Generating Folded Protein Structures with a Lattice Chain Growth Algorithm

We present a new application of the chain growth algorithm to lattice generation of protein structures and thermodynamics. Given the difficulty of ab initio protein structure prediction, this approach provides an alternative to current folding algorithms. The chain growth algorithm, unlike Metropolis folding algorithms, generates independent protein structures to achieve rapid and efficient exploration of configurational space. It is a modified version of the Rosenbluth algorithm where the chain-growth transition probability is a normalized Boltzmann factor; it was previously applied only to simple polymers and protein models with two residue types. The independent protein configurations, generated segment-by-segment on a refined cubic lattice, are based on a single interaction site for each amino acid and a statistical interaction energy derived by Miyazawa and Jernigan (MJ). We examine for several proteins the algorithm's ability to produce nativelike folds and its effectiveness for calculating protein thermodynamics. Thermal transition profiles associated with the internal energy, entropy, and radius of gyration show characteristic folding/unfolding transitions and provide evidence for unfolding via partially unfolded (molten-globule) states. From the configurational ensembles, the protein structures with the lowest distance root-mean-square deviations (dRMSD) vary between 2.2 to 3.8 Å, a range comparable to results of an exhaustive enumeration search. Though the ensemble-averaged dRMSD values are about 1.5 to 2 Å larger, the lowest dRMSD structures have similar overall folds to the native proteins. These results demonstrate that the chain growth algorithm is a viable alternative to protein simulations using the whole chain.

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