Accurate Molecular Modeling in Structural Genomics

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The overall aim of this proposal is easily stated: provide automated comparative or homology modeling with the same accuracy as the best CASP (Critical Assessment of Structure Prediction) predictions. At CASP meeting in 1998 and 2000, some 40 target sequences were predicted by over 100 groups, for a total effort of over a man-year per structure. With a programming system that does as well in a few hours of computer time, we will be able to greatly increase the value of protein structures determined in the Structural Genomics Initiative. Our specific efforts are: (1) Reliable fold recognition is the essential first step that matches up the target sequence being predicted with a known sequence and associated known structure (the template). Our method combines the results from the best available world wide web servers in a way that is insensitive to noise or limitations of any one method. Collaborations with Dr. Wooley at the Joint Center for Structural Genomics, San Diego and with Dr. Fidelis at the Protein Structure Prediction Center, Livermore, will allow us to subject our methods to continuous blind testing. (2) We will calibrate our structure enhanced sequence alignment method to fit a large number of accurate structural alignments generated with the improved program, Structal. We will use a new method of multiple structure superposition to make multiple sequence profiles that may give better alignments. (3) Adding atomic detail is a key stage in all homology modeling. Here, we will augment our well-tested method, SegMod, with a new method for combining data from different template structures by mean-field averaging. (4) Energy minimization unconstrained by NMR or x-ray data generally spoils the conformation of a structure rather than making it more native-like. This is the Refinement Problem that we aim to solve using a novel method for deriving continuous, differentiable energy functions from highly refined x-ray structures. Cartesian and torsion angle minimization will be combined to give the largest possible radius of convergence.