CFG Subgroup: 3-D structural glycobiology group: topic
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Issues and advances in applying 3D structure methods to Glycoscience
Rob Woods
Wednesday, 09 July 2008 15:55 UTC
As everyone is aware, there are difficulties in applying any of the established 3D methods to glycans, glycoproteins and carbohydrate-protein complexes.
I would like to initiate a discussion outlining these challenges, but also presenting the most recent advances toward addressing them. Hopefully this can help us refine our mandate as a subgroup.
From the computational modeling perspective, let me kick things off by listing one of the biggest challenges:
Challenge 1) carbohydrates are often flexible and so necessitate a sophisticated modeling approach to capture the motion or to generate an ensemble of conformations that are experimentally consistent.
Advances: More efficient algorithms mean that molecular dynamics simulations can be used to reach into the sub-microsecond regime. Better force fields mean that the results from MD simulations are often experimentally-consistent, without the need to employ experimental constrains.
What about challenges/advances for other methods, such as NMR, x-ray, emerging techniques?
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Replies
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I agree with Rob. The main hurdles lie in an area that people who like pretty pictures don’t usually think about. A snapshot does not capture the complexity of the energy landscape that molecules, especially carbohydrates, sample.
Challenge 1:
As an NMR spectroscopist, I and those in my group typically measure averaged parameters and we must rely on MD simulations to help in interpreting the averaged data. Unfortunately,we cannot test whether the detailed conformational analysis provided by computational studies tell the full story. In part, this is a vicious cycle because NMR cannot easily detect motion that does not influence nuclear relaxation.MD simulations can address some of this with longer trajectories. But there are a few advances in NMR as well that can help. Residual dipolar couplings are extremely sensitive to motion on ALL timescales and can therefore augment the typical NMR relaxation data. In this way, it is possible to “see” molecular motion in carbohydrates that relaxation data cannot detect. Furthermore, we can now bracket what time regime the motion must occur on (usually microseconds-to-nanoseconds, see Venable et al. Carbohydr. Res., 2005, 340, p.863 & Freedberg, J. Am. Chem. Soc. 2002, 124, 2358).
The typical Lipari and Szabo analysis used for proteins is difficult to apply to carbohydrates because the analysis relies on a large difference between the overall reorientation time of the molecules and the local (or internal) motion. Furthermore, carbohydrates do not always have compact shapes. Relaxation analysis at multiple fields should add to the mix, allowing a wider range of motion to be detected. With the increase in magnetic field strength, probing somewhat higher frequency motion will be easier.
I can now begin to address Rob’s challenge: MD simulations can be used to calculate T1s. Multiple fields, temperatures and longer simulation times will help to bridge the gap between structural ensembles and calculations.
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