Peak height versus peak volume
 

Given a standard NOESY-based protein structure determination: Does anyone have any information on the benefits of measuring peak intensity by a volume integration method rather than simply measuring the peak height.

Obviously integration is theoretically more accurate, but does it make any difference to the quality of the structures produced? especially if peak lineshapes are comparable?

I was hoping to find some study comparing structures produced by both methods.....


I'm also curious about the benefits of distance-calbrating NOEs to a curve rather than simply putting restraints in a few different bins?

Re
 

Hi!
I think in a NOESY based structure determination, all the program that are available are dealing with the volume based integration.
In my view volume based integration would be more better as the peaks are considered to be in a 3D space, where as line shape involving measurement of the peak height in all the 3D dimension will become more tedious.
If you get more better explanation do write to me.

Cheers!
Prem Prakash Pathak

Peak height vs volume
 

I haven't seen any studies comparing structures calculated from peak heights or volumes but Peter Wright's group did analysis a few years ago about the differences in relaxation rates calculated from intensities and volumes as well as data fitting.

Viles JH, Duggan BM, Zaborowski E, Schwarzinger S, Huntley JJ, Kroon GJ, Dyson HJ, Wright PE. Related Articles, Links
Abstract Potential bias in NMR relaxation data introduced by peak intensity analysis and curve fitting methods.
J Biomol NMR. 2001 Sep;21(1):1-9.
PMID: 11693564 [PubMed - indexed for MEDLINE]

I believe they found that routines that found intensities by checking for the highest point in an area around a peak maximum biased the measurements to higher value because they were subject to influence of noise but that the results were otherwise quite similar. In my experience most people use intensities when they a large number of NOEs as in protein spectra and use volumes in combination with careful relaxation analysis/simulation routines (ie CORMA) where few distance constraints are available as is the case in DNA. For protein structures a large number of loose constraints serves to restrain the structure quite well and the extra time, difficulties and overlap problems of peak integration are not worth the trouble.

NOE peak intensities are less prone to baseline offset errors than NOE peak volumes
 

If processing is not perfect (e.g. improper 1st data point scaling in the indirect dimension, which depends on the existence of a non-zero 1st order phase correction), the baseline (in other words the noise ) can have an offset from 0. This can be spotted for example when looking at 1D traces in the indirect dimension of the ROESY or NOESY spectrum: it looks like the average noise line for the most intense peaks (e.g. diagonal peaks or methyl peaks) lies above (or below) the true 0 line (i.e. the average noise line of the traces that don't contain strong peaks).

This results into a larger relative error in peak volumes compared to peak intensities. Say the average noise level is offset above (below) 0, it will add (subtract) a large quantity to the peak volume because it's close to the peak base, which is broad (basically the additional volume added/subtracted will be be approx. the noise average times the broad area of the peak base). The error propagated in the intensity is only the noise average. Again, the noise average is nonzero because the noise is artificially above or below the true 0 baseline.

Since this error is non-uniform (it applies only to cross peaks that align in F2 frequency to the strong peaks, or ultimately only to peaks with a baseline offset), it may decrease significantly the overall accuracy of the resulting NOE constraint set.