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Yersinia Protein Tyrosine Phosphatase (YopH) is the most efficient enzyme among all known PTPases and relies on its catalytic loop movements for substrate binding and catalysis. Fluorescence, NMR, and UV resonance Raman (UVRR) techniques have been used to study the thermodynamic and dynamic properties of the loop motions. In this study, a computational approach based on the pathway refinement methods nudged elastic band (NEB) and harmonic Fourier beads (HFB) has been developed to provide structural interpretations for the experimentally observed kinetic processes. In this approach, the minimum potential energy pathways for the loop open/closure conformational changes were determined by NEB using a one-dimensional global coordinate. Two dimensional data analyses of the NEB results were performed as an efficient method to qualitatively evaluate the energetics of transitions along several specific physical coordinates. The free energy barriers for these transitions were then determined more precisely using the HFB method. Kinetic parameters were estimated from the energy barriers using transition state theory and compared against experimentally determined kinetic parameters. When the calculated energy barriers are calibrated by a simple "scaling factor", as have been done in our previous vibrational frequency calculations to explain the ligand frequency shift upon its binding to protein, it is possible to make structural interpretations of several observed enzyme dynamic rates. For example, the nanosecond kinetics observed by fluorescence anisotropy may be assigned to the translational motion of the catalytic loop and microsecond kinetics observed in fluorescence T-jump can be assigned to the loop backbone dihedral angle flipping. Furthermore, we can predict that a Trp354 conformational conversion associated with the loop movements would occur on the tens of nanoseconds time scale, to be verified by future UVRR T-jump studies.

Computational Studies of Catalytic Loop Dynamics in Yersinia Protein Tyrosine Phosphatase Using Pathway Optimization Methods