Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11861/7518
Title: | Subtypes of associated protein-DNA (Transcription Factor-Transcription Factor Binding Site) patterns |
Authors: | Chan, Tak-Ming Prof. LEUNG Kwong Sak Lee, Kin-Hong Wong, Man-Hon Lau, Terrence Chi-Kong Tsui, Stephen Kwok-Wing |
Issue Date: | 2012 |
Source: | Nucleic Acids Research, 2012, 40(19), pp. 9392-9403 |
Journal: | Nucleic Acids Research |
Abstract: | In protein-DNA interactions, particularly transcription factor (TF) and transcription factor binding site (TFBS) bindings, associated residue variations form patterns denoted as subtypes. Subtypes may lead to changed binding preferences, distinguish conserved from flexible binding residues and reveal novel binding mechanisms. However, subtypes must be studied in the context of core bindings. While solving 3D structures would require huge experimental efforts, recent sequence-based associated TF-TFBS pattern discovery has shown to be promising, upon which a large-scale subtype study is possible and desirable. In this article, we investigate residue-varying subtypes based on associated TF-TFBS patterns. By re-categorizing the patterns with respect to varying TF amino acids, statistically significant (P values≤0.005) subtypes leading to varying TFBS patterns are discovered without using TF family or domain annotations. Resultant subtypes have various biological meanings. The subtypes reflect familial and functional properties and exhibit changed binding preferences supported by 3D structures. Conserved residues critical for maintaining TF-TFBS bindings are revealed by analyzing the subtypes. In-depth analysis on the subtype pair PKVVIL-CACGTG versus PKVEILCAGCTG shows the V/E variation is indicative for distinguishing Myc from MRF families. Discovered from sequences only, the TF-TFBS subtypes are informative and promising for more biological findings, complementing and extending recent one-sided subtype and familial studies with comprehensive evidence. © 2012 The Author(s). |
Type: | Peer Reviewed Journal Article |
URI: | http://hdl.handle.net/20.500.11861/7518 |
ISSN: | 13624962 |
DOI: | 10.1093/nar/gks749 |
Appears in Collections: | Applied Data Science - Publication |
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