Abstract:
The prediction of proteins' conformation helps to understand their exhibited functions,
allows for modeling and allows for the possible synthesis of the studied protein. Our
research is focused on a sub-problem of protein folding known as side-chain packing. Its
computational complexity has been proven to be NP-Hard. The motivation behind our
study is to offer the scientific community a means to obtain faster conformation
approximations for small to large proteins over currently available methods. As the size
of proteins increases, current techniques become unusable due to the exponential nature
of the problem. We investigated the capabilities of a hybrid genetic algorithm / simulated
annealing technique to predict the low-energy conformational states of various sized
proteins and to generate statistical distributions of the studied proteins' molecular
ensemble for pKa predictions. Our algorithm produced errors to experimental results
within .acceptable margins and offered considerable speed up depending on the protein
and on the rotameric states' resolution used.
