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

Kumaraswamy Naidu Chitrala,Mitzi Nagarkatti,Suneetha Yeguvapalli,Prakash Nagarkatti

doi:10.3390/ijms20122962

Abstract

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.

Highlights

Breast cancer is one of the leading causes of cancer deaths faced by women around the world today and is a major health issue faced in the western part of the world
Among the high penetrance genes, p53 has a significant role in the malignancy of breast cancer with it mutations were more frequently observed in 30% of the breast carcinomas of which 26% are in luminal tumors (17% of luminal A, 41% of luminal B), 50% are in HER2 amplified tumors, 69% are in molecular apocrine breast carcinomas and 88% are in basal-like carcinomas [37]
Results from our study demonstrated that the mutants P151T and P278A show a large deviation from the native p53-ERα complex in the surface atoms and surface residues compared to R110P

Summary

Introduction

Breast cancer is one of the leading causes of cancer deaths faced by women around the world today and is a major health issue faced in the western part of the world. Body mass index, hormone replacement therapy with estrogen and progesterone, radiation exposure, early menarche, late menopause, age at first childbirth, current age, past history of breast cancer, family history and germline mutations are some of the non-genetic factors that confer risk to breast cancer [6]. Among these risk factors, prolonged exposure to sex steroid estrogen (early menarche, late menopause, or postmenopausal hormone replacement therapy) is associated with a high breast cancer risk [7]. Estrogen ablation or anti-estrogen strategy is an effective means of prevention or treatment of breast cancer, especially in estrogen receptors (ERs)-dependent breast cancer

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