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Analysis of the TP53 Deleterious Single Nucleotide Polymorphisms Impact on Estrogen Receptor Alpha-p53 Interaction: A Machine Learning Approach

Breast cancer is a leading cancer type and one of the major health issues faced by women around the world. Some of its major risk factors include body mass index, hormone replacement therapy, family history and germline mutations. Of these risk factors, estrogen levels play a crucial role. Among the...

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Autores principales: Chitrala, Kumaraswamy Naidu, Nagarkatti, Mitzi, Nagarkatti, Prakash, Yeguvapalli, Suneetha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6627686/
https://www.ncbi.nlm.nih.gov/pubmed/31216622
http://dx.doi.org/10.3390/ijms20122962
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author Chitrala, Kumaraswamy Naidu
Nagarkatti, Mitzi
Nagarkatti, Prakash
Yeguvapalli, Suneetha
author_facet Chitrala, Kumaraswamy Naidu
Nagarkatti, Mitzi
Nagarkatti, Prakash
Yeguvapalli, Suneetha
author_sort Chitrala, Kumaraswamy Naidu
collection PubMed
description Breast cancer is a leading cancer type and one of the major health issues faced by women around the world. Some of its major risk factors include body mass index, hormone replacement therapy, family history and germline mutations. Of these risk factors, estrogen levels play a crucial role. Among the estrogen receptors, estrogen receptor alpha (ERα) is known to interact with tumor suppressor protein p53 directly thereby repressing its function. Previously, we have studied the impact of deleterious breast cancer-associated non-synonymous single nucleotide polymorphisms (nsnps) rs11540654 (R110P), rs17849781 (P278A) and rs28934874 (P151T) in TP53 gene on the p53 DNA-binding core domain. In the present study, we aimed to analyze the impact of these mutations on p53–ERα interaction. To this end, we, have modelled the full-length structure of human p53 and validated its quality using PROCHECK and subjected it to energy minimization using NOMAD-Ref web server. Three-dimensional structure of ERα activation function-2 (AF-2) domain was downloaded from the protein data bank. Interactions between the modelled native and mutant (R110P, P278A, P151T) p53 with ERα was studied using ZDOCK. Machine learning predictions on the interactions were performed using Weka software. Results from the protein–protein docking showed that the atoms, residues and solvent accessibility surface area (SASA) at the interface was increased in both p53 and ERα for R110P mutation compared to the native complexes indicating that the mutation R110P has more impact on the p53–ERα interaction compared to the other two mutants. Mutations P151T and P278A, on the other hand, showed a large deviation from the native p53-ERα complex in atoms and residues at the surface. Further, results from artificial neural network analysis showed that these structural features are important for predicting the impact of these three mutations on p53–ERα interaction. Overall, these three mutations showed a large deviation in total SASA in both p53 and ERα. In conclusion, results from our study will be crucial in making the decisions for hormone-based therapies against breast cancer.
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spelling pubmed-66276862019-07-23 Analysis of the TP53 Deleterious Single Nucleotide Polymorphisms Impact on Estrogen Receptor Alpha-p53 Interaction: A Machine Learning Approach Chitrala, Kumaraswamy Naidu Nagarkatti, Mitzi Nagarkatti, Prakash Yeguvapalli, Suneetha Int J Mol Sci Article Breast cancer is a leading cancer type and one of the major health issues faced by women around the world. Some of its major risk factors include body mass index, hormone replacement therapy, family history and germline mutations. Of these risk factors, estrogen levels play a crucial role. Among the estrogen receptors, estrogen receptor alpha (ERα) is known to interact with tumor suppressor protein p53 directly thereby repressing its function. Previously, we have studied the impact of deleterious breast cancer-associated non-synonymous single nucleotide polymorphisms (nsnps) rs11540654 (R110P), rs17849781 (P278A) and rs28934874 (P151T) in TP53 gene on the p53 DNA-binding core domain. In the present study, we aimed to analyze the impact of these mutations on p53–ERα interaction. To this end, we, have modelled the full-length structure of human p53 and validated its quality using PROCHECK and subjected it to energy minimization using NOMAD-Ref web server. Three-dimensional structure of ERα activation function-2 (AF-2) domain was downloaded from the protein data bank. Interactions between the modelled native and mutant (R110P, P278A, P151T) p53 with ERα was studied using ZDOCK. Machine learning predictions on the interactions were performed using Weka software. Results from the protein–protein docking showed that the atoms, residues and solvent accessibility surface area (SASA) at the interface was increased in both p53 and ERα for R110P mutation compared to the native complexes indicating that the mutation R110P has more impact on the p53–ERα interaction compared to the other two mutants. Mutations P151T and P278A, on the other hand, showed a large deviation from the native p53-ERα complex in atoms and residues at the surface. Further, results from artificial neural network analysis showed that these structural features are important for predicting the impact of these three mutations on p53–ERα interaction. Overall, these three mutations showed a large deviation in total SASA in both p53 and ERα. In conclusion, results from our study will be crucial in making the decisions for hormone-based therapies against breast cancer. MDPI 2019-06-18 /pmc/articles/PMC6627686/ /pubmed/31216622 http://dx.doi.org/10.3390/ijms20122962 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chitrala, Kumaraswamy Naidu
Nagarkatti, Mitzi
Nagarkatti, Prakash
Yeguvapalli, Suneetha
Analysis of the TP53 Deleterious Single Nucleotide Polymorphisms Impact on Estrogen Receptor Alpha-p53 Interaction: A Machine Learning Approach
title Analysis of the TP53 Deleterious Single Nucleotide Polymorphisms Impact on Estrogen Receptor Alpha-p53 Interaction: A Machine Learning Approach
title_full Analysis of the TP53 Deleterious Single Nucleotide Polymorphisms Impact on Estrogen Receptor Alpha-p53 Interaction: A Machine Learning Approach
title_fullStr Analysis of the TP53 Deleterious Single Nucleotide Polymorphisms Impact on Estrogen Receptor Alpha-p53 Interaction: A Machine Learning Approach
title_full_unstemmed Analysis of the TP53 Deleterious Single Nucleotide Polymorphisms Impact on Estrogen Receptor Alpha-p53 Interaction: A Machine Learning Approach
title_short Analysis of the TP53 Deleterious Single Nucleotide Polymorphisms Impact on Estrogen Receptor Alpha-p53 Interaction: A Machine Learning Approach
title_sort analysis of the tp53 deleterious single nucleotide polymorphisms impact on estrogen receptor alpha-p53 interaction: a machine learning approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6627686/
https://www.ncbi.nlm.nih.gov/pubmed/31216622
http://dx.doi.org/10.3390/ijms20122962
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