Tuning the "violin" of protein kinases: The role of dynamics-based allostery

Abstract

The intricacies of allosteric regulation of protein kinases continue to engage the research community. Allostery, or control from a distance, is seen as a fundamental biomolecular mechanism for proteins. From the traditional methods of conformational selection and induced fit, the field has grown to include the role of protein motions in defining a dynamics-based allosteric approach. Harnessing of these continuous motions in the protein to exert allosteric effects can be defined by a "violin" model that focuses on distributions of protein vibrations as opposed to concerted pathways. According to this model, binding of an allosteric modifier causes global redistribution of dynamics in the protein kinase domain that leads to changes in its catalytic properties. This model is consistent with the "entropy-driven allostery" mechanism proposed by Cooper and Dryden in 1984 and does not require, but does not exclude, any major structural changes. We provide an overview of practical implementation of the violin model and how it stands amidst the other known models of protein allostery. Protein kinases have been described as the biomolecules of interest. © 2019 IUBMB Life, 71(6):685-696, 2019.

Keywords: MD simulation; community maps; conformation; entropy; protein allostery; protein dynamics; protein kinases.

FIG 1

The classical model of allostery is based on structural descriptions of two end states that are defined by two discrete basins in the free energy landscape of the protein. Transition between the two states defines conformation-based allostery into paths: the “induced-fit”/KNF model and the “conformation-selection”/MWC model. As decided by the allosteric effector, one path may be taken over the other, but at any given time, significant flux is expected to be present in both pathways.

FIG 2

The conserved kinase core harbors a hydrophobic motif known as the regulatory or R-spine. An assembled R-spine is the signature of the “active” kinase conformation. This assembly and disassembly of the R-spine in accordance with effects like activation loop phosphorylation and kinase dimerization forms a conformation-based allosteric descriptor of protein kinase activation.

FIG 3

The kinase R-spine can be disassembled in many ways, indicating that the “inactive” state is specific to the kinase. The R-spine-assembled conformation defines the “active” conformation that is conserved for the superfamily of proteins.

FIG 4

Computational umbrella-sampling-based assessment of conformation-based allostery in the Src kinase domain. The free energy landscape shows the presence of “inactive” and “active” conformation basins and also two intermediates. Assembly of the R-spine by activation loop phosphorylation or the L297F/V281F double mutant shifts the Src populations into the “active” basin of the free energy landscape. Data are adapted from references –.

FIG 5

Internal motions of the protein allow for dynamics-based allostery wherein the conformation of the protein is limited to the same free energy basin. Reorganization of internal motions of the protein allow for allosteric effects that are entropically driven.

FIG 6

The “violin” model of dynamics-based allostery. Harmonic nodes at the stem of the violin create a “Chladni pattern” on the body of the violin. These Chladni patterns are descriptors of parts of the body of the violin that vibrate in sync or out of sync. Each harmonic creates a distinct Chladni pattern. Using this analogy, we argue that a protein kinase domain works like the body of a violin wherein the internal vibrations/motions of the protein are remodeled based on the allosteric effector. Every effector creates a distinct “community map” much like a distinct Chladni pattern for each harmonic of the violin. Chladni patterns were taken from reference . Colored regions of the violin denote the regions that vibrate together. This is analogous to the “communities” of residues in the protein structure that share the same dynamics-based information.

FIG 7

Girvan–Newman algorithm-based “community map” of protein kinase A (PKA) describes the sets of 40–60 amino acid large communities that share the maximum mutual information. Each community is mapped onto the structure to explore its biological function. Graph theory-based representation shows each the community map a 2D graph where communities as circles are connected to each other by edges. Sizes of the community circle are indicative of the number of residues contained in them. Thickness of edges connecting the communities denotes the strength of interaction.

FIG 8

Community map of PKA explored in the context of a distal Y204A mutation. The mutation about 8 Å away from the active site debilitates the kinetics of the kinase. Comparison of the community maps of the mutant and the wild-type PKA allow for understanding the dynamic allostery-based modulations that effect kinase activity. As shown in the active-site cleft, the distal Y204A mutation alters the distribution of the dynamics of the protein and reorganizes its community map. As a result, the mutant is unable to synchronize the nucleotide and peptide substrates optimally at the active site.