Intrinsic microtubule GTP-cap dynamics in semi-confined systems: kinetochore–microtubule interface

In order to quantify the intrinsic dynamics associated with the tip of a GTP-cap under semi-confined conditions, such as those within a neuronal cone and at a kinetochore–microtubule interface, we propose a novel quantitative concept of critical nano local GTP-tubulin concentration (CNLC). A simulation of a rate constant of GTP-tubulin hydrolysis, under varying conditions based on this concept, generates results in the range of 0-420 s−1. These results are in agreement with published experimental data, validating our model. The major outcome of this model is the prediction of 11 random and distinct outbursts of GTP hydrolysis per single layer of a GTP-cap. GTP hydrolysis is accompanied by an energy release and the formation of discrete expanding zones, built by less-stable, skewed GDP-tubulin subunits. We suggest that the front of these expanding zones within the walls of the microtubule represent soliton-like movements of local deformation triggered by energy released from an outburst of hydrolysis. We propose that these solitons might be helpful in addressing a long-standing question relating to the mechanism underlying how GTP-tubulin hydrolysis controls dynamic instability. This result strongly supports the prediction that large conformational movements in tubulin subunits, termed dynamic transitions, occur as a result of the conversion of chemical energy that is triggered by GTP hydrolysis (Satarić et al., Electromagn Biol Med 24:255–264, 2005). Although simple, the concept of CNLC enables the formulation of a rationale to explain the intrinsic nature of the “push-and-pull” mechanism associated with a kinetochore–microtubule complex. In addition, the capacity of the microtubule wall to produce and mediate localized spatio-temporal excitations, i.e., soliton-like bursts of energy coupled with an abundance of microtubules in dendritic spines supports the hypothesis that microtubule dynamics may underlie neural information processing including neurocomputation (Hameroff, J Biol Phys 36:71–93, 2010; Hameroff, Cognit Sci 31:1035–1045, 2007; Hameroff and Watt, J Theor Biol 98:549–561, 1982).