Double Optimization for Design of Protein Energy Function
We propose an automated protocol for designing the energy landscape suitable for the description of a given set of protein sequences with known structures, by optimizing the parameters of the energy function. The parameters are optimized so that not only the global minimum-energy conformation becomes native-like, but also the conformations distinct from the native structure have higher energies than those close to the native one, for each protein sequence in the set. In order to achieve this goal, one has to sample protein conformations that are local minima of the energy function for given parameters. Then the parameters are optimized using linear approximation, and then local minimum conformations are searched with the new energy parameters. We develop an algorithm that repeats this process of parameter optimization based on linear approximation, and conformational optimization for the current parameters, ultimately leading to the optimization of the energy parameters. We test the feasibility of this algorithm by optimizing a coarse grained energy function, called the UNRES energy function, for a set of ten proteins.
KeywordsEnergy Function Structural Database Protein Structure Prediction Conformational Search Automate Protocol
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