Identification of 14-3-3 Proteins Phosphopeptide-Binding Specificity Using an Affinity-Based Computational Approach

PloS One
Zhao LiFei Guo

Abstract

The 14-3-3 proteins are a highly conserved family of homodimeric and heterodimeric molecules, expressed in all eukaryotic cells. In human cells, this family consists of seven distinct but highly homologous 14-3-3 isoforms. 14-3-3σ is the only isoform directly linked to cancer in epithelial cells, which is regulated by major tumor suppressor genes. For each 14-3-3 isoform, we have 1,000 peptide motifs with experimental binding affinity values. In this paper, we present a novel method for identifying peptide motifs binding to 14-3-3σ isoform. First, we propose a sampling criteria to build a predictor for each new peptide sequence. Then, we select nine physicochemical properties of amino acids to describe each peptide motif. We also use auto-cross covariance to extract correlative properties of amino acids in any two positions. Finally, we consider elastic net to predict affinity values of peptide motifs, based on ridge regression and least absolute shrinkage and selection operator (LASSO). Our method tests on the 1,000 known peptide motifs binding to seven 14-3-3 isoforms. On the 14-3-3σ isoform, our method has overall pearson-product-moment correlation coefficient (PCC) and root mean squared error (RMSE) values of 0.84 and 252.3...Continue Reading

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Methods Mentioned

BETA
feature extraction

Software Mentioned

Elastic Net
LASSO

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