More for less in structural genomics.

Structural genomics is the idea of covering protein space so that every protein sequence comes within model building distance of a protein of known structure. Unfortunately, reproducing the structural alignment of distantly related proteins is a difficult challenge to existing sequence alignment and motif search software. We have developed a new transitive alignment algorithm (MaxFlow), which generates accurate alignments between proteins deep in the twilight zone of sequence similarity, below 20% sequence identity. In particular, MaxFlow reliably identifies conserved core motifs between proteins which are only indirect PSI-Blast neighbours. Based on MaxFlow alignments, useful 3D models can be generated for all members of a superfamily from as few as a single structural template--despite hundreds of representatives at 40% sequence identity level and patchy detection of homology by PSI-Blast. We propose novel strategies for target prioritization using MaxFlow scores to predict the optimal templates in a superfamily. Our results support an increase in the granularity of covering protein space that has potentially enormous economic implications for planning the transition to the full production phase of structural genomics.