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Automated tertiary structure prediction with accurate local model quality assessment using the intfold‐ts method
Author(s) -
McGuffin Liam J.,
Roche Daniel B.
Publication year - 2011
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.23120
Subject(s) - computer science , data mining , quality assessment , quality (philosophy) , artificial intelligence , evaluation methods , reliability engineering , engineering , philosophy , epistemology
The IntFOLD‐TS method was developed according to the guiding principle that the model quality assessment (QA) would be the most critical stage for our template‐based modeling pipeline. Thus, the IntFOLD‐TS method firstly generates numerous alternate models, using in‐house versions of several different sequence‐structure alignment methods, which are then ranked in terms of global quality using our top performing QA method—ModFOLDclust2. In addition to the predicted global quality scores, the predictions of local errors are also provided in the resulting coordinate files, using scores that represent the predicted deviation of each residue in the model from the equivalent residue in the native structure. The IntFOLD‐TS method was found to generate high quality 3D models for many of the CASP9 targets, whilst also providing highly accurate predictions of their per‐residue errors. This important information may help to make the 3D models that are produced by the IntFOLD‐TS method more useful for guiding future experimental work. Proteins 2011; © 2011 Wiley‐Liss, Inc.