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Time-Resolved Studies of Crossbridge Movement: Why Use X-Rays? Why Use Fish Muscle?

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Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 332)

Abstract

The advantages of using time-resolved X-ray diffraction as a means of probing myosin cross-bridge behaviour in active muscle are outlined, together with the reasons that bony fish muscle has advantages in such studies. We show that the observed X-ray diffraction patterns from fish muscle can be analysed in a way that is rigorous enough to allow reliable information about crossbridge activity to be defined. Among the advantages of this muscle are that diffraction patterns from resting, active and rigor muscles are all well-sampled at least out to the 30 row-line, that the resting myosin layer-line pattern can be’ solved’ crystallographically to define the starting position of the crossbridges in resting muscle, and that the equatorial intensity distribution, which in all patterns from vertebrate skeletal muscles comprises overlapping peaks from the A-band and the Z-band, can be analysed sufficiently rigorously to allow separation of the the two patterns, both of which change when the muscle is active. Finally, we present results both on a new set of myosin-based layer-lines in patterns from active muscle (consistent with the presence of low-force bridges as also indicated by the time-courses of the intensity changes on the equator and the changing mass distribution in the A-band unit cell) and also on changes of the actin-based layer-lines (consistent with stereospecific labelling of the actin filaments by force-producing crossbridges). Our results to date, which demonstrate the enormous power of time-resolved X-ray diffraction studies, strongly support the swinging of myosin heads on actin as part of the contractile cycle.

Keywords

Actin Filament Fish Muscle Sarcomere Length Myosin Head Myosin Filament 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© Springer Science+Business Media New York 1993

Authors and Affiliations

  1. 1.Biophysics SectionBlackett Laboratory Imperial CollegeLondonUK

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