Discovering functional patterns from pattern signatures of P53 protein sequence association structure

NEUROCOMPUTING

卷:145

Article

Chiu, David K. Y.¹  Manjunath, Ramya¹ 

[1] Univ Guelph, Sch Comp Sci, Guelph, ON N1G 2W1, Canada

关键词: Association network;    Pattern signature;    Statistical granular computing;    Nanostructure analysis;    p53 tumor suppressor protein;   

DOI  :  10.1016/j.neucom.2014.06.036

来源: Elsevier

【 摘 要 】

The relationship connecting the biomolecular sequence, the molecular structure, and the biological function is of extreme importance in nanostructure analysis of a protein. Previous studies involving multiple sequence alignment of biomolecules have demonstrated that associated sites are indicative of the structural and functional characteristics of biomolecules, comparable to methods such as consensus sequences analysis. In this paper, an association network structure is constructed from detected significant associated sites in aligned p53 sequence ensemble. From the structure, pattern signatures are measured. These signatures are then compared to selected functionality of the p53 proteins. The results indicate that the extracted site patterns are significantly associated with some known properties of p53, a tumor suppressor. Furthermore, when the sites are aligned with p63 and p73, the homologs of p53 without the same cancer suppressing property, using the common domains, the sites significantly discriminate between the human sequences of the p53 family. Therefore, the study confirms the importance of these detected sites that may indicate their differences in cancer suppressing property. (C) 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

【 授权许可】

Free