Monitoring aromatic picosecond to nanosecond dynamics in proteins via 13C relaxation: expanding perturbation mapping of the rigidifying core mutation, V54A, in eglin c.

Long-range effects, such as allostery, have evolved in proteins as a means of regulating function via communication between distal sites. An NMR-based perturbation mapping approach was used to more completely probe the dynamic response of the core mutation V54A in the protein eglin c by monitoring changes in picosecond to nanosecond aromatic side-chain dynamics and H/D exchange stabilities. Previous side-chain dynamics studies on this mutant were limited to methyl-bearing residues, most of which were found to rigidify on the picosecond to nanosecond time scale in the form of a contiguous "network". Here, high precision (13)C relaxation data from 13 aromatic side chains were acquired by applying canonical relaxation experiments to a newly developed carbon labeling scheme [Teilum et al. (2006) J. Am. Chem. Soc. 128, 2506-2507]. The fitting of model-free parameters yielded S (2) variability which is intermediate with respect to backbone and methyl-bearing side-chain variability and tau e values that are approximately 1 ns. Inclusion of the aromatic dynamic response results in an expanded network of dynamically coupled residues, with some aromatics showing increases in flexibility, which partially offsets the rigidification in methyl side chains. Using amide hydrogen exchange, dynamic propagation on a slower time scale was probed in response to the V54A perturbation. Surprisingly, regional stabilization (slowed exchange) 10-12 A from the site of mutation was observed despite a global destabilization of 1.5 kcal x mol (-1). Furthermore, this unlikely pocket of stabilized residues colocalizes with increases in aromatic flexibility on the faster time scale. Because the converse is also true (destabilized residues colocalize with rigidification on the fast time scale), a plausible entropy-driven mechanism is discussed for relating colocalization of opposing dynamic trends on vastly different time scales.