Protein modeling by multiple sequence threading and distance geometry.

Abstract

The application of homology modeling is often limited by the lack of known structures with sufficiently high sequence similarity to the target protein. The recent development of threading methods now enable the identification of likely folding patterns in a number of cases where the structural relatedness between target and template(s) is not detectable at the sequence level. We devised a hybrid method in which fold recognition was performed using the Multiple Sequence Threading (MST) method. The structural equivalences deduced from the threading output were used to guide the distance geometry program DRAGON in the construction of low-resolution C alpha/C beta models. The initial structures were converted to full-atom representation and refined using the general-purpose molecular modeling package QUANTA. The performance of the approach is illustrated on the CASP2 target T0004 (polyribonucleotide nucleotidyl-transferase S1 motif (PNS1) from Escherichia coli, PDB code: 1SRO) for which no obvious homologues with known structure were available. The correct fold of PNS1 was successfully identified, and the model was found to be more similar to the experimental PNS1 structure than the scaffold (C alpha RMSD of 6.2 A compared with 6.4 A). Our results indicate that a sensitive fold recognition algorithm coupled with a distance geometry program capable of rapidly generating initial structures can successfully complement high-resolution homology modeling methods in cases where sequential similarity is low.