An evaluation of automated homology modelling methods at low target template sequence similarity

@article{citeulike:1562564, abstract = {Motivation: There are two main areas of difficulty in homology modelling that are particularly important when sequence identity between target and template falls below 50\%: sequence alignment and loop building. These problems become magnified with automatic modelling processes, as there is no human input to correct mistakes. As such we have benchmarked several stand-alone strategies that could be implemented in a workflow for automated high-throughput homology modelling. These include three new sequence-structure alignment programs: 3D-Coffee, Staccato and SAlign, plus five homology modelling programs and their respective loop building methods: Builder, Nest, Modeller, SegMod/ENCAD and Swiss-Model. The SABmark database provided 123 targets with at least five templates from the same SCOP family and sequence identities [\≤]50\%. Results: When using Modeller as the common modelling program, 3D-Coffee outperforms Staccato and SAlign using both multiple templates and the best single template, and across the sequence identity range 2050\%. The mean model RMSD generated from 3D-Coffee using multiple templates is 15 and 28\% (or using single templates, 3 and 13\%) better than those generated by Staccato and Salign, respectively. 3D-Coffee gives equivalent modelling accuracy from multiple and single templates, but Staccato and SAlign are more successful with single templates, their quality deteriorating as additional lower sequence identity templates are added. Evaluating the different homology modelling programs, on average Modeller performs marginally better in overall modelling than the others tested. However, on average Nest produces the best loops with an 8\% improvement by mean RMSD compared to the loops generated by Builder. Contact: r.m.jackson@leeds.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online. 10.1093/bioinformatics/btm262}, author = {Dalton, James A. and Jackson, Richard M. }, citeulike-article-id = {1562564}, doi = {10.1093/bioinformatics/btm262}, journal = {Bioinformatics}, keywords = {bioinformatics, evaluation, homology, modelling, prediction, protein, structure}, month = {August}, number = {15}, pages = {1901--1908}, posted-at = {2008-04-26 12:05:56}, priority = {2}, title = {An evaluation of automated homology modelling methods at low target template sequence similarity}, url = {http://dx.doi.org/10.1093/bioinformatics/btm262}, volume = {23}, year = {2007} }

TY - JOUR ID - citeulike:1562564 L3 - citeulike-article-id:1562564 TI - An evaluation of automated homology modelling methods at low target template sequence similarity JF - Bioinformatics VL - 23 IS - 15 SP - 1901 EP - 1908 N2 - Motivation: There are two main areas of difficulty in homology modelling that are particularly important when sequence identity between target and template falls below 50%: sequence alignment and loop building. These problems become magnified with automatic modelling processes, as there is no human input to correct mistakes. As such we have benchmarked several stand-alone strategies that could be implemented in a workflow for automated high-throughput homology modelling. These include three new sequence-structure alignment programs: 3D-Coffee, Staccato and SAlign, plus five homology modelling programs and their respective loop building methods: Builder, Nest, Modeller, SegMod/ENCAD and Swiss-Model. The SABmark database provided 123 targets with at least five templates from the same SCOP family and sequence identities [≤]50%. Results: When using Modeller as the common modelling program, 3D-Coffee outperforms Staccato and SAlign using both multiple templates and the best single template, and across the sequence identity range 2050%. The mean model RMSD generated from 3D-Coffee using multiple templates is 15 and 28% (or using single templates, 3 and 13%) better than those generated by Staccato and Salign, respectively. 3D-Coffee gives equivalent modelling accuracy from multiple and single templates, but Staccato and SAlign are more successful with single templates, their quality deteriorating as additional lower sequence identity templates are added. Evaluating the different homology modelling programs, on average Modeller performs marginally better in overall modelling than the others tested. However, on average Nest produces the best loops with an 8% improvement by mean RMSD compared to the loops generated by Builder. Contact: r.m.jackson@leeds.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online. 10.1093/bioinformatics/btm262 KW - bioinformatics KW - evaluation KW - homology KW - modelling KW - prediction KW - protein KW - structure AU - Dalton, James AU - Jackson, Richard PY - 2007/08/01/ UR - http://dx.doi.org/10.1093/bioinformatics/btm262 ER -