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A fundamental biological challenge is to understand how the linear information in an organism's genome is processed to produce the resulting behavior or phenotype. Genes, made up of DNA, are transcribed into RNA, and translated into proteins which together form the vast majority of functional elements in an organism. Evolutionary processes ensure that these functional elements interact with their environment in a manner that is beneficial to the organism, using a variety of diverse molecules. Our research elucidates these processes by developing computational algorithms to model, annotate, and understand the relationships between the sequences, structures, functions, and interactions of proteins, DNA, proteins and metabolites, at both the molecular and the genomic/systems levels. The goal is to develop a coherent picture of the mechanistic basis (wiring diagram) of molecular and organismal structure, function, networks, and evolution within a fundamental scientific framework: * Structure: Predict atomic level three dimensional structures of biologically important molecules (with focus on proteins) given their sequence. * Function: Predict function using the resulting models with the aid of available experimental information. * Interaction: Predict interactions between and among these molecules, including biological substrates and inhibitors. * Systems: Integrate the structure, function, and interaction information with the expression (copy number) of these molecules. * Application: Apply the methodologies developed to study specific biological problems of interest. * Infrastructure: Develop an infrastructure to publish the integrated information so that it is useful for biologists to pose and answer precise scientific questions about molecular, systems and organismal biology. We have applied the basic research developed as part of this framework to determine interactions between all human ingestible small molecules and the universe of protein structures to detect homology at the compound-proteome interaction level with the goal of repurposing existing drugs. The Computational Analysis of Novel Drug Opportunities (CANDO) project has made predictions of new drugs for a large number of indications (http://cando.compbio.washington.edu) which are currently being validated in the lab and the clinic.
Copyright © 2003-2014 Molecular & Cellular Biology Program, University of Washington
Fred Hutchison Cancer Research Center | University of Washington
Institute for Systems Biology | Seattle Biomed