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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.

SUBMITTER: Huang T

PROVIDER: S-EPMC3152557 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Predicting transcriptional activity of multiple site p53 mutants based on hybrid properties.

Huang Tao T Niu Shen S Xu Zhongping Z Huang Yun Y Kong Xiangyin X Cai Yu-Dong YD Chou Kuo-Chen KC

PloS one 20110808 8

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 f ...[more]

PMID: 21857971

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Project description:The majority of TP53 missense mutations identified in cancer patients are in the DNA-binding domain and are characterized as either structural or contact mutations. These missense mutations exhibit inhibitory effects on wild-type p53 activity. More importantly, these mutations also demonstrate gain-of-function (GOF) activities characterized by increased metastasis, poor prognosis, and drug resistance. To better understand the activities by which TP53 mutations, identified in Li-Fraumeni syndrome, contribute to tumorigenesis, we generated mice harboring a novel germline Trp53R245W allele (contact mutation) and compared them with existing models with Trp53R172H (structural mutation) and Trp53R270H (contact mutation) alleles. Thymocytes from heterozygous mice showed that all three hotspot mutations exhibited similar inhibitory effects on wild-type p53 transcription in vivo, and tumors from these mice had similar levels of loss of heterozygosity. However, the overall survival of Trp53R245W/+ and Trp53R270H/+ mice, but not Trp53R172H/+ mice, was significantly shorter than that of Trp53+/- mice, providing strong evidence for p53-mutant-specific GOF contributions to tumor development. Furthermore, Trp53R245W/+ and Trp53R270H/+ mice had more osteosarcoma metastases than Trp53R172H/+ mice, suggesting that these two contact mutants have stronger GOF in driving osteosarcoma metastasis. Transcriptomic analyses using RNA sequencing data from Trp53R172H/+, Trp53R245W/+, and Trp53R270H/+ primary osteosarcomas in comparison with Trp53+/- indicated that GOF of the three mutants was mediated by distinct pathways. Thus, both the inhibitory effect of mutant over wild-type p53 and GOF activities of mutant p53 contributed to tumorigenesis in vivo. Targeting p53 mutant-specific pathways may be important for therapeutic outcomes in osteosarcoma.Significancep53 hotspot mutants inhibit wild-type p53 similarly but differ in their GOF activities, with stronger tumor-promoting activity in contact mutants and distinct protein partners of each mutant driving tumorigenesis and metastasis.

Dataset Information

Predicting transcriptional activity of multiple site p53 mutants based on hybrid properties.

Publications

Predicting transcriptional activity of multiple site p53 mutants based on hybrid properties.

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