Pharmacophore Model Generation Research Articles

Generation of 2D-QSAR and pharmacophore models for fishing better anti-leishmanial therapeutics

Generation of 2D-QSAR and pharmacophore models for fishing better anti-leishmanial therapeutics

Apo2ph4: A Versatile Workflow for the Generation of Receptor-based Pharmacophore Models for Virtual Screening.

Pharmacophore models are widely used as efficient virtual screening (VS) filters for the target-directed enrichment of large compound libraries. However, the generation of pharmacophore models that have the power to discriminate between active and inactive molecules traditionally requires structural information about ligand-target complexes or at the very least knowledge of one active ligand. The fact that the discovery of the first known active ligand of a newly investigated target represents a major hurdle at the beginning of every drug discovery project underscores the need for methods that are able to derive high-quality pharmacophore models even without the prior knowledge of any active ligand structures. In this work, we introduce a novel workflow, called apo2ph4, that enables the rapid derivation of pharmacophore models solely from the three-dimensional structure of the target receptor. The utility of this workflow is demonstrated retrospectively for the generation of a pharmacophore model for the M2 muscarinic acetylcholine receptor. Furthermore, in order to show the general applicability of apo2ph4, the workflow was employed for all 15 targets of the recently published LIT-PCBA dataset. Pharmacophore-based VS runs using the apo2ph4-derived models achieved a significant enrichment of actives for 13 targets. In the last presented example, a pharmacophore model derived from the etomidate site of the α1β2γ2 GABAA receptor was used in VS campaigns. Subsequent in vitro testing of selected hits revealed that 19 out of 20 (95%) tested compounds were able to significantly enhance GABA currents, which impressively demonstrates the applicability of apo2ph4 for real-world drug design projects.

Structure-guided identification of a selective sulfonamide-based inhibitor targeting the human carbonic anhydrase VA isoform.

In recent years, multistep hybrid computational protocols have attracted attention for their application in the drug discovery of enzyme inhibitors. So far, there are large collections of human carbonic anhydrase (hCA) inhibitors, but only a few of them selectively inhibit the mitochondrial isoforms hCA VA and VB as potential therapeutics in obesity treatment. Most sulfonamide-based inhibitors show poor selectivity for inhibiting isoforms of therapeutic interest over ubiquitous hCA I and hCA II. Herein, we propose a combination of ligand- and structure-based approaches to generate pharmacophore models for hCA VA inhibitors. Then, we performed a virtual screening (VS) campaign on a database of commercially available sulfonamides. Finally, the in silico screening followed by docking studies suggested several "hit compounds" that demonstrated to inhibit hCA VA at a low nanomolar concentration in a stopped-flow CO2 hydrase assay. Notably, the best candidate, 2-(3,4-dihydro-2H-quinolin-1-yl)-N-(4-sulfamoylphenyl)acetamide (code name VAME-28) proved to be a potent hCA VA inhibitor (Ki value of 54.8 nM) and a more selective agent over hCA II when compared to the reference compound topiramate.

Computer-aided Design of Novel CDK5 Inhibitors; Towards New Treatments of Glioblastoma

Abstract AIMS Design and statistically optimize an in silico screening protocol; apply the screening protocol to a compound database; in vitro validation of selected inhibitors (isolated CDK5 enzyme binding assay). METHOD Two large compound databases, ZINC15 and Analyticon Discovery, were used to screen for potential CDK5 ATP-binding site inhibitors. They were first filtered using a generated pharmacophore model and then virtually screened using Glide SP docking with a solved CDK5-p25 structure (PDB: 3O0G). The selected candidates were validated using enzyme binding assays to determine their inhibitory activity on CDK5, as well as against a panel of kinases. RESULTS Over 17,000 compounds were screened during molecular docking studies and of these, ~11,600 compounds returned which are predicted to bind to CDK5. The top 10% of docked compounds were analysed and 30 candidates were selected for single concentration screening at 50 µM concentrations. The 9 most potent compounds were selected for IC50 determinations and revealed 6 compounds with IC50 <10 µM. The selected novel compounds were also screened against a panel of kinases (CDK2, CDK5, CDK9, GSK-3β) with varying selective profiles observed. CONCLUSION Low micromolar potent compounds have been identified through in silico screening and validated in using in vitro binding assays. One flavonoid compound in particular, cirsiliol (IC50 = 5.90 µM), displayed best selectivity towards CDK5, a challenge in kinase inhibitor design. The identified compounds will now pass to cellular studies to determine their effect on Glioblastoma cell lines.

Common Structural Features of Hydrophobic α-Helical Hot Spots: Insights for the Design of Novel α-Helix Mimetics.

The binding conformations of α-helical hydrophobic hot spots are convergent into two spatial areas in protein-protein complex structures. The physical basis for convergence was disclosed, which allows the development of pharmacophore models for i/i + 4/i + 7 or i/i + 3/i + 4 α-helical hot spots. Further investigation revealed that this convergence of binding conformations is common among all hydrophobic hot spots regardless of their α-helical positions. This observation led to a streamlined generation of pharmacophore models for hydrophobic hot spots at any positions along the α-helix. These successfully evaluated pharmacophore models may be useful for designing novel α-helical hot spot mimetics.

A Multistage In Silico Study of Natural Potential Inhibitors Targeting SARS-CoV-2 Main Protease.

Among a group of 310 natural antiviral natural metabolites, our team identified three compounds as the most potent natural inhibitors against the SARS-CoV-2 main protease (PDB ID: 5R84), Mpro. The identified compounds are sattazolin and caprolactin A and B. A validated multistage in silico study was conducted using several techniques. First, the molecular structures of the selected metabolites were compared with that of GWS, the co-crystallized ligand of Mpro, in a structural similarity study. The aim of this study was to determine the thirty most similar metabolites (10%) that may bind to the Mpro similar to GWS. Then, molecular docking against Mpro and pharmacophore studies led to the choice of five metabolites that exhibited good binding modes against the Mpro and good fit values against the generated pharmacophore model. Among them, three metabolites were chosen according to ADMET studies. The most promising Mpro inhibitor was determined by toxicity and DFT studies to be caprolactin A (292). Finally, molecular dynamics (MD) simulation studies were performed for caprolactin A to confirm the obtained results and understand the thermodynamic characteristics of the binding. It is hoped that the accomplished results could represent a positive step in the battle against COVID-19 through further in vitro and in vivo studies on the selected compounds.

Pathogenomic in silico approach identifies NSP-A and Fe-IIISBP as possible drug targets in Neisseria Meningitidis MC58 and development of pharmacophores as novel therapeutic candidates.

Meningitis creates a life-threatening clinical crisis. Moreover, the administered antibiotics result into multi-drug resistance, thereby necessitating development of alternative therapeutic strategies. This study aimed at identifying novel-drug targets in Neisseria meningitidis and therapeutic molecules which can be exploited for the treatment of meningitis. Novel targets were identified by applying a pathogenomic approach involving protein data-set mining, subtractive channel analysis and subsequent qualitative analysis comprising of in silico pharmacokinetics, molecular docking and pharmacophore generation. Pathogenomic studies revealed Neisserial Surface Protein A (NSP-A) and Iron-III-Substrate Binding Protein (Fe-IIISBP) as potential targets. Two pharmacophore models comprising of 2-(biaryl) carbapenems, efavirenz, praziquantel and pyrimethamine for NSP-A and 2-(biaryl) carbapenems, trimipramine and pyrimethamine for Fe-IIISBP, showed successful docking, followed drug-likeness criteria and generated pharmacophore model with a score of 8.08 and 8.818, respectively, which had further been docked to the target stably. Thus, our study identifies NSP-A and Fe-IIISBP as novel targets in Neisseria meningitidis for which 2-(biaryl) carbapenems, efavirenz, praziquantel, trimipramine and pyrimethamine may be employed for effective treatment of meningitis.

Pharmacophore modeling for biological targets with high flexibility: LXRβ case study

Many biological targets are characterized by high binding pocket flexibility posing a challenge in identifying important ligand binding features. Liver X receptors (LXRs) are members of the nuclear hormone receptor super family. Although several X-ray structures for LXRβ bound ligands are available, differences in ligands’ binding poses and interactions in the ligand binding pocket causes a challenge in identifying general elements of ligand binding. In this study, we used several LXR X-ray structures and known LXR ligands to study how LXRβ ligands interact with the LXRβ receptor. Using this information, we generated several pharmacophore models that represents important chemical features necessary for LXR binding and activation. Our results show that generating pharmacophore models based on a combined approach of multiple ligands alignments and considering the ligands’ binding coordinates yielded the best results. The generated pharmacophore model will be useful for future ligand discovery of LXRβ modulators using virtual screening.

A Method for Automated Pharmacophore Model Generation Using Multiple Copy Simultaneous Search

Pharmacophores are three‐dimensional arrangements of molecular features required for biological activity that are often used in virtual screening efforts to prioritize ligands for experimental screening. G protein‐coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for ligand discovery and drug development. Ligand‐based pharmacophore models can be constructed to identify structural commonalities between known bioactive ligands for targets including GPCR. However, structure‐based pharmacophores (which only require a resolved or predictive structure for a protein) have gained more attention as the number of publicly available, high‐resolution GPCR structures have increased (113 unique GPCR represented as of November 24, 2021). Thus, the goal of this study was to develop a method of structure‐based pharmacophore model generation applicable to ligand discovery for GPCR with few known ligands. Pharmacophore models have been generated using the active sites of 8 class A GPCR crystal structures via automated feature annotation of 5 randomly selected functional group fragments in an attempt to sample diverse combinations of pharmacophore features to aid in virtual screening efforts. Each of the 5000 generated pharmacophores was then used to search a database containing active and decoy/inactive compounds for 30 class A GPCR and scored using enrichment factor and goodness‐of‐hit metrics to assess performance. As proof‐of‐principle for this method, pharmacophore models were also generated within two dopamine receptor 2 (D2) crystal structures to elucidate novel ligands possessing activity for this receptor. Application of this method to the set of 8 class A GPCR generated pharmacophore models possessing the theoretical maximum enrichment factor value in both resolved structures (8 of 8 cases) and predictive models (7 of 8 cases), implying that generated pharmacophore models will prove useful in the context of virtual screening. Furthermore, application of this protocol to the D2 receptor resulted in the identification of novel candidate ligands whose activity has been confirmed with two in vitroactivity assays.

Automated, Score‐Based Pharmacophore Generation Using Multiple Copy Simultaneous Search

Pharmacophores are three‐dimensional arrangements of molecular features required for biological activity that are often used in virtual screening efforts to prioritize ligands for experimental screening. G protein‐coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for ligand discovery and drug development. Pharmacophore models are most typically ligand‐based and constructed via the identification of structural commonalities between known bioactive ligands. However, structure‐based pharmacophore models (requiring only a target protein’s structure) provide an alternative to ligand‐based pharmacophore models. Although both ligand‐based and structure‐based pharmacophore models have exhibited success in prior virtual screening studies, most pharmacophore modeling efforts are not applicable to GPCR targets lacking known ligands. Consequently, the development of a purely structure‐based pharmacophore modeling protocol for GPCR has become an increasingly attractive approach as the number of publicly available, high‐resolution GPCR structures has increased (113 unique GPCR represented as of November 24, 2021). Thus, the aim of this work was to develop a structure‐based pharmacophore modeling approach that can generate well‐performing pharmacophore models in GPCR crystal structures and homology models. Pharmacophore models were generated in crystal structures and homology models of 13 class A GPCR via automated feature annotation of differing subsets of functional group fragments placed with Multiple Copy Simultaneous Search (MCSS). During the feature annotation process, distance cutoffs were enforced to generate combinations of pharmacophore features that reflect spatial arrangements of interactions typically observed in GPCR pharmacophore models. Resulting pharmacophore models for each target GPCR were used to search a database containing active and decoy/inactive compounds for 30 class A GPCR and scored using enrichment factor (EF) and goodness‐of‐hit (GH) metrics to assess performance. To identify well‐performing pharmacophore models for cases where active ligands are unknown, pharmacophore models were classified using cluster‐then‐predict logistic regression. As a proof‐of principle for this method, pharmacophore models were generated for the orphan GPCR GPR101 to elucidate candidate ligands that may possess activity for GPR101. Application of this method to the set of 13 class A GPCR targets resulted in the generation of pharmacophore models possessing EF scores ≥ 2 in both crystal structures (12 of 13 cases) and homology models (9 of 13 cases). In addition, classification of pharmacophore models with cluster‐then‐predict logistic regression resulted in positive predictive values (PPV) of at least 0.67 for pharmacophore models generated with selected MCSS fragment subsets. Lastly, implementation of generated pharmacophore models in a virtual screening workflow identified 33 candidate ligands for GPR101.

Structure-Based Virtual Screening towards the Discovery of Novel ULK1 Inhibitors with Anti-HCC Activities.

There is an urgent need to develop new effective therapies for HCC. Our previous study identified ULK1 as the potential target for HCC therapy and screened the compound XST-14 as a specific inhibitor of ULK1 to suppress HCC progression. However, the poor manufacturability of XST-14 impeded the process of its clinical translation. In this study, we first generated pharmacophore models of ULK1 based on the X-ray structure of UKL1 in complex with ligands. We then screened the Specs chemical library for potential UKL1 inhibitors. By molecular docking, we screened out the 19 compounds through structure-based virtual screening. Through CCK8 activity screening on HCC cells, we found that ZZY-19 displayed obvious cell killing effects on HCC cells. SPR assay indicated that ZZY-19 had a higher binding affinity for ULK1 than XST-14. Moreover, ZZY-19 induced the effects of anti-proliferation, anti-invasion and anti-migration in HCC cells. Mechanistically, ZZY-19 induces autophagy inhibition by reducing the expression of ULK1 on HCC cells. Especially, the combination of ZZY-19 with sorafenib synergistically suppresses the progression of HCC in vivo. Taken together, ZZY-19 was a potential candidate compound that targeted ULK1 and possessed promising anti-HCC activities by inhibiting autophagy.

Pharmacophore-guided virtual screening and dynamic simulation of Kallikrein-5 inhibitor: Discovery of potential molecules for rosacea therapy

Rosacea is a common chronic inflammatory skin disease characterized by flushing, transient erythema, persistent erythema, telangiectasia, papules, pustules, phymata, edema, pain, stinging, or burning, and its treatment is still being developed. Rosacea is known to be regulated by excessive activation of Kallikrein-5 (KLK5). This research aimed to discover potent compounds for rosacea therapy based on virtual screening guided by pharmacophore and molecular dynamic simulation targeting KLK5. First, we generated and validated the pharmacophore model for target using the model to screen the compounds from the ZINC database and then conducted molecular docking and molecular dynamic simulations on KLK5. The generated pharmacophore model of KLK5 inhibitor has several features, such as F1: aromatic, F2: aromatic, F3: aromatic, F4: aromatic and hydrophobic, and F5: hydrogen bonding acceptors and donors. Subsequently, we used the pharmacophore model to screen the ZINC database and obtained 102 hits. Six best hits were obtained based on the docking score of KLK5, and the molecular interactions were revealed. Furthermore, the molecular dynamic simulation was conducted for 50 ns, and the system stability was assessed via RMSD, RMSF, MM-PBSA binding free energy, Rg, SASA, and PCA analyses. ZINC000022339916 was found to exhibit better stability compared with other hit compounds, azelaic acid, and native ligand of co-crystal KLK5 inhibitor. The compound is currently in the ordering process, and the study of biological activity will be held and reported soon.

Ligand-based G Protein Coupled Receptor pharmacophore modeling: Assessing the role of ligand function in model development

Integral membrane proteins in the G Protein-Coupled Receptor (GPCR) class are attractive drug development targets. However, computational methods applicable to ligand discovery for many GPCR targets are restricted by limited numbers of known ligands. Pharmacophore models can be developed using variously sized training sets and applied in database mining to prioritize candidate ligands for subsequent validation. This in silico study assessed the impact of key pharmacophore modeling decisions that arise when known ligand numbers for a target of interest are low. GPCR included in this study are the adrenergic alpha-1A, 1D and 2A, adrenergic beta 2 and 3, kappa, delta and mu opioid, serotonin 1A and 2A, and the muscarinic 1 and 2 receptors, all of which have rich ligand data sets suitable to assess the performance of protocols intended for application to GPCR with limited ligand data availability. Impact of ligand function, potency and structural diversity in training set selection was assessed to define when pharmacophore modeling targeting GPCR with limited known ligands becomes viable. Pharmacophore elements and pharmacophore model selection criteria were also assessed. Pharmacophore model assessment was based on percent pharmacophore model generation failure, as well as Güner-Henry enrichment and goodness-of-hit scores. Three of seven pharmacophore element schemes evaluated in MOE 2018.0101, Unified, PCHD, and CHD, showed substantially lower failure rates and higher enrichment scores than the others. Enrichment and GH scores were used to compare construction protocol for pharmacophore models of varying purposes— such as function specific versus nonspecific ligand identification. Notably, pharmacophore models constructed from ligands of mixed functions (agonists and antagonists) were capable of enriching hitlists with active compounds, and therefore can be used when available sets of known ligands are limited in number.

Exploration of the structural requirements of Aurora Kinase B inhibitors by a combined QSAR, modelling and molecular simulation approach

Aurora kinase B plays an important role in the cell cycle to orchestrate the mitotic process. The amplification and overexpression of this kinase have been implicated in several human malignancies. Therefore, Aurora kinase B is a potential drug target for anticancer therapies. Here, we combine atom-based 3D-QSAR analysis and pharmacophore model generation to identify the principal structural features of acylureidoindolin derivatives that could potentially be responsible for the inhibition of Aurora kinase B. The selected CoMFA and CoMSIA model showed significant results with cross-validation values (q2) of 0.68, 0.641 and linear regression values (r2) of 0.971, 0.933 respectively. These values support the statistical reliability of our model. A pharmacophore model was also generated, incorporating features of reported crystal complex structures of Aurora kinase B. The pharmacophore model was used to screen commercial databases to retrieve potential lead candidates. The resulting hits were analyzed at each stage for diversity based on the pharmacophore model, followed by molecular docking and filtering based on their interaction with active site residues and 3D-QSAR predictions. Subsequently, MD simulations and binding free energy calculations were performed to test the predictions and to characterize interactions at the molecular level. The results suggested that the identified compounds retained the interactions with binding residues. Binding energy decomposition identified residues Glu155, Trp156 and Ala157 of site B and Leu83 and Leu207 of site C as major contributors to binding affinity, complementary to 3D-QSAR results. To best of our knowledge, this is the first comparison of WaterSwap field and 3D-QSAR maps. Overall, this integrated strategy provides a basis for the development of new and potential AK-B inhibitors and is applicable to other protein targets.

Identification of CDK7 Inhibitors from Natural Sources Using Pharmacoinformatics and Molecular Dynamics Simulations.

The cyclin-dependent kinase 7 (CDK7) plays a crucial role in regulating the cell cycle and RNA polymerase-based transcription. Overexpression of this kinase is linked with various cancers in humans due to its dual involvement in cell development. Furthermore, emerging evidence has revealed that inhibiting CDK7 has anti-cancer effects, driving the development of novel and more cost-effective inhibitors with enhanced selectivity for CDK7 over other CDKs. In the present investigation, a pharmacophore-based approach was utilized to identify potential hit compounds against CDK7. The generated pharmacophore models were validated and used as 3D queries to screen 55,578 natural drug-like compounds. The obtained compounds were then subjected to molecular docking and molecular dynamics simulations to predict their binding mode with CDK7. The molecular dynamics simulation trajectories were subsequently used to calculate binding affinity, revealing four hits—ZINC20392430, SN00112175, SN00004718, and SN00262261—having a better binding affinity towards CDK7 than the reference inhibitors (CT7001 and THZ1). The binding mode analysis displayed hydrogen bond interactions with the hinge region residues Met94 and Glu95, DFG motif residue Asp155, ATP-binding site residues Thr96, Asp97, and Gln141, and quintessential residue outside the kinase domain, Cys312 of CDK7. The in silico selectivity of the hits was further checked by docking with CDK2, the close homolog structure of CDK7. Additionally, the detailed pharmacokinetic properties were predicted, revealing that our hits have better properties than established CDK7 inhibitors CT7001 and THZ1. Hence, we argue that proposed hits may be crucial against CDK7-related malignancies.

Identification of ACK1 inhibitors as anticancer agents by using computer-aided drug designing

ACK1, an intracellular non-receptor tyrosine kinase when abnormally activated and amplified causes numerous sorts of human cancers, therefore, act as a major target of cancer drug development. Currently, there are very small numbers of inhibitors reported for ACK1 inhibition and none of them are in clinical trials. In this study, to identify potential ACK1 inhibitors, a small dataset was prepared from already known inhibitors as a training set for ligand-based pharmacophore model generation using the Hip-Hop algorithm available in the Discovery Studio. Selected pharmacophore hypothesis, Hypo1 displayed the highest rank and consists of five features including two hydrogen bond acceptors (HBA), two-ring aromatic (RA) and one hydrophobic (HYP). Hypo1 was further validated by Receiver Operating Characteristic (ROC) curve and Güner-Henry (GH) approach which give an ROC value of 0.92 and highest GH score of 0.78. Drug-like database was generated from ZINC, ANX, NCI and Princeton database using Lipinski's rule of five and ADMET descriptors. Validated Hypo1 was used for searching new potential hit compounds from the generated drug-like database. From molecular docking analysis, ten potential inhibitor compounds were selected on the basis of Goldscore >67.72, the score of reference inhibitor Dasatinib. Furthermore, molecular dynamics simulation study was performed to study the stability of docking conformations. Subsequently, molecular dynamics simulation analyses revealed that the Hit1 and Hit2 compounds have desirable molecular interaction with key residues Thr205 and Ala208 in the hinge region of ACK1 with better binding affinity. Finally, various pharmacokinetic properties over the Hit1 and Hit2 compounds were analyzed using pkCSM tool and it was found that our hit compounds have shown comparable results with reference. Therefore, we propose the Hit1 and Hit2 compounds may be crucial against ACK1 as potential anticancer agents subjected to experimental validation.

Discovery of beta-lactamase CMY-10 inhibitors for combination therapy against multi-drug resistant Enterobacteriaceae.

β-lactam antibiotics are the most widely used antimicrobial agents since the discovery of benzylpenicillin in the 1920s. Unfortunately, these life-saving antibiotics are vulnerable to inactivation by continuously evolving β-lactamase enzymes that are primary resistance determinants in multi-drug resistant pathogens. The current study exploits the strategy of combination therapeutics and aims at identifying novel β-lactamase inhibitors that can inactivate the β-lactamase enzyme of the pathogen while allowing the β-lactam antibiotic to act against its penicillin-binding protein target. Inhibitor discovery applied the Site-Identification by Ligand Competitive Saturation (SILCS) technology to map the functional group requirements of the β-lactamase CMY-10 and generate pharmacophore models of active site. SILCS-MC, Ligand-grid Free Energy (LGFE) analysis and Machine-learning based random-forest (RF) scoring methods were then used to screen and filter a library of 700,000 compounds. From the computational screens 74 compounds were subjected to experimental validation in which β-lactamase activity assay, in vitro susceptibility testing, and Scanning Electron Microscope (SEM) analysis were conducted to explore their antibacterial potential. Eleven compounds were identified as enhancers while 7 compounds were recognized as inhibitors of CMY-10. Of these, compound 11 showed promising activity in β-lactamase activity assay, in vitro susceptibility testing against ATCC strains (E. coli, E. cloacae, E. agglomerans, E. alvei) and MDR clinical isolates (E. cloacae, E. alvei and E. agglomerans), with synergistic assay indicating its potential as a β-lactam enhancer and β-lactamase inhibitor. Structural similarity search against the active compound 11 yielded 28 more compounds. The majority of these compounds also exhibited β-lactamase inhibition potential and antibacterial activity. The non-β-lactam-based β-lactamase inhibitors identified in the current study have the potential to be used in combination therapy with lactam-based antibiotics against MDR clinical isolates that have been found resistant against last-line antibiotics.

Pharmacophore-based screening of autoinducer-2 inhibitor in bacteria for prevention of oral biofilm formation: An exploratory study

Aim: The aim of this study was to predict the 3D structure of luxS of Fusobacterium nucleatum and identification of key residues by in silico homology modeling and molecular docking for inhibiting autoinducer-2 (AI-2), responsible for biofilm formation. Materials and Methods: The current study was to explore 3D structure of luxS protein along with its major residues that are directly responsible for the activity by homology modeling employing Discovery Studio V4.0. Further, a ligand-based pharmacophore was generated, employing the known ligands to control AI-2 producing enzymes and to ultimately regulate biofilm formation in the oral environment subjecting to key bioinformatics tools. Results: The multiple sequence alignment study showed 26 and 56% similarity in target and template sequences of luxS protein, respectively. Homology modeling indicated high structural similarity between the two luxS proteins (homocysteine and 4, 5-dihydroxy-2, 3 pentanedione) with a low root mean square value of 0.6 A, in addition to leading to major mutations that are responsible for the production of AI-2. A pharmacophore model was generated as a template for virtual screening to explore potent luxS inhibitor. ZINC15 database was used for similarity search algorithms and screening against a generated pharmacophore model. During molecular docking, a molecule was structurally identified with a high libdock score and significant binding energy, in addition to establishing a regulatory mechanism with the receptor protein. Conclusions: This investigation paves way for the high throughput virtual screening to characterize luxS and associated proteins resulting in considerable minimization of time and funds before taking up biological confirmatory tests in the wet laboratories during exploration of possible inhibitors.

Exploiting Water Dynamics for Pharmacophore Screening.

Three-dimensional pharmacophore models have been proven extremely valuable in exploring novel chemical space through virtual screening. However, traditional pharmacophore-based approaches need ligand information and rely on static snapshots of highly dynamic systems. In this chapter, we describe PyRod, a novel tool to generate three-dimensional pharmacophore models based on water traces of a molecular dynamics simulation of an apo-protein.The protocol described herein was successfully applied for the discovery of novel drug-like inhibitors of West Nile virus NS2B-NS3 protease. By using this recent example, we highlight the key steps of the generation and validation of PyRod-derived pharmacophore models and their application for virtual screening.

Identification of novel anti-cryptosporidial inhibitors through a combined approach of pharmacophore modeling, virtual screening, and molecular docking

Abstract Background and objective Cryptosporidiosis is a diarrheal disease that affects millions of people worldwide and can cause dire effects in immunocompromised, HIV/AIDS patients and younger children. Cryptosporidium species are causative agents for cryptosporidiosis. The presently available chemotherapeutic options are not fully effective in curbing the menace of disease in all patients. The deprived experimental traceability of the pathogen, lack of a suitable in-vivo model are few factors that hindered the development of reliable chemotherapeutic options despite continuous efforts. The underway COVID-19 pandemic widens this gap as most of the scientific resources and researches shifted priority. The genome sequencing of the Cryptosporidium parvum (C. parvum) has discovered many new targets, such as Inosine 5′-Monophosphate Dehydrogenase (IMPDH), Lactate Dehydrogenase and many more. The parasite uses the host adenosine pool to synthesize guanine nucleotide in a streamlined pathway, where IMPDH catalyzes the first rate-limiting step. The present study aims to discover new anti-cryptosporidial agents that work against the IMPDH of the parasite. Methodology and results In this study, The PharmaGist ( https://bioinfo3d.cs.tau.ac.il/PharmaGist/ ) tool was deployed for the pharmacophore model generation employing previously reported Cryptosporidium parvum IMPDH (CpIMPDH) inhibitors having IC50 values ranging from 0.009 μM to 0.028 μM. The model having the highest scores was used as a 3D query for high throughput screening of the Zinc database via ZINC pharmer ( http://zincpharmer.csb.pitt.edu/ ) tool. The screened 'hits' were further subjected to molecular docking in the active site of CpIMPDH (PDB ID: 4IXH ), Lipinski's rule of 5, and SwissADME ( http://www.swissadme.ch/ ) filter. Finally, the best three 'hits' or proposed leads (ZINC09672610, ZINC16511373, and ZINC39780256) docked CpIMPDH were further subjected to 50 ns molecular dynamic simulation (MDL) analysis for stability analysis. Conclusion and significance The ZINC09672610 and ZINC39780256 compounds show excellent in-silico inhibitory efficacy against CpIMPDH. These compounds were used as a starting point for the discovery and development of new anti-cryptosporidial compounds. Future anti-cryptosporidial agents could be designed by utilizing these three molecules.