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Article

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

1
Department of Zoology, Sri Venkateswara University, Tirupati 517502, India
2
Department of Pathology, Microbiology and Immunology, University of South Carolina School of Medicine, Columbia, SC 29208, USA
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2019, 20(12), 2962; https://doi.org/10.3390/ijms20122962
Received: 8 April 2019 / Revised: 20 May 2019 / Accepted: 22 May 2019 / Published: 18 June 2019
(This article belongs to the Special Issue Kinase Signal Transduction 2019)
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. View Full-Text
Keywords: breast cancer; TP53; ERα; single nucleotide polymorphism; genetic factors; machine learning breast cancer; TP53; ERα; single nucleotide polymorphism; genetic factors; machine learning
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MDPI and ACS Style

Chitrala, K.N.; Nagarkatti, M.; Nagarkatti, P.; Yeguvapalli, S. Analysis of the TP53 Deleterious Single Nucleotide Polymorphisms Impact on Estrogen Receptor Alpha-p53 Interaction: A Machine Learning Approach. Int. J. Mol. Sci. 2019, 20, 2962. https://doi.org/10.3390/ijms20122962

AMA Style

Chitrala KN, Nagarkatti M, Nagarkatti P, Yeguvapalli S. Analysis of the TP53 Deleterious Single Nucleotide Polymorphisms Impact on Estrogen Receptor Alpha-p53 Interaction: A Machine Learning Approach. International Journal of Molecular Sciences. 2019; 20(12):2962. https://doi.org/10.3390/ijms20122962

Chicago/Turabian Style

Chitrala, Kumaraswamy N., Mitzi Nagarkatti, Prakash Nagarkatti, and Suneetha Yeguvapalli. 2019. "Analysis of the TP53 Deleterious Single Nucleotide Polymorphisms Impact on Estrogen Receptor Alpha-p53 Interaction: A Machine Learning Approach" International Journal of Molecular Sciences 20, no. 12: 2962. https://doi.org/10.3390/ijms20122962

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