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Updated: Jun 8, 2026
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Realistic Membrane Modeling Using Complex Lipid Mixtures in Simulation Studies
Published on: September 1, 2023
MEDELLER: homology-based coordinate generation for membrane proteins
Sebastian Kelm1, Jiye Shi, Charlotte M Deane
1Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK. kelm@stats.ox.ac.uk
Bioinformatics (Oxford, England)
|October 8, 2010
Summary
A new method, MEDELLER, improves membrane protein (MP) structure prediction. This tool offers higher accuracy than existing methods for these important drug targets, advancing structural biology.
Area of Science:
- Structural Biology
- Computational Biology
- Biophysics
Background:
- Membrane proteins (MPs) are crucial drug targets, yet their structural data is scarce.
- Current homology-based structure prediction methods are unsuitable for MPs.
- Growing MP structure data necessitates specialized prediction tools.
Purpose of the Study:
- To develop a novel homology-based method for membrane protein structure prediction.
- To enhance the accuracy and reliability of predicted MP models.
Main Methods:
- MEDELLER, a new MP-specific homology-based coordinate generation method.
- Optimization focused on generating highly reliable core models.
- Implementation in Python, Bash, and Perl CGI for Linux systems.
Main Results:
- MEDELLER outperforms the standard Modeller program for MP structure prediction.
- Comparison on 616 target-template pairs showed superior performance.
- MEDELLER achieved an average backbone RMSD of 2.62 Å, compared to 3.16 Å for Modeller.
Conclusions:
- MEDELLER provides a significant advancement in predicting membrane protein structures.
- The method offers improved accuracy, particularly for easier prediction targets.
- MEDELLER is a valuable tool for structural biology research and drug discovery.
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