TASSER_low‐zsc: An approach to improve structure prediction using low z ‐score–ranked templates

In a variety of threading methods, often poorly ranked (low z ‐score) templates have good alignments. Here, a new method, TASSER_low‐zsc that identifies these low z ‐score–ranked templates to improve protein structure prediction accuracy, is described. The approach consists of clustering of threading templates by affinity propagation on the basis of structural similarity ( thread_cluster ) followed by TASSER modeling, with final models selected by using a TASSER_QA variant. To establish the generality of the approach, templates provided by two threading methods, SP 3 and SPARKS 2 , are examined. The SP 3 and SPARKS 2 benchmark datasets consist of 351 and 357 medium/hard proteins (those with moderate to poor quality templates and/or alignments) of length ≤250 residues, respectively. For SP 3 medium and hard targets, using thread_cluster , the TM‐scores of the best template improve by ∼4 and 9% over the original set (without low z ‐score templates) respectively; after TASSER modeling/refinement and ranking, the best model improves by ∼7 and 9% over the best model generated with the original template set. Moreover, TASSER_low‐zsc generates 22% (43%) more foldable medium (hard) targets. Similar improvements are observed with low‐ranked templates from SPARKS 2 . The template clustering approach could be applied to other modeling methods that utilize multiple templates to improve structure prediction. Proteins 2010. © 2010 Wiley‐Liss, Inc.