MEDELLER: homology-based coordinate generation for membrane proteins

By Sebastian Kelm, Jiye Shi, Charlotte M. Deane. 2010

100 citations recorded by the source corpus.

Motivation: Membrane proteins (MPs) are important drug targets but knowledge of their exact structure is limited to relatively few examples. Existing homology-based structure prediction methods are designed for globular, water-soluble proteins. However, we are now beginning to have enough MP structures to justify the development of a homology-based approach specifically for them. Results: We present a MP-specific homology-based coordinate generation method, MEDELLER, which is optimized to build highly reliable core models. The method outperforms the popular structure prediction programme Modeller on MPs. The comparison of the two methods was performed on 616 target–template pairs of MPs, which were classified into four test sets by their sequence identity. Across all targets, MEDELLER gave an average backbone root mean square deviation (RMSD) of 2.62 Å versus 3.16 Å for Modeller. On our ‘easy’ test set, MEDELLER achieves an average accuracy of 0.93 Å backbone RMSD versus 1.56 Å for Modeller. Availability and Implementation: http://medeller.info; Implemented in Python, Bash and Perl CGI for use on Linux systems; Supplementary data are available at http://www.stats.ox.ac.uk/proteins/resources. Contact: kelm@stats.ox.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

Computational modeling of membrane proteins (2014, methodology): “101 MEDELLER,102 is a coordinate-generation tool designed for MPs that outperforms MODELLER for all types of targets from easy to difficult.”
Structural Bioinformatics (unknown): “FREAD uses environment-specific substitution scores (Choi and Deane, 2010; Kelm et al., 2010; Lee and Blundell, 2009) to identify the best fragments for building the structure of the loops.”
Accurate de novo structure prediction of large transmembrane protein domains using fragment-assembly and correlated mutation analysis (2012, background): “As with globular proteins, predicting the structure of TMPs by homology modeling is very effective particularly when TMP-spe-cific methods are used (5); however, the paucity of solved structures means that homology modeling can only be applied to a minority of TMP families.”
An Integrated Framework Advancing Membrane Protein Modeling and Design (2015, methodology): “Homology models fromMEDELLER [70] and Rosetta [16] can be used as input to MPdock and MPsymdock for modeling of large membrane protein complexes.”