Using multiple structure alignments, fast model building, and energetic analysis in fold recognition and homology modeling

Abstract

We participated in the fold recognition and homology sections of CASP5 using primarily in-house software. The central feature of our structure prediction strategy involved the ability to generate good sequence-to-structure alignments and to quickly transform them into models that could be evaluated both with energy-based methods and manually. The in-house tools we used include: a) HMAP (Hybrid Multidimensional Alignment Profile)-a profile-to-profile alignment method that is derived from sequence-enhanced multiple structure alignments in core regions, and sequence motifs in non-structurally conserved regions. b) NEST-a fast model building program that applies an "artificial evolution" algorithm to construct a model from a given template and alignment. c) GRASP2-a new structure and alignment visualization program incorporating multiple structure superposition and domain database scanning modules. These methods were combined with model evaluation based on all atom and simplified physical-chemical energy functions. All of these methods were under development during CASP5 and consequently a great deal of manual analysis was carried out at each stage of the prediction process. This interactive model building procedure has several advantages and suggests important ways in which our and other methods can be improved, examples of which are provided.