ArticlePloS one2011
Predicting transcriptional activity of multiple site p53 mutants based on hybrid properties.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
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- Implications of Newly Identified Brain eQTL Genes and Their Interactors in Schizophrenia.Molecular therapy. Nucleic acids · 2018Article
- In silico identification of rescue sites by double force scanning.Bioinformatics (Oxford, England) · 2018Article
- REGULATOR: a database of metazoan transcription factors and maternal factors for developmental studies.BMC bioinformatics · 2015Article
- Discriminating between deleterious and neutral non-frameshifting indels based on protein interaction networks and hybrid properties.Molecular genetics and genomics : MGG · 2015Article
- Improved classification of lung cancer tumors based on structural and physicochemical properties of proteins using data mining models.PloS one · 2013Article
- A Bayesian ensemble approach with a disease gene network predicts damaging effects of missense variants of human cancers.Human genetics · 2013Article
- Prediction of P53 mutants (multiple sites) transcriptional activity based on structural (2D&3D) properties.PloS one · 2013Article
- Signal propagation in protein interaction network during colorectal cancer progression.BioMed research international · 2013Article
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- Dysfunctions associated with methylation, microRNA expression and gene expression in lung cancer.PloS one · 2012Article
- Hepatitis C virus network based classification of hepatocellular cirrhosis and carcinoma.PloS one · 2012Article
- Identification of colorectal cancer related genes with mRMR and shortest path in protein-protein interaction network.PloS one · 2012Article
- SySAP: a system-level predictor of deleterious single amino acid polymorphisms.Protein & cell · 2012Article
- 3D QSAR pharmacophore modeling, in silico screening, and density functional theory (DFT) approaches for identification of human chymase inhibitors.International journal of molecular sciences · 2011Article
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Authors and funding
7 authors.
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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.
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