>Modeling loops is a necessary step in protein structure determination, even with experimental nuclear magnetic resonance (NMR) data, it is widely known to be difficult. Database techniques have the advantage of producing a higher proportion of predictions with subangstrom accuracy when compared with ab initio techniques, but the disadvantage of also producing a higher proportion of clashing or highly inaccurate predictions. We introduce LoopWeaver, a database method that uses multidimensional scaling to achieve better, clash-free placement of loops obtained from a database of protein structures. This allows us to maintain the above-mentioned advantage while avoiding the disadvantage. Test results show that we achieve significantly better results than all other methods, including Modeler, Loopy, SuperLooper, and Rapper, before refinement. With refinement, our results (LoopWeaver and Loopy consensus) are better than ROSETTA, with 0.42 Å RMSD on average for 206 length 6 loops, 0.64 Å local RMSD for 168 length 7 loops, 0.81Å RMSD for 117 length 8 loops, and 0.98 Å RMSD for length 9 loops, while ROSETTA has 0.55, 0.79, 1.16, 1.42, respectively, at the same average time limit (3 hours). When we allow ROSETTA to run for over a week, it approaches, but does not surpass, our accuracy.
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