Human aromatase enzyme is a microsomal cytochrome P450 and catalyzes aromatization of androgens into estrogens during steroidogenesis. For breast cancer therapy, third-generation aromatase inhibitors (AIs) have proven to be effective; however patients acquire resistance to current AIs. Thus there is a need to predict aromatase-related proteins to develop efficacious AIs. A machine learning method was established to identify aromatase-related proteins using a five-fold cross validation technique. In this study, different SVM approach-based models were built using the following approaches like amino acid, dipeptide composition, hybrid and evolutionary profiles in the form of position-specific scoring matrix (PSSM); with maximum accuracy of 87.42%, 84.05%, 85.12%, and 92.02% respectively. Based on the primary sequence, the developed method is highly accurate to predict the aromatase-related proteins. Prediction scores graphs were developed using the known dataset to check the performance of the method. Based on the approach described above, a webserver for predicting aromatase-related proteins from primary sequence data was developed and implemented at https://bioinfo.imtech.res.in/servers/muthu/aromatase/home.html. We hope that the developed method will be useful for aromatase protein related research.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10057777 | PMC |
| http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0283567 | PLOS |
Publication Analysis
Top Keywords
Similar Publications
Computational method for aromatase-related proteins using machine learning approach.
PLoS One
March 2023
Department of Biophysics, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, India.
Human aromatase enzyme is a microsomal cytochrome P450 and catalyzes aromatization of androgens into estrogens during steroidogenesis. For breast cancer therapy, third-generation aromatase inhibitors (AIs) have proven to be effective; however patients acquire resistance to current AIs. Thus there is a need to predict aromatase-related proteins to develop efficacious AIs.
View Article and Find Full Text PDFNovel F-Labeled PET Tracers Specific to Aromatase: Design, Synthesis, and Biological Evaluation.
Mol Pharm
July 2022
Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing 100069, China.
The abnormal expression of aromatase is associated with the occurrence and development of a variety of neurological diseases and tumors. A series of F-labeled and Ga-labeled potential aromatase-binding candidate compounds were designed and synthesized based on the structures of aromatase inhibitors. Competitive inhibition experiments in vitro and molecular docking showed that BIBD-069 and BIBD-071 have high affinity for aromatase.
View Article and Find Full Text PDFIn Silico Prediction of Chemicals Binding to Aromatase with Machine Learning Methods.
Chem Res Toxicol
May 2017
Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
Environmental chemicals may affect endocrine systems through multiple mechanisms, one of which is via effects on aromatase (also known as CYP19A1), an enzyme critical for maintaining the normal balance of estrogens and androgens in the body. Therefore, rapid and efficient identification of aromatase-related endocrine disrupting chemicals (EDCs) is important for toxicology and environment risk assessment. In this study, on the basis of the Tox21 10K compound library, in silico classification models for predicting aromatase binders/nonbinders were constructed by machine learning methods.
View Article and Find Full Text PDFProtective effect of estrogens on the brain of rats with essential and endocrine hypertension.
Horm Mol Biol Clin Investig
December 2010
Estrogen neuroprotection has been shown in pathological conditions damaging the hippocampus, such as trauma, aging, neurodegeneration, excitotoxicity, oxidative stress, hypoglycemia, amyloid-β peptide exposure and ischemia. Hypertensive encephalopathy also targets the hippocampus; therefore, hypertension seems an appropriate circumstance to evaluate steroid neuroprotection. Two experimental models of hypertension, spontaneously hypertensive rats (SHR) and deoxycorticosterone (DOCA)-salt hypertensive rats, develop hippocampal abnormalities, which include decreased neurogenesis in the dentate gyrus, astrogliosis, low expression of brain-derived neurotrophic factor (BDNF) and decreased number of neurons in the hilar region, with respect of their normotensive strains Wistar Kyoto (WKY) and Sprague-Dawley rats.
View Article and Find Full Text PDFA gene-to-gene interaction between aromatase and estrogen receptors influences bone mineral density.
Eur J Endocrinol
July 2006
Department of Internal Medicine, Hospital U.M. Valdecilla, University of Cantabria, 39008 Santander, Spain.
Objective: The aromatization of androgenic precursors is the main source of estrogens in postmenopausal women. We tested the hypothesis that allelic variants of the genes coding for aromatase and estrogen receptors (ER) could interact to determine the estrogenic signals on the bone tissue and, consequently, bone mineral density (BMD).
Design: Cross-sectional study including 331 postmenopausal women.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!