NOVEL STATISTICAL ENERGY FUNCTIONS AND APPLICATIONS TO PROTEIN STRUCTURE PREDIC

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Protein structure prediction is one of the most challenging areas in theoretical biophysics, and it plays an essential role in structural genomics and rational drug design. We have recently developed two novel statistical energy functions: OPUS-Ca, a C-alpha-based potential function composed of seven representative molecular interactions; and OPUS-PSP, an orientation-dependent statistical all-atom potential derived from side-chain packing. We wish to apply these two potential functions to OPUS, a conceptually new structure prediction method that employs multi-scale, multi-layer and top-down prediction strategies. OPUS combines template-based and de novo methods to predict 3D protein structures from primary sequences.