  A protein is a linear chain containing a set of amino acids, which folds on
itself to create a specific native structure, also called the minimum energy
conformation. It is the native structure that determines the functionality of
each protein. The protein folding problem (PFP) remains one of the more
difficult problems in computational and chemical biology. The principal
challenge of PFP is to predict the optimal conformation of a given protein by
considering only its amino acid sequence. As the conformational space contains
a very large number of conformations, even when addressing short sequences,
different simplified models have been developed and applied to make the PFP
less complex. In the last few years, many computational approaches have been
proposed to solve the PFP. They are based on simplified lattice models such as
the hydrophobic-polar model. In this paper, we present a new Hybrid Cuckoo
Search Algorithm (HCSA) to solve the 3D-HP protein folding optimization
problem. Our proposed algorithm consists of combining the Cuckoo Search
Algorithm (CSA) with the Hill Climbing (HC) algorithm. Simulation results on
different benchmark sequences are presented and compared to the
state-of-the-art algorithms.
