User:Steven J. Koch/Notebook/Kochlab/2010/09/04/Curr Bio (2009) Muthukrishnan, Hancock et al kinesin-2 processivity
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Citation
Muthukrishnan, G., Zhang, Y., Shastry, S., & Hancock, W. O. (2009). The processivity of kinesin-2 motors suggests diminished front-head gating. Current biology : CB, 19(5), 442-7. http://dx.doi.org/10.1016/j.cub.2009.01.058.
Other links
- Mendeley: http://www.mendeley.com/research/the-processivity-of-kinesin2-motors-suggests-diminished-fronthead-gating/
- Follow-on paper:
- my summary Shastry, S., & Hancock, W. O. (2010). Neck linker length determines the degree of processivity in kinesin-1 and kinesin-2 motors. Current biology : CB, 20(10), 939-43. http://dx.doi.org/10.1016/j.cub.2010.03.065.
Notes
They use kinesin-1, kinesin-2, kinesin-2 homodimeric structures, and stochastic simulation of discrete state models to investigate the role of neck linker tension in processivity.
- kinesin-2 is 4x less processive than kinesin-1 (in the assay conditions they use)
- neck linker lengths are 14 aa for k-1 and 17 aa for k-2. In undocked / undocked configuration, they use WLC to model the forces as 43 pN for kinesin-1 and 18 pN for kinesin-2 (assuming 8.1 nm between neck linker attachment sites)
- as far as I can see, no discussion of docking
- WLC parameters are 0.5 nm persistence length, 0.38 nm/aa contour length; not extensible (infiinite stretch modulus)
- inserting three amino acids into kinesin-1 neck linker reduces the processivity.
- stochastic model for kinesin-1 is able to replicate run length and time seen experimentally, with some tweaking.
- they combine the rear-head detachment step with the Pi release step for a combined rate constant of 250/s, which they label as "empirical." In our model, we had to increase the Pi release to 200 to 300/s as well. This remains a puzzle compared to published values for Pi release.
- stochastic model they use for kinesin-1 is to simple to account for the behavior of kinesin-2. Namely, that kinesin-2 processivity decreases with increasing ATP concentration. They expand the model to allow for front-head ATP binding when back head is in ADP-P state. Doing this and tweaking rate constants, they are able to fit the observed behavior with stochastic simulations.
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