Iterative cluster-NMA: A tool for generating conformational transitions in proteins.
Computational models provide insight into the structure-function relationship in proteins. These approaches, especially those based on normal mode analysis, can identify the accessible motion space around a given equilibrium structure. The large magnitude, collective motions identified by these methods are often well aligned with the general direction of the expected conformational transitions. However, these motions cannot realistically be extrapolated beyond the local neighborhood of the starting conformation. In this article, the iterative cluster-NMA (icNMA) method is presented for traversing the energy landscape from a starting conformation to a desired goal conformation. This is accomplished by allowing the evolving geometry of the intermediate structures to define the local accessible motion space, and thus produce an appropriate displacement. Following the derivation of the icNMA method, a set of sample simulations are performed to probe the robustness of the model. A detailed analysis of beta1,4-galactosyltransferase-T1 is also given, to highlight many of the capabilities of icNMA. Remarkably, during the transition, a helix is seen to be extended by an additional turn, emphasizing a new unknown role for secondary structures to absorb slack during transitions. The transition pathway for adenylate kinase, which has been frequently studied in the literature, is also discussed.
Schuyler
Adam D
AD
Department of Neurology, University of Michigan, Ann Arbor, Michigan 48109, USA.
Jernigan
Robert L
RL
Qasba
Pradman K
PK
Ramakrishnan
Boopathy
B
Chirikjian
Gregory S
GS
D016000:Cluster Analysis; D019295:Computational Biology; D003198:Computer Simulation; D030562:Databases, Protein; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry
D011506:Proteins