A Machine-Learning Strategy for Protein Analysis
or function (or both) is largely unknown. Proteomics is the protein counterpart to genomics, the large-scale analysis of complete genomes. Proteomes contain a cell’s total protein expression at a given time. Proteome analysis not only deals with determining protein-encoding genes’ sequence and function, but is also strongly concerned with the precise biochemical state of each protein in its posttranslational form—that is, the form it takes after it has been translated from its original DNA encoding. Traditional experimental techniques for determining a protein’s structure and function, such as x-ray diffraction or Nuclear Magnetic Resonance methods, remain slow and laborious, and do not scale up to current sequencing speeds. Furthermore, using experiments to determine how proteins function is a daunting task: Protein interactions are complex, and their native operating environments are very specific, which can be difficult to replicate in the laboratory. Researchers are developing many new highthroughput experimental techniques for proteomics applications, including mass spectrometry and protein chips. Still, given proteins’ fundamental importance to biology, biotechnology, and medicine, we must continue developing computer methods that can rapidly sift through massive amounts of data and help determine the structure and function of all the proteins in a given genome. We’re applying machine-learning methods to proteomic problems, and have developed a novel strategy for completely predicting protein 3D coordinates. The strategy has three stages: structural features prediction, topology prediction, and coordinate prediction. Here, we offer an overview of the domain and our machine-learning techniques, and describe the software suite we’ve developed, which is available at http://promoter.ics.uci.edu/BRNN-PRED/.