A New Approach to Optimize a Protein Energy Function on a Folding Pathway Using Gō-Like Potential and All-Atom, Ab Initio Monte Carlo Simulations




Safronova, Aleksandra

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Prediction of a protein structure is important for understanding the function of a protein. The process of protein structure prediction employs the approximation of a protein free energy that guides protein folding to the protein's native state. A function with a good approximation of the protein free energy should allow estimation of the structural distance of the evaluated candidate structure to the protein native state. Currently the energy optimization process relies on the correlation between the energy and the similarity to the native structure. The energy function is presented as a weighted sum of components which are designed by human experts with the use of statistical analysis of solved protein strictures. Values of the weights are derived through the procedure that maximizes the correlation between the energy and the similarity to the native structure measured by a root mean square deviation between coordinates of the protein backbone. Two major components are required for a successful ab initio modelling: (1) an effective energy function that discriminates the native protein structure out of all possible decoy structures; (2) an efficient sampling algorithm that quickly searches for the low-energy states. In this dissertation a new method for energy optimization is proposed. The method relies on a fast sampling algorithm and targets successful protein folding. The weights for energy components are optimized on a found with the Gō potential energy fast folding pathway. The Lennard-Jones potential, the Lazaridis-Karplus solvation potential, hydrogen bonding potential are used in the optimization algorithm. The optimized weights successfully predict all α and α/β proteins. The proposed strategy is conceptually different from the existing methods that optimize the energy on solved protein structures. The developed algorithm is a novel concept that allows the optimization of a more complex functional combination of the energy components that would improve the prediction quality.

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