Study of molecular processes through calculation of multidimensional free energy landscapes.
PhD Thesis, University of Basel,
Faculty of Science.
Official URL: http://edoc.unibas.ch/diss/DissB_11986
The potential of mean force describing conformational changes of biomolecules is a central quantity for understanding the function of biomolecular systems. Calculating an energy landscape of a process that depends on three or more reaction coordinates requires extensive computational power, making some multi-dimensional calculations practically impossible. Here, I present an efficient automatized umbrella sampling strategy for calculating a multidimensional potential of mean force. The method progressively learns by itself, through a feedback mechanism, which regions of a multidimensional space are worth exploring and automatically generates a set of umbrella sampling windows that is adapted to the system. The self-learning adaptive umbrella sampling method is first explained with illustrative examples based on simplified reduced model systems and then applied to two nontrivial situations: the conformational equilibrium of the pentapeptide Met-enkephalin in solution and ion permeation in the KcsA potassium channel. With this method, it is demonstrated that a significant smaller number of umbrella windows needs to be employed to characterize the free energy landscape over the most relevant regions without any loss in accuracy.
|Advisors:||Schwede, Torsten and Bernèche, Simon and Dal Peraro, Matteo|
|Faculties and Departments:||05 Faculty of Science > Departement Biozentrum > Computational & Systems Biology > Bioinformatics (Schwede)|
|Bibsysno:||Link to catalogue|
|Number of Pages:||1 Online-Ressource (83 Seiten)|
|Last Modified:||28 Dec 2016 10:37|
|Deposited On:||28 Dec 2016 10:36|
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