How well can we understand large-scale protein motions using normal modes of elastic network models?
In this article, we apply a coarse-grained elastic network model (ENM) to study conformational transitions to address the following questions: How well can a conformational change be predicted by the mode motions? Is there a way to improve the model to gain better results? To answer these questions, we use a dataset of 170 pairs having "open" and "closed" structures from Gerstein's protein motion database. Our results show that the conformational transitions fall into three categories: 1), the transitions of these proteins that can be explained well by ENM; 2), the transitions that are not explained well by ENM, but the results are significantly improved after considering the rigidity of some residue clusters and modeling them accordingly; and 3), the intrinsic nature of these transitions, specifically the low degree of collectivity, prevents their conformational changes from being represented well with the low frequency modes of any elastic network models. Our results thus indicate that the applicability of ENM for explaining conformational changes is not limited by the size of the studied protein or even the scale of the conformational change. Instead, it depends strongly on how collective the transition is.
Yang
Lei
L
Program of Bioinformatics and Computational Biology, Iowa State University, Ames, Iowa, USA.
Song
Guang
G
Jernigan
Robert L
RL
D030562:Databases, Protein; D004548:Elasticity; D008954:Models, Biological; D009068:Movement; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000502:physiology*; D015203:Reproducibility of Results; D012399:Rotation
D011506:Proteins