Development and benchmarking of TASSER iter for the iterative improvement of protein structure predictions

To improve the accuracy of TASSER models especially in the limit where threading provided template alignments are of poor quality, we have developed the TASSER iter algorithm which uses the templates and contact restraints from TASSER generated models for iterative structure refinement. We apply TASSER iter to a large benchmark set of 2,773 nonhomologous single domain proteins that are ≤ 200 in length and that cover the PDB at the level of 35% pairwise sequence identity. Overall, TASSER iter models have a smaller global average RMSD of 5.48 Å compared to 5.81 Å RMSD of the original TASSER models. Classifying the targets by the level of prediction difficulty (where Easy targets have a good template with a corresponding good threading alignment, Medium targets have a good template but a poor alignment, and Hard targets have an incorrectly identified template), TASSER iter (TASSER) models have an average RMSD of 4.15 Å (4.35 Å) for the Easy set and 9.05 Å (9.52 Å) for the Hard set. The largest reduction of average RMSD is for the Medium set where the TASSER iter models have an average global RMSD of 5.67 Å compared to 6.72 Å of the TASSER models. Seventy percent of the Medium set TASSER iter models have a smaller RMSD than the TASSER models, while 63% of the Easy and 60% of the Hard TASSER models are improved by TASSER iter . For the foldable cases, where the targets have a RMSD to the native <6.5 Å, TASSER iter shows obvious improvement over TASSER models: For the Medium set, it improves the success rate from 57.0 to 67.2%, followed by the Hard targets where the success rate improves from 32.0 to 34.8%, with the smallest improvement in the Easy targets from 82.6 to 84.0%. These results suggest that TASSER iter can provide more reliable predictions for targets of Medium difficulty, a range that had resisted improvement in the quality of protein structure predictions. Proteins 2007. © 2007 Wiley‐Liss, Inc.