Exploring the fitness landscapes of lattice proteins

We present methods to investigate the sequence to structure relation for proteins. We use random structures of HP-type lattice models as a coarse gained model to study generic properties of biopolymers. To circumvent the computational limitations imposed by most lattice protein folding algorithms we apply a simple and fast deterministic approximation algorithm with a tunable accuracy. We investigate ensemble properties such as the conditional probability to find structures with a certain similarity at a given distance of the underlying sequence for various alphabets. Our results suggest that the structure landscapes for lattice proteins are generally very rugged, while larger alphabets fine tune the folding process and smoothen the map. This implies a simplification for evolutionary strategies. The applied methods appear to be helpful in the study of the complex interplay between folding strategies, energy functions and alphabets. Possible implications to the investigation of evolutionary strategies or the optimization of biopolymers are discussed.