  Protein structure prediction (PSP) is computationally a very challenging
problem. The challenge largely comes from the fact that the energy function
that needs to be minimised in order to obtain the native structure of a given
protein is not clearly known. A high resolution 20x20 energy model could better
capture the behaviour of the actual energy function than a low resolution
energy model such as hydrophobic polar. However, the fine grained details of
the high resolution interaction energy matrix are often not very informative
for guiding the search. In contrast, a low resolution energy model could
effectively bias the search towards certain promising directions. In this
paper, we develop a genetic algorithm that mainly uses a high resolution energy
model for protein structure evaluation but uses a low resolution HP energy
model in focussing the search towards exploring structures that have
hydrophobic cores. We experimentally show that this mixing of energy models
leads to significant lower energy structures compared to the state-of-the-art
results.
