Using motion planning to study protein folding pathways
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Protein Structure Prediction: A Practical Approach
Protein Structure Prediction: A Practical Approach
Molecular Dynamics Simulation: Elementary Methods
Molecular Dynamics Simulation: Elementary Methods
Proceedings of the sixth annual international conference on Computational biology
Proceedings of the sixth annual international conference on Computational biology
A Motion Planning Approach to Flexible Ligand Binding
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Using motion planning to study RNA folding kinetics
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
A motion planning approach to protein folding
A motion planning approach to protein folding
Tools for simulating and analyzing RNA folding kinetics
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
A multi-directional rapidly exploring random graph (mRRG) for protein folding
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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Protein motions, ranging from molecular flexibility to large-scale conformational change, play an essential role in many biochemical processes. Despite the explosion in our knowledge of structural and functional data, our understanding of protein movement is still very limited. In previous work, we developed and validated a motion planning based method for mapping protein folding pathways from unstructured conformations to the native state. In this paper, we propose a novel method based on rigidity theory to sample conformation space more effectively, and we describe extensions of our framework to automate the process and to map transitions between specified conformations. Our results show that these additions both improve the accuracy of our maps and enable us to study a broader range of motions for larger proteins. For example, we show that rigidity-based sampling results in maps that capture subtle folding differences between protein G and its mutations, NuG1 and NuG2, and we illustrate how our technique can be used to study large-scale conformational changes in calmodulin, a 148 residue signaling protein known to undergo conformational changes when binding to Ca2+. Finally, we announce our web-based protein folding server which includes a publically available archive of protein motions: http://parasol.tamu.edu/foldingserver/