Browsing by Subject "energy landscape"
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Item The impact of protein fluctuations on molecular recognition(2008-12-05) anthony C manson; Dr. Wlodek Bujalowski; Dr. Werner Braun; Dr. Montgomery Pettitt; Dr. Mary MoslenThe effect of protein fluctuations on molecular recognition is poorly understood. Prediction of useful properties such as binding affinity using rigid structures has produced sporadic success. Although attempts have been made to model the effect of\r\nconformational fluctuations, capturing the impact of backbone relaxation has remained\r\nparticularly elusive. In order to investigate these effects, a series of surface exposed\r\nAla/Gly mutants were designed in the flexible RT loop of the C-terminal SH3 domain of\r\nSEM5. One set of mutations was designed to perturb the ensemble of accessible\r\nconformations in the unbound ensemble while leaving the interaction surface with the\r\nligand unchanged. The other set was designed to perturb both the interaction surface as\r\nwell as the ensembles of bound and free conformations. The effects of these mutations\r\nwere investigated by generating random conformations of the RT loop and performing\r\nprincipal component analysis to organize the randomly generated conformational states\r\ninto a coherent landscape. To predict the effect of these mutations, we developed a\r\nstatistical mechanical technique using a simplified energy function that only applied the\r\neffects of excluded volume and implicit solvation. This energy function was utilized to\r\nweight an ensemble of conformational states from which aggregate thermodynamic\r\nproperties could be derived. The computed effects of the mutations on the binding\r\naffinity agreed with experimentally determined values (R= 0.97) from isothermal titration\r\ncalorimetry. The results indicate that the bound state of SEM5 SH3 domain contains a\r\nconsiderable repertoire of conformational variants of the high-resolution structure and\r\nthat the determinants of binding cannot be elucidated from the static structure of the\r\nbound complex.\r\nItem Techniques for modeling and analyzing RNA and protein folding energy landscapes(2009-05-15) Tang, XinyuRNA and protein molecules undergo a dynamic folding process that is important to their function. Computational methods are critical for studying this folding pro- cess because it is difficult to observe experimentally. In this work, we introduce new computational techniques to study RNA and protein energy landscapes, includ- ing a method to approximate an RNA energy landscape with a coarse graph (map) and new tools for analyzing graph-based approximations of RNA and protein energy landscapes. These analysis techniques can be used to study RNA and protein fold- ing kinetics such as population kinetics, folding rates, and the folding of particular subsequences. In particular, a map-based Master Equation (MME) method can be used to analyze the population kinetics of the maps, while another map analysis tool, map-based Monte Carlo (MMC) simulation, can extract stochastic folding pathways from the map. To validate the results, I compared our methods with other computational meth- ods and with experimental studies of RNA and protein. I first compared our MMC and MME methods for RNA with other computational methods working on the com- plete energy landscape and show that the approximate map captures the major fea- tures of a much larger (e.g., by orders of magnitude) complete energy landscape. Moreover, I show that the methods scale well to large molecules, e.g., RNA with 200+ nucleotides. Then, I correlate the computational results with experimental findings. I present comparisons with two experimental cases to show how I can pre- dict kinetics-based functional rates of ColE1 RNAII and MS2 phage RNA and their mutants using our MME and MMC tools respectively. I also show that the MME and MMC tools can be applied to map-based approximations of protein energy energy landscapes and present kinetics analysis results for several proteins.