Predicting protein-protein interactions, interaction sites and residue-residue contact matrices with machine learning techniques.

摘要

Protein-protein interactions (PPIs) play crucial roles in many biological processes in living organisms, such as immune response, enzyme catalysis, and signal transduction. Acquiring knowledge of the interfacial regions between interacting proteins is not only helpful in understanding protein functions and elucidating signal transduction networks but also critical for structure-based drug design and disease treatment. The cost, time and other limitations associated with the current experimental methods to obtain PPI information have motivated the development of computational methods for predicting PPIs and their interfaces.;In the dissertation, I propose to use deep learning algorithms, mainly Stacked Autoencoders and Deep Neural Networks, along with other machine learning techniques to predict the protein-protein interactions, interaction sites, and amino acid residue-residue contacts. These machine learning techniques include Hidden Markov Models, Fisher Scores, Support Vector Machines, logistic regression, and clustering. Specifically, I developed computational methods based on these machine learning techniques to tackle the following three questions about protein-protein interaction: 1) whether two given protein sequences can interact (protein-protein interaction predictions), 2) if they interact, where are the interacting residues in individual proteins (interaction site predictions), 3) how these interacting residues are paired up across the interacting proteins (contact matrix predictions).;Lastly, a web server, DDI2PPI, has been developed to make available the PPI and residue-residue contact matrix predictions to the public. The DDI2PPI provides a large-scale implementation of the machine learning algorithms that have been developed from the in-depth research work of this dissertation. DDI2PPI is freely available at http://annotation.dbi.udel.edu/ppi_prediction/.