Efficiency of photosynthesis in a Chl d-utilizing cyanobacterium is comparable to or higher than that in Chl a-utilizing oxygenic species.

The cyanobacterium Acaryochloris marina uses chlorophyll d to carry out oxygenic photosynthesis in environments depleted in visible and enhanced in lower-energy, far-red light. However, the extent to which low photon energies limit the efficiency of oxygenic photochemistry in A. marina is not known. Here, we report the first direct measurements of the energy-storage efficiency of the photosynthetic light reactions in A. marina whole cells, and find it is comparable to or higher than that in typical, chlorophyll a-utilizing oxygenic species. This finding indicates that oxygenic photosynthesis is not fundamentally limited at the photon energies employed by A. marina, and therefore is potentially viable in even longer-wavelength light environments.

Estimation of protein secondary structure content directly from NMR spectra using an improved empirical correlation with averaged chemical shift.

We have recently shown that the averaged chemical shift (ACS) of a nucleus in the protein backbone correlates well empirically to its secondary structure content (SSC). This allows the estimation of SSC directly from the NMR spectrum without the time intensive process of chemical shift assignment. Here, we present an empirical correlation that accounts both for contributions to the relevant protein and chemical shift databases made subsequent to the original analysis, and for missing or inconsistently referenced resonances. Our results affirm that this method provides a significant tool for initial structural prediction from NMR data prior to complete chemical shift assignment.

An evaluation of chemical shift index-based secondary structure determination in proteins: influence of random coil chemical shifts.

Random coil chemical shifts are commonly used to detect protein secondary structural elements in chemical shift index (CSI) calculations. Though this technique is widely used and seems reliable for folded proteins, the choice of reference random coil chemical shift values can significantly alter the outcome of secondary structure estimation. In order to evaluate these effects, we present a comparison of secondary structure content calculated using CSI, based on five different reference random coil chemical shift value sets, to that derived from three-dimensional structures. Our results show that none of the reference random coil data sets chosen for evaluation fully reproduces the actual secondary structures. Among the reference values generally available to date, most tend to be good estimators only of helices. Based on our evaluation, we recommend the experimental values measured by Schwarzinger et al.(2000), and statistical values obtained by Lukin et al. (1997), as good estimators of both helical and sheet content.

Protein structural class identification directly from NMR spectra using averaged chemical shifts.

Knowledge of the three-dimensional structure of proteins is integral to understanding their functions, and a necessity in the era of proteomics. A wide range of computational methods is employed to estimate the secondary, tertiary, and quaternary structures of proteins. Comprehensive experimental methods, on the other hand, are limited to nuclear magnetic resonance (NMR) and X-ray crystallography. The full characterization of individual structures, using either of these techniques, is extremely time intensive. The demands of high throughput proteomics necessitate the development of new, faster experimental methods for providing structural information. As a first step toward such a method, we explore the possibility of determining the structural classes of proteins directly from their NMR spectra, prior to resonance assignment, using averaged chemical shifts. This is achieved by correlating NMR-based information with empirical structure-based information available in widely used electronic databases. The results are analyzed statistically for their significance. The robustness of the method as a structure predictor is probed by applying it to a set of proteins of unknown structure. Our results show that this NMR-based method can be used as a low-resolution tool for protein structural class identification.