Discovering protein structure and function through nonlinear system identification

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Predicting protein structure and function from sequence is of fundamental importance to biomedical research. An effective solution has the potential to accelerate the drug discovery process and may lead to an increased understanding of cancer and other disease processes. Unfortunately, experimental methods for elucidating a protein's structure are very costly and are not always applicable. Computational prediction techniques provide an attractive alternative; however, the accurate prediction of 3D tertiary protein structure directly from amino acid sequence data continues to elude researchers when highly similar previously solved protein structures are not available or for longer domains. Even then, knowing a protein's tertiary structure may not be sufficient to fully understand its function: the majority of eukaryotic proteins are not functional in their original structure and must be activated through some form of post-translational modification (PTM). We have recently developed a novel approach to protein secondary structure prediction making use of Parallel Cascade Identification (PCI), a powerful method of nonlinear system identification. Resulting accuracy not only compared well with top contemporary prediction methods based on neural networks and hidden Markov models, but PCI was also shown to be highly effective when used in concert with such methods. While predicting secondary structure is a useful intermediate step, the ultimate goal of protein structure prediction is to predict the complete 3D structure of the active conformation(s) of a protein, including PTM information, given knowledge only of its amino acid sequence and its environment. Building on the established success of PCI as a proteomics tool, the research program proposed herein will make significant progress in these new and challenging areas, and make a real contribution towards in silico drug prediction of protein structure and function.