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Meiler Lab Computational Chemical and Structural Biology |
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Methods Development
Contact Prediction BCL Align Sequence Alignment and Fold Recognition Sparse EPR Data guided Protein Structure Elucidation Jufo9D Secondary Structure and TM Span Prediction Protein Structure Prediction using Fold Templates BCL::Cluster: Clustering biological molecules with PyMOL visualization Gradient Minimization for Structure Refinement RosettaTMHApplications
Modeling of Protein IIIA Structure Determination of DsbB using Paramagnetic Restraints Structure determination of KCNE3 using paramagnetic restraints
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Methods Development
Protein Structure Prediction Cryo-EM Fit Cryo-EM Guided Protein Fold Elucidation Membrane Protein Structure Prediction using Sparse NMR Restraints RosettaEPR BCL::EMFitMinimize Protein Structure Prediction using SAXS DataApplications
Modeling Rhodopsin Interaction with G-Protein and Arrestin Mechanism of Activation of the heterotrimeric G protein alpha subunit
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Methods Development
Chemical Shift Prediction PharmMap Method Development for QSAR Ligand-based Virtual Screening Development of RosettaQSAR Knowledge-Based Small Molecule Conformational Sampling Novel HTS Datasets composed to benchmark ligand-based QSAR ModelingApplications
mGluR5 Related Neurological Diseases Allosteric Modulators of Neuropeptide Y Receptors Malaria - Inhibition of beta-Hematin Crystallization in Plasmodium falciparum Rapid in silico target identification for anti-malarials
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Methods Development
Rosetta Drug Design Rosetta Design Modeling Partial Covalent Interactions in Protein Ligand Interfaces RosettaLigandEnsemble Drug Design through Foldit Video GameApplications
Structural Determinants of Antidepressant Drugs GPCR Model Building, Refinement, and Ligand Docking HIV-1 Protease Inhibitor Docking Protein-Ligand Interaction as Cancer Therapeutics
Protein structure prediction using fold templates
The current BCL::Fold protein structure prediction method uses a pure de novo algorithm. Secondary structure elements (SSEs) are assembled in a Monte Carlo fashion and scored based on coarse knowledge-based potentials. The probability of generating meaningful SSE placements can be enhanced by using structures known to occur in nature. Fold templates that describe SSE orientations were directly derived from protein structures in the PDB. Assembling protein models using these fold templates should both speed up the algorithm and improve accuracy of the predicted final models.
Alumni Project Members: Brian Weiner , Axel Fischer , Marcin J. Skwark
Center for Structural Biology
465 21st Ave South, BIOSCI/MRBIII
Vanderbilt University, Nashville, TN