Pharmacophore-Based Screening, Molecular Docking, and Dynamic Simulation of Fungal Metabolites as Inhibitors of Multi-Targets in Neurodegenerative Disorders
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SUBMITTER: Iqbal D
PROVIDER: S-EPMC10669353 | biostudies-literature | 2023 Nov
REPOSITORIES: biostudies-literature
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Project description:AIM: This study was conducted to compare the efficiencies of two virtual screening approaches, pharmacophore-based virtual screening (PBVS) and docking-based virtual screening (DBVS) methods. METHODS: All virtual screens were performed on two data sets of small molecules with both actives and decoys against eight structurally diverse protein targets, namely angiotensin converting enzyme (ACE), acetylcholinesterase (AChE), androgen receptor (AR), D-alanyl-D-alanine carboxypeptidase (DacA), dihydrofolate reductase (DHFR), estrogen receptors alpha (ERalpha), HIV-1 protease (HIV-pr), and thymidine kinase (TK). Each pharmacophore model was constructed based on several X-ray structures of protein-ligand complexes. Virtual screens were performed using four screening standards, the program Catalyst for PBVS and three docking programs (DOCK, GOLD and Glide) for DBVS. RESULTS: Of the sixteen sets of virtual screens (one target versus two testing databases), the enrichment factors of fourteen cases using the PBVS method were higher than those using DBVS methods. The average hit rates over the eight targets at 2% and 5% of the highest ranks of the entire databases for PBVS are much higher than those for DBVS. CONCLUSION: The PBVS method outperformed DBVS methods in retrieving actives from the databases in our tested targets, and is a powerful method in drug discovery.
| S-EPMC4007494 | biostudies-literature
Project description: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 9.2 million 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.
| S-EPMC11564549 | biostudies-literature
Project description:Presently, humanity is confronted with a range of diseases that have high death rates, especially those linked to cancerous growths. Several enzymes and proteins have been discovered as highly attractive targets for cancer treatment. The PARP family consists of 17 members and plays a crucial role in repairing DNA damage, which enables the survival of cancer cells. PARP-1 and, to a lesser extent, PARP-2 display above 90% activity in response to DNA damage, thereby distinguishing them apart from other members of the PARP family. Elevated levels of PARP-1 were observed in many types of tumor cells, such as breast, lung, ovarian, prostate, and melanomas. In an attempt to provide a future guide for developing selective inhibitors for PARP-1 over PARP-2 to minimize the resulting side effects from PARP-2 inhibitors, we constructed a structure-based virtual screening approach (SBVS). Firstly. A 3D pharmacophore was constructed based on the interaction of the selective inhibitor compound IV. After that, a database of nearly 450,000 phthalimide-containing inhibitors was screened through the validated pharmacophore, and 165 compounds were retrieved. The retrieved compounds were docked into the active site of PARP-1 where only 5 compounds MWGS-1-5 achieved a favorable docking score than the reference IV (-16.8 Kcal/mol). Redocking of the five compounds should have excellent selectivity for PARP-1 over PARP-2, especially compound MWGS-1. Further endorsement via molecular dynamics has proven higher affinity and selectivity for MWGS-1 towards PARP-1 over PARP-2, in which PARP-1- MWGS-1 and PARP-1- MWGS-1 achieved RMSD values of 1.42 and 2.8 Å, respectively.
| S-EPMC11786381 | biostudies-literature
Project description: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.
| S-EPMC10449137 | biostudies-literature
Project description:Alzheimer's disease (AD) is a leading cause of dementia in elderly patients. The pathophysiology of AD includes various pathways, such as the degradation of acetylcholine, amyloid-beta deposition, neurofibrillary tangle formation, and neuroinflammation. Many studies showed that targeting acetylcholinesterase enzyme (AChE) to improve acetylcholine can be an effective option to treat AD. In the current work, we employed a 3D QSAR-based approach to generate a pharmacophore to screen a chemical library of compounds that may inhibit AChE. Data from experimental studies were collected and used for the generation of pharmacophores. More than 1 million compounds were screened, and further drug-like properties were determined via in-silico ADMET studies. Techniques like molecular docking and molecular dynamics simulation were performed to analyze the binding of novel AChE inhibitors. A novel AChE inhibitor ligand-1 was identified as best with a docking score of -13.560 kcal/mol with RMSD of 1.71 Å during a 100 ns MD run. Further biological studies can give an insight into the potential of ligand-1 as a therapeutic agent for AD.Graphical abstractSupplementary informationThe online version contains supplementary material available at 10.1007/s40203-024-00189-1.
| S-EPMC10873251 | biostudies-literature
Project description:Main protease (Mpro) plays a key role in replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This study was designed for finding natural inhibitors of SARS-CoV-2 Mpro by in silico methods. To this end, the co-crystal structure of Mpro with telaprevir was explored and receptor-ligand pharmacophore models were developed and validated using pharmit. The database of "ZINC Natural Products" was screened, and 288 compounds were filtered according to pharmacophore features. In the next step, Lipinski's rule of five was applied and absorption, distribution, metabolism, excretion, and toxicity (ADMET) of the filtered compounds were calculated using in silico methods. The resulted 15 compounds were docked into the active site of Mpro and those with the highest binding scores and better interaction including ZINC61991204, ZINC67910260, ZINC61991203, and ZINC08790293 were selected. Further analysis by molecular dynamic simulation studies showed that ZINC61991203 and ZINC08790293 dissociated from Mpro active site, while ZINC426421106 and ZINC5481346 were stable. Root mean square deviation (RMSD), radius of gyration (Rg), number of hydrogen bonds between ligand and protein during the time of simulation, and root mean square fluctuations (RMSF) of protein and ligands were calculated, and components of binding free energy were calculated using the molecular mechanic/Poisson-Boltzmann surface area (MM/PBSA) method. The result of all the analysis indicated that ZINC61991204 and ZINC67910260 are drug-like and nontoxic and have a high potential for inhibiting Mpro.
| S-EPMC9420677 | biostudies-literature
Project description:Viral diseases pose a serious global health threat due to their rapid transmission and widespread impact. The RNA-dependent RNA polymerase (RdRp) participates in the synthesis, transcription, and replication of viral RNA in host. The current study investigates the antiviral potential of secondary metabolites particularly those derived from bacteria, fungi, and plants to develop novel medicines. Using a virtual screening approach that combines molecular docking and molecular dynamics (MD) simulations, we aimed to discover compounds with strong interactions with RdRp of five different retroviruses. The top five compounds were selected for each viral RdRp based on their docking scores, binding patterns, molecular interactions, and drug-likeness properties. The molecular docking study uncovered several metabolites with antiviral activity against RdRp. For instance, cytochalasin Z8 had the lowest docking score of -8.9 (kcal/mol) against RdRp of SARS-CoV-2, aspulvinone D (-9.2 kcal/mol) against HIV-1, talaromyolide D (-9.9 kcal/mol) for hepatitis C, aspulvinone D (-9.9 kcal/mol) against Ebola and talaromyolide D also maintained the lowest docking score of -9.2 kcal/mol against RdRp enzyme of dengue virus. These compounds showed remarkable antiviral potential comparable to standard drug (remdesivir -7.4 kcal/mol) approved to target RdRp and possess no significant toxicity. The molecular dynamics simulation confirmed that the best selected ligands were firmly bound to their respective target proteins for a simulation time of 200 ns. The identified lead compounds possess distinctive pharmacological characteristics, making them potential candidates for repurposing as antiviral drugs against SARS-CoV-2. Further experimental evaluation and investigation are recommended to ascertain their efficacy and potential.
| S-EPMC11299834 | biostudies-literature
Project description:BackgroundVirtual 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.ResultsWe 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.ConclusionThe 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.
| S-EPMC3152774 | biostudies-literature
Project description:AimTo construct a quantitative pharmacophore model of tubulin inhibitors and to discovery new leads with potent antitumor activities.MethodsLigand-based pharmacophore modeling was used to identify the chemical features responsible for inhibiting tubulin polymerization. A set of 26 training compounds was used to generate hypothetical pharmacophores using the HypoGen algorithm. The structures were further validated using the test set, Fischer randomization method, leave-one-out method and a decoy set, and the best model was chosen to screen the Specs database. Hit compounds were subjected to molecular docking study using a Molecular Operating Environment (MOE) software and to biological evaluation in vitro.ResultsHypo1 was demonstrated to be the best pharmacophore model that exhibited the highest correlation coefficient (0.9582), largest cost difference (70.905) and lowest RMSD value (0.6977). Hypo1 consisted of one hydrogen-bond acceptor, a hydrogen-bond donor, a hydrophobic feature, a ring aromatic feature and three excluded volumes. Hypo1 was validated with four different methods and had a goodness-of-hit score of 0.81. When Hypo1 was used in virtual screening of the Specs database, 952 drug-like compounds were revealed. After docking into the colchicine-binding site of tubulin, 5 drug-like compounds with the required interaction with the critical amino acid residues and the binding free energies < -4 kcal/mol were selected as representative leads. Compounds 1 and 3 exhibited inhibitory activity against MCF-7 human breast cancer cells in vitro.ConclusionHypo1 is a quantitative pharmacophore model for tubulin inhibitors, which not only provides a better understanding of their interaction with tubulin, but also assists in discovering new potential leads with antitumor activities.
| S-EPMC4088285 | biostudies-literature
Project description:The performance of molecular docking can be improved by comparing the shape similarity of the flexibly sampled poses against the target proteins' inverted binding cavities. The effectiveness of these pseudo-ligands or negative image-based models in docking rescoring is boosted further by performing enrichment-driven optimization. Here, we introduce a novel shape-focused pharmacophore modeling algorithm O-LAP that generates a new class of cavity-filling models by clumping together overlapping atomic content via pairwise distance graph clustering. Top-ranked poses of flexibly docked active ligands were used as the modeling input and multiple alternative clustering settings were benchmark-tested thoroughly with five demanding drug targets using random training/test divisions. In docking rescoring, the O-LAP modeling typically improved massively on the default docking enrichment; furthermore, the results indicate that the clustered models work well in rigid docking. The C+ +/Qt5-based algorithm O-LAP is released under the GNU General Public License v3.0 via GitHub ( https://github.com/jvlehtonen/overlap-toolkit ). SCIENTIFIC CONTRIBUTION: This study introduces O-LAP, a C++/Qt5-based graph clustering software for generating new type of shape-focused pharmacophore models. In the O-LAP modeling, the target protein cavity is filled with flexibly docked active ligands, the overlapping ligand atoms are clustered, and the shape/electrostatic potential of the resulting model is compared against the flexibly sampled molecular docking poses. The O-LAP modeling is shown to ensure high enrichment in both docking rescoring and rigid docking based on comprehensive benchmark-testing.
| S-EPMC11312248 | biostudies-literature