IntFOLD: an integrated server for modelling protein structures and functions from amino acid sequences

Liam J. Mcguffin 1 Jennifer D. Atkins 1 Bajuna R. Salehe 1 Ahmad N. Shuid 1 Daniel Roche 2, 3
1 School of Biological Sciences [Reading]
2 MAB - Méthodes et Algorithmes pour la Bioinformatique
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
3 IBC - Institut de Biologie Computationnelle
Abstract : IntFOLD is an independent web server that integrates our leading methods for structure and function prediction. The server provides a simple unified interface that aims to make complex protein modelling data more accessible to life scientists. The server web interface is designed to be intuitive and integrates a complex set of quantitative data, so that 3D modelling results can be viewed on a single page and interpreted by non-expert modellers at a glance. The only required input to the server is an amino acid sequence for the target protein. Here we describe major performance and user interface updates to the server, which comprises an integrated pipeline of methods for: tertiary structure prediction, global and local 3D model quality assessment, disorder prediction, structural domain prediction, function prediction and modelling of protein-ligand interactions. The server has been independently validated during numerous CASP (Critical Assessment of Techniques for Protein Structure Prediction) experiments, as well as being continuously evaluated by the CAMEO (Continuous Automated Model Evaluation) project. The IntFOLD server is available at: http://www.reading.ac.uk/bioinf/IntFOLD/.
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Journal articles
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01287105
Contributor : Laurent Brehelin <>
Submitted on : Friday, March 11, 2016 - 5:56:08 PM
Last modification on : Friday, March 15, 2019 - 1:15:07 AM

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Liam J. Mcguffin, Jennifer D. Atkins, Bajuna R. Salehe, Ahmad N. Shuid, Daniel Roche. IntFOLD: an integrated server for modelling protein structures and functions from amino acid sequences. Nucleic Acids Research, Oxford University Press, 2015, 43 (W1), pp.W169-W173. ⟨10.1093/nar/gkv236⟩. ⟨lirmm-01287105⟩

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