Modeling Conformational Ensembles of Slow Functional Motions in Pin1-WW

Protein-protein interactions are often mediated by flexible loops that experience conformational dynamics on the microsecond to millisecond time scales. NMR relaxation studies can map these dynamics. However, defining the network of inter-converting conformers that underlie the relaxation data remains generally challenging. Here, we combine NMR relaxation experiments with simulation to visualize networks of inter-converting conformers. We demonstrate our approach with the apo Pin1-WW domain, for which NMR has revealed conformational dynamics of a flexible loop in the millisecond range. We sample and cluster the free energy landscape using Markov State Models (MSM) with major and minor exchange states with high correlation with the NMR relaxation data and low NOE violations. These MSM are hierarchical ensembles of slowly interconverting, metastable macrostates and rapidly interconverting microstates. We found a low population state that consists primarily of holo-like conformations and is a “hub” visited by most pathways between macrostates. These results suggest that conformational equilibria between holo-like and alternative conformers pre-exist in the intrinsic dynamics of apo Pin1-WW. Analysis using MutInf, a mutual information method for quantifying correlated motions, reveals that WW dynamics not only play a role in substrate recognition, but also may help couple the substrate binding site on the WW domain to the one on the catalytic domain. Our work represents an important step towards building networks of inter-converting conformational states and is generally applicable.Author Summary: Proteins in their native state can adopt a plethora of shapes, or conformations; this conformational plasticity is critical for regulation and function in many systems. However, it has remained difficult to determine what these different conformations look like at the atomic level. We present a novel way to use Nuclear Magnetic Resonance, Molecular Dynamics Simulations, and Markov State Models to uncover a map of this plethora of conformations that is consistent with the available data. We applied this method to study the intrinsic dynamics used in substrate binding by the WW domain of the Pin1 proline cis-trans isomerase and found that the NMR data were best explained by two slowly-interconverting sets of many metastable conformations rather than two distinct macrostates. Substantial value is added to the NMR data by our method since it provides a kinetic “map” of conformational changes consistent with the observed relaxation data. Such an approach, in combination with information theory, helped us to identify specific conformational changes that might couple substrate binding at the Pin1 WW domain to the catalytic subunit.