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Molecular Dynamics of DNA Mechanical Contributions of Various Promoter Sequences in p53 Complexes Motivated by a Novel Additive Binding Energy Model
Author(s) -
Han In Sub,
Thayer Kelly M.
Publication year - 2015
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.29.1_supplement.712.22
Subject(s) - dna , molecular dynamics , biophysics , promoter , molecular mechanics , computational biology , binding site , hydrogen bond , dna replication , binding energy , replication protein a , chemistry , dna damage , hmg box , biology , microbiology and biotechnology , biochemistry , molecule , dna binding protein , physics , gene , computational chemistry , gene expression , transcription factor , organic chemistry , nuclear physics
The tumor suppressor protein p53 binds genomic DNA as a cell cycle regulator that ensures DNA gets repaired before replication. When excessive damage renders cells non‐salvageable, the protein initiates apoptosis; however, disruption of this process may lead to tumor growth. An additive binding‐energy model was developed as a means to categorize known DNA binding sequences based on their hydrogen bonding patterns and DNA bending mechanics. The model incorporates reported key discriminatory hydrogen bonding interactions and the mechanical DNA bending caused by the “TG step” that occurs in the 1TUP wild type p53 crystal structure. We have found that binding categorically occurs in five groups hypothesized also differ in binding mechanisms. The high frequency of poly(A) and poly(G) tract regions in the non‐consensus p53 binding sites has prompted us to pay special attention to the DNA mechanics and its role in DNA‐protein interactions specifically in the p53 binding interface. To further investigate this hypothesis, sequences representing these features taken from known biological promoters were simulated with molecular dynamics using the AMBER suite programs. The results can iteratively refine the additive binding energy model as more detailed mechanistic properties are elucidated.