Advanced Sequence-Based Prediction of Protein Function

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The goal of this project is to improve the prediction of protein structure and function and thereby enhance our understanding of cellular processes mediated by their associated proteins. The specific aims are: (i) to deepen our understanding of the relationship between sequence and structure, (ii) to develop fast and sensitivity tools for searching a database of advanced, family-specific hidden Markov models of protein domains; (iii) to improve the accuracy of multiple sequence alignments; (iv) to develop tools for coordinated analysis of various forms of biological data; and (v) to advance our understanding of specific cellular processes through comprehensive analysis of the proteins that collectively orchestrate those processes. Methods to be used include hidden Markov models, simulated annealing, Gibbs sampling and other Monte Carlo methods, secondary structure and structural threading methods, gapped BLAST and PSI-BLAST heuristic routines, phylogenetic and clustering techniques, as well as other statistical and algorithmic methods. During their development, these methods will be applied to the analysis of various cellular processes, including signaling pathways, RNA processing, chromosome dynamics, and chaperone-mediated assembly and disassembly of protein complexes.