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Enhanced sampling development for accessing long time scale protein dynamics


Computational modeling has played a great role in solving many questions in biochemical and biomedical research. However, many biologically relevant processes occur on long time scales, which are inaccessible to conventional modeling techniques. Accessing these long time scales has been a great challenge for computational scientists, which has led to the development of numerous methods for enhanced sampling. In this work the well established accelerated molecular dynamics (aMD) method is implemented into several different codes in both a classical mechanics framework as well as a quantum mechanics framework in order to access events occurring on time scales otherwise inaccessible with conventional molecular dynamics codes. Recently there has been a great interest in developing codes to run on graphics processing units (GPUs), which, have been shown to be well suited for conventional molecular dynamics. The research presented in this dissertation shows that the combination of the highly parallelized, inexpensive GPU and the efficient enhanced sampling method, aMD, allow access to events occurring on the millisecond time scale. Importantly, this development is made available to the general scientific community with the release of the Amber12 simulation package

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