I am working on a cytoskeletal protein and need to generate free energy landscape models of the protein in its apo form and nucleotide bound form
“Key concept to interpret and illustrate biomolecular processes is provide by the molecule ’ s free energy landscape G(r) kBT ln P(r) where P is the probability distribution of the molecular system along some (in general multidimensional) coordinate r . Popular choices for the coordinate r include the fraction of native contacts, the radius of gyration, and the root mean square deviation of the molecule with respect to the native state. aracterized by its minima (which represent the metastable conformational states of the systems) and its barriers (which connect these states), the energy landscape allows us to account for the pathways and their kinetics occurring in a biomolecular process.”
If you did your simulation in Gromacs you can use g_sham tool and scrip for generating input for free energy landscapes can be found in the following link(http://www.bevanlab.biochem.vt.edu/Pages/Personal/justin/Scripts/sham.txt)
“Key concept to interpret and illustrate biomolecular processes is provide by the molecule ’ s free energy landscape deltaG(r)=-kBT ln P(r) where P is the probability distribution of the molecular system along some (in general multidimensional) coordinate r . Popular choices for the coordinate r include the fraction of native contacts, the radius of gyration, and the root mean square deviation of the molecule with respect to the native state. Characterized by its minima (which represent the metastable conformational states of the systems) and its barriers (which connect these states), the energy landscape allows us to account for the pathways and their kinetics occurring in a biomolecular process.”
If you did your simulation in Gromacs you can use g_sham tool and scrip for generating input for free energy landscapes can be found in the following link(http://www.bevanlab.biochem.vt.edu/Pages/Personal/justin/Scripts/sham.txt)
Reference
http://onlinelibrary.wiley.com/doi/10.1002/9781118183373.ch2/pdf
http://www.moldyn.uni-freiburg.de/publications/papers/115.pdf
Dear Manan,
MD does not provide reliable estimates of the landscape even if they are combined with MC and umbrella sampling. You may try methods developed by Wolynes and Onuchic. I actually found useful COREX by Hilser and methods originated by Erman with eclidean gaussian network models. But really, these are all only models. We actually showed in the past that the NMR provides an insight into the landscapes. Kondrashov, Proteins: Structure, Function, and Bioinformatics 70 (2), 353-362
I suggest you use the axes of a principal components analysis to draw your FEL.
For GROMACS, you have the plugin PLUMED for calculation of Free-Energies Landscapes through Umbrella Sampling, Metadynamics, etc.
Using appropriate CVs for your metadynamics or well-tempered metadynamics simulation can give you more precise results of the free energy landscape for the targeted protein. Using distances of mass centers of different moieties or dihedral angles could get good results. Good luck
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