Seminar by Prof. George Stan
University of Cincinnati, Department of Chemistry
AAA+ nanomachines, such as the hexameric ring-shaped Clp (Caseinolytic protease) ATPases or the 26S eukaryotic proteasome, assist protein degradation and disaggregation by unfolding and translocating substrate proteins (SPs) through a narrow central channel. Repetitive mechanical forces mediating these actions are effected through sequential ATP-driven axial motions of flexible pore loops protruding into the channel. The primary remodeling action involves applying repetitive mechanical forces onto the substrate proteins through a set of ATPase loops that protrude into the channel. The fate of the substrate protein is largely dependent not on its global stability, but on the local mechanical strength near the pulled terminal, and on the direction of force application. To elucidate the effect of SP structure and force directionality, we probed diverse substrates, such as the I27 domain of the muscle protein titin, dihydrofolate reductase, HaloTag, green fluorescent protein, and knotted proteins. Our atomistic simulations of Clp-mediated degradation of knotted proteins revealed dependence of unknotting and translocation on tension propagation, sequence direction, non-native contacts and intermediates with strong local mechanical resistance.
All-atom, solvated, simulations of ClpB dynamics reveal that requisite dynamic pore stability and flexibility involve a complex interplay between networks of inter- and intraprotomer interactions. Predicted relaxation times of the pore loop 1, obtained noting that the decay in the autocorrelation function is observation-time-dependent, are in excellent
agreement with experimental single-molecule FRET (smFRET) values. Analysis of allosteric pathways, using graph theory approaches, reveals that mutations abolishing ATP hydrolysis weakly affect communication between the nucleotide binding site with pore loop 1, but strongly affect those with pore loops 2 and 3, in accord with results of smFRET experiments. Simulations of the ClpP peptidase, analyzed using graph theory and machine learning approaches, identify structural features and their relative importance for characterizing the conformational transition that controls access to the ClpP degradation chamber.