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Assessing the quality of modelled 3D protein structures using the ModFOLD server

Roche, D. B., Buenavista, M. T. and McGuffin, L. J. (2014) Assessing the quality of modelled 3D protein structures using the ModFOLD server. In: Protein Structure Prediction. Methods in Molecular Biology, 1137. Springer, pp. 83-103. ISBN 9781493903658

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To link to this item DOI: 10.1007/978-1-4939-0366-5_7


Model quality assessment programs (MQAPs) aim to assess the quality of modelled 3D protein structures. The provision of quality scores, describing both global and local (per-residue) accuracy are extremely important, as without quality scores we are unable to determine the usefulness of a 3D model for further computational and experimental wet lab studies.Here, we briefly discuss protein tertiary structure prediction, along with the biennial Critical Assessment of Techniques for Protein Structure Prediction (CASP) competition and their key role in driving the field of protein model quality assessment methods (MQAPs). We also briefly discuss the top MQAPs from the previous CASP competitions. Additionally, we describe our downloadable and webserver-based model quality assessment methods: ModFOLD3, ModFOLDclust, ModFOLDclustQ, ModFOLDclust2, and IntFOLD-QA. We provide a practical step-by-step guide on using our downloadable and webserver-based tools and include examples of their application for improving tertiary structure prediction, ligand binding site residue prediction, and oligomer predictions.

Item Type:Book or Report Section
Divisions:Interdisciplinary centres and themes > Institute for Cardiovascular and Metabolic Research (ICMR)
Faculty of Life Sciences > School of Biological Sciences > Biomedical Sciences
ID Code:36144
Uncontrolled Keywords:Model quality assessment - Protein tertiary structure prediction - Critical Assessment of Techniques for Protein Structure Prediction (CASP) - Web servers - Single-model quality assessment methods - Consensus-based (clustering) model quality assessment methods - Per-residue error - Fold recognition - Ligand binding site residue prediction - Oligomer prediction

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