TY - GEN
T1 - Loop weaver - Loop modeling by the weighted scaling of verified proteins
AU - Holtby, Daniel
AU - Li, Shuai Cheng
AU - Li, Ming
PY - 2012
Y1 - 2012
N2 - Modeling loops is a necessary step in protein structure determination even with experimental NMR data. It is well known to be difficult. Database techniques have the advantage of producing a higher proportion of predictions with sub-angstrom 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 0.42Å RMSD on average for 206 length 6 loops, 0.64Å local RMSD for 168 length 7 loops, 0.81Å RMSD for 117 length 8 loops, and 0.98Å 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 run for over a week, it approaches, but does not surpass, our accuracy. © 2012 Springer-Verlag Berlin Heidelberg.
AB - Modeling loops is a necessary step in protein structure determination even with experimental NMR data. It is well known to be difficult. Database techniques have the advantage of producing a higher proportion of predictions with sub-angstrom 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 0.42Å RMSD on average for 206 length 6 loops, 0.64Å local RMSD for 168 length 7 loops, 0.81Å RMSD for 117 length 8 loops, and 0.98Å 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 run for over a week, it approaches, but does not surpass, our accuracy. © 2012 Springer-Verlag Berlin Heidelberg.
KW - database search
KW - loop modeling
KW - loop prediction
KW - molecular structural biology
UR - https://www.scopus.com/pages/publications/84860794694
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84860794694&origin=recordpage
U2 - 10.1007/978-3-642-29627-7_11
DO - 10.1007/978-3-642-29627-7_11
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9783642296260
VL - 7262 LNBI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 113
EP - 126
BT - Research in Computational Molecular Biology
PB - Springer Verlag
T2 - 16th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2012
Y2 - 21 April 2012 through 24 April 2012
ER -