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Protein refolding in silico with atom-based statistical potentials and conformational search using a simple genetic algorithm.

Qiaojun Fang·David Shortle
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Abstract

A distance-dependent atom-pair potential that treats long range and local interactions separately has been developed and optimized to distinguish native protein structures from sets of incorrect or decoy structures. Atoms are divided into 30 types based on chemical properties and relative position in the amino acid side-chains. Several parameters affecting the calculation and evaluation of this statistical potential, such as the reference state, the bin width, cutoff distances between pairs, and the number of residues separating the atom pairs, are adjusted to achieve the best discrimination. The native structure has the lowest energy for 39 of the 40 sets of original ROSETTA decoys (1000 structures per set) and 23 of the 25 improved decoys (approximately 1900 structures per set). Combined with the orientation-dependent backbone hydrogen bonding potential used by ROSETTA and a statistical solvation potential based on the solvent exclusion model of Lazaridis & Karplus, this potential is used as a scoring function for conformational search based on a genetic algorithm method. After unfolding the native structure by changing every phi and psi angle by either +/-3, +/-5 or +/-7 degrees, five small proteins can be efficiently refolded, in some cases to within 0.5 A C(alpha) distance matrix error (DME) to the native state. Although no significant correlation is found between the total energy and structural similarity to the native state, a surprisingly strong correlation exists between the radius of gyration and the DME for low energy structures.

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AlgorithmsAmino AcidsComputational BiologyHydrogen BondingProtein ConformationProtein FoldingStatic ElectricityJournal ArticleResearch Support, N.I.H., Extramural

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Qiaojun Fang, David Shortle. 2006-04-27. Protein refolding in silico with atom-based statistical potentials and conformational search using a simple genetic algorithm.. https://doi.org/10.1016/j.jmb.2006.04.033

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