Machine Learning Structural and Functional Proteomics
While new high-throughput experimental techniques are being developed for proteomics applications (e.g. mass spectrometry, protein chips), it is clear that given the fundamental importance of proteins to biology, biotechnology, and medicine, computer methods that can rapidly sift through massive amounts of data and help determine the structure and function of a large number of proteins in a given genome remain important. We provide a brief overview of the application of machine learning methods to proteomic problems. In particular, we outline a novel strategy for the complete prediction of protein 3D coordinates. The strategy relies on three main successive stages: prediction of structural features, prediction of topology, and prediction of actual coordinates. We provide a progress report under this strategy and describe the corresponding suite of web servers available through http://promoter.ics.uci.edu/BRNN-PRED/. Applications to functional proteomics are brie y discussed.