ReFOLD: a server for the refinement of 3D protein models guided by accurate quality estimates

By Ahmad N. Shuid, Robert Kempster, Liam J. McGuffin. 2017

38 citations recorded by the source corpus.

Abstract ReFOLD is a novel hybrid refinement server with integrated high performance global and local Accuracy Self Estimates (ASEs). The server attempts to identify and to fix likely errors in user supplied 3D models of proteins via successive rounds of refinement. The server is unique in providing output for multiple alternative refined models in a way that allows users to quickly visualize the key residue locations, which are likely to have been improved. This is important, as global refinement of a full chain model may not always be possible, whereas local regions, or individual domains, can often be much improved. Thus, users may easily compare the specific regions of the alternative refined models in which they are most interested e.g. key interaction sites or domains. ReFOLD was used to generate hundreds of alternative refined models for the CASP12 experiment, boosting our group's performance in the main tertiary structure prediction category. Our successful refinement of initial server models combined with our built-in ASEs were instrumental to our second place ranking on Template Based Modeling (TBM) and Free Modeling (FM)/TBM targets. The ReFOLD server is freely available at: http://www.reading.ac.uk/bioinf/ReFOLD/.

Subfields: Materials Chemistry, 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): “The IntFOLD server provides a single point of access to an integrated suite of six component methods: IntFOLD-TS, for tertiary structure prediction (9–11,18,19); ModFOLD, for 3D model Accuracy Self-Estimate (ASE) scoring (9,20); ReFOLD, for 3D model refinement (9,21); DISOclust, for disorder predict”
Accurate template‐based modeling in CASP12 using the IntFOLD4‐TS, ModFOLD6, and ReFOLD methods (2018, methodology): “Additionally, we gained a small overall performance boost in GDT_TS by attempting refinement for the T0 targets using ReFOLD.12 We will continue to work on improving our model ranking, selection and ASE scoring with future versions of ModFOLD.”