Bioinformatics Methods To Study Protein Interactions

Since the human genome sequence has been finished and the function-genome was developed roundly, the post-genome era is coming. It will become an important object that Elucidating the expression law and biology function of the all protein about the genomes, because just the protein is the executor of the life activity. An important task of post-genome era is to annotate genome function from the protein-protein interactions. Since the conception of bioinformatics has been mentioned, along with the rapid development of experimental science and internet, bioinformatics has been an important means in research of biology; the software and database of biology that have been developed with bioinformatics were made up of biologist's scientific research in the contemporary era. A fully automatic and reliable system for protein-protein interaction prediction is highly needed especially for the analysis of large-scale genome sequences. In this postgraduate thesis, firstly, introduce the application of the computational methods in protein-protein interaction study. Secondly, summarize the application of the machine learning methods in the bioinformatics and expatiate on the rationale of the Support Vector Machine (SVM). Here, we propose a method to predict protein-protein interaction based on the Support Vector Machine (SVM). In this method, efficient features of protein sequence were selected to construct vectors for SVM. Different sets of training and testing data were compared to ensure the efficient model that can determine between interacting and non-interacting protein pairs. The prediction accuracy, precision, recall, correlation coefficient (CC) for Yeast testing dataset reached 72.46%, 70.00%, 78.64% and 0.45 respectively. Our system provided a complementary way to existing 'wet'experimental methods for protein-protein interaction research. Finally, we develop a useful and efficient tool for protein-protein interactions and the PPIPre web server is available at http://www.ddib.net/DIDWeb/tools.jsp?type=SVMppip.