Browse
Submit Data
Databases
API
Help

Dataset Information

30 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Screening of Potential Breast Cancer Inhibitors through Molecular Docking and Molecular Dynamics Simulation.

ABSTRACT: Cyclooxygenase-2 (COX-2) is a key enzyme involved in overexpression in several human cancerous diseases including breast cancer. By performing efficient virtual screening in a series of active molecules or compounds from the Maybridge, NCI (National Cancer Institute), and Enamine databases, potential identification of COX-2 inhibitors could lead to new prognostic strategies in the treatment of breast cancer. Based on a 50% structural similitude, compounds were chosen as the inductive model of COX-2 inhibitions from these databases. Selected compounds were filtered and tested with Lipinski's rule of five followed by absorption, distribution, metabolism, and excretion (ADME) properties. Subsequently, molecular docking was performed to achieve accuracy in screening and also to find an interactive mechanism between hit compounds with their respective binding sites. Simultaneously, molecular simulations of top-scored compounds were selected and coded such as Maybridge_55417, NCI_30552, and Enamine_62410. Chosen compounds were analyzed and interpreted with COX-2 affinity. Results endorsed that hydrophobic affinity and optimum hydrogen bonds were the forces driven in the interactive mechanism of in silico hits compounds with COX-2 and can be used as efficient alternative therapeutic agents targeting deleterious breast cancer. With these in silico findings, compounds identified may prevent the action of the COX-2 enzyme and thereby diminish the incidence of breast cancer.

SUBMITTER: Pandi S

PROVIDER: S-EPMC9256436 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Discovery of Novel Tankyrase Inhibitors through Molecular Docking-Based Virtual Screening and Molecular Dynamics Simulation Studies.

Project description:Tankyrase enzymes (TNKS), a core part of the canonical Wnt pathway, are a promising target in the search for potential anti-cancer agents. Although several hundreds of the TNKS inhibitors are currently known, identification of their novel chemotypes attracts considerable interest. In this study, the molecular docking and machine learning-based virtual screening techniques combined with the physico-chemical and ADMET (absorption, distribution, metabolism, excretion, toxicity) profile prediction and molecular dynamics simulations were applied to a subset of the ZINC database containing about 1.7 M commercially available compounds. Out of seven candidate compounds biologically evaluated in vitro for their inhibition of the TNKS2 enzyme using immunochemical assay, two compounds have shown a decent level of inhibitory activity with the IC50 values of less than 10 nM and 10 μM. Relatively simple scores based on molecular docking or MM-PBSA (molecular mechanics, Poisson-Boltzmann, surface area) methods proved unsuitable for predicting the effect of structural modification or for accurate ranking of the compounds based on their binding energies. On the other hand, the molecular dynamics simulations and Free Energy Perturbation (FEP) calculations allowed us to further decipher the structure-activity relationships and retrospectively analyze the docking-based virtual screening performance. This approach can be applied at the subsequent lead optimization stages.

| S-EPMC7397142 | biostudies-literature

Identification of Potential Cytochrome P450 3A5 Inhibitors: An Extensive Virtual Screening through Molecular Docking, Negative Image-Based Screening, Machine Learning and Molecular Dynamics Simulation Studies.

Project description:Cytochrome P450 3A5 (CYP3A5) is one of the crucial CYP family members and has already proven to be an important drug target for cardiovascular diseases. In the current study, the PubChem database was screened through molecular docking and high-affinity molecules were adopted for further assessment. A negative image-based (NIB) model was used for a similarity search by considering the complementary shape and electrostatics of the target and small molecules. Further, the molecules were segregated into active and inactive groups through six machine learning (ML) matrices. The active molecules found in each ML model were used for in silico pharmacokinetics and toxicity assessments. A total of five molecules followed the acceptable pharmacokinetics and toxicity profiles. Several potential binding interactions between the proposed molecules and CYP3A5 were observed. The dynamic behavior of the selected molecules in the CYP3A5 was explored through a molecular dynamics (MD) simulation study. Several parameters obtained from the MD simulation trajectory explained the stability of the protein-ligand complexes in dynamic states. The high binding affinity of each molecule was revealed by the binding free energy calculation through the MM-GBSA methods. Therefore, it can be concluded that the proposed molecules might be potential CYP3A5 molecules for therapeutic application in cardiovascular diseases subjected to in vitro/in vivo validations.

| S-EPMC9409045 | biostudies-literature

Computer-aided screening for potential TMPRSS2 inhibitors: a combination of pharmacophore modeling, molecular docking and molecular dynamics simulation approaches.

Project description:Transmembrane serine protease 2 (TMPRSS2) has been established as one of the host proteins that facilitate entry of coronaviruses into host cells. One of the approaches often employed towards preventing the entry and proliferation of viruses is computer-aided inhibition studies to identify potent compounds that can inhibit activity of viral targets in the host through binding at the active site. In this study, we developed a pharmacophore model of reportedly potent drugs against severe acute respiratory syndrome coronaviruses 1 and 2 (SARS-CoV-1 and -2). The model was used to screen the ZINC database for commercially available compounds having similar features with the experimentally tested drugs. The top 3000 compounds retrieved were docked into the active sites of a homology-modelled TMPRSS2. Docking scores of the top binders were validated and the top-ranked compounds were subjected to ADME, Lipinski's and medicinal Chemistry property predictions for druglikeness analyses. Two lead compounds, ZINC64606047 and ZINC05296775, were identified having binding affinities higher than those of the reference inhibitors, favorable interactions with TMPRSS2 active site residues and good ADME and medicinal chemistry properties. Molecular dynamics simulation was used to assess the stability and dynamics of the interactions of these compounds with TMPRSS2. Binding free energy and contribution energy evaluations were determined using MMPBSA method. Analyses of the trajectory dynamics collectively established further that the lead compounds bound and interacted stably with active site residues of TMPRSS2. Nonetheless, experimental studies are needed to further assess the potentials of these compounds as possible therapeutics against coronaviruses. Communicated by Ramaswamy H. Sarma.

| S-EPMC7441808 | biostudies-literature

Identification of Marine-Derived SLC7A11 Inhibitors: Molecular Docking, Structure-Based Virtual Screening, Cytotoxicity Prediction, and Molecular Dynamics Simulation.

Project description:The search for anticancer drugs that target ferroptosis is a promising avenue of research. SLC7A11, a key protein involved in ferroptosis, has been identified as a potential target for drug development. Through screening efforts, novel inhibitors of SLC7A11 have been designed with the aim of promoting ferroptosis and ultimately eliminating cancer cells. We initially screened 563 small molecules using pharmacophore and 2D-QSAR models. Molecular docking and ADMET toxicity predictions, with Erastin as a positive control, identified the small molecules 42711 and 27363 as lead compounds with strong inhibitory activity against SLC7A11. Further optimization resulted in the development of a new inhibitor structure (42711_11). Molecular docking and ADMET re-screening demonstrated successful fragment substitution for this small molecule. Final molecular dynamics simulations also confirmed its stable interaction with the protein. These findings represent a significant step towards the development of new therapeutic strategies for ferroptosis-related diseases.

| S-EPMC11355350 | biostudies-literature

Structure-based virtual screening and molecular docking for the identification of potential multi-targeted inhibitors against breast cancer.

Project description:Breast cancer is characterized by an uncontrolled growth of cells in breast tissue. Genes that foster cell growth in breast cells are overexpressed, giving rise to breast tumors. The identification of effective inhibitors represents a rational chemopreventive strategy. The current in silico study provides a pharmacoinformatic approach for the identification of active compounds against a co-chaperone HSP90 and the human epidermal growth factor receptors EGFR and HER2/neu receptor. The elevated levels of expression of these target proteins have been documented in breast cancer. The utilization of drug-likeness filters helped to evaluate the pharmacological activity of potential lead compounds. Those fulfilling this criterion were subjected to energy minimization for 1000 steepest descent steps at a root means square gradient of 0.02 with an Amber ff12SB force field. Based on molecular docking results and binding interaction analysis, this study represents five chemical compounds (S-258282355, S-258012947, S-259417539, S-258002927, and S-259411474) that indicate high binding energies that range between -8.7 to -10.3 kcal/mol. With high cytochrome P inhibitory promiscuity activity, these multi-targeted potential hits portray not only good physiochemical interactions but also an excellent profile of absorption, distribution, metabolism, excretion, and toxicity, which hypothesizes that these compounds can be developed as anticancer drugs in the near future.

| S-EPMC5476443 | biostudies-literature

Discovery of potential SARS-CoV 3CL protease inhibitors from approved antiviral drugs using: virtual screening, molecular docking, pharmacophore mapping evaluation and dynamics simulation.

Project description:The spread of corona-virus disease 2019 (COVID-19) has been faster than any other corona-viruses that have succeeded in crossing the animal-human barrier. This disease, caused by the severe acute respiratory syndrome corona-virus 2 (SARS-CoV-2/2019-nCoV) posing a serious threat to global public health and local economies. There are three responsible for this disease; SARS-CoV-2, SARS-CoV and MERS-CoV. Whereas our goal is to test the affinity for a new class of compounds obtained from a hybridization of Chloroquine, Amodiaquine and Mefloquine with three targets SARS-CoV-2, SARS-CoV and MERS-CoV, in order to find new compounds as new inhibitors against Covid-19. In this work, we first used: the molecular docking/dynamics methods and ADME properties to study interaction and affinity between eight new compounds against three targets involved in the Covid-19. The results of the docking simulations and dynamics revealed that inhibitor of the malaria (Ligand 87) has an affinity to interact with SARS-CoV-2, SARS-CoV and MERS-CoV targets and they can be good inhibitors for treatment of Covid-19. Moreover, they give best affinity compared to the Remdesivir and Chloroquine and other clinical tests. The Pharmacokinetics was justified by means of lipophilicity and high coefficient of skin permeability. The in silico evaluation of ADME and drug-likeness revealed that L87 has higher absorption in the intestines with good bioavailability. However, an additional in vitro and/or in vivo experimental study should make it possible to verify the theoretical results obtained in silico.Communicated by Ramaswamy H. Sarma.

| S-EPMC8459931 | biostudies-literature

Screening Malaria-box compounds to identify potential inhibitors against SARS-CoV-2 M<sup>pro</sup>, using molecular docking and dynamics simulation studies.

Project description:Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) Main protease (Mpro) is one of the vital drug targets amongst all the coronaviruses, as the protein is indispensable for virus replication. The study aimed to identify promising lead molecules against Mpro enzyme through virtual screening of Malaria Venture (MMV) Malaria Box (MB) comprising of 400 experimentally proven compounds. The binding affinities were studied using virtual screening based molecular docking, which revealed five molecules having the highest affinity scores compared to the reference molecules. Utilizing the established 3D structure of Mpro the binding affinity conformations of the docked complexes were studied by Molecular Dynamics (MD) simulations. The MD simulation trajectories were analysed to monitor protein deviation, relative fluctuation, atomic gyration, compactness covariance, residue-residue map and free energy landscapes. Based on the present study outcome, we propose three Malaria_box (MB) compounds, namely, MB_241, MB_250 and MB_266 to be the best lead compounds against Mpro activity. The compounds may be evaluated for their inhibitory activities using experimental techniques.

| S-EPMC7584923 | biostudies-literature

In Silico Screening of Natural Products as Potential Inhibitors of SARS-CoV-2 Using Molecular Docking Simulation.

Project description:OBJECTIVE:To explore potential natural products against severe acute respiratory syndrome coronavirus (SARS-CoV-2) via the study of structural and non-structural proteins of human coronaviruses. METHODS:In this study, we performed an in-silico survey of 25 potential natural compounds acting against SARS-CoV-2. Molecular docking studies were carried out using compounds against 3-chymotrypsin-like protease (3CLPRO), papain-like protease (PLPRO), RNA-dependent RNA polymerase (RdRp), non-structural protein (nsp), human angiotensin converting enzyme 2 receptor (hACE2R), spike glycoprotein (S protein), abelson murine leukemia viral oncogene homolog 1 (ABL1), calcineurin-nuclear factor of activated T-cells (NFAT) and transmembrane protease serine 2. RESULTS:Among the screened compounds, amentoflavone showed the best binding affinity with the 3CLPRO, RdRp, nsp13, nsp15, hACE2R. ABL1 and calcineurin-NFAT; berbamine with hACE2R and ABL1; cepharanthine with nsp10, nsp14, nsp16, S protein and ABL1; glucogallin with nsp15; and papyriflavonol A with PLPRO protein. Other good interacting compounds were juglanin, betulinic acid, betulonic acid, broussooflavan A, tomentin A, B and E, 7-methoxycryptopleurine, aloe emodin, quercetin, tanshinone I, tylophorine and furruginol, which also showed excellent binding affinity towards a number of target proteins. Most of these compounds showed better binding affinities towards the target proteins than the standard drugs used in this study. CONCLUSION:Natural products or their derivatives may be one of the potential targets to fight against SARS-CoV-2.

| S-EPMC8672856 | biostudies-literature

3D-QSAR, Molecular Docking and Molecular Dynamics Simulation of Pseudomonas aeruginosa LpxC Inhibitors.

Project description:As an important target for the development of novel antibiotics, UDP-3-O-(R-3-hydroxymyristoyl)-N-acetylglucosamine deacetylase (LpxC) has been widely studied. Pyridone methylsulfone hydroxamate (PMH) compounds can effectively inhibit the catalytic activity of LpxC. In this work, the three-dimensional quantitative structure-activity relationships of PMH inhibitors were explored and models with good predictive ability were established using comparative molecular field analysis and comparative molecular similarity index analysis methods. The effect of PMH inhibitors' electrostatic potential on the inhibitory ability of Pseudomonas aeruginosa LpxC (PaLpxC) is revealed at the molecular level via molecular electrostatic potential analyses. Then, two molecular dynamics simulations for the PaLpxC and PaLpxC-inhibitor systems were also performed respectively to investigate the key residues of PaLpxC hydrolase binding to water molecules. The results indicate that orderly alternative water molecules can form stable hydrogen bonds with M62, E77, T190, and H264 in the catalytic center, and tetracoordinate to zinc ion along with H78, H237, and D241. It was found that the conformational transition space of PaLpxC changes after association with PMH inhibitors through free energy landscape and cluster analyses. Finally, a possible inhibitory mechanism of PMH inhibitors was proposed, based on our molecular simulation. This paper will provide a theoretical basis for the molecular design of LpxC-targeting antibiotics.

| S-EPMC5454807 | biostudies-literature

Identification of Potential WSB1 Inhibitors by AlphaFold Modeling, Virtual Screening, and Molecular Dynamics Simulation Studies.

Project description:WD40 repeat and SOCS box containing 1 (WSB1) consists of seven WD40 repeat structural domains at the N-terminal end and one SOCS box structural domain at the C-terminal end. WSB1 promotes cancer progression by affecting the Von Hippel-Lindau tumor suppressor protein (pVHL) and upregulating hypoxia inducible factor-1α (HIF-1α) target gene expression. However, the crystal structure of WSB1 has not been reported, which is not beneficial to the research on WSB1 inhibitors. Therefore, we focused on specific small molecule inhibitors of WSB1. This study applied virtual screening and molecular dynamics simulations; finally, 20 compounds were obtained. Among them, compound G490-0341 showed the best stable structure and was a promising composite for further development of WSB1 inhibitors.

| S-EPMC9122669 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data