SUMMARY

The function of a protein is largely dictated by its natural shape called the “native conformation.” Since the native conformation and the global minimum energy configuration highly correlate, predicting this conformation is a global optimization known as the “protein folding problem.” It is computationally intensive due to the high-dimensional and complex energy landscape. Typical conformation algorithms combine a probabilistic search with analytical optimization. The analytical part typically takes longer than the probabilistic part since more function evaluations are required, which are algorithm bottlenecks. To reduce the computational cost, this research studies the effects of energy landscaping on three analytical optimization algorithms: Newton's method, a quasi-Newton algorithm, and the Broyden-Fletcher-Goldfarb-Shanno algorithm. The investigated landscaping changes the heights and the depths of the extrema but keeps their location the same, which eliminates the troublesome process of remapping minima onto the original landscape. With this energy landscaping, the computational cost can be reduced by up to 40% and a protein conformation with up to 30% lower energy can be located. Although both results were not achieved simultaneously, the method of energy landscape modification can make the prediction process more efficient.