SCWRL and MolIDE: programs for protein side-chain prediction and homology modeling

Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA 19111, USA.
Nature Protocol (Impact Factor: 9.67). 02/2008; 3(12):1832-47. DOI: 10.1038/nprot.2008.184
Source: PubMed


SCWRL and MolIDE are software applications for prediction of protein structures. SCWRL is designed specifically for the task of prediction of side-chain conformations given a fixed backbone usually obtained from an experimental structure determined by X-ray crystallography or NMR. SCWRL is a command-line program that typically runs in a few seconds. MolIDE provides a graphical interface for basic comparative (homology) modeling using SCWRL and other programs. MolIDE takes an input target sequence and uses PSI-BLAST to identify and align templates for comparative modeling of the target. The sequence alignment to any template can be manually modified within a graphical window of the target-template alignment and visualization of the alignment on the template structure. MolIDE builds the model of the target structure on the basis of the template backbone, predicted side-chain conformations with SCWRL and a loop-modeling program for insertion-deletion regions with user-selected sequence segments. SCWRL and MolIDE can be obtained at (

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Available from: Roland Dunbrack
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    • "The most reliable model was evaluated on the basis of root mean square deviation (RMSD) and TM score [25]. The selected models were further refined using SCWRL 4.0 [33] and CHARMm [34] energy minimization. The GROMOS [35] algorithm implemented in DeepView [36] was used for energy minimization of the predicted models. "
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    • "Such distortions are for example well-handled using the approach of Claessens et al. [53] or the ModRefiner tool [59] (which unfortunately doesn't handle multimeric protein chains). For the second rebuilding step, side chain reconstruction, the SCWRL program can be recommended [60]. "

    Full-text · Dataset · Oct 2015
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    • "The most reliable models were evaluated on the basis of root mean square deviation (RMSD), TM score and DOPE Profile. The selected models were further refined using SCWRL 4.0 (Wang et al., 2008) and CHARMm (Vanommeslaeghe et al., 2010) energy minimization using ChiRotor algorithm of DS. The GROMOS (van Gunsteren et al., 1996) algorithm implemented in DeepView (Kaplan and Littlejohn, 2001) was used for energy minimization of the predicted chitinase II structure. "
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