TY - JOUR
T1 - Protein Refolding in Silico with Atom-based Statistical Potentials and Conformational Search Using a Simple Genetic Algorithm
AU - Fang, Qiaojun
AU - Shortle, David
N1 - Funding Information:
This work is supported by NIH grant GM34171. We thank Greg Franson for administration of our computer cluster and Erika Gebel for reading the manuscript.
PY - 2006/6/23
Y1 - 2006/6/23
N2 - 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 (∼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 Å Cα 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.
AB - 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 (∼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 Å Cα 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.
KW - atom-pair potential
KW - decoy discrimination
KW - implicit solvation
KW - simple genetic algorithm
KW - statistical potentials
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U2 - 10.1016/j.jmb.2006.04.033
DO - 10.1016/j.jmb.2006.04.033
M3 - Article
C2 - 16678202
AN - SCOPUS:33745152349
SN - 0022-2836
VL - 359
SP - 1456
EP - 1467
JO - Journal of molecular biology
JF - Journal of molecular biology
IS - 5
ER -