Predicting the folding pathway of engrailed homeodomain with a probabilistic roadmap enhanced reaction-path algorithm.
To predict a protein-folding pathway, we present an alternative to the time-consuming dynamic simulation of atomistic models. We replace the actual dynamic simulation with variational optimization of a reaction path connecting known initial and final protein conformations in such a way as to maximize an estimate of the reactive flux or minimize the mean first passage time at a given temperature, referred to as MaxFlux. We solve the MaxFlux global optimization problem with an efficient graph-theoretic approach, the probabilistic roadmap method (PRM). We employed CHARMM19 and the EEF1 implicit solvation model to describe the protein solution. The effectiveness of our MaxFlux-PRM is demonstrated in our promising simulation results on the folding pathway of the engrailed homeodomain. Our MaxFlux-PRM approach provides the direct evidence to support that the previously reported intermediate state is a genuine on-pathway intermediate, and the demand of CPU power is moderate.
Li
Da-Wei
DW
Gustaf H. Carlson School of Chemistry and Department of Mathematics and Computer Science, Clark University, Worcester, Massachusetts, USA.
Yang
Haijun
H
Han
Li
L
Huo
Shuanghong
S
D000465:Algorithms; D019295:Computational Biology; D003198:Computer Simulation; D005544:Forecasting; D015233:Models, Statistical; D011336:Probability; D011485:Protein Binding; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry*; D012996:Solutions / Q000737:chemistry; D013696:Temperature
D011506:Proteins; D012996:Solutions