Non-uniform sampling of NMR relaxation data.

The use of non-uniform sampling of NMR spectra may give significant reductions in the data acquisition time. For quantitative experiments such as the measurement of spin relaxation rates, non-uniform sampling is however not widely used as inaccuracies in peak intensities may lead to errors in the extracted dynamic parameters. By systematic reducing the coverage of the Nyquist grid of (15)N Carr-Purcell-Meiboom-Gill (CPMG) relaxation dispersion datasets for four different proteins and performing a full data analysis of the resulting non-uniform sampled datasets, we have compared the performance of the multi-dimensional decomposition and iterative re-weighted least-squares algorithms in reconstructing spectra with accurate peak intensities. As long as a single fully sampled spectrum is included in a series of otherwise non-uniform sampled two-dimensional spectra, multi-dimensional decomposition reconstructs the non-uniform sampled spectra with high accuracy. For two of the four analyzed datasets, a coverage of only 20% results in essentially the same results as the fully sampled data. As exemplified by other data, such a low coverage is in general not enough to produce reliable results. We find that a coverage level not compromising the final results can be estimated by recording a single full two-dimensional spectrum and reducing the spectrum quality in silico.

relax: the analysis of biomolecular kinetics and thermodynamics using NMR relaxation dispersion data.

UNLABELLED: Nuclear magnetic resonance (NMR) is a powerful tool for observing the motion of biomolecules at the atomic level. One technique, the analysis of relaxation dispersion phenomenon, is highly suited for studying the kinetics and thermodynamics of biological processes. Built on top of the relax computational environment for NMR dynamics is a new dispersion analysis designed to be comprehensive, accurate and easy-to-use. The software supports more models, both numeric and analytic, than current solutions. An automated protocol, available for scripting and driving the graphical user interface (GUI), is designed to simplify the analysis of dispersion data for NMR spectroscopists. Decreases in optimization time are granted by parallelization for running on computer clusters and by skipping an initial grid search by using parameters from one solution as the starting point for another -using analytic model results for the numeric models, taking advantage of model nesting, and using averaged non-clustered results for the clustered analysis. AVAILABILITY AND IMPLEMENTATION: The software relax is written in Python with C modules and is released under the GPLv3+ license. Source code and precompiled binaries for all major operating systems are available from http://www.nmr-relax.com. CONTACT: edward@nmr-relax.com.

Protein structure validation and refinement using amide proton chemical shifts derived from quantum mechanics.

We present the ProCS method for the rapid and accurate prediction of protein backbone amide proton chemical shifts--sensitive probes of the geometry of key hydrogen bonds that determine protein structure. ProCS is parameterized against quantum mechanical (QM) calculations and reproduces high level QM results obtained for a small protein with an RMSD of 0.25 ppm (r = 0.94). ProCS is interfaced with the PHAISTOS protein simulation program and is used to infer statistical protein ensembles that reflect experimentally measured amide proton chemical shift values. Such chemical shift-based structural refinements, starting from high-resolution X-ray structures of Protein G, ubiquitin, and SMN Tudor Domain, result in average chemical shifts, hydrogen bond geometries, and trans-hydrogen bond ((h3)J(NC')) spin-spin coupling constants that are in excellent agreement with experiment. We show that the structural sensitivity of the QM-based amide proton chemical shift predictions is needed to obtain this agreement. The ProCS method thus offers a powerful new tool for refining the structures of hydrogen bonding networks to high accuracy with many potential applications such as protein flexibility in ligand binding.