title: Predicting transcriptional activity of multiple site p53 mutants based on hybrid properties.
abstract: As an important tumor suppressor protein, reactivate mutated p53 was found in many kinds of human cancers and that restoring active p53 would lead to tumor regression. In this work, we developed a new computational method to predict the transcriptional activity for one-, two-, three- and four-site p53 mutants, respectively. With the approach from the general form of pseudo amino acid composition, we used eight types of features to represent the mutation and then selected the optimal prediction features based on the maximum relevance, minimum redundancy, and incremental feature selection methods. The Mathew's correlation coefficients (MCC) obtained by using nearest neighbor algorithm and jackknife cross validation for one-, two-, three- and four-site p53 mutants were 0.678, 0.314, 0.705, and 0.907, respectively. It was revealed by the further optimal feature set analysis that the 2D (two-dimensional) structure features composed the largest part of the optimal feature set and maybe played the most important roles in all four types of p53 mutant active status prediction. It was also demonstrated by the optimal feature sets, especially those at the top level, that the 3D structure features, conservation, physicochemical and biochemical properties of amino acid near the mutation site, also played quite important roles for p53 mutant active status prediction. Our study has provided a new and promising approach for finding functionally important sites and the relevant features for in-depth study of p53 protein and its action mechanism.
authors.affiliation: Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People's Republic of China.
authors.forename: Tao
authors.initials: T
authors.lastname: Huang
authors.forename: Shen
authors.initials: S
authors.lastname: Niu
authors.forename: Zhongping
authors.initials: Z
authors.lastname: Xu
authors.forename: Yun
authors.initials: Y
authors.lastname: Huang
authors.forename: Xiangyin
authors.initials: X
authors.lastname: Kong
authors.forename: Yu-Dong
authors.initials: YD
authors.lastname: Cai
authors.forename: Kuo-Chen
authors.initials: KC
authors.lastname: Chou
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D001665:Binding Sites / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D009154:Mutation; D009369:Neoplasms / Q000235:genetics; D017434:Protein Structure, Tertiary; D015203:Reproducibility of Results; D014158:Transcription, Genetic; D016159:Tumor Suppressor Protein p53 / Q000737:chemistry / Q000235:genetics* / Q000378:metabolism
chemical_list: D000596:Amino Acids; D016159:Tumor Suppressor Protein p53