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Assessing the Quality of Modelled 3D Protein Structures Using the ModFOLD Server

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Protein Structure Prediction

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1137))

Abstract

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.

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Acknowledgments

University of Reading Faculty Studentship, MRC Harwell and the Diamond Light Source Ltd. (to. M.T.B.). This research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement No. 246556 (to D.B.R.).

Author information

Authors and Affiliations

  1. Genoscope, Institut de Génomique, Commissariat à l’Energie Atomique et aux Energies Alternatives, Evry, France

    Daniel Barry Roche

  2. Centre National de la Recherche Scientifique, UMR Evry, Evry, France

    Daniel Barry Roche

  3. Université d’Evry-Val-d’Essonne, Evry, France

    Daniel Barry Roche

  4. PRES UniverSud Paris, Les Algorithmes, Bâtiment Euripide, Saint-Aubin, France

    Daniel Barry Roche

  5. School of Biological Sciences, University of Reading, Reading, UK

    Maria Teresa Buenavista & Liam James McGuffin

  6. BioComputing Section, Medical Research Council Harwell, Harwell Oxford, Oxfordshire, UK

    Maria Teresa Buenavista

  7. Diamond Light Source, Didcot, UK

    Maria Teresa Buenavista

Authors
  1. Daniel Barry Roche
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  2. Maria Teresa Buenavista
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  3. Liam James McGuffin
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Editor information

Editors and Affiliations

  1. Purdue University Dept. Biological Science, West Lafayette, Indiana, USA

    Daisuke Kihara

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Roche, D.B., Buenavista, M.T., McGuffin, L.J. (2014). Assessing the Quality of Modelled 3D Protein Structures Using the ModFOLD Server. In: Kihara, D. (eds) Protein Structure Prediction. Methods in Molecular Biology, vol 1137. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-0366-5_7

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  • DOI: https://doi.org/10.1007/978-1-4939-0366-5_7

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-0365-8

  • Online ISBN: 978-1-4939-0366-5

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