DISTANCE GEOMETRY GENERATES NATIVE-LIKE FOLDS FOR SMALL HELICAL PROTEINS USING THE CONSENSUS DISTANCES OF PREDICTED PROTEIN STRUCTURES

For successful ab initio protein structure prediction, a method is nee ded to identify native-like structures from a set containing both nati ve and non-native protein-like conformations. In this regard, the use of distance geometry has shown promise when accurate inter-residue dis tances are available. We describe a method by which distance geometry restraints are culled from sets of 500 protein-like conformations for four small helical proteins generated by the method of Simons et al. ( 1997). A consensus-based approach was applied in which every inter-C a lpha distance was measured, and the most frequently occurring distance s were used as input restraints for distance geometry. For each protei n, a structure with lower coordinate root-mean-square (RMS) error than the mean of the original set was constructed; in three cases the topo logy of the fold resembled that of the native protein. When the fold s ets were filtered for the best scoring conformations with respect to a n all-atom knowledge-based scoring function, the remaining subset of 5 0 structures yielded restraints of higher accuracy. A second round of distance geometry using these restraints resulted in an average coordi nate RMS error of 4.38 Angstrom.