative binding modes of the ligands were suggested which are consistent with established structure-activity relationships. The applied screening methods were complementary in discovering hit compounds, and a comparison of both approaches highlights their strengths and weaknesses. It is noteworthy that compounds identified by virtual screening methods provided the controls for the biochemical screens.

Combining cheminformatics methods and pathway analysis to identify molecules with whole-cell activity against Mycobacterium tuberculosis

PURPOSE

New strategies for developing inhibitors of Mycobacterium tuberculosis (Mtb) are required in order to identify the next generation of tuberculosis (TB) drugs. Our approach leverages the integration of intensive data mining and curation and computational approaches, including cheminformatics combined with bioinformatics, to suggest biological targets and their small molecule modulators.

METHODS

We now describe an approach that uses the TBCyc pathway and genome database, the Collaborative Drug Discovery database of molecules with activity against Mtb and their associated targets, a 3D pharmacophore approach and Bayesian models of TB activity in order to select pathways and metabolites and ultimately prioritize molecules that may be acting as substrate mimics and exhibit activity against TB.

RESULTS

In this study we combined the TB cheminformatics and pathways databases that enabled us to computationally search >80,000 vendor available molecules and ultimately test 23 compounds in vitro that resulted in two compounds (N-(2-furylmethyl)-N'-[(5-nitro-3-thienyl)carbonyl]thiourea and N-[(5-nitro-3-thienyl)carbonyl]-N'-(2-thienylmethyl)thiourea) proposed as mimics of D-fructose 1,6 bisphosphate, (MIC of 20 and 40 μg/ml, respectively).

CONCLUSION

This is a simple yet novel approach that has the potential to identify inhibitors of bacterial growth as illustrated by compounds identified in this study that have activity against Mtb.

Metrics and the effective computational scientist: process, quality and communication

Recent treatments of computational knowledge worker productivity have focused upon the value the discipline brings to drug discovery using positive anecdotes. While this big picture approach provides important validation of the contributions of these knowledge workers, the impact accounts do not provide the granular detail that can help individuals and teams perform better. I suggest balancing the impact-focus with quantitative measures that can inform the development of scientists. Measuring the quality of work, analyzing and improving processes, and the critical evaluation of communication can provide immediate performance feedback. The introduction of quantitative measures can complement the longer term reporting of impacts on drug discovery. These metric data can document effectiveness trends and can provide a stronger foundation for the impact dialogue.

Switching reversibility to irreversibility in glycogen synthase kinase 3 inhibitors: clues for specific design of new compounds

Development of kinase-targeted therapies for central nervous system (CNS) diseases is a great challenge. Glycogen synthase kinase 3 (GSK-3) offers a great potential for severe CNS unmet diseases, being one of the inhibitors on clinical trials for different tauopathies. Following our hypothesis based on the enhanced reactivity of residue Cys199 in the binding site of GSK-3, we examine here the suitability of phenylhalomethylketones as irreversible inhibitors. Our data confirm that the halomethylketone unit is essential for the inhibitory activity. Moreover, addition of the halomethylketone moiety to reversible inhibitors turned them into irreversible inhibitors with IC(50) values in the nanomolar range. Overall, the results point out that these compounds might be useful pharmacological tools to explore physiological and pathological processes related to signaling pathways regulated by GSK-3 opening new avenues for the discovery of novel GSK-3 inhibitors.

Systematic exploitation of multiple receptor conformations for virtual ligand screening

The role of virtual ligand screening in modern drug discovery is to mine large chemical collections and to prioritize for experimental testing a comparatively small and diverse set of compounds with expected activity against a target. Several studies have pointed out that the performance of virtual ligand screening can be improved by taking into account receptor flexibility. Here, we systematically assess how multiple crystallographic receptor conformations, a powerful way of discretely representing protein plasticity, can be exploited in screening protocols to separate binders from non-binders. Our analyses encompass 36 targets of pharmaceutical relevance and are based on actual molecules with reported activity against those targets. The results suggest that an ensemble receptor-based protocol displays a stronger discriminating power between active and inactive molecules as compared to its standard single rigid receptor counterpart. Moreover, such a protocol can be engineered not only to enrich a higher number of active compounds, but also to enhance their chemical diversity. Finally, some clear indications can be gathered on how to select a subset of receptor conformations that is most likely to provide the best performance in a real life scenario.

Discovery of novel GSK-3β inhibitors with potent in vitro and in vivo activities and excellent brain permeability using combined ligand- and structure-based virtual screening

Dysregulation of glycogen synthase kinase (GSK-3β) is implicated in the pathophysiology of many diseases, including type-2 diabetes, stroke, Alzheimer's, and others. A multistage virtual screening strategy designed so as to overcome known caveats arising from the considerable flexibility of GSK-3β yielded, from among compounds in our in-house database and two commercial databases, new GSK-3β inhibitors with novel scaffold structures. The two most potent and selective validated hits, a 2-anilino-5-phenyl-1,3,4-oxadiazole (24) and a phenylmethylene hydantoin (28), both exhibited nanomolar affinity and selectivity over CDK2 and were potent enough for direct in vivo validation. Both were able to cause significant increases in liver glycogen accumulation in dose-dependent fashion. One also exhibited excellent blood-brain barrier permeability, the other adequate for a lead compound. Analogues of the oxadiazole 24 were synthesized to experimentally corroborate or rule out ligand-bound structures arising from docking studies. SAR results supported one docking study among a number of alternatives.

IKKβ inhibitor identification: a multi-filter driven novel scaffold

Background

Nuclear factor kappa B (NF-κB) is a chief nuclear transcription factor that controls the transcription of various genes; and its activation is tightly controlled by Inhibitor kappa B kinase (IKK). The irregular transcription of NF-κB has been linked to auto-immune disorders, cancer and other diseases. The IKK complex is composed of three units, IKKα, IKKβ, and the regulatory domain NEMO, of which IKKβ is well understood in the canonical pathway. Therefore, the inhibition of IKKβ by drugs forms the molecular basis for anti-inflammatory drug research.

Results

The ligand- and structure-based virtual screening (VS) technique has been applied to identify IKKβ inhibitors from the ChemDiv database with 0.7 million compounds. Initially, a 3D-QSAR pharmacophore model has been deployed to greatly reduce the database size. Subsequently, recursive partitioning (RP) and docking filters were used to screen the pharmacophore hits. Finally, 29 compounds were selected for IKKβ enzyme inhibition assay to identify a novel small molecule inhibitor of IKKβ protein.

Conclusions

In the present investigation, we have applied various computational models sequentially to virtually screen the ChemDiv database, and identified a small molecule that has an IC₅₀ value of 20.3μM. This compound is novel among the known IKKβ inhibitors. Further optimization of the hit compound can reveal a more potent anti-inflammatory agent.

Complementarity between a docking and a high-throughput screen in discovering new cruzain inhibitors

Virtual and high-throughput screens (HTS) should have complementary strengths and weaknesses, but studies that prospectively and comprehensively compare them are rare. We undertook a parallel docking and HTS screen of 197861 compounds against cruzain, a thiol protease target for Chagas disease, looking for reversible, competitive inhibitors. On workup, 99% of the hits were eliminated as false positives, yielding 146 well-behaved, competitive ligands. These fell into five chemotypes: two were prioritized by scoring among the top 0.1% of the docking-ranked library, two were prioritized by behavior in the HTS and by clustering, and one chemotype was prioritized by both approaches. Determination of an inhibitor/cruzain crystal structure and comparison of the high-scoring docking hits to experiment illuminated the origins of docking false-negatives and false-positives. Prioritizing molecules that are both predicted by docking and are HTS-active yields well-behaved molecules, relatively unobscured by the false-positives to which both techniques are individually prone.

GSK3beta: role in therapeutic landscape and development of modulators

Glycogen synthase kinase-3 beta (GSK3beta) is a multifunctional serine/threonine kinase which was originally identified as a regulator of glycogen metabolism. It plays a key role in the regulation of numerous signalling pathways including cellular process such as cell cycle, inflammation and cell proliferation. Over the last few years there is a considerable rise in the number of journals and patents publication by different workers worldwide. Many pharmaceutical companies are focusing on GSK3beta as a therapeutic target for the treatment of disease conditions. The present review is focused on signalling pathways of different disease conditions where GSK3beta is implicated. In this review, we present a comprehensive map of GSK3beta signalling pathways in disease physiologies. Structural analysis of GSK3beta along with molecular modelling reports from numerous workers are reviewed in context of design and development of GSK3beta inhibitors. Patent landscape of the small molecule modulators is profiled. The chemo space for small molecule modulators extracted from public and proprietary Kinase Chembiobase for GSK3beta are discussed. Compounds in different clinical phases of discovery are analysed. The review ends with the overall status of this important therapeutic target and challenges in development of its modulators.

An Evaluation of Explicit Receptor Flexibility in Molecular Docking Using Molecular Dynamics and Torsion Angle Molecular Dynamics

Incorporating receptor flexibility into molecular docking should improve results for flexible proteins. However, the incorporation of explicit all-atom flexibility with molecular dynamics for the entire protein chain may also introduce significant error and "noise" that could decrease docking accuracy and deteriorate the ability of a scoring function to rank native-like poses. We address this apparent paradox by comparing the success of several flexible receptor models in cross-docking and multiple receptor ensemble docking for p38α mitogen-activated protein (MAP) kinase. Explicit all-atom receptor flexibility has been incorporated into a CHARMM-based molecular docking method (CDOCKER) using both molecular dynamics (MD) and torsion angle molecular dynamics (TAMD) for the refinement of predicted protein-ligand binding geometries. These flexible receptor models have been evaluated, and the accuracy and efficiency of TAMD sampling is directly compared to MD sampling. Several flexible receptor models are compared, encompassing flexible side chains, flexible loops, multiple flexible backbone segments, and treatment of the entire chain as flexible. We find that although including side chain and some backbone flexibility is required for improved docking accuracy as expected, docking accuracy also diminishes as additional and unnecessary receptor flexibility is included into the conformational search space. Ensemble docking results demonstrate that including protein flexibility leads to to improved agreement with binding data for 227 active compounds. This comparison also demonstrates that a flexible receptor model enriches high affinity compound identification without significantly increasing the number of false positives from low affinity compounds.

Docking and chemoinformatic screens for new ligands and targets

Computer-based docking screens are now widely used to discover new ligands for targets of known structure; in the last two years alone, the discovery of ligands for more than 20 proteins has been reported. Recently, investigators have also turned to predicting new substrates for enzymes of unknown function, taking docking in a wholly new direction. Increasingly, the hit rates, the true-positives, and the false-positives from the docking screens are being compared to those from empirical, high-throughput screens, revealing the strengths, weaknesses, and complementarities of both techniques. The recent efflorescence of GPCR structures has made these quintessential drug targets available to structure-based approaches. Consistent with their 'druggability', the docking screens have returned high hit rates and potent molecules. Finally, in the last several years, an approach almost exactly opposite to docking has also appeared; this pharmacological network approach begins not with the structure of the target but rather those of drug molecules and asks, given a pattern of chemistry in the ligands, what targets may a particular drug bind to? This method, which returns to an older, pharmacology logic, has been surprisingly successful in predicting new 'off-targets' for established drugs.

Consistent improvement of cross-docking results using binding site ensembles generated with elastic network normal modes

The representation of protein flexibility is still a challenge for the state-of-the-art flexible ligand docking protocols. In this article, we use a large and diverse benchmark to prove that is possible to improve consistently the cross-docking performance against a single receptor conformation, using an equilibrium ensemble of binding site conformers. The benchmark contained 28 proteins, and our method predicted the top-ranked near native ligand poses 20% more efficiently than using a single receptor. The multiple conformations were derived from the collective variable space defined by all heavy-atom elastic network normal modes, including backbone and side chains. We have found that the binding site displacements for best positioning of the ligand seem rather independent from the global collective motions of the protein. We also found that the number of binding site conformations needed to represent nonredundant flexibility was < 100. The ensemble of receptor conformations can be generated at our Web site at http://abagyan.scripps.edu/MRC.

The identification of novel PLC-gamma inhibitors using virtual high throughput screening

Phospholipase C-gamma (PLC-gamma) has been identified as a possible biological target for anticancer drug therapy but suitable inhibitors are lacking. Therefore, in order to identify active compounds (hits) virtual high throughput screening was performed. The crystal structure of the PLC-delta isoform was used as a model docking scaffold since no crystallographic data are available on its gamma counterpart. A pilot screen was performed using approximately 9.2x10(4) compounds, where the robustness of the methodology was tested. This was followed by the main screening effort where approximately 4.4x10(5) compounds were used. In both cases, plausible compounds were identified (virtual hits) and a selection of these was experimentally tested. The most potent compounds were in the single digit micro-molar range as determined from the biochemical (Flashplate) assay. This translated into approximately 15 microM in a functional assay in cells. About 30% of the virtual hits showed activity against PLC-gamma (IC(50)<50 microM).

Computational chemistry approaches to drug discovery in signal transduction

The advent of therapeutic strategies aimed at targeting specific macromolecular components of deregulated signaling pathways associated with particular disease states has given rise to the idea that it should be possible to design ligands as drug candidates to these targets from first principles. This concept has been beckoning for a long time but structure-based ligand design only became feasible once it was possible to determine the 3-D structures of molecular targets at atomic resolution. However, structure-based design turned out to be difficult, chiefly because under physiological conditions both receptors and ligands are not static but they behave dynamically. While it is possible to design ligands with high steric and electronic complementarity to a receptor site, it is always uncertain how biologically relevant the assumed conformations of both ligand and receptor actually are. The fact that it remains beyond our current abilities to predict with sufficient accuracy the affinity between hypothetical ligand and receptor poses is in part connected with this problem and continues to confound the reliable prediction of drug-like ligands for therapeutic targets. Nevertheless, significant progress has been made and so-called virtual screening methods that use computational methods to dock candidate ligands into receptor sites and to score the resulting complexes are now used routinely as one of the components in drug discovery screening campaigns. Here an overview is given of the underlying principles, implementations, and applications of structure-guided computational design technologies. Although the emphasis is on receptor-based strategies, mention will also be made of some of the more established ligand-based approaches, such as similarity analyses and quantitative structure-activity relationship methods.

Protein protein interaction inhibition (2P2I) combining high throughput and virtual screening: Application to the HIV-1 Nef protein

Protein-protein recognition is the cornerstone of multiple cellular and pathological functions. Therefore, protein-protein interaction inhibition (2P2I) is endowed with great therapeutic potential despite the initial belief that 2P2I was refractory to small-molecule intervention. Improved knowledge of complex molecular binding surfaces has recently stimulated renewed interest for 2P2I, especially after identification of "hot spots" and first inhibitory compounds. However, the combination of target complexity and lack of starting compound has thwarted experimental results and created intellectual barriers. Here we combined virtual and experimental screening when no previously known inhibitors can be used as starting point in a structure-based research program that targets an SH3 binding surface of the HIV type I Nef protein. High-throughput docking and application of a pharmacophoric filter on one hand and search for analogy on the other hand identified drug-like compounds that were further confirmed to bind Nef in the micromolar range (isothermal titration calorimetry), to target the Nef SH3 binding surface (NMR experiments), and to efficiently compete for Nef-SH3 interactions (cell-based assay, GST pull-down). Initial identification of these compounds by virtual screening was validated by screening of the very same library of compounds in the cell-based assay, demonstrating that a significant enrichment factor was attained by the in silico screening. To our knowledge, our results identify the first set of drug-like compounds that functionally target the HIV-1 Nef SH3 binding surface and provide the basis for a powerful discovery process that should help to speed up 2P2I strategies and open avenues for new class of antiviral molecules.