Structure-based Pharmacophore Research Articles

Identification of novel PAD2 inhibitors using pharmacophore-based virtual screening, molecular docking, and MD simulation studies.

In the realm of epigenetic regulation, Protein arginine deiminase 2 (PAD2) stands out as a therapeutic target due to its significant role in neurological disorders, rheumatoid arthritis (RA), multiple sclerosis (MS), and various cancers. To date, no in silico studies have focused on PAD2 for lead compound identification. Therefore, we conducted structure-based pharmacophore modeling, virtual screening, molecular docking, molecular dynamics (MD) simulations, and essential dynamics studies using PCA and free energy landscape analyses to identify repurposed drugs and selective inhibitors against PAD2. The best pharmacophore model, 'Pharm_01,' had a selectivity score of 10.485 and an excellent ROC curve quality of 0.972. Pharm1 consisted of three hydrogen bond donors (HBD) and two hydrophobic (Hy) features (DDDHH). A virtual screening of about 9.2million compounds yielded 2575 hits using a fit value threshold of 2.5 and drug-likeness criteria. Molecular docking identified the top ten molecules, which were verified using MD simulations. Stability was verified using MM-PBSA studies, whereas conformational differences were investigated using PCA and free energy landscape analyses. Two hits (Leads 1 and 2) from the DrugBank dataset showed promise for repurposing as PAD2 inhibitors, while one hit compound (Lead 8) from the ZINC database emerged as a novel PAD2 inhibitor. These findings indicate that the discovered compounds may be potent PAD2 inhibitors, necessitating additional preclinical and clinical research to produce viable treatments for cancer and neurological disorders.

Unlocking Neuraminidase Inhibitors: Insights from Natural Products through Pharmacophore Modeling, Virtual Screening, and Molecular Docking

Background: Neuraminidase enzyme plays a major role in the life cycle of influenza viruses. Targeting neuraminidases is a key strategy in preventing and treating influenza A and B spread by inhibiting the release of new viral particles from infected cells. Developing new neuraminidase inhibitors with enhanced efficacy and reduced risk of resistance is the ultimate of ongoing research. Objective: In this study, we tried to shed light on molecules of natural origin that inhibit neuraminidase enzyme by utilizing different aspects of in silico studies. Methods: The work started by generating structure-based pharmacophore, later used for the virtual screening of electronic libraries constructed from chemical and natural compounds. Hits raised from virtual screening were subjected to molecular docking to assess and explain different modes of interactions with the neuraminidase enzyme. Drug likeness and ADME filters were applied to choose compounds that may be taken effectively by oral route with good gastrointestinal absorption and acceptable pharmacokinetics with respect to Lipinski and Ghose rules. The hits were also inspected for toxicity risks, including mutagenic, tumorigenic, irritant, and reproductive effects. Results: 7 features of the generated pharmacophore from the potent inhibitor Oselatamiver were the base for virtual screening. The results obtained by database screening highlighted 4 natural compounds listed in the COCONUT library (CNP 0125691 (Penicitrinol K), CNP0256196 (Frenolicin D), CNP 0138184, and CNP 0206296) as potential neuraminidase inhibitors. The compounds showed a similar interaction profile to Oseltamivir by molecular docking with high binding affinity. The key residue TYR 406 hydrogen bonded to Penicitrinol and Frenolicin D as well as to Oseltamevir. The 4 natural compounds exerted drug-like properties and promising pharmacokinetic characters. Toxicity risks estimations put Penicitrinol K and Frenolicin as devoted to mutagenic, tumorigenic, irritant and reproductive potentials. Penicitrinol K and Frenolicin are assigned for further in vitro and in vivo studies as promising neuraminidase inhibitors. Conclusion: Penicitrinol K, and Frenolicin D are promising natural neuraminidase inhibitors, exhibiting favourable drug-like properties and low toxicity risks in addition to showing the pattern of interaction profiles with neuraminidase enzymes similar to the known drug Oseltamivir contributing to the development of effective antiviral therapies against influenza, suggesting further in vitro and in vivo evaluation.

In Silico Design, Chemical Synthesis, Biophysical and in Vitro Evaluation for the Identification of 1-Ethyl-1H-Pyrazolo[3,4-b]Pyridine-Based BRD9 Binders.

In this work, we report the identification of novel bromodomain-containing protein 9 (BRD9) binders through a virtual screening based on our developed 3D structure-based pharmacophore model. The in silico workflow here described led to the identification of a promising initial hit (1) featuring the 1-ethyl-1H-pyrazolo[3,4-b]pyridine motif which represented an unexplored chemotype for the development of a new class of BRD9 ligands. The encouraging biophysical results achieved for compound 1 prompted us to explore further tailored structural modification around the C-4 and C-6 positions of the central core. Hence, the design and synthesis of a set of 19 derivatives (2-20) were performed to extensively investigate the chemical space of BRD9 binding site. Among them, four compounds (5, 11, 12, and 19) stood out in biophysical assays as new valuable BRD9 ligands featuring IC50 values in the low-micromolar range. Noteworthy, a promising antiproliferative activity was detected in vitro for compound 5 on HeLa and A375 cancer cell line. The successful combination and application of in silico tools, chemical synthesis, and biological assays allowed to identify novel BRD9 binders and to expand the arsenal of promising chemical entities amenable to the recognition of this important epigenetic target.

Novel glucokinase activators: A structure-based pharmacophore modeling, QSAR analysis, and molecular dynamics approach

Novel glucokinase activators: A structure-based pharmacophore modeling, QSAR analysis, and molecular dynamics approach

Identification of Novel Bromodomain-Containing Protein 4 (BRD4) Binders through 3D Pharmacophore-Based Repositioning Screening Campaign.

A 3D structure-based pharmacophore model built for bromodomain-containing protein 4 (BRD4) is reported here, specifically developed for investigating and identifying the key structural features of the (+)-JQ1 known inhibitor within the BRD4 binding site. Using this pharmacophore model, 273 synthesized and purchased compounds previously considered for other targets but yielding poor results were screened in a drug repositioning campaign. Subsequently, only six compounds showed potential as BRD4 binders and were subjected to further biophysical and biochemical assays. Compounds 2, 5, and 6 showed high affinity for BRD4, with IC50 values of 0.60 ± 0.25 µM, 3.46 ± 1.22 µM, and 4.66 ± 0.52 µM, respectively. Additionally, these compounds were tested against two other bromodomains, BRD3 and BRD9, and two of them showed high selectivity for BRD4. The reported 3D structure-based pharmacophore model proves to be a straightforward and useful tool for selecting novel BRD4 ligands.

Antibacterial activity of natural flavones against bovine mastitis pathogens: in vitro, SAR analysis, and computational study

Bovine mastitis is a worldwide disease affecting dairy cattle and causes major economic losses in the dairy industry. Recently, the emergence of microbial resistance to the current antibiotics complicates the treatment protocol which necessitates antibiotic stewardship and further research to find new active compounds. Recently, phytobiotics have gained interest in being used as an alternative to antibiotics in the poultry industry as an antibiotic stewardship intervention. This study evaluated the in vitro antibacterial activity of 16 flavonoids against bovine mastitis pathogens. Two flavones: 2-(4-methoxyphenyl)chromen-4-one (1) and 2-(3-hydroxyphenyl)chromen-4-one (4) showed inhibition of the growth of Klebsiella oxytoca with MIC values range (25–50 µg mL− 1) followed by a structure-activity relationship (SAR) study indicating that the presence of a hydroxyl group at C-3` or methoxy at C-4` increases the activity against Klebsiella oxytoca while the presence of hydroxyl group at C-7 decreases the activity. Furthermore, a structure-based drug development approach was applied using several in silico tools to understand the interactions of active flavones at the active site of the DNA gyrase protein. Compound (4) showed a higher docking score than quercetin (standard) which is known to have antibacterial activity by inhibiting the DNA gyrase. In addition, the structure-based pharmacophores of compound (4) and quercetin showed similar pharmacophoric features and interactions with DNA gyrase. Based on our findings, compounds (1) and (4) are promising for further study as potential anti-microbial phytochemicals that can have a role in controlling bovine mastitis as well as to investigate their mechanism of action further.

Hierarchical Virtual Screening of SARS-CoV-2 Main Protease Potential Inhibitors: Similarity Search, Pharmacophore Modeling, and Molecular Docking Study

Background: The outbreak of COVID-19 caused by severe acute respiratory syndrome coronavirus2 (SARS-CoV-2) resulted in a widespread pandemic. Various approaches involved the repositioning of antiviral remedies and other medications. Several therapies, including oral antiviral treatments, represent some approaches to adapting to the long existence of the COVID-19 pandemic. In silico studies provide valuable insights throughout drug discovery and development in compliance with global efforts to overcome the pandemic. The main protease is an essential target in the viral cycle. Computer-aided drug design accelerates the identification of potential treatments, including oral therapy. Aims: This work aims to identify potential SARS-CoV-2 main protease inhibitors using different aspects of in silico approaches. Methods: In this work, we conducted a hierarchical virtual screening of SARS-CoV-2 main protease inhibitors. A similarity search was conducted to screen molecules similar to the inhibitor PF-07321332. Concurrently, structure-based pharmacophores, besides ligand-based pharmacophores, were derived. A drug-likeness filter filtered the compounds retrieved from similarity search and pharmacophore modeling before being subjected to molecular docking. The candidate molecules that showed higher affinity to the main protease than the reference inhibitor were further filtered by absorption, distribution, metabolism, and excretion (ADME) parameters. Results: According to binding affinity and ADME analysis, four molecules (CHEMBL218022, PubChem163362029, PubChem166149100, and PubChem 162396459) were prioritized as promising hits. The compounds above were not reported before; no previous experimental studies and bioactive assays are available. Conclusion: Our time-saving approach represents a strategy for discovering novel SARS-CoV- 2 main protease inhibitors. The ultimate hits may be nominated as leads in discovering novel SARS-CoV-2 main protease inhibitors.

Structure-based pharmacophore modeling for precision inhibition of mutant ESR2 in breast cancer: A systematic computational approach.

Breast cancer, a leading cause of female mortality, is closely linked to mutations in estrogen receptor beta (ESR2), particularly in the ligand-binding domain, which contributed to altered signaling pathways and uncontrolled cell growth. This study investigates the molecular and structural aspects of ESR2 mutant proteins to identify shared pharmacophoric regions of ESR2 mutant proteins and potential therapeutic targets aligned within the pharmacophore model. This study was initiated by establishing a common pharmacophore model among three mutant ESR2 proteins (PDB ID: 2FSZ, 7XVZ, and 7XWR). The generated shared feature pharmacophore (SFP) includes four primary binding interactions: Hydrogen bond donors (HBD), hydrogen bond acceptors (HBA), hydrophobic interactions (HPho), and Aromatic interactions (Ar), along with halogen bond donors (XBD) and totalling 11 features (HBD: 2, HBA: 3, HPho: 3, Ar: 2, XBD: 1). By employing an in-house Python script, these 11 features distributed into 336 combinations, which were used as query to isolate a drug library of 41,248 compounds and subjected to virtual screening through the generated SFP. The virtual screening demonstrated 33 hits showing potential pharmacophoric fit scores and low RMSD value. The top four compounds: ZINC94272748, ZINC79046938, ZINC05925939, and ZINC59928516 showed a fit score of more than 86% and satisfied the Lipinski rule of five. These four compounds and a control underwent molecular (XP Glide mode) docking analysis against wild-type ESR2 protein (PDB ID: 1QKM), resulting in binding affinity of -8.26, -5.73, -10.80, and -8.42 kcal/mol, respectively, along with the control -7.2 kcal/mol. Furthermore, the stability of the selected candidates was determined through molecular dynamics (MD) simulations of 200 ns and MM-GBSA analysis. Based on MD simulations and MM-GBSA analysis, our study identified ZINC05925939 as a promising ESR2 inhibitor among the top four hits. However, it is essential to conduct further wet lab evaluation to assess its efficacy.

Virtual Screening of Indonesian Herbal Compounds with Neuraminidase Inhibitor Activity against N2 Influenza Virus Protein: An in silico Study

Background: Neuraminidase inhibitor (NAI) is one of anti-influenza drugs recommended for use by the World Health Organization (WHO). However, after treatment with NAI drugs in human, resistance to influenza antiviral drugs is begun to rise. Therefore, identification of compounds from Indonesian herbal plants as natural inhibitors of the influenza virus neuraminidase protein needs to be conducted for the development of new anti-influenza drugs.Materials and methods: The crystal structure of the neuraminidase protein complex used in this study was obtained from the Protein Data Bank (PDB). Structure-based pharmacophore modeling was performed using LigandScout version 4.4.5 software. Indonesian herbal plant compounds were collected from the HerbalDB database. Protein and ligand processing was carried out using Autodock 4.2 software. The 3D interaction visualization was carried out with Autodock software, while 2D interaction visualization was carried out with LigPlot software. To determine the toxicity and drug-likeliness of the ligand, the test ligands that had the best docking results were predicted using SwissADME and AdmetSAR.Results: From the virtual screening results, 24 hits were found, and five compounds had the best binding energy among the 24 tested compounds, these were pollenitin (ΔG=-7.22 kcal/mol), OPC-4:0 (ΔG=-7.11 kcal/mol), 6-hydroxykaempferol (ΔG=-7.08 kcal/mol), 5,8-dihydroxy-7,4'-dimethoxyflavone (ΔG=-7.07 kcal/mol), and 3,5,6,7-tetrahydroxy-4'-methoxyflavone (ΔG=-6.95 kcal/mol). The best five compounds were then chosen for further analysis.Conclusion: OPC-4:0 is found to be the best compound for the NAI based on its binding energy, pharmacokinetics, toxicity, and drug-likeliness. Thus, OPC-4:0 might be a potential candidate as a NAI of HxN2 virus. Keywords: influenza, molecular docking, neuraminidase, resistance, virtual screening

Repositioning of Small Molecules through the Inverse Virtual Screening in silico Tool: Case of Benzothiazole-Based Inhibitors of Soluble Epoxide Hydrolase (sEH).

Computational techniques accelerate drug discovery by identifying bioactive compounds for specific targets, optimizing molecules with moderate activity, or facilitating the repositioning of inactive items onto new targets. Among them, the Inverse Virtual Screening (IVS) approach is aimed at the evaluation of one or a small set of molecules against a panel of targets for addressing target identification. In this work, a focused library of benzothiazole-based compounds was re-investigated by IVS. Four items, originally synthesized and tested on bromodomain-containing protein 9 (BRD9) but yielding poor binding, were critically re-analyzed, disclosing only a partial fit with 3D structure-based pharmacophore models, which, in the meanwhile, were developed for this target. Afterwards, these compounds were re-evaluated through IVS on a panel of proteins involved in inflammation and cancer, identifying soluble epoxide hydrolase (sEH) as a putative interacting target. Three items were subsequently confirmed as able to interfere with sEH activity, leading to inhibition percentages spanning from 70 % up to 30 % when tested at 10 μM. Finally, one benzothiazole-based compound emerged as the most promising inhibitor featuring an IC50 in the low micromolar range (IC50=6.62±0.13 μM). Our data confirm IVS as a predictive tool for accelerating the target identification and repositioning processes.

Molecular Modeling and simulation-based identification of inhibitors against new Delhi Metallo-Lactamase 1: Implications for bacterial antibiotic resistance

ObjectivesBacterial infections expressing New Delhi metallo-lactamase-1 (NDM-1) pose an escalating global threat to healthcare systems. NDM-1 is an enzyme that renders β-lactam antibiotics ineffective, leading to resistance against numerous antibiotics used in clinical practice. Therefore, there is an urgent need to identify and develop a clinically relevant inhibitor for NDM-1. MethodsVitas-M laboratory database was screened for small molecules with abilities to bind NDM-1, by generating structure-based pharmacophore hypothesis. Thereafter, molecular docking was performed between NDM-1 and the potential small molecule inhibitors. The outcomes of molecular docking were validated by molecular dynamics simulation and MM-GBSA protocols. ResultsBased upon initial NDM-1-binding characteristics, two ligands (STK115225 and STK107343) were nominated for further analyses for stability and affinity of protein–ligand interactions. Assessment of conformational change parameters indicated that these showed tight and stable binding to the active site pocket of NDM-1 protein. Principal component analysis (PCA) further illustrated that the protein ligand complexes were highly stable. Molecular dynamics simulation along with high numbers of static hydrogen bonds signifies the potency of STK115225 and STK107343 in inhibiting NDM-1. Further, MM-GBSA-based binding free energy maps verified favorable energy changes for the binding of the two small molecules, indicating their abilities for high affinity-binding with NDM-1. ConclusionsThis study has significant implications for addressing antibiotic resistance mediated by NDM-1. The identification of STK115225 and STK107343 as high-affinity binding ligands against NDM-1 provides a strong foundation for developing new therapeutic agents. However, to fully ascertain their clinical relevance, these findings must be validated through in vitro and in vivo experiments. If successful, these inhibitors could restore the efficacy of β-lactam antibiotics and offer a new approach to combat NDM-1 mediated antibiotic resistance, ultimately improving patient outcomes and reducing the global burden of resistant bacterial infections.

Pharmacophore Virtual Screening Identifies Riboflavin as an Inhibitor of the Schistosome Cathepsin B1 Protease with Antiparasitic Activity.

Schistosomiasis is a neglected disease of poverty that affects over 200 million people worldwide and relies on a single drug for therapy. The cathepsin B1 cysteine protease (SmCB1) of Schistosoma mansoni has been investigated as a potential target. Here, a structure-based pharmacophore virtual screening (VS) approach was used on a data set of approved drugs to identify potential antischistosomal agents targeting SmCB1. Pharmacophore (PHP) models underwent validation through receiver operating characteristics curves achieving values >0.8. The data highlighted riboflavin (RBF) as a compound of particular interest. A 1 μs molecular dynamics simulation demonstrated that RBF altered the conformation of SmCB1, causing the protease's binding site to close around RBF while maintaining the protease's overall integrity. RBF inhibited the activity of SmCB1 at low micromolar values and killed the parasite in vitro. Finally, in a murine model of S. mansoni infection, oral administration of 100 mg/kg RBF for 7 days significantly decreased worm burdens by ∼20% and had a major impact on intestinal and fecal egg burdens, which were decreased by ∼80%.

Developing Dynamic Structure-Based Pharmacophore and ML-Trained QSAR Models for the Discovery of Novel Resistance-Free RET Tyrosine Kinase Inhibitors Through Extensive MD Trajectories and NRI Analysis.

Activation of RET tyrosine kinase plays a critical role in the pathogenesis of various cancers, including non-small cell lung cancer, papillary thyroid cancers, multiple endocrine neoplasia type 2A and 2B (MEN2A, MEN2B), and familial medullary thyroid cancer. Gene fusions and point mutations in the RET proto-oncogene result in constitutive activation of RET signaling pathways. Consequently, developing effective inhibitors to target RET is of utmost importance. Small molecules have shown promise as inhibitors by binding to the kinase domain of RET and blocking its enzymatic activity. However, the emergence of resistance due to single amino acid changes poses a significant challenge. In this study, a structure-based dynamic pharmacophore-driven approach using E-pharmacophore modeling from molecular dynamics trajectories is proposed to select low-energy favorable hypotheses, and ML-trained QSAR models to predict pIC50 values of compounds. For this aim, extensive small molecule libraries were screened using developed ligand-based models, and potent compounds that are capable of inhibiting RET activation were proposed.

PharmaCore: The Automatic Generation of 3D Structure-Based Pharmacophore Models from Protein/Ligand Complexes.

In this work, we present PharmaCore: a new, completely automatic workflow aimed at generating three-dimensional (3D) structure-based pharmacophore models toward any target of interest. The proposed approach relies on using cocrystallized ligands to create the input files for generating the pharmacophore hypotheses, integrating not only the three-dimensional structural information on the ligand but also data concerning the binding mode of these molecules put in the protein cavity. We developed a Python library that, starting from the specific UniProt ID of the protein under investigation as the only element that requires user intervention, subsequently collects and aligns the corresponding structures bearing a known ligand in a fully automated fashion, bringing them all into the same coordinate system. The protocol includes a final phase in which the aligned small molecules are used to produce the pharmacophore hypotheses directly onto the protein structure using a specific software, e.g., Phase (Schrödinger LLC). To validate the entire procedure and highlight the possible applications in the field of drug discovery and repositioning, we first generated pharmacophores for soluble epoxide hydrolase (sEH) and compared with already-published ones. Then, we reproduced the binding profile of a reported selective binder of ATAD2 bromodomain (AM879), testing it against a panel of 1741 pharmacophores related to 16 epigenetic proteins and automatically generated with PharmaCore, finally disclosing putative unprecedented off-targets. The computational predictions were successfully validated with AlphaScreen assays, highlighting the applicability of the proposed workflow in drug discovery and repositioning. Finally, the process was also validated on tankyrase 2 and SARS-CoV-2 MPro, confirming the robustness of PharmaCore.

A structure-based pharmacophore modelling approach to identify and design new neprilysin (NEP) inhibitors: An in silico-based investigation

Neutral endopeptidase or neprilysin (NEP) cleaves the natriuretic peptides, bradykinin, endothelin, angiotensin II, amyloid β protein, substance P, etc., thus modulating their effects on heart, kidney, and other organs. NEP has a proven role in hypertension, heart disease, renal disease, Alzheimer's, diabetes, and some cancers. NEP inhibitor development has been in focus since the US FDA approved a combination therapy of angiotensin II type 1 receptor inhibitor (valsartan) and NEP inhibitor (sacubitril) for use in heart failure. Considering the importance of NEP inhibitors the present work focuses on the designing of a potential lead for NEP inhibition. A structure-based pharmacophore modelling approach was employed to identify NEP inhibitors from the pool of 1140 chemical entities obtained from the ZINC database. Based on the docking score and pivotal interactions, ten molecules were selected and subjected to binding free energy calculations and ADMET predictions. The top two compounds were studied further by molecular dynamics simulations to determine the stability of the ligand-receptor complex. ZINC0000004684268, a phenylalanine derivative, showed affinity and complex stability comparable to sacubitril. However, in silico studies indicated that it may have poor pharmacokinetic parameters. Therefore, the molecule was optimized using bioisosteric replacements, keeping the phenylalanine moiety intact, to obtain five potential lead molecules with an acceptable pharmacokinetic profile. The works thus open up the scope to further corroborate the present in silico findings with the biological analysis.

In silico exploration of phenolics as modulators of penicillin binding protein (PBP) 2× of Streptococcus pneumoniae

Infections caused by multidrug-resistant Streptococcus pneumoniae remain the leading cause of pneumonia-related deaths in children < 5 years globally, and mutations in penicillin-binding protein (PBP) 2 × have been identified as the major cause of resistance in the organism to beta-lactams. Thus, the development of new modulators with enhanced binding of PBP2x is highly encouraged. In this study, phenolics, due to their reported antibacterial activities, were screened against the active site of PBP2x using structure-based pharmacophore and molecular docking techniques, and the ability of the top-hit phenolics to inhibit the active and allosteric sites of PBP2x was refined through 120 ns molecular dynamic simulation. Except for gallocatechin gallate and lysidicichin, respectively, at the active and allosteric sites of PBP2x, the top-hit phenolics had higher negative binding free energy (ΔGbind) than amoxicillin [active site (− 19.23 kcal/mol), allosteric site (− 33.75 kcal/mol)]. Although silicristin had the best broad-spectrum effects at the active (− 38.41 kcal/mol) and allosteric (− 50.54 kcal/mol) sites of PBP2x, the high thermodynamic entropy (4.90 Å) of the resulting complex might suggest the need for its possible structural refinement for enhanced potency. Interestingly, silicristin had a predicted synthetic feasibility score of < 5 and quantum calculations using the DFT B3LYP/6-31G+ (dp) revealed that silicristin is less stable and more reactive than amoxicillin. These findings point to the possible benefits of the top-hit phenolics, and most especially silicristin, in the direct and synergistic treatment of infections caused by S. pneumoniae. Accordingly, silicristin is currently the subject of further confirmatory in vitro research.

Hit discovery of potential CDK8 inhibitors and analysis of amino acid mutations for cancer therapy through computer-aided drug discovery

Cyclin-dependent kinase 8 (CDK8) has emerged as a promising target for inhibiting cancer cell function, intensifying efforts towards the development of CDK8 inhibitors as potential cancer therapeutics. Mutations in CDK8, a protein kinase, are also implicated as a primary factor associated with tumor formation. In this study, we identified potential inhibitors through virtual screening for CDK8 and single amino acid mutations in CDK8, namely D173A (Aspartate 173 mutate to Alanine), D189N (Aspartate 189 mutate to Asparagine), T196A (Threonine 196 mutate to Alanine) and T196D (Threonine 196 mutate to Aspartate). Four databases (CHEMBEL, ZINC, MCULE, and MolPort) containing 65,209,131 molecules have been searched to identify new inhibitors for CDK8 and its single mutations. In the first step, structure-based pharmacophore modeling in the Pharmit server was used to select the compounds to know the inhibitors. Then molecules with better predicted drug-like molecule properties were selected. The final filter used to select more effective inhibitors among the previously selected molecules was molecular docking. Finally, 13 hits for CDK8, 11 hits for D173A, 11 hits for D189N, 15 hits for T196A, and 12 hits for T196D were considered potential inhibitors. A majority of the virtual screening hits exhibited satisfactorily predict pharmacokinetic characteristics and toxicity properties.

Unveiling potential repurposed drug candidates for Plasmodium falciparum through in silico evaluation: A synergy of structure-based approaches, structure prediction, and molecular dynamics simulations

The rise of drug resistance in Plasmodium falciparum, rendering current treatments ineffective, has hindered efforts to eliminate malaria. To address this issue, the study employed a combination of Systems Biology approach and a structure-based pharmacophore method to identify a target against P. falciparum. Through text mining, 448 genes were extracted, and it was discovered that plasmepsins, found in the Plasmodium genus, play a crucial role in the parasite's survival. The metabolic pathways of these proteins were determined using the PlasmoDB genomic database and recreated using CellDesigner 4.4.2. To identify a potent target, Plasmepsin V (PF13_0133) was selected and examined for protein-protein interactions (PPIs) using the STRING Database. Topological analysis and global-based methods identified PF13_0133 as having the highest centrality. Moreover, the static protein knockout PPIs demonstrated the essentiality of PF13_0133 in the modeled network. Due to the unavailability of the protein’s crystal structure, it was modeled and subjected to a molecular dynamics simulation study. The structure-based pharmacophore modeling utilized the modeled PF13_0133 (PfPMV), generating 10 pharmacophore hypotheses with a library of active and inactive compounds against PfPMV. Through virtual screening, two potential candidates, hesperidin and rutin, were identified as potential drugs which may be repurposed as potential anti-malarial agents.

In silico analysis of small molecule compounds based on pharmacophore- and backbone-leaping peroxiredoxin 1 (PRDX1) inhibitors

Abnormal expression of PRDX has been found to play a significant role in the growth of colorectal cancer and other types of tumors. Despite the identification of several PRDX1 inhibitory compounds in recent years, none of them have been utilized in clinical treatments. Therefore, we conducted a virtual screening of 210,331 small molecules from the SPECS library using PRDX1 and multiple methods. From this screening, we identified 13 compounds with the highest scores from the molecular docking analysis. To further validate the accuracy of our pharmacophore model, we constructed a structure-based pharmacophore model and analyzed the receiver operating characteristic curve (ROC curve). Through this process, we selected nine compounds using skeleton jumping and virtual screening based on the highest pharmacophore model scores. Subsequently, we examined the ADMET properties of these nine compounds to assess their drug-forming potential, resulting in three compounds with the best drug properties. Finally, we assessed the binding stability of these three candidate molecules to proteins using molecular dynamics and MM-PBSA calculations. After a comprehensive evaluation, we found that compounds 6 and 9 formed stable complexes with PRDX1 proteins and could potentially serve as competitive inhibitors of PRDX1 substrates. Communicated by Ramaswamy H. Sarma

Mutant anaplastic lymphoma kinase inhibitor identification by integrated in silico approaches

ABSTRACT Non-small-cell lung cancer (NSCLC) is the primary form of lung cancer globally and remains a leading cause of mortality. Anaplastic lymphoma kinase (ALK) mutations, such as I1171N + L1198H, have been discovered to confer resistance to current ALK inhibitors, reducing their therapeutic effectiveness. Addressing drug resistance necessitates exploring selective inhibitors for innovative therapeutic approaches. In this study, a structure-based pharmacophore model, using ALK-approved inhibitors, was developed to screen an In-house database for potential mutant ALK inhibitors. Compounds with requisite pharmacophoric features were evaluated for binding potential against the I1171N + L1198H ALK mutant phenotype. Selected hits underwent assessment for chemical reactivity, and dynamics stability. The study identified five chemical scaffolds (NS1-5) with favorable binding modes and pharmacokinetic properties. The conformational ensembles featured the average RMSD values, ranging from 0.4 to 0.6 nm. RMSF analysis revealed consistent side chain fluctuations with reduced flexibility, while Rog analysis indicated convergence of most complexes. NS1 and NS5, in particular emerged as promising candidates, exhibiting remarkable performance than others, with binding free energies of −210.12 ± 9.94 and −163.68 ± 11.14 kcal/mol, respectively. These findings thus suggest further exploration and optimisation of NS1 and NS5 for mutant ALK inhibitors for the treatment of NSCLC.