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Study on Law and Predicting Methods of Protein-protein Interaction

Author: PeiZhiYong
Tutor: CaiLu
School: Inner Mongolia University of Science and Technology
Course: Genetics
Keywords: Protein Interactions Relative bias Class Structure Secondary structure Support Vector Machine
CLC: Q51
Type: Master's thesis
Year: 2010
Downloads: 122
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Abstract


Exercise functions as the main form of the protein , protein - protein interactions in numerous biological processes play a key role . Today a major topic of biological research , is to explore the mechanisms of protein interactions , protein-protein and determine whether a particular interaction . Study protein interactions of the law, and build predictive models has important practical significance. First, based on protein interaction databases (DIP), Gene Ontology database (GO) and Structural Classification of Proteins database (SCOP), combined with other relevant databases SWISS-PROT protein functional annotation information that defines the parameters describe the tendency of protein-protein interactions ( PIRB), Saccharomyces cerevisiae positioned on different organelles, some membrane proteins of Saccharomyces cerevisiae , C. elegans and E. coli SCOP classification by different structural classes to study protein-protein interactions regularity . The results show that a variety of organelles , membrane protein-protein interactions and structural classes do exist obvious bias . This article also biological significance of the results and protein interaction law are briefly discussed. Protein interactions and protein structure information are closely linked . In this article , the secondary structure of the protein is used as the feature data for protein - protein interactions and protein subunits prediction . The interaction between the protein subunits forecasting , through to the secondary structure and analysis of the information super- secondary structure , which is characterized by extraction , using support vector institutions to build a prediction model . The predictive model to be more meaningful forecast results, the overall prediction accuracy, sensitivity and correlation coefficients were reached 64.52% , 66.87% and 0.291 . The protein - protein interaction prediction work, the protein structure data, and regional location information is used to build the model. Also the use of support vector machines to Saccharomyces cerevisiae as a model target protein interaction prediction accuracy rate of 88.01 percent overall correlation coefficient reached 0.761 . This work was constructed protein interaction prediction model and its associated data sets available online. (http://ibt.imust.cn/PPIP.html)

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CLC: > Biological Sciences > Biochemistry > Protein
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