Computational methods for protein-protein interface prediction

Protein-protein interactions underlie all biological processes and are a field of study that has wide implications throughout many other fields including medicine, genetics, biology, and ecology. Proteins are the building blocks and primary actors of life. They work together to accomplish virtually every task within a cell, including, metabolism, signal propagation, immune responses, and cell signaling. This problem is a logical successor to the Human Genome Project: now that we know so much about the DNA of living organisms, how do we advance our knowledge? The Human Genome and other DNA sequencing efforts have provided complete genetic sequences for more than 180 living organisms. However, these efforts fall short of describing or predicting life processes because the sequence of a protein is not enough to elucidate its function. Knowing this, the National Institute of Health started the Protein Structure Initiative, which seeks to increase knowledge of protein structure and has led to an increase in the number of known proteins structures. Unfortunately, even these efforts fall short as there are over 80,000 known protein structures but the function of many is completely unknown. The fledgling field of interface prediction seeks to use this wealth of structural information to be able to describe protein function and drastically increase our understanding of life processes. Presented herein is a novel methodology for solving the protein-protein interface prediction problem leveraging a variety of Computer Science techniques. Specifically detailed is a process for decomposing this 3-dimensional problem into a feature extraction and classification problem using algorithms from computer vision and machine learning

In Silico Methodologies for Selection and Prioritization of Compounds in Drug Discovery

Ph.DDOCTOR OF PHILOSOPH