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Yeast As a Chassis for Developing Functional Assays to Study Human P53
14:57

Yeast As a Chassis for Developing Functional Assays to Study Human P53

Published on: August 4, 2019

Regression based predictor for p53 transactivation.

Sivakumar Gowrisankar1, Anil G Jegga

  • 1Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. sgowrisankar@partners.org

BMC Bioinformatics
|July 16, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a novel computational tool to predict the transactivation capability of p53 response elements (REs), outperforming existing methods. The predictor analyzes nucleotide interactions and spacer length to assess p53 binding affinity and the impact of genetic variations.

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genetics

Background:

  • The p53 protein is a crucial regulator of gene transcription in response to cellular stress.
  • p53 binding affinity to response elements (REs) is key to its regulatory function.
  • Traditional methods using position weight matrices (PWM) are limited in predicting binding affinity due to factors like nucleotide interactions and spacer length.

Purpose of the Study:

  • To develop a novel in-silico predictor for p53-RE transactivation capability.
  • To improve the prediction of p53 binding affinity by considering nucleotide interactions and spacer length.
  • To analyze validated and novel p53-REs and predict the impact of single nucleotide polymorphisms (SNPs).

Main Methods:

  • Developed a novel in-silico predictor combining multidimensional scaling and multinomial logistic regression.
  • Trained the predictor using experimentally validated p53-REs and their transactivation capabilities.
  • Employed cross-validation studies to compare the new method with existing approaches.

Main Results:

  • The novel in-silico predictor demonstrated superior performance compared to existing methods in cross-validation studies.
  • Ranked putative p53-REs for target genes and microRNAs based on predicted transactivation capability.
  • Analyzed the impact of polymorphisms overlapping p53-REs on their transactivation capability.

Conclusions:

  • A novel regression-based in-silico predictor for p53-RE transactivation capability was created, accounting for nucleotide interactions and spacer length.
  • The predictor offers a more accurate assessment of p53 binding affinity than traditional PWM scores.
  • The tool facilitates the analysis of known and novel p53-REs and the prediction of SNP effects.