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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