National Cancer Institute Drug Information System 3D database

Discovery of Novel Spike Inhibitors against SARS-CoV-2 Infection

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is responsible for the current coronavirus disease pandemic. With the rapid evolution of variant strains, finding effective spike protein inhibitors is a logical and critical priority. Angiotensin-converting enzyme 2 (ACE2) has been identified as the functional receptor for SARS-CoV-2 viral entry, and thus related therapeutic approaches associated with the spike protein-ACE2 interaction show a high degree of feasibility for inhibiting viral infection. Our computer-aided drug design (CADD) method meticulously analyzed more than 260,000 compound records from the United States National Cancer Institute (NCI) database, to identify potential spike inhibitors. The spike protein receptor-binding domain (RBD) was chosen as the target protein for our virtual screening process. In cell-based validation, SARS-CoV-2 pseudovirus carrying a reporter gene was utilized to screen for effective compounds. Ultimately, compounds C2, C8, and C10 demonstrated significant antiviral activity against SARS-CoV-2, with estimated EC50 values of 8.8 μM, 6.7 μM, and 7.6 μM, respectively. Using the above compounds as templates, ten derivatives were generated and robust bioassay results revealed that C8.2 (EC50 = 5.9 μM) exhibited the strongest antiviral efficacy. Compounds C8.2 also displayed inhibitory activity against the Omicron variant, with an EC50 of 9.3 μM. Thus, the CADD method successfully discovered lead compounds binding to the spike protein RBD that are capable of inhibiting viral infection.

Discovery of novel RARα agonists using pharmacophore-based virtual screening, molecular docking, and molecular dynamics simulation studies

Nuclear retinoic acid receptors (RARs) are ligand-dependent transcription factors involved in various biological processes, such as embryogenesis, cell proliferation, differentiation, reproduction, and apoptosis. These receptors are regulated by retinoids, i.e., retinoic acid (RA) and its analogs, as receptor agonists. RAR agonists are promising therapeutic agents for the treatment of serious dermatological disorders, including some malignant conditions. By inducing apoptosis, they are able to inhibit the proliferation of diverse cancer cell lines. Also, RAR agonists have recently been identified as therapeutic options for some neurodegenerative diseases. These features make retinoids very attractive molecules for medical purposes. Synthetic selective RAR agonists have several advantages over endogenous ones, but they suffer poor pharmacokinetic properties. These compounds are normally lipophilic acids with unfavorable drug-like features such as poor oral bioavailability. Recently, highly selective, potent, and less toxic RAR agonists with proper lipophilicity, thus, good oral bioavailability have been developed for some therapeutic applications. In the present study, ligand and structure-based virtual screening technique was exploited to introduce some novel RARα agonists. Pharmacokinetic assessment was also performed in silico to suggest those compounds which have optimized drug-like features. Finally, two compounds with the best in silico pharmacological features are proposed as lead molecules for future development of RARα agonists.

Recent advances in the self-referencing embedded strings (SELFIES) library

String-based molecular representations play a crucial role in cheminformatics applications, and with the growing success of deep learning in chemistry, have been readily adopted into machine learning pipelines. However, traditional string-based representations such as SMILES are often prone to syntactic and semantic errors when produced by generative models. To address these problems, a novel representation, SELF-referencing embedded strings (SELFIES), was proposed that is inherently 100% robust, alongside an accompanying open-source implementation called selfies. Since then, we have generalized SELFIES to support a wider range of molecules and semantic constraints, and streamlined its underlying grammar. We have implemented this updated representation in subsequent versions of selfies, where we have also made major advances with respect to design, efficiency, and supported features. Hence, we present the current status of selfies (version 2.1.1) in this manuscript. Our library, selfies, is available at GitHub (https://github.com/aspuru-guzik-group/selfies).

Identification of 11-Hydroxytephrosin and Torosaflavone A as Potential Inhibitors of 3-Phosphoinositide-Dependent Protein Kinase 1 (PDPK1): Toward Anticancer Drug Discovery

The 3-phosphoinositide-dependent protein kinase 1 (PDPK1) has a significant role in cancer progression and metastasis as well as other inflammatory disorders, and has been proposed as a promising therapeutic target for several malignancies. In this work, we conducted a systematic virtual screening of natural compounds from the IMPPAT database to identify possible PDPK1 inhibitors. Primarily, the Lipinski rules, ADMET, and PAINS filter were applied and then the binding affinities, docking scores, and selectivity were carried out to find effective hits against PDPK1. Finally, we identified two natural compounds, 11-Hydroxytephrosin and Torosaflavone A, bearing substantial affinity with PDPK1. Both compounds showed drug-likeness as predicted by the ADMET analysis and their physicochemical parameters. These compounds preferentially bind to the ATP-binding pocket of PDPK1 and interact with functionally significant residues. The conformational dynamics and complex stability of PDPK1 with the selected compounds were then studied using interaction analysis and molecular dynamics (MD) simulations for 100 ns. The simulation results revealed that PDPK1 forms stable docked complexes with the elucidated compounds. The findings show that the newly discovered 11-Hydroxytephrosin and Torosaflavone A bind to PDPK1 in an ATP-competitive manner, suggesting that they could one day be used as therapeutic scaffolds against PDPK1-associated diseases including cancer.

Modern drug discovery applications for the identification of novel candidates for COVID-19 infections

In early December 2019, a large pneumonia epidemic occurred in Wuhan, China. The World Health Organization is concerned about the outbreak of another coronavirus with the powerful, rapid, and contagious transmission. Anyone with minor symptoms like fever and cough or travel history to contaminated places might be suspected of having COVID-19. COVID-19 therapy focuses on treating the disease's symptoms. So far, no such therapeutic molecule has been shown effective in treating this condition. So the treatment is mostly supportive and plasma. Globally, numerous studies and researchers have recently started fighting this virus. Vaccines and chemical compounds are also being investigated against infection. COVID-19 was successfully diagnosed using RNA detection and very sensitive RT-PCR (reverse transcription-polymerase chain reaction). The evolution of particular vaccinations is required to reduce illness severity and spread. Numerous computational analyses and molecular docking have predicted various target compounds that might stop this condition. This paper examines the main characteristics of coronavirus and the computational analyses necessary to avoid infection.

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Importance of advanced genome sequencing in analyzing the sequential change in DNA and RNA due to SARS-CoV-2 genome.

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Lead Identification studies with computer drug-aided design.

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Prevalence of some flavonoid glycosides in medicinal plants which have potential protective effects against COVID-19 infections.

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Docking studies with alkaloids, vanillin derivatives and their actions suggest possible SARS- CoV-2 inhibition.

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Modeling studies outcomes about ligand-based and structure based activity.

Natural therapeutics and nutraceuticals for lung diseases: Traditional significance, phytochemistry, and pharmacology

BACKGROUND

Lung diseases including chronic obstructive pulmonary disease (COPD), infections like influenza, acute respiratory distress syndrome (ARDS), asthma and pneumonia lung cancer (LC) are common causes of sickness and death worldwide due to their remoteness, cold and harsh climatic conditions, and inaccessible health care facilities.

PURPOSE

Many drugs have already been proposed for the treatment of lung diseases. Few of them are in clinical trials and have the potential to cure infectious diseases. Plant extracts or herbal products have been extensively used as Traditional Chinese Medicine (TCM) and Indian Ayurveda. Moreover, it has been involved in the inhibition of certain genes/protiens effects to promote regulation of signaling pathways. Natural remedies have been scientifically proven with remarkable bioactivities and are considered a cheap and safe source for lung disease.

METHODS

This comprehensive review highlighted the literature about traditional plants and their metabolites with their applications for the treatment of lung diseases through experimental models in humans. Natural drugs information and mode of mechanism have been studied through the literature retrieved by Google Scholar, ScienceDirect, SciFinder, Scopus and Medline PubMed resources against lung diseases.

RESULTS

In vitro, in vivo and computational studies have been explained for natural metabolites derived from plants (like flavonoids, alkaloids, and terpenoids) against different types of lung diseases. Probiotics have also been biologically active therapeutics against cancer, anti-inflammation, antiplatelet, antiviral, and antioxidants associated with lung diseases.

CONCLUSION

The results of the mentioned natural metabolites repurposed for different lung diseases especially for SARS-CoV-2 should be evaluated more by advance computational applications, experimental models in the biological system, also need to be validated by clinical trials so that we may be able to retrieve potential drugs for most challenging lung diseases especially SARS-CoV-2.

EMT-mediated regulation of CXCL1/5 for resistance to anti-EGFR therapy in colorectal cancer

The emergence of RAS/RAF mutant clone is the main feature of EGFR inhibitor resistance in KRAS wild-type colon cancer. However, its molecular mechanism is thought to be multifactorial, mainly due to cellular heterogeneity. In order to better understand the resistance mechanism in a single clone level, we successfully isolated nine cells with cetuximab-resistant (CR) clonality from in vitro system. All CR cells harbored either KRAS or BRAF mutations. Characteristically, these cells showed a higher EMT (Epithelial to mesenchymal transition) signature, showing increased EMT markers such as SNAI2. Moreover, the expression level of CXCL1/5, a secreted protein, was significantly higher in CR cells compared to the parental cells. In these CR cells, CXCL1/5 expression was coordinately regulated by SNAI2/NFKB and transactivated EGFR through CXCR/MMPI/EGF axis via autocrine singling. We also observed that combined cetuximab/MEK inhibitor not only showed growth inhibition but also reduced the secreted amounts of CXCL1/5. We further found that serum CXCL1/5 level was positively correlated with the presence of RAS/RAF mutation in colon cancer patients during cetuximab therapy, suggesting its role as a biomarker. These data indicated that the application of serum CXCL1/5 could be a potential serologic biomarker for predicting resistance to EGFR therapy in colorectal cancer.

Combined Pharmacophore and Grid-Independent Molecular Descriptors (GRIND) Analysis to Probe 3D Features of Inositol 1,4,5-Trisphosphate Receptor (IP3R) Inhibitors in Cancer

Inositol 1, 4, 5-trisphosphate receptor (IP₃R)-mediated Ca²⁺ signaling plays a pivotal role in different cellular processes, including cell proliferation and cell death. Remodeling Ca²⁺ signals by targeting the downstream effectors is considered an important hallmark in cancer progression. Despite recent structural analyses, no binding hypothesis for antagonists within the IP₃-binding core (IBC) has been proposed yet. Therefore, to elucidate the 3D structural features of IP₃R modulators, we used combined pharmacoinformatic approaches, including ligand-based pharmacophore models and grid-independent molecular descriptor (GRIND)-based models. Our pharmacophore model illuminates the existence of two hydrogen-bond acceptors (2.62 Å and 4.79 Å) and two hydrogen-bond donors (5.56 Å and 7.68 Å), respectively, from a hydrophobic group within the chemical scaffold, which may enhance the liability (IC₅₀) of a compound for IP₃R inhibition. Moreover, our GRIND model (PLS: Q² = 0.70 and R² = 0.72) further strengthens the identified pharmacophore features of IP₃R modulators by probing the presence of complementary hydrogen-bond donor and hydrogen-bond acceptor hotspots at a distance of 7.6-8.0 Å and 6.8-7.2 Å, respectively, from a hydrophobic hotspot at the virtual receptor site (VRS). The identified 3D structural features of IP₃R modulators were used to screen (virtual screening) 735,735 compounds from the ChemBridge database, 265,242 compounds from the National Cancer Institute (NCI) database, and 885 natural compounds from the ZINC database. After the application of filters, four compounds from ChemBridge, one compound from ZINC, and three compounds from NCI were shortlisted as potential hits (antagonists) against IP₃R. The identified hits could further assist in the design and optimization of lead structures for the targeting and remodeling of Ca²⁺ signals in cancer.

Structure-based virtual screening for novel potential selective inhibitors of class IIa histone deacetylases for cancer treatment

The fundamental cause of human cancer is strongly influenced by down- or up-regulations of epigenetic factors. Upregulated histone deacetylases (HDAC) have been shown to be effectively neutralized by the action of HDACs inhibitors (HDACi). However, cytotoxicity has been reported in normal cells because of non-specificity of several available HDACis that are in clinical use or at different phases of clinical trials. Because of the high amino acid sequence and structural similarity among HDAC enzymes, it is believed to be a challenging task to obtain isoform-selectivity. The essential aim of the present research work was to identify isoform-selective inhibitors against class IIa HDACs via structure-based drug design. Based on the highest binding affinity and isoform-selectivity, the top-ranked inhibitors were in silico tested for their absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties, which were classified as drug-like compounds. Later, molecular dynamics simulation (MD) was carried out for all compound-protein complexes to evaluate the structural stability and the biding mode of the inhibitors, which showed high stability throughout the 100 ns simulation. Free binding energy predictions by MM-PBSA method showed the high binding affinity of the identified compounds toward their respective targets. Hence, these inhibitors could be used as drug candidates or as lead compounds for more in silico or in vitro optimization to design safe isoform-selective HDACs inhibitors.

Receptor-Based Pharmacophore Modeling in the Search for Natural Products for COVID-19 Mpro

Considering the urgency of the COVID-19 pandemic, we developed a receptor-based pharmacophore model for identifying FDA-approved drugs and hits from natural products. The COVID-19 main protease (Mpro) was selected for the development of the pharmacophore model. The model consisted of a hydrogen bond acceptor, donor, and hydrophobic features. These features demonstrated good corroboration with a previously reported model that was used to validate the present model, showing an RMSD value of 0.32. The virtual screening was carried out using the ZINC database. A set of 208,000 hits was extracted and filtered using the ligand pharmacophore mapping, applying the lead-like properties. Lipinski's filter and the fit value filter were used to minimize hits to the top 2000. Simultaneous docking was carried out for 200 hits for natural drugs belonging to the FDA-approved drug database. The top 28 hits from these experiments, with promising predicted pharmacodynamic and pharmacokinetic properties, are reported here. To optimize these hits as Mpro inhibitors and potential treatment options for COVID-19, bench work investigations are needed.

Pharmacophore-based approaches in the rational repurposing technique for FDA approved drugs targeting SARS-CoV-2 Mpro

Novel coronavirus (CoV) is the primary etiological virus responsible for the pandemic that started in Wuhan in 2019–2020. This viral disease is extremely prevalent and has spread around the world. Preventive steps are restricted social contact and isolation of the sick individual to avoid person-to-person transmission. There is currently no cure available for the disease and the search for novel medications or successful therapeutics is intensive, time-consuming, and laborious. An effective approach in managing this pandemic is to develop therapeutically active drugs by repurposing or repositioning existing drugs or active molecules. In this work, we developed a feature-based pharmacophore model using reported compounds that inhibit SARS-CoV-2. This model was validated and used to screen the library of 565 FDA-approved drugs against the viral main protease (M^pro), resulting in 66 drugs interacting with M^pro with higher binding scores in docking experiments than drugs previously reported for the target diseases. The study identified drugs from many important classes, viz. D₂ receptor antagonist, HMG-CoA inhibitors, HIV reverse transcriptase and protease inhibitors, anticancer agents and folate inhibitors, which can potentially interact with and inhibit the SARS-CoV-2 M^pro. This validated approach may help in finding the urgently needed drugs for the SARS-CoV-2 pandemic with infinitesimal chances of failure.

Novel coronavirus (CoV) is the primary etiological virus responsible for the pandemic that started in Wuhan in 2019–2020.

Design, 3D QSAR modeling and docking of TGF-β type I inhibitors to target cancer

Transforming growth factor-β (TGF-β) family members plays a vital role in regulating hormonal function, bone formation, tissue remodeling, and erythropoiesis, cell growth and apoptosis. TGF-β super-family members mediate signal transduction via serine/threonine kinase receptors located on the cell membrane. Variation in expression of the TGF-β type I and II receptors in the cancer cells compromise its tumor suppressor activities which direct to oncogenic functions. The present study was aimed to screen the potent TGF-β type I inhibitors through atom based 3D-QSAR and pharmacophore modelling. For this purpose, we have chosen known TGF-β type I inhibitors from the binding database. The PHASE module of Schrodinger identified the best Pharmacophore model which includes three hydrogen bond acceptors (A), one hydrophobic region (H), and one ring (R) as the structural features. The top pharmacophore model AAAHR.27 was chosen with the R² value of 0.94 and validated externally using molecules of the test set. Moreover the AAAHR.27 model underwent virtual screening using the molecules from the NCI, ZINC and Maybridge database. The screened molecules were further filtered using molecular docking and ADME properties prediction. Additionally, the 7 lead molecules were compared with a newly identified compound "SB431542" (well known TGF-β type I receptor inhibitor) and Galunisertib, a small molecule inhibitor of TGF-β type I, under clinical development (phase II trials) using the docking score and other binding properties. Also a top scored screened molecule from our study has been compared and confirmed using molecular dynamic simulation studies. By this way, we have obtained 7 distinct drug-like TGF-β type I inhibitors which can be beneficial in suppressing cancers reported with up-regulation of TGF-β type I. This result highlights the guidelines for designing molecules with TGF-β Type I inhibitory properties.

Chemical curation to improve data accuracy: recent development of the 3DMET database

We have developed a three-dimensional structure database of natural metabolites (3DMET). Early development of the 3DMET database relied on content auto-generated from 2D-structures of other chemical databases. From 2009, we began manual curation, obtaining new compounds from published works. In the process of curation, problems of digitizing 3D-structures from structure drawings of documents were accumulated. As the same as auto-generation, structure drawings should be also payed attention about stereochemistry. Our experiences in manual curation of 3DMET, as described herein, may be useful to others in this field of research and for the development of supporting systems of a chemical structure database. Manual curation is still necessary for proper database entry of the 3D-configurations of chiral atoms, a problem encountered frequently among natural products.

NMR-Fragment Based Virtual Screening: A Brief Overview

Fragment-based drug discovery (FBDD) using NMR has become a central approach over the last twenty years for development of small molecule inhibitors against biological macromolecules, to control a variety of cellular processes. Yet, several considerations should be taken into account for obtaining a therapeutically relevant agent. In this review, we aim to list the considerations that make NMR fragment screening a successful process for yielding potent inhibitors. Factors that may govern the competence of NMR in fragment based drug discovery are discussed, as well as later steps that involve optimization of hits obtained by NMR-FBDD.

Computational modeling of the bat HKU4 coronavirus 3CLpro inhibitors as a tool for the development of antivirals against the emerging Middle East respiratory syndrome (MERS) coronavirus

The Middle East respiratory syndrome coronavirus (MERS‐CoV) is an emerging virus that poses a major challenge to clinical management.

The 3C‐like protease (3CL^pro) is essential for viral replication and thus represents a potential target for antiviral drug development. Presently, very few data are available on MERS‐CoV 3CL^pro inhibition by small molecules. We conducted extensive exploration of the pharmacophoric space of a recently identified set of peptidomimetic inhibitors of the bat HKU4‐CoV 3CL^pro. HKU4‐CoV 3CL^pro shares high sequence identity (81%) with the MERS‐CoV enzyme and thus represents a potential surrogate model for anti‐MERS drug discovery. We used 2 well‐established methods: Quantitative structure‐activity relationship (QSAR)‐guided modeling and docking‐based comparative intermolecular contacts analysis. The established pharmacophore models highlight structural features needed for ligand recognition and revealed important binding‐pocket regions involved in 3CL^pro‐ligand interactions. The best models were used as 3D queries to screen the National Cancer Institute database for novel nonpeptidomimetic 3CL^pro inhibitors. The identified hits were tested for HKU4‐CoV and MERS‐CoV 3CL^pro inhibition. Two hits, which share the phenylsulfonamide fragment, showed moderate inhibitory activity against the MERS‐CoV 3CL^pro and represent a potential starting point for the development of novel anti‐MERS agents. To the best of our knowledge, this is the first pharmacophore modeling study supported by in vitro validation on the MERS‐CoV 3CL^pro.

Highlights

MERS‐CoV is an emerging virus that is closely related to the bat HKU4‐CoV.

3CL^pro is a potential drug target for coronavirus infection.

HKU4‐CoV 3CL^pro is a useful surrogate model for the identification of MERS‐CoV 3CL^pro enzyme inhibitors.

dbCICA is a very robust modeling method for hit identification.

The phenylsulfonamide scaffold represents a potential starting point for MERS coronavirus 3CL^pro inhibitors development.

Lead identification for the K-Ras protein: virtual screening and combinatorial fragment-based approaches

OBJECTIVE

Kirsten rat sarcoma (K-Ras) protein is a member of Ras family belonging to the small guanosine triphosphatases superfamily. The members of this family share a conserved structure and biochemical properties, acting as binary molecular switches. The guanosine triphosphate-bound active K-Ras interacts with a range of effectors, resulting in the stimulation of downstream signaling pathways regulating cell proliferation, differentiation, and apoptosis. Efforts to target K-Ras have been unsuccessful until now, placing it among high-value molecules against which developing a therapy would have an enormous impact. K-Ras transduces signals when it binds to guanosine triphosphate by directly binding to downstream effector proteins, but in case of guanosine diphosphate-bound conformation, these interactions get disrupted.

METHODS

In the present study, we targeted the nucleotide-binding site in the "on" and "off" state conformations of the K-Ras protein to find out suitable lead compounds. A structure-based virtual screening approach has been used to screen compounds from different databases, followed by a combinatorial fragment-based approach to design the apposite lead for the K-Ras protein.

RESULTS

Interestingly, the designed compounds exhibit a binding preference for the "off" state over "on" state conformation of K-Ras protein. Moreover, the designed compounds' interactions are similar to guanosine diphosphate and, thus, could presumably act as a potential lead for K-Ras. The predicted drug-likeness properties of these compounds suggest that these compounds follow the Lipinski's rule of five and have tolerable absorption, distribution, metabolism, excretion and toxicity values.

CONCLUSION

Thus, through the current study, we propose targeting only "off" state conformations as a promising strategy for the design of reversible inhibitors to pharmacologically inhibit distinct conformations of K-Ras protein.

Identification of a putative Tdp1 inhibitor (CD00509) by in vitro and cell-based assays

Mutations of DNA repair pathways contribute to tumorigenesis and provide a therapeutic target for synthetic lethal interactions in tumor cells. Given that tyrosyl-DNA phosphodiesterase 1 (Tdp1) repairs stalled topoisomerase-I DNA complexes, we hypothesized that inhibition of Tdp1 has synthetic lethal effects in some cancers. To test this, we screened tumor arrays for Tdp1 expression and observed that Tdp1 is expressed in many tumors, including more than 90% of human breast tumors. Subsequent chemical screening identified putative Tdp1 inhibitors. Treatment of control human mammary epithelial cells and the breast cancer cell line MCF-7 with compound CD00509 preferentially sensitized MCF-7 cells to camptothecin and decreased cell proliferation 25% more than camptothecin treatment alone. This suggests that CD00509 specifically targeted Tdp1 in vitro, and CD00509 increased the sensitivity of wild-type murine embryonic fibroblasts (MEFs) to camptothecin to a degree comparable to that of Tdp1(-/-) MEFs. In addition, consistent with poly ADP-ribose polymerase-1 (PARP-1) collaborating with Tdp1 in DNA repair, combined Tdp1 and PARP-1 inhibition was more detrimental to MCF-7 cells than either treatment alone, whereas the combination was not additively harmful to control mammary cells. We conclude that targeting Tdp1 in anticancer therapy preferentially enhances the sensitivity of some breast cancer cells to camptothecin and may be an effective adjuvant for breast cancer therapy.

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

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

Computational methods in drug discovery

Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.

Identification of novel liver X receptor activators by structure-based modeling

Liver X receptors (LXRs) are members of the nuclear receptor family. Activators of LXRs are of high pharmacological interest as LXRs regulate cholesterol, fatty acid, and carbohydrate metabolism as well as inflammatory processes. On the basis of different X-ray crystal structures, we established a virtual screening workflow for the identification of novel LXR modulators. A two-step screening concept to identify active compounds included 3D-pharmacophore filters and rescoring by shape alignment. Eighteen virtual hits were tested in vitro applying a reporter gene assay, where concentration-dependent activity was proven for four novel lead structures. The most active compound 10, a 1,4-naphthochinone, has an estimated EC₅₀ of around 5 μM.

Novel pharmacological chaperones that correct phenylketonuria in mice

Phenylketonuria (PKU) is caused by inherited phenylalanine-hydroxylase (PAH) deficiency and, in many genotypes, it is associated with protein misfolding. The natural cofactor of PAH, tetrahydrobiopterin (BH(4)), can act as a pharmacological chaperone (PC) that rescues enzyme function. However, BH(4) shows limited efficacy in some PKU genotypes and its chemical synthesis is very costly. Taking an integrated drug discovery approach which has not been applied to this target before, we identified alternative PCs for the treatment of PKU. Shape-focused virtual screening of the National Cancer Institute's chemical library identified 84 candidate molecules with potential to bind to the active site of PAH. An in vitro evaluation of these yielded six compounds that restored the enzymatic activity of the unstable PAHV106A variant and increased its stability in cell-based assays against proteolytic degradation. During a 3-day treatment study, two compounds (benzylhydantoin and 6-amino-5-(benzylamino)-uracil) substantially improved the in vivo Phe oxidation and blood Phe concentrations of PKU mice (Pah(enu1)). Notably, benzylhydantoin was twice as effective as tetrahydrobiopterin. In conclusion, we identified two PCs with high in vivo efficacy that may be further developed into a more effective drug treatment of PKU.

Structural diversity of biologically interesting datasets: a scaffold analysis approach

Background

The recent public availability of the human metabolome and natural product datasets has revitalized "metabolite-likeness" and "natural product-likeness" as a drug design concept to design lead libraries targeting specific pathways. Many reports have analyzed the physicochemical property space of biologically important datasets, with only a few comprehensively characterizing the scaffold diversity in public datasets of biological interest. With large collections of high quality public data currently available, we carried out a comparative analysis of current day leads with other biologically relevant datasets.

Results

In this study, we note a two-fold enrichment of metabolite scaffolds in drug dataset (42%) as compared to currently used lead libraries (23%). We also note that only a small percentage (5%) of natural product scaffolds space is shared by the lead dataset. We have identified specific scaffolds that are present in metabolites and natural products, with close counterparts in the drugs, but are missing in the lead dataset. To determine the distribution of compounds in physicochemical property space we analyzed the molecular polar surface area, the molecular solubility, the number of rings and the number of rotatable bonds in addition to four well-known Lipinski properties. Here, we note that, with only few exceptions, most of the drugs follow Lipinski's rule. The average values of the molecular polar surface area and the molecular solubility in metabolites is the highest while the number of rings is the lowest. In addition, we note that natural products contain the maximum number of rings and the rotatable bonds than any other dataset under consideration.

Conclusions

Currently used lead libraries make little use of the metabolites and natural products scaffold space. We believe that metabolites and natural products are recognized by at least one protein in the biosphere therefore, sampling the fragment and scaffold space of these compounds, along with the knowledge of distribution in physicochemical property space, can result in better lead libraries. Hence, we recommend the greater use of metabolites and natural products while designing lead libraries. Nevertheless, metabolites have a limited distribution in chemical space that limits the usage of metabolites in library design.

Novel neuraminidase inhibitors: identification, biological evaluation and investigations of the binding mode

Background: The pathogenicity of influenza A and B viruses depends on the function of influenza neuraminidase (NA). Emerging resistant influenza A viruses of subtype H1N1 increasingly challenge the effectiveness of established NA inhibitors. Recent computational studies have indicated several weak points of NA that can be exploited for rational inhibitor design to conquer this imminent threat, such as the opening of the binding pocket due to the flexibility of the 150-, 245- and 430-loops. Methods: We employed shape-focused virtual screening based on a recently discovered lead compound, katsumadain A, to identify novel promising compounds with significant inhibitory efficacy on NA and resistance-breaking capacity on oseltamivir-resistant strains. A potential binding mode of these compounds was derived employing ligand-based techniques and protein–ligand docking using representative protein conformations selected from molecular dynamics simulations. Results: Five novel compounds were identified by virtual screening. Their IC50 values, determined in chemiluminescence-based NA inhibition assays, are in the range of 0.18–17 µM. In particular, artocarpin exhibits high affinity toward three H1N1 oseltamivir-sensitive influenza A viruses. It also inhibits the NA of an oseltamivir-resistant H1N1 isolate.

Identification of small-molecule inhibitors against human leukocyte antigen-death receptor 4 (HLA-DR4) through a comprehensive strategy

Rheumatoid arthritis (RA) is an autoimmune disease mediated by T-lymphocytes and associated with the human leukocyte antigen-death receptor 4 (HLA-DR4). The HLA-DR4 protein selectively interacts with the antigenic peptides on the cell surface and presents them to the T cell receptor (TCR) on CD4+ T cells. The HLA-DR4-antigen-TCR complex initiates the autoimmune response and eventually causes the chronic inflammation within patients bodies. To inhibit HLA-DR4-restricted T cell activation, an ideal approach is to discover non-T cell stimulating substrates that specifically bind to HLA-DR4. In this paper, a comprehensive structure-based design strategy involved de novo design approach, pharmacophore search, and dock method was presented and applied to "simplify" the known binding peptide ligand of HLA-DR4 and identified specific small-molecule inhibitors for HLA-DR4. The designed three-step strategy successfully identified five nonpeptide ligands with novel scaffolds from a chemical library containing 4 × 10(6) commercially available compounds within a tolerable computing time. The identified five chemicals, BAS-0219606, T0506-2494, 6436645, 3S-71981, and KM 11073, are all non-T cell stimulators and are able to significantly inhibit HLA-DR4-restricted T cell activation induced by type II collagen (CII) 263-272 peptide. IC(50) for the best two potentials, BAS-0219606 and T0506-2494, was 31 and 17 μM, respectively, which is equivalent or better than the known peptide ligands. It is hopeful that they can be used as effective therapeutic means for further treatment of RA patients. In addition, the comprehensive strategy presented in this paper exhibited itself to be an effective flow line from peptide ligands to small-molecule inhibitors and will have applications to other targets.

Computer tools in the discovery of HIV-1 integrase inhibitors

Computer-aided drug design (CADD) methodologies have made great advances and contributed significantly to the discovery and/or optimization of many clinically used drugs in recent years. CADD tools have likewise been applied to the discovery of inhibitors of HIV-1 integrase, a difficult and worthwhile target for the development of efficient anti-HIV drugs. This article reviews the application of CADD tools, including pharmacophore search, quantitative structure-activity relationships, model building of integrase complexed with viral DNA and quantum-chemical studies in the discovery of HIV-1 integrase inhibitors. Different structurally diverse integrase inhibitors have been identified by, or with significant help from, various CADD tools.

Stimulation of N-cadherin-dependent neurite outgrowth by small molecule peptide mimetic agonists of the N-cadherin HAV motif

N-cadherin is a cell adhesion molecule that promotes axon outgrowth and synapse formation during the development of the central nervous system. In addition, N-cadherin promotes glial cell adhesion and myelination of axons. Therefore, stimulating N-cadherin function with N-cadherin agonists could be used therapeutically to promote regeneration of the nervous system and remyelination after injury or disease. In the extracellular domain of N-cadherin, the amino acid sequence HAV is required for N-cadherin-mediated adhesion and neurite outgrowth. The ADH-1 cyclic peptide, derived from the N-cadherin HAV site, is an effective antagonist of N-cadherin-mediated neurite outgrowth and is currently being tested in clinical trials for cancer chemotherapy. Of interest, a dimeric version of this cyclic peptide, N-Ac-CHAVDINGHAVDIC-NH(2), functions as an N-cadherin agonist. This dimeric peptide agonist and the peptide antagonist ADH-1 both have limitations as drugs due to their metabolic instability and lack of oral delivery. To address this issue Adherex Technologies Inc. generated a small molecule library of peptidomimetics to the HAV region of N-cadherin, which would be more amenable to therapeutic use. We screened the Adherex library for compounds that altered neurite outgrowth and identified eight N-cadherin agonists that stimulated N-cadherin-dependent neurite outgrowth. Five of these agonists also stimulated retinal cell migration. These small molecule agonists may be effective reagents for promoting axon growth and remyelination after injury or disease.

Non-peptidic thrombospondin-1 mimics as fibroblast growth factor-2 inhibitors: an integrated strategy for the development of new antiangiogenic compounds

Endogenous inhibitors of angiogenesis, such as thrombospondin-1 (TSP-1), are promising sources of therapeutic agents to treat angiogenesis-driven diseases, including cancer. TSP-1 regulates angiogenesis through different mechanisms, including binding and sequestration of the angiogenic factor fibroblast growth factor-2 (FGF-2), through a site located in the calcium binding type III repeats. We hypothesized that the FGF-2 binding sequence of TSP-1 might serve as a template for the development of inhibitors of angiogenesis. Using a peptide array approach followed by binding assays with synthetic peptides and recombinant proteins, we identified a FGF-2 binding sequence of TSP-1 in the 15-mer sequence DDDDDNDKIPDDRDN. Molecular dynamics simulations, taking the full flexibility of the ligand and receptor into account, and nuclear magnetic resonance identified the relevant residues and conformational determinants for the peptide-FGF interaction. This information was translated into a pharmacophore model used to screen the NCI2003 small molecule databases, leading to the identification of three small molecules that bound FGF-2 with affinity in the submicromolar range. The lead compounds inhibited FGF-2-induced endothelial cell proliferation in vitro and affected angiogenesis induced by FGF-2 in the chicken chorioallantoic membrane assay. These small molecules, therefore, represent promising leads for the development of antiangiogenic agents. Altogether, this study demonstrates that new biological insights obtained by integrated multidisciplinary approaches can be used to develop small molecule mimics of endogenous proteins as therapeutic agents.

Novel peptide mimetic small molecules of the HAV motif in N-cadherin inhibit N-cadherin-mediated neurite outgrowth and cell adhesion

The cell adhesion molecule, N-cadherin, stabilizes cell-cell junctions and promotes cellular migration during tissue morphogenesis in development. N-cadherin is also implicated in mediating tumor progression and metastasis in cancer. Therefore, developing antagonists of N-cadherin adhesion may be of therapeutic value in cancer treatment. The amino acid sequence HAV in the extracellular domain of N-cadherin is required for N-cadherin-mediated adhesion and migration. A cyclic peptide, ADH-1, derived from the N-cadherin HAV site is an effective antagonist of N-cadherin-mediated processes and is now in clinical trials for cancer chemotherapy. Because it is a peptide, ADH-1 has certain limitations as a drug, namely its metabolic instability and lack of oral delivery. Adherex set out to identify small molecule antagonists of N-cadherin, which would be more amenable to therapeutic use. Using three-dimensional computational screening, Adherex identified a set of small molecules as potential antagonists with sufficient structural similarity to the HAV region of N-cadherin. We tested the ability of these small molecules to interfere with two N-cadherin-dependent processes: neurite outgrowth (axonal migration) and N-cadherin-dependent cell adhesion. We identified 21 N-cadherin antagonists of varying potency. More importantly, our studies demonstrate that these compounds are significantly more potent than ADH-1 at perturbing N-cadherin-mediated processes. The IC(50) of ADH-1 is 2.33 mM while the IC(50) of the small molecules ranges from 4.5 to 30 microM. Given the efficacy of ADH-1 for treating cancer, these small molecule antagonists will be highly effective in treatment of cancer metastasis and conditions of aberrant neurite outgrowth, such as neuropathic pain.

Computational selection of inhibitors of Abeta aggregation and neuronal toxicity

Alzheimer's disease (AD) is characterized by the cerebral accumulation of misfolded and aggregated amyloid-beta protein (Abeta). Disease symptoms can be alleviated, in vitro and in vivo, by 'beta-sheet breaker' pentapeptides that reduce plaque load. However the peptide nature of these compounds, made them biologically unstable and unable to penetrate membranes with high efficiency. The main goal of this study was to use computational methods to identify small molecule mimetics with better drug-like properties. For this purpose, the docked conformations of the active peptides were used to identify compounds with similar activities. A series of related beta-sheet breaker peptides were docked to solid state NMR structures of a fibrillar form of Abeta. The lowest energy conformations of the active peptides were used to design three dimensional (3D)-pharmacophores, suitable for screening the NCI database with Unity. Small molecular weight compounds with physicochemical features and a conformation similar to the active peptides were selected, ranked by docking and biochemical parameters. Of 16 diverse compounds selected for experimental screening, 2 prevented and reversed Abeta aggregation at 2-3microM concentration, as measured by Thioflavin T (ThT) fluorescence and ELISA assays. They also prevented the toxic effects of aggregated Abeta on neuroblastoma cells. Their low molecular weight and aqueous solubility makes them promising lead compounds for treating AD.

Combining docking with pharmacophore filtering for improved virtual screening

BACKGROUND

Virtual screening is used to distinguish potential leads from inactive compounds in a database of chemical samples. One method for accomplishing this is by docking compounds into the structure of a receptor binding site in order to rank-order compounds by the quality of the interactions they form with the receptor. It is generally established that docking can be reasonably successful at generating good poses of a ligand in an active site. However, the scoring functions that are used with docking are typically not successful at correctly ranking ligands according to binding affinity or even distinguishing correct poses of a given ligand from incorrect ones.

RESULTS

We have developed a simple method for reducing the number of false positives in a virtual screen, meaning ligands which are scored highly by the docking program but do not bind well in reality. This method uses a docking program for pose generation without regard to scoring, followed by filtering with receptor-based pharmacophore searches. We applied it to three test-case targets: neuraminidase A, cyclin-dependent kinase 2, and the C1 domain of protein kinase C.

CONCLUSION

The pharmacophore filtering method can perform better than more traditional docking + scoring methods, and allows the advantages of both docking-based and pharmacophore-based approaches to virtual screening to be fully realized.

Small-molecule inhibitors of the MDM2-p53 protein-protein interaction to reactivate p53 function: a novel approach for cancer therapy

Tumor suppressor p53 is an attractive cancer therapeutic target because it can be functionally activated to eradicate tumors. Direct gene alterations in p53 or interaction between p53 and MDM2 proteins are two alternative mechanisms for the inactivation of p53 function. Designing small molecules to block the MDM2-p53 interaction and reactivate the p53 function is a promising therapeutic strategy for the treatment of cancers retaining wild-type p53. This review will highlight recent advances in the design and development of small-molecule inhibitors of the MDM2-p53 interaction as new cancer therapies. A number of these small-molecule inhibitors, such as analogs of MI-219 and Nutlin-3, have progressed to advanced preclinical development or early phase clinical trials.

Structure-based tailoring of compound libraries for high-throughput screening: discovery of novel EphB4 kinase inhibitors

High-throughput docking is a computational tool frequently used to discover small-molecule inhibitors of enzymes or receptors of known three-dimensional structure. Because of the large number of molecules in chemical libraries, automatic procedures to prune multimillion compound collections are useful for high-throughput docking and necessary for in vitro screening. Here, we propose an anchor-based library tailoring approach (termed ALTA) to focus a chemical library by docking and prioritizing molecular fragments according to their binding energy which includes continuum electrostatics solvation. In principle, ALTA does not require prior knowledge of known inhibitors, but receptor-based pharmacophore information (hydrogen bonds with the hinge region) is additionally used here to identify molecules with optimal anchor fragments for the ATP-binding site of the EphB4 receptor tyrosine kinase. The 21,418 molecules of the focused library (from an initial collection of about 730,000) are docked into EphB4 and ranked by force-field-based energy including electrostatic solvation. Among the 43 compounds tested in vitro, eight molecules originating from two different anchors show low-micromolar activity in a fluorescence-based enzymatic assay. Four of them are active in a cell-based assay and are potential anti-angiogenic compounds.

Design of ligand binding to an engineered protein cavity using virtual screening and thermal up-shift evaluation

Proteins could be used to carry and deliver small compounds. As a tool for designing ligand binding sites in protein cores, a three-step virtual screening method is presented that has been optimised using existing data on T4 lysozyme complexes and tested in a newly engineered cavity in flavodoxin. The method can pinpoint, in large databases, ligands of specific protein cavities. In the first step, physico-chemical filters are used to screen the library and discard a majority of compounds. In the second step, a flexible, fast docking procedure is used to score and select a smaller number of compounds as potential binders. In the third step, a finer method is used to dock promising molecules of the hit list into the protein cavity, and an optimised free energy function allows discarding the few false positives by calculating the affinity of the modelled complexes. To demonstrate the portability of the method, several cavities have been designed and engineered in the flavodoxin from Anabaena PCC 7119, and the W66F/L44A double mutant has been selected as a suitable host protein. The NCI database has then been screened for potential binders, and the binding to the engineered cavity of five promising compounds and three tentative non-binders has been experimentally tested by thermal up-shift assays and spectroscopic titrations. The five tentative binders (some apolar and some polar), unlike the three tentative non-binders, are shown to bind to the host mutant and, importantly, not to bind to the wild type protein. The three-step virtual screening method developed can thus be used to identify ligands of buried protein cavities. We anticipate that the method could also be used, in a reverse manner, to identify natural or engineerable protein cavities for the hosting of ligands of interest.

Introduction to cheminformatics

Cheminformatics is a relatively new field of information technology that focuses on the collection, storage, analysis, and manipulation of chemical data. The chemical data of interest typically includes information on small molecule formulas, structures, properties, spectra, and activities (biological or industrial). Cheminformatics originally emerged as a vehicle to help the drug discovery and development process, however cheminformatics now plays an increasingly important role in many areas of biology, chemistry, and biochemistry. The intent of this unit is to give readers some introduction into the field of cheminformatics and to show how cheminformatics not only shares many similarities with the field of bioinformatics, but that it can also enhance much of what is currently done in bioinformatics.

Identification of non-lipid LPA3 antagonists by virtual screening

In the present study, we utilized virtual screening to identify LPA(3) antagonists. We have developed a three-point structure-based pharmacophore model based on known LPA(3) antagonists. This model was used to mine the NCI database. Docking, pharmacophore development, and database mining produced new, non-lipid leads. Experimental testing of seven computationally selected pharmacophore hits produced one potentiator and three antagonists, one of which displays both LPA(3) selectivity and nanomolar potency. Similarity searching in the ChemBridge database using the most promising lead as the search target produced four additional LPA(3) antagonists and a potent dual LPA(1&2) antagonist.

SHEF: a vHTS geometrical filter using coefficients of spherical harmonic molecular surfaces

SHEF (spherical harmonic coefficient filter), a geometrical matching procedure constituting a preliminary step in the virtual high throughput screening of large databases of small drug-like molecules, is demonstrated. This filter uses a description of both the binding site of the target and the ligand surfaces using spherical harmonic polynomial expansions. Using this representation, which is based on limited sets of spherical harmonic coefficients, considerably reduces the complexity of surface complementarity calculation. As a first test, 188 known protein-ligand complexes were used, and the results of docking the abstracted ligands into the bare proteins using SHEF were compared to the original X-ray structures. The ability of SHEF to retrieve known ligands "hidden" in a virtual library of 1,000 randomly selected drug-like compounds is also demonstrated.

Receptor flexibility for large-scale in silico ligand screens: chances and challenges

An important contribution to today's computer-aided drug design is the automated screening of large compound databases against structurally resolved protein receptors targets. The introduction of ligand flexibility has, by now, become a standardized procedure. In contrast, a general approach to treat target degrees of freedom is still to be found, a consequence of the extreme increase of computational complexity, which comes along with the relaxation of protein degrees of freedom. In this chapter, we discuss in some detail both benefits and present limitations of target flexibility for high-throughput in silico database screens. Among the benefits are an improved diversity of binding modes, which allows one to identify a wider class of drug candidates. The limitations are related to a diminishing docking accuracy and an increased number of false hits. Using the thymidine kinase receptor and ten known inhibitors as an example, we describe in detail how target flexibility was implemented and how it affected the screening performance.

Identification of inhibitors to papillomavirus type 16 E6 protein based on three-dimensional structures of interacting proteins

Human papillomaviruses (HPV) cause cutaneous and genital warts. A subset of HPV types is associated with a high-risk for progression to malignancy. The E6 protein from the high-risk HPV types represents an attractive target for intervention because of its roles in viral propagation and cellular transformation. E6 functions in part by interaction with human cellular proteins, several of which possess a helical E6-binding motif. The role for each amino acid in this motif for binding E6 has been tested through structure determination and site-directed mutagenesis. These structural and molecular biological approaches defined the spatial geometry of functional groups necessary for binding to E6. This E6-binding information (the E6-binding pharmacophore) was transferred into a three-dimensional query format suitable for computational screening of large chemical databases. Compounds were identified and tested using in vitro and cell culture-based assays. Several compounds selectively inhibited E6 interaction with the E6-binding protein E6AP and interfered with the ability of E6 to promote p53 degradation. Such compounds provide leads for the development of new pharmacologic agents to treat papillomavirus infections and their associated cancers.

Mining the NCI antiviral compounds for HIV-1 integrase inhibitors

HIV-1 integrase (IN) is an essential enzyme for effective viral replication and is a validated target for the development of antiretroviral drugs. Currently, there are no approved drugs targeting this enzyme. In this study, we have identified 11 structurally diverse small-molecule inhibitors of IN. These compounds have been selected by mining the moderately active antiviral molecules from a collection of 90,000 compounds screened by the National Cancer Institute (NCI) Antiviral Program. These compounds, which were screened at the NCI during the past 20 years, resulted in approximately 4000 compounds labeled as 'moderately active.' In our study, chalcone 11 shows the most potent activity with an IC(50) of 2+/-1 microM against purified IN in the presence of both Mn(2+) and Mg(2+) as cofactors. Docking simulations using the 11 identified inhibitors as a training set have elucidated two unique binding areas within the active site: the first encompasses the conserved D64-D116-E152 motif, while the other involves the flexible loop region formed by amino acid residues 140-149. The tested inhibitors exhibit favorable interactions with important amino acid residues through van der Waals and H-bonding contacts.

The Tropical Biominer Project: Mining Old Sources for New Drugs

The Tropical Biominer Project is a recent initiative from the Federal University of Minas Gerais (UFMG) and the Oswaldo Cruz foundation, with the participation of the Biominas Foundation (Belo Horizonte, Minas Gerais, Brazil) and the start-up Homologix. The main objective of the project is to build a new resource for the chemogenomics research, on chemical compounds, with a strong emphasis on natural molecules. Adopted technologies include the search of information from structured, semi-structured, and non-structured documents (the last two from the web) and datamining tools in order to gather information from different sources. The database is the support for developing applications to find new potential treatments for parasitic infections by using virtual screening tools. We present here the midpoint of the project: the conception and implementation of the Tropical Biominer Database. This is a Federated Database designed to store data from different resources. Connected to the database, a web crawler is able to gather information from distinct, patented web sites and store them after automatic classification using datamining tools. Finally, we demonstrate the interest of the approach, by formulating new hypotheses on specific targets of a natural compound, violacein, using inferences from a Virtual Screening procedure.