CORE--MOLECULAR MODELING FACILITY

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The Molecular Modeling Facility provides state-of-the-art services in protein sequence analysis arid structure prediction to Fox Chase Cancer Center (FCCC) investigators. These services include database search, multiple sequence alignment, phylogenetic trees, secondary structure prediction, transmembrane, coiled-coil and disordered region prediction, homology modeling of single proteins and complexes, protein-protein and protein-ligand docking, and ligand design. This is a new Facility in this renewal application of the CCSG. Currently, one-third to one-half of sequenced proteins are homologous at least in part to a protein of known structure. Homology modeling methods use known structures to build three-dimensional models of target proteins of unknown structure. These models can be used to predict functional interactions with other molecules, to explain existing experimental data, to generate testable hypotheses, and in some cases to become the basis for design of specific inhibitors for translational research. The Facility was created in April 2003, and since that time has performed services for 15 principal investigators with peer-reviewed funding in eight Research Programs and all three Divisions of FCCC. The Facility has also developed new software for automating the modeling process to allow Facility staff more time to concentrate on the biological problem under study in collaboration with the principal investigator. This software is extensible, so that it allows new tools to be incorporated into the same graphical user interface as they become available. As new technologies such as two-hybrid interaction mapping and two-dimensional gel electrophoresis allow investigators to identify functional and physical interactions of larger numbers of proteins in fully sequenced genomes, the demand for detailed structural information will grow rapidly. The use of this Facility is therefore expected to grow significantly over the next five years. The services of the Facility will provide increasingly important information for understanding complex biological systems.