Unknown

Dataset Information

0

Predicting the folding pathway of engrailed homeodomain with a probabilistic roadmap enhanced reaction-path algorithm.


ABSTRACT: 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.

SUBMITTER: Li DW 

PROVIDER: S-EPMC2242771 | biostudies-literature | 2008 Mar

REPOSITORIES: biostudies-literature

altmetric image

Publications

Predicting the folding pathway of engrailed homeodomain with a probabilistic roadmap enhanced reaction-path algorithm.

Li Da-Wei DW   Yang Haijun H   Han Li L   Huo Shuanghong S  

Biophysical journal 20071116 5


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-theo  ...[more]

Similar Datasets

| S-EPMC2905463 | biostudies-literature
| S-EPMC2931720 | biostudies-literature
| S-EPMC1890484 | biostudies-literature
| S-EPMC2527276 | biostudies-other
2011-04-19 | GSE21407 | GEO
| S-EPMC3738247 | biostudies-literature
2011-04-19 | E-GEOD-21407 | biostudies-arrayexpress
| S-EPMC6278053 | biostudies-literature
| S-EPMC6237354 | biostudies-literature
| S-EPMC3522382 | biostudies-literature