In Silico Criterion for Prediction of Effects of p53 Gene Missense Mutations on p53-Mdm2 Feedback Loop

Abstract

The Informational Spectrum Method (ISM) is the tool for the in silico analysis of proteins which interprets protein sequence linear information using signal analyses methods. In this paper the ISM was employed to characterize the products of genetic variants of tumor suppressor gene p53 and its natural binding regulator protein Mdm2. Based on this we propose the criterion for identification of missense mutations that have impact on the p53-Mdm2 feedback loop. The efficiency of the proposed criterion was confirmed by the ISM analyses of p53 mutants reported in: (i) healthy individuals, (ii) germline mutations database and (iii) somatic mutations database.

Lipoprotein lipase: A bioinformatics criterion for assessment of mutations as a risk factor for cardiovascular disease

“…Once the characteristic frequency for a particular protein function/interaction is identified, it is possible to utilize the ISM approach to predict the amino acids in the sequence, which essentially contribute to this frequency and are likely to be crucial for the observed function. Thus far, the ISM was successfully applied in structure‐function analysis of different protein sequences and assessment of biological effects of mutations and SNPs 14–21…”