Solution structure of a minor and transiently formed state of a T4 lysozyme mutant

Structure of a protein intermediate The function of a protein depends critically on structural dynamics, and on the nature of the transient conformation intermediates that the molecule can adopt. These transients can be elusive and therefore hard to characterize. This paper reports the use of a combination of relaxation-dispersion nuclear magnetic resonance with Rosetta computational structure predictions to design T4 lysozyme mutations that stabilize 'excited' states that are normally too transient to be observed. Proteins are inherently plastic molecules, whose function often critically depends on excursions between different molecular conformations (conformers) 1 , 2 , 3 . However, a rigorous understanding of the relation between a protein’s structure, dynamics and function remains elusive. This is because many of the conformers on its energy landscape are only transiently formed and marginally populated (less than a few per cent of the total number of molecules), so that they cannot be individually characterized by most biophysical tools. Here we study a lysozyme mutant from phage T4 that binds hydrophobic molecules 4 and populates an excited state transiently (about 1 ms) to about 3% at 25 °C (ref. 5 ). We show that such binding occurs only via the ground state, and present the atomic-level model of the ‘invisible’, excited state obtained using a combined strategy of relaxation-dispersion NMR (ref. 6 ) and CS-Rosetta 7 model building that rationalizes this observation. The model was tested using structure-based design calculations identifying point mutants predicted to stabilize the excited state relative to the ground state. In this way a pair of mutations were introduced, inverting the relative populations of the ground and excited states and altering function. Our results suggest a mechanism for the evolution of a protein’s function by changing the delicate balance between the states on its energy landscape. More generally, they show that our approach can generate and validate models of excited protein states.