Computational Investigation Identified Potential Chemical Scaffolds for Heparanase as Anticancer Therapeutics

Heparanase (Hpse) is an endo-β-D-glucuronidase capable of cleaving heparan sulfate side chains. Its upregulated expression is implicated in tumor growth, metastasis and angiogenesis, thus making it an attractive target in cancer therapeutics. Currently, a few small molecule inhibitors have been reported to inhibit Hpse, with promising oral administration and pharmacokinetic (PK) properties. In the present study, a ligand-based pharmacophore model was generated from a dataset of well-known active small molecule Hpse inhibitors which were observed to display favorable PK properties. The compounds from the InterBioScreen database of natural (69,034) and synthetic (195,469) molecules were first filtered for their drug-likeness and the pharmacophore model was used to screen the drug-like database. The compounds acquired from screening were subjected to molecular docking with Heparanase, where two molecules used in pharmacophore generation were used as reference. From the docking analysis, 33 compounds displayed higher docking scores than the reference and favorable interactions with the catalytic residues. Complex interactions were further evaluated by molecular dynamics simulations to assess their stability over a period of 50 ns. Furthermore, the binding free energies of the 33 compounds revealed 2 natural and 2 synthetic compounds, with better binding affinities than reference molecules, and were, therefore, deemed as hits. The hit compounds presented from this in silico investigation could act as potent Heparanase inhibitors and further serve as lead scaffolds to develop compounds targeting Heparanase upregulation in cancer.     

Identification of New KRAS G12D Inhibitors through Computer-Aided Drug Discovery Methods

Owing to several mutations, the oncogene Kirsten rat sarcoma 2 viral oncogene homolog (KRAS) is activated in the majority of cancers, and targeting it has been pharmacologically challenging. In this study, using an in silico approach comprised of pharmacophore modeling, molecular docking, and molecular dynamics simulations, potential KRAS G12D inhibitors were investigated. A ligand-based common feature pharmacophore model was generated to identify the framework necessary for effective KRAS inhibition. The chemical features in the selected pharmacophore model comprised two hydrogen bond donors, one hydrogen bond acceptor, two aromatic rings and one hydrophobic feature. This model was used for screening in excess of 214,000 compounds from InterBioScreen (IBS) and ZINC databases. Eighteen compounds from the IBS and ten from the ZINC database mapped onto the pharmacophore model and were subjected to molecular docking. Molecular docking results highlighted a higher affinity of four hit compounds towards KRAS G12D in comparison to the reference inhibitor, BI-2852. Sequential molecular dynamics (MD) simulation studies revealed all four hit compounds them possess higher KRAS G12D binding free energy and demonstrate stable polar interaction with key residues. Further, Principal Component Analysis (PCA) analysis of the hit compounds in complex with KRAS G12D also indicated stability. Overall, the research undertaken provides strong support for further in vitro testing of these newly identified KRAS G12D inhibitors, particularly Hit1 and Hit2.     

Identification of Novel Potential β-N-Acetyl-D-Hexosaminidase Inhibitors by Virtual Screening, Molecular Dynamics Simulation and MM-PBSA Calculations

Chitinolytic β-N-acetyl-d-hexosaminidases, as a class of chitin hydrolysis enzyme in insects, are a potential species-specific target for developing environmentally-friendly pesticides. Until now, pesticides targeting chitinolytic β-N-acetyl-d-hexosaminidase have not been developed. This study demonstrates a combination of different theoretical methods for investigating the key structural features of this enzyme responsible for pesticide inhibition, thus allowing for the discovery of novel small molecule inhibitors. Firstly, based on the currently reported crystal structure of this protein (OfHex1.pdb), we conducted a pre-screening of a drug-like compound database with 8 × 10(6) compounds by using the expanded pesticide-likeness criteria, followed by docking-based screening, obtaining 5 top-ranked compounds with favorable docking conformation into OfHex1. Secondly, molecular docking and molecular dynamics simulations are performed for the five complexes and demonstrate that one main hydrophobic pocket formed by residues Trp424, Trp448 and Trp524, which is significant for stabilization of the ligand-receptor complex, and key residues Asp477 and Trp490, are respectively responsible for forming hydrogen-bonding and π-π stacking interactions with the ligands. Finally, the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) analysis indicates that van der Waals interactions are the main driving force for the inhibitor binding that agrees with the fact that the binding pocket of OfHex1 is mainly composed of hydrophobic residues. These results suggest that screening the ZINC database can maximize the identification of potential OfHex1 inhibitors and the computational protocol will be valuable for screening potential inhibitors of the binding mode, which is useful for the future rational design of novel, potent OfHex1-specific pesticides.     

Discovery of Novel Focal Adhesion Kinase Inhibitors Using a Hybrid Protocol of Virtual Screening Approach Based on Multicomplex-Based Pharmacophore and Molecular Docking

Focal adhesion kinase (FAK) is a tyrosine kinase that functions as a key orchestrator of signals leading to invasion and metastasis. In the current study, the multicomplex-based pharmacophore (MCBP)-guided method has been suggested to generate a comprehensive pharmacophore of FAK kinase based on seven crystal structures of FAK-inhibitor complexes. In this investigation, a hybrid protocol of virtual screening methods, comprising of pharmacophore model-based virtual screening (PB-VS) and docking-based virtual screening (DB-VS), is used for retrieving new FAK inhibitors from commercially available chemical databases. This hybrid virtual screening approach was then applied to screen several chemical databases, including the Specs (202,408 compounds) database. Thirty-five compounds were selected from the final hits and should be shifted to experimental studies. These results may provide important information for further research of novel FAK inhibitors.     

Novel Hybrid Virtual Screening Protocol Based on Molecular Docking and Structure-Based Pharmacophore for Discovery of Methionyl-tRNA Synthetase Inhibitors as Antibacterial Agents

Methione tRNA synthetase (MetRS) is an essential enzyme involved in protein biosynthesis in all living organisms and is a potential antibacterial target. In the current study, the structure-based pharmacophore (SBP)-guided method has been suggested to generate a comprehensive pharmacophore of MetRS based on fourteen crystal structures of MetRS-inhibitor complexes. In this investigation, a hybrid protocol of a virtual screening method, comprised of pharmacophore model-based virtual screening (PBVS), rigid and flexible docking-based virtual screenings (DBVS), is used for retrieving new MetRS inhibitors from commercially available chemical databases. This hybrid virtual screening approach was then applied to screen the Specs (202,408 compounds) database, a structurally diverse chemical database. Fifteen hit compounds were selected from the final hits and shifted to experimental studies. These results may provide important information for further research of novel MetRS inhibitors as antibacterial agents.     

Three-dimensional pharmacophore design and biochemical screening identifies substituted 1,2,4-triazoles as inhibitors of the annexin A2-S100A10 protein interaction

Protein interactions are increasingly appreciated as targets in small‐molecule drug discovery. The interaction between the adapter protein S100A10 and its binding partner annexin A2 is a potentially important drug target. To obtain small‐molecule starting points for inhibitors of this interaction, a three‐dimensional pharmacophore model was constructed from the X‐ray crystal structure of the complex between S100A10 and annexin A2. The pharmacophore model represents the favourable hydrophobic and hydrogen bond interactions between the two partners, as well as spatial and receptor site constraints (excluded volume spheres). Using this pharmacophore model, UNITY flex searches were carried out on a 3D library of 0.7 million commercially available compounds. This resulted in 568 hit compounds. Subsequently, GOLD docking studies were performed on these hits, and a set of 190 compounds were purchased and tested biochemically for inhibition of the protein interaction. Three compounds of similar chemical structure were identified as genuine inhibitors of the binding of annexin A2 to S100A10. The binding modes predicted by GOLD were in good agreement with their UNITY‐generated conformations. We synthesised a series of analogues revealing areas critical for binding. Thus computational predictions and biochemical screening can be used successfully to derive novel chemical classes of protein–protein interaction blockers.     

A Combined Pharmacophore Modeling, 3D QSAR and Virtual Screening Studies on Imidazopyridines as B-Raf Inhibitors

B-Raf kinase is an important target in treatment of cancers. In order to design and find potent B-Raf inhibitors (BRIs), 3D pharmacophore models were created using the Genetic Algorithm with Linear Assignment of Hypermolecular Alignment of Database (GALAHAD). The best pharmacophore model obtained which was used in effective alignment of the data set contains two acceptor atoms, three donor atoms and three hydrophobes. In succession, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on 39 imidazopyridine BRIs to build three dimensional quantitative structure-activity relationship (3D QSAR) models based on both pharmacophore and docking alignments. The CoMSIA model based on the pharmacophore alignment shows the best result (q² = 0.621, r²_pred = 0.885). This 3D QSAR approach provides significant insights that are useful for designing potent BRIs. In addition, the obtained best pharmacophore model was used for virtual screening against the NCI2000 database. The hit compounds were further filtered with molecular docking, and their biological activities were predicted using the CoMSIA model, and three potential BRIs with new skeletons were obtained.     

Novel Natural Inhibitors of CYP1A2 Identified by in Silico and in Vitro Screening

Inhibition of cytochrome P450 (CYP) is a major cause of herb-drug interactions. The CYP1A2 enzyme plays a major role in the metabolism of drugs in humans. Its broad substrate specificity, as well as its inhibition by a vast array of structurally diverse herbal active ingredients, has indicated the possibility of metabolic herb-drug interactions. Therefore nowadays searching inhibitors for CYP1A2 from herbal medicines are drawing much more attention by biological, chemical and pharmological scientists. In our work, a pharmacophore model as well as the docking technology is proposed to screen inhibitors from herbal ingredients data. Firstly different pharmaphore models were constructed and then validated and modified by 202 herbal ingredients. Secondly the best pharmaphore model was chosen to virtually screen the herbal data (a curated database of 989 herbal compounds). Then the hits (147 herbal compounds) were continued to be filtered by a docking process, and were tested in vitro successively. Finally, five of eighteen candidate compounds (272, 284, 300, 616 and 817) were found to have inhibition of CYP1A2 activity. The model developed in our study is efficient for in silico screening of large herbal databases in the identification of CYP1A2 inhibitors. It will play an important role to prevent the risk of herb-drug interactions at an early stage of the drug development process.     

A Comprehensive Survey of Prospective Structure-Based Virtual Screening for Early Drug Discovery in the Past Fifteen Years

Structure-based virtual screening (SBVS), also known as molecular docking, has been increasingly applied to discover small-molecule ligands based on the protein structures in the early stage of drug discovery. In this review, we comprehensively surveyed the prospective applications of molecular docking judged by solid experimental validations in the literature over the past fifteen years. Herein, we systematically analyzed the novelty of the targets and the docking hits, practical protocols of docking screening, and the following experimental validations. Among the 419 case studies we reviewed, most virtual screenings were carried out on widely studied targets, and only 22% were on less-explored new targets. Regarding docking software, GLIDE is the most popular one used in molecular docking, while the DOCK 3 series showed a strong capacity for large-scale virtual screening. Besides, the majority of identified hits are promising in structural novelty and one-quarter of the hits showed better potency than 1 μM, indicating that the primary advantage of SBVS is to discover new chemotypes rather than highly potent compounds. Furthermore, in most studies, only in vitro bioassays were carried out to validate the docking hits, which might limit the further characterization and development of the identified active compounds. Finally, several successful stories of SBVS with extensive experimental validations have been highlighted, which provide unique insights into future SBVS drug discovery campaigns.     

Dynophore-Based Approach in Virtual Screening: A Case of Human DNA Topoisomerase IIα

In this study, we utilized human DNA topoisomerase IIα as a model target to outline a dynophore-based approach to catalytic inhibitor design. Based on MD simulations of a known catalytic inhibitor and the native ATP ligand analog, AMP-PNP, we derived a joint dynophore model that supplements the static structure-based-pharmacophore information with a dynamic component. Subsequently, derived pharmacophore models were employed in a virtual screening campaign of a library of natural compounds. Experimental evaluation identified flavonoid compounds with promising topoisomerase IIα catalytic inhibition and binding studies confirmed interaction with the ATPase domain. We constructed a binding model through docking and extensively investigated it with molecular dynamics MD simulations, essential dynamics, and MM-GBSA free energy calculations, thus reconnecting the new results to the initial dynophore-based screening model. We not only demonstrate a new design strategy that incorporates a dynamic component of molecular recognition, but also highlight new derivates in the established flavonoid class of topoisomerase II inhibitors.     

A Search for Cyclin-Dependent Kinase 4/6 Inhibitors by Pharmacophore-Based Virtual Screening,Molecular Docking,and Molecular Dynamic Simulations

The G1 phase of cell cycle progression is regulated by Cyclin-Dependent Kinase 4 (CDK4) as well as Cyclin-Dependent Kinase 6 (CDK6), and the acivities of these enzymes are regulated by the catalytic subunit, cyclin D. Cell cycle control through selective pharmacological inhibition of CDK4/6 has proven to be beneficial in the treatment of estrogen receptor-positive (ER-positive) breast cancer, particularly improving the progression-free survival of patients. Thus, targeting specific inhibition on CDK4/6 is bound to increase therapeutic efficiency. This study aimed to obtain CDK4/6 inhibitors through a pharmacophore-based virtual screening of the ZINC15 purchasable compound database using the in silico method. The pharmacophore model was designed based on the FDA-approved cdk4/6 inhibitor structures, and molecular docking was performed to further screen the hit compounds obtained. A total of eight compounds were selected based on docking results and interactions with CDK4 and CDK6, using palbociclib as the reference drug. According to the results, the compounds of ZINC585292724 and ZINC585291674 were the best compounds based on free binding energy, as well as hydrogen bond stability, and, therefore, exhibit potential as starting points in the development of CDK4/6 inhibitors.     

The Discovery of Potentially Selective Human Neuronal Nitric Oxide Synthase (nNOS) Inhibitors: A Combination of Pharmacophore Modelling,CoMFA, Virtual Screening and Molecular Docking Studies

Neuronal nitric oxide synthase (nNOS) plays an important role in neurotransmission and smooth muscle relaxation. Selective inhibition of nNOS over its other isozymes is highly desirable for the treatment of neurodegenerative diseases to avoid undesirable effects. In this study, we present a workflow for the identification and prioritization of compounds as potentially selective human nNOS inhibitors. Three-dimensional pharmacophore models were constructed based on a set of known nNOS inhibitors. The pharmacophore models were evaluated by Pareto surface and CoMFA (Comparative Molecular Field Analysis) analyses. The best pharmacophore model, which included 7 pharmacophore features, was used as a search query in the SPECS database (SPECS^®, Delft, The Netherlands). The hit compounds were further filtered by scoring and docking. Ten hits were identified as potential selective nNOS inhibitors.     

Identification of important chemical features of 11β-hydroxysteroid dehydrogenase type1 inhibitors: application of ligand based virtual screening and density functional theory

11β-Hydroxysteroid dehydrogenase type1 (11βHSD1) regulates the conversion from inactive cortisone to active cortisol. Increased cortisol results in diabetes, hence quelling the activity of 11βHSD1 has been thought of as an effective approach for the treatment of diabetes. Quantitative hypotheses were developed and validated to identify the critical chemical features with reliable geometric constraints that contribute to the inhibition of 11βHSD1 function. The best hypothesis, Hypo1, which contains one-HBA; one-Hy-Ali, and two-RA features, was validated using Fischer's randomization method, a test and a decoy set. The well validated, Hypo1, was used as 3D query to perform a virtual screening of three different chemical databases. Compounds selected by Hypo1 in the virtual screening were filtered by applying Lipinski's rule of five, ADMET, and molecular docking. Finally, five hit compounds were selected as virtual novel hit molecules for 11βHSD1 based on their electronic properties calculated by Density functional theory.     

The Discovery of Potential MDM2 Inhibitors: A Combination of Pharmacophore Modeling,Virtual Screening,Molecular Docking Studies,and in vitro/in vivo Biological Evaluation

Small-molecule inhibitors of MDM2 that block the MDM2-p53 protein-protein interaction have been considered as potential therapeutic agents for the treatment of cancer. Here, we identify five highly potent inhibitors of MDM2 (termed as WY 1–5) that display significant inhibitory effects on MDM2-p53 interaction by using a combined strategy of pharmacophore modeling, virtual screening, and molecular docking studies. Among them, WY-5 is the most active MDM2 inhibitor with an IC₅₀ value of 14.1±2.8 nM. Moreover, WY-5 significantly up-regulate the protein level of p53 in SK-Hep-1 cells harboring wild-type p53. In vitro anticancer study reveals that WY-5 markedly inhibits the survival of SK-Hep-1 cells. In vivo anticancer study suggests that WY-5 significantly inhibits the growth of SK-Hep-1 cells-derived xenograft in nude mice, with no observable toxicity. Our results demonstrate that WY-5 may be a promising candidate for the treatment of cancer harboring wild-type p53.     

Combined Pharmacophore Modeling,Docking, and 3D QSAR Studies of ABCB1 and ABCC1 Transporter Inhibitors

Quinazolinones, indolo‐ and pyrrolopyrimidines with inhibitory effects toward ABCB1 (P‐gp) and ABCC1 (MRP1) transporters were studied by pharmacophore modeling, docking, and 3D QSAR to describe the binding preferences of the proteins. The pharmacophore overlays between dual and/or highly selective inhibitors point to binding sites of different topology and physiochemical properties for MRP1 and P‐gp. Docking of selective inhibitors into the P‐gp binding cavity by the use of a structural model based on the recently resolved P‐gp structure confirms the P‐gp pharmacophore features identified, and reveals the interactions of some functional groups and atoms in the structures with particular protein residues. The 3D QSAR analysis of the dual‐effect inhibitors allows satisfactory prediction of the selectivity index of the compounds and outlines electrostatics as most important for selectivity. The results from the combined modeling approach complement each other and could improve our understanding of the protein–ligand interactions involved, and could aid in the development of highly selective and potent inhibitors of P‐gp and MRP1.