The IntFOLD server: an integrated web resource for protein fold recognition, 3D model quality assessment, intrinsic disorder prediction, domain prediction and ligand binding site prediction

By Daniel B. Roche, Maria T. Buenavista, Stuart J. Tetchner, Liam J. McGuffin. 2011

107 citations recorded by the source corpus.

The IntFOLD server is a novel independent server that integrates several cutting edge methods for the prediction of structure and function from sequence. Our guiding principles behind the server development were as follows: (i) to provide a simple unified resource that makes our prediction software accessible to all and (ii) to produce integrated output for predictions that can be easily interpreted. The output for predictions is presented as a simple table that summarizes all results graphically via plots and annotated 3D models. The raw machine readable data files for each set of predictions are also provided for developers, which comply with the Critical Assessment of Methods for Protein Structure Prediction (CASP) data standards. The server comprises an integrated suite of five novel methods: nFOLD4, for tertiary structure prediction; ModFOLD 3.0, for model quality assessment; DISOclust 2.0, for disorder prediction; DomFOLD 2.0 for domain prediction; and FunFOLD 1.0, for ligand binding site prediction. Predictions from the IntFOLD server were found to be competitive in several categories in the recent CASP9 experiment. The IntFOLD server is available at the following web site: http://www.reading.ac.uk/bioinf/IntFOLD/.

Subfields: Molecular Biology

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Citations in the corpus

IntFOLD: an integrated web resource for high performance protein structure and function prediction (2019, methodology): “Since its inception, the high performance tertiary structure prediction algorithms with integrated model quality assessment have been at the core of IntFOLD server (10,11,18), and these factors have been key contributors to the historical success of the component methods (5,7,9,18,26–30).”
Local Error Estimates Dramatically Improve the Utility of Homology Models for Solving Crystal Structures by Molecular Replacement (2015, methodology): “Predictions that did not obey this latter relationship were of lower completeness; e.g., side chains or whole loops were missing.”
Protein Modelling: What Happened to the “Protein Structure Gap”? (2013, unknown): “…(Hildebrand et al., 2009) IMP http://www.salilab.org/imp (Russel et al., 2012; Yang et al., 2012) IntFOLD http://www.reading.ac.uk/bioinf/IntFOLD/ (Roche et al., 2011) I-Tasser http://zhanglab.ccmb.med.umich.edu/I-TASSER/ (Zhang, 2013) ModBase http://salilab.org/modbase/ (Pieper et al.,…”
Improvement of 3D protein models using multiple templates guided by single-template model quality assessment (2012, background): “…Zhang, 2007), 3D-Jury (Ginalski et al., 2003), 3D- SHOTGUN (Fischer, 2003), Pcons (Lundstrom et al., 2001) and IntFOLD-TS (McGuffin and Roche, 2011; Roche et al., 2011) improve the selection of single-templates by the use of alternative models from several independent servers or in-house version…”
IntFOLD: an integrated server for modelling protein structures and functions from amino acid sequences (2015, methodology): “Here we describe the significant major updates to the IntFOLD server, which was originally published in the NAR Web Server 2011 issue (1).”
Disorder Prediction Methods, Their Applicability to Different Protein Targets and Their Usefulness for Guiding Experimental Studies (2015, methodology): “An example of this approach is used in DISOclust, which is now integrated with the IntFOLD server [51,52].”
Methods for estimation of model accuracy in CASP12 (2017, methodology): “Additionally, 3 alternative quasi-single model methods were used to score models including: the newly developed Disorder B-factor Agreement, the ModFOLD5_single and the ModFOLDclustQ_single scores(16)—each of which made use of a set of 130 reference 3D models that were generated using the latest ver”
FunFOLD: an improved automated method for the prediction of ligand binding residues using 3D models of proteins (2011, methodology): “However, the top model from the IntFOLD-TS server (TS275) [33] was used for testing performance on each of the CASP9 targets.”
Automated tertiary structure prediction with accurate local model quality assessment using the intfold‐ts method (2011, methodology): “The IntFOLD-TS method fully described here is now available via the IntFOLD server,17 which also fully integrates the latest versions of our automated disorder prediction (IntFOLD-DR, DISOclust), 28 domain prediction (IntFOLD-DP, DomFOLD), binding site residue prediction (IntFOLD-FN, FunFOLD),29…”