  Fragment-based assembly has been widely used in Ab initio protein folding
simulation which can effectively reduce the conformational space and thus
accelerate sampling. The efficiency of fragment-based movement as well as the
quality of fragment library determine whether the folding process can lead the
free energy landscape to the global minimum and help the protein to reach
near-native folded state. We designed an improved fragment-based movement,
"fragmove", which substituted multiple backbone dihedral angles in every
simulation step. This movement strategy was derived from the fragment library
generated by LRFragLib, an effective fragment detection algorithm using
logistic regression model. We show in replica exchange Monte Carlo (REMC)
simulation that "fragmove", when compared with a set of existing movements in
REMC, shows significant improved ability at increasing secondary and tertiary
predicted model accuracy by 11.24% and 17.98%, respectively and reaching energy
minima decreased by 5.72%. Our results demonstrate that this improved movement
is more powerful to guide proteins faster to low energy regions of
conformational space and promote folding efficiency and predicted model
accuracy.
