Ab initio fold prediction of small helical proteins using distance geometry and knowledge-based scoring functions

The problem of protein tertiary structure prediction from primary sequence can be separated into two subproblems: generation of a library of possible folds;md specification of a best fold given the library. A distance geometr y procedure based on random pairwise metrization with good sampling propert ies was used to generate a library of 500 possible structures for each of 1 1 small helical proteins. The input to distance geometry consisted of sets of restraints to enforce predicted helical secondary structure and a generi c range of 5 to 11 Angstrom between predicted contact residues on all pairs of helices. For each of the 11. targets, the resulting library contained s tructures with low RMSD versus the native structure. Near-native sampling w as enhanced by at least three orders of magnitude compared to a random samp ling of compact folds. All library members were scored with a combination o f an all-atom distance-dependent function, a residue pair-potential, and a hydrophobicity function. In six of the 11 cases, the best-ranking fold was considered to be near native. Each library was also reduced to a final ab i nitio prediction via consensus distance geometry performed over the 50 best -ranking structures from the full set of 500. The consensus results were of generally higher quality, yielding six predictions within 6.5 Angstrom of the native fold. These favorable predictions corresponded to those for whic h the correlation between the RMSD and the scoring function were highest. T he advantage of the reported methodology is its extreme simplicity and pote ntial for including other types of structural restraints. (C) 1999 Academic Press.