Accessibility navigation

IntFOLD: an integrated web resource for high performance protein structure and function prediction

McGuffin, L. J. ORCID:, Adiyaman, R., Maghrabi, A. H. A., Shuid, A. N., Brackenridge, D. A., Nealon, J. O. and Philomina, L. S. (2019) IntFOLD: an integrated web resource for high performance protein structure and function prediction. Nucleic Acids Research. gkz322. ISSN 1362-4962

Text (Open Access) - Published Version
· Available under License Creative Commons Attribution.
· Please see our End User Agreement before downloading.


It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1093/nar/gkz322


The IntFOLD server provides a unified resource for the automated prediction of: protein tertiary structures with built-in estimates of model accuracy (EMA), protein structural domain boundaries, natively unstructured or disordered regions in proteins, and protein–ligand interactions. The component methods have been independently evaluated via the successive blind CASP experiments and the continual CAMEO benchmarking project. The IntFOLD server has established its ranking as one of the best performing publicly available servers, based on independent official evaluation metrics. Here, we describe significant updates to the server back end, where we have focused on performance improvements in tertiary structure predictions, in terms of global 3D model quality and accuracy self-estimates (ASE), which we achieve using our newly improved ModFOLD7 rank algorithm. We also report on various upgrades to the front end including: a streamlined submission process, enhanced visualization of models, new confidence scores for ranking, and links for accessing all annotated model data. Furthermore, we now include an option for users to submit selected models for further refinement via convenient push buttons.

Item Type:Article
Divisions:Interdisciplinary centres and themes > Institute for Cardiovascular and Metabolic Research (ICMR)
Interdisciplinary centres and themes > Reading Systems Biology Network (RSBN)
Life Sciences > School of Biological Sciences > Biomedical Sciences
ID Code:83525
Publisher:Oxford Academic


Downloads per month over past year

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation