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This suggests that an environment-specific approach to membrane protein modelling will yield greater improvements than did the environment-specific approach to soluble protein modelling.
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A more realistic membrane protein model for Glycophorin A is also examined.
In addition, our results tested with the bacterial potassium channel model demonstrated the potential usefulness of the approach in assessing the quality of membrane protein models.
OPM was used to identify reference structures (resolution <3.0 Å) to generate membrane protein models.
> -wrap-foot> Not many independent compilations of membrane protein models with known target structures exist.
The torsion term trained on soluble structures is applied to the whole membrane protein model.
With QMEANBrane, we present a framework that widely covers the aspects of membrane protein model quality assessment.
We show that reliable local quality estimation of membrane protein models is possible, thereby extending local quality estimation to these biologically relevant molecules.
ProQM (Ray et al., 2010) is an MQAP that uses a support vector machine to predict the quality of a membrane protein model by combining structural and sequence-based features calculated from the model.
To assess the membrane protein models quality, we mainly rely on statistical potential terms, combined with the relative solvent accessibility of each residue as calculated by DSSP (Kabsch and Sander, 1983).
As a result of such efforts, we present QMEANBrane, a combination of statistical potentials targeted at local quality estimation of membrane protein models in their naturally occurring oligomeric state: after identifying the transmembrane region using an implicit solvation model, specifically trained statistical potentials get applied on the different regions of a protein model (Figs 1 and 2).
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