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Molecular docking analysis of phytochemicals with estrogen receptor alpha.

ABSTRACT: Breast cancer (BC) is linked to estrogen receptor alpha (ER-α) positive. Tamoxifen and other estrogen selective modulators have proven to be beneficial in slowing the progression of ER-α BC. However, tamoxifen resistance emerges as a result of long-term treatment and cancer development. Therefore, it is of interest to document data on the molecular docking analysis of phytochemicals targeting with Estrogen Receptor-alpha. The screening of the phytochemicals from the ZINC database (a total of 87133 compounds) against ER-α protein was completed. We show that ZINC69481841 and ZINC95486083bind strongly to ER- with binding energies of 10.47 and 11.88 Kcal/mol, respectively, which were significantly greater than the control compound (-8.32Kcal/mol). ZINC69481841 and ZINC95486083 were found to bind with the key residues (Leu387, Arg394, Glu353, and Thr347) of ER-α protein. Data shows that the lead compounds (ZINC69481841 and ZINC95486083) have an acceptable range of ADMET and drug-likeness properties for further consideration in drug discovery.

SUBMITTER: M Rafeeq M

PROVIDER: S-EPMC10266367 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Molecular docking analysis of phytochemicals with estrogen receptor alpha.

M Rafeeq Misbahuddin M

Bioinformation 20220831 8

Breast cancer (BC) is linked to estrogen receptor alpha (ER-α) positive. Tamoxifen and other estrogen selective modulators have proven to be beneficial in slowing the progression of ER-α BC. However, tamoxifen resistance emerges as a result of long-term treatment and cancer development. Therefore, it is of interest to document data on the molecular docking analysis of phytochemicals targeting with Estrogen Receptor-alpha. The screening of the phytochemicals from the ZINC database (a total of 871 ...[more]

PMID: 37323553

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Project description:BackgroundEndocrine disrupting chemicals (EDCs) are exogenous compounds that interfere with the endocrine system of vertebrates, often through direct or indirect interactions with nuclear receptor proteins. Estrogen receptors (ERs) are particularly important protein targets and many EDCs are ER binders, capable of altering normal homeostatic transcription and signaling pathways. An estrogenic xenobiotic can bind ER as either an agonist or antagonist to increase or inhibit transcription, respectively. The receptor conformations in the complexes of ER bound with agonists and antagonists are different and dependent on interactions with co-regulator proteins that vary across tissue type. Assessment of chemical endocrine disruption potential depends not only on binding affinity to ERs, but also on changes that may alter the receptor conformation and its ability to subsequently bind DNA response elements and initiate transcription. Using both agonist and antagonist conformations of the ERα, we developed an in silico approach that can be used to differentiate agonist versus antagonist status of potential binders.MethodsThe approach combined separate molecular docking models for ER agonist and antagonist conformations. The ability of this approach to differentiate agonists and antagonists was first evaluated using true agonists and antagonists extracted from the crystal structures available in the protein data bank (PDB), and then further validated using a larger set of ligands from the literature. The usefulness of the approach was demonstrated with enrichment analysis in data sets with a large number of decoy ligands.ResultsThe performance of individual agonist and antagonist docking models was found comparable to similar models in the literature. When combined in a competitive docking approach, they provided the ability to discriminate agonists from antagonists with good accuracy, as well as the ability to efficiently select true agonists and antagonists from decoys during enrichment analysis.ConclusionThis approach enables evaluation of potential ER biological function changes caused by chemicals bound to the receptor which, in turn, allows the assessment of a chemical's endocrine disrupting potential. The approach can be used not only by regulatory authorities to perform risk assessments on potential EDCs but also by the industry in drug discovery projects to screen for potential agonists and antagonists.

Project description:There is a vast gulf between the two primary strategies for simulating protein-ligand interactions. Docking methods significantly limit or eliminate protein flexibility to gain great speed at the price of uncontrolled inaccuracy, whereas fully flexible atomistic molecular dynamics simulations are expensive and often suffer from limited sampling. We have developed a flexible docking approach geared especially for highly flexible or poorly resolved targets based on mixed-resolution Monte Carlo (MRMC), which is intended to offer a balance among speed, protein flexibility, and sampling power. The binding region of the protein is treated with a standard atomistic force field, while the remainder of the protein is modeled at the residue level with a Gō model that permits protein flexibility while saving computational cost. Implicit solvation is used. Here we assess three facets of the MRMC approach with implications for other docking studies: (i) the role of receptor flexibility in cross-docking pose prediction; (ii) the use of non-equilibrium candidate Monte Carlo (NCMC) and (iii) the use of pose-clustering in scoring. We examine 61 co-crystallized ligands of estrogen receptor α, an important cancer target known for its flexibility. We also compare the performance of the MRMC approach with Autodock smina. Adding protein flexibility, not surprisingly, leads to significantly lower total energies and stronger interactions between protein and ligand, but notably we document the important role of backbone flexibility in the improvement. The improved backbone flexibility also leads to improved performance relative to smina. Somewhat unexpectedly, our implementation of NCMC leads to only modestly improved sampling of ligand poses. Overall, the addition of protein flexibility improves the performance of docking, as measured by energy-ranked poses, but we do not find significant improvements based on cluster information or the use of NCMC. We discuss possible improvements for the model including alternative coarse-grained force fields, improvements to the treatment of solvation, and adding additional types of NCMC moves.

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Molecular docking analysis of phytochemicals with estrogen receptor alpha.

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Molecular docking analysis of phytochemicals with estrogen receptor alpha.

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