Multiple marker abundance profiling: combining selected reaction monitoring and data-dependent acquisition for rapid estimation of organelle abundance in subcellular samples.

Measuring changes in protein or organelle abundance in the cell is an essential, but challenging aspect of cell biology. Frequently-used methods for determining organelle abundance typically rely on detection of a very few marker proteins, so are unsatisfactory. In silico estimates of protein abundances from publicly available protein spectra can provide useful standard abundance values but contain only data from tissue proteomes, and are not coupled to organelle localization data. A new protein abundance score, the normalized protein abundance scale (NPAS), expands on the number of scored proteins and the scoring accuracy of lower-abundance proteins in Arabidopsis. NPAS was combined with subcellular protein localization data, facilitating quantitative estimations of organelle abundance during routine experimental procedures. A suite of targeted proteomics markers for subcellular compartment markers was developed, enabling independent verification of in silico estimates for relative organelle abundance. Estimation of relative organelle abundance was found to be reproducible and consistent over a range of tissues and growth conditions. In silico abundance estimations and localization data have been combined into an online tool, multiple marker abundance profiling, available in the SUBA4 toolbox (http://suba.live).

First-principles Hubbard U approach for small molecule binding in metal-organic frameworks.

We apply first-principles approaches with Hubbard U corrections for calculation of small molecule binding energetics to open-shell transition metal atoms in metal-organic frameworks (MOFs). Using density functional theory with van der Waals dispersion-corrected functionals, we determine Hubbard U values ab initio through an established linear response procedure for M-MOF-74, for a number of different metal centers (M = Ti, V, Cr, Mn, Fe, Co, Ni, and Cu). While our ab initio U values differ from those used in previous work, we show that they result in lattice parameters and electronic contributions to CO2-MOF binding energies that lead to excellent agreement with experiments and previous results, yielding lattice parameters within 3%. In addition, U-dependent calculations for an example system, Co-MOF-74, suggest that the CO2 binding energy grows monotonically with the value of Hubbard U, with the binding energy shifting 4 kJ/mol (or 0.041 eV) over the range of U = 0-5.4 eV. These results provide insight into an approximate but computationally efficient means for calculation of small molecule binding energies to open-shell transition metal atoms in MOFs and suggest that the approach can be predictive with good accuracy, independent of the cations used and the availability of experimental data.

MASCP gator: an overview of the Arabidopsis proteomic aggregation portal.

A key challenge in the area of bioinformatics in the coming decades is the ability to manage the wealth of information that is being generated from the variety of high throughput methodologies currently being undertaken in laboratories across the world. While these approaches have made available large volumes of data to the research community, less attention has been given to the problem of how to intuitively present the data to enable greater biological insights. Recently, an attempt was made to tackle this problem in the area of Arabidopsis proteomics. The model plant has been the target of countless proteomics surveys producing an exhaustive array of data and online repositories. The MASCP Gator is an aggregation portal for proteomic data currently being produced by the community and unites a large collection of specialized resources to a single portal (http://gator.masc-proteomics.org/). Here we describe the latest additions, upgrades and features to this resource further expanding its role into protein modifications and genome sequence variations.

Testing the results of municipal mixed-use zoning ordinances: a novel methodological approach.

Municipal mixed-use zoning (MUZ) is one public health strategy to create more walkable neighborhoods by reducing the separation of daily activities. This study uses a novel data-gathering methodology to evaluate municipal zoning ordinances in twenty-two California cities in conjunction with the walkability potential of resulting mixed-use zones, to explore the extent to which variations in uses mandated by MUZ ordinances are correlated with variations in walking opportunities. We find that, after controlling for population, socioeconomic status, and zone size, significant relationships exist between the range and precision of uses mandated by MUZ ordinances and the mixture and breadth of walking destinations in these zones. The study also demonstrates that analysis of municipal zoning codes and a novel data-gathering methodology yield valid data. The analysis of MUZ ordinances is a significant complement to other approaches to measuring walkability and can be used across cities.

Proteome coverage of the model plant Arabidopsis thaliana: implications for shotgun proteomic studies.

The recent aggregation of matched proteomics data for the model plant Arabidopsis has enabled the assessment of a diverse array of large scale shotgun proteomics data. A collection of over nine million matched peptides was used to assess proteome coverage and experimental parameters when compared to the theoretical tryptic peptide population. The analysis indicated that the experimentally identified median peptide mass was significantly higher than the theoretical median tryptic peptide in Arabidopsis. This finding led to a critical examination of precursor scan ranges currently being employed by shotgun proteomic studies. The analysis revealed diminishing returns at the high end scan range and opportunities for greater coverage and identifications at the low mass range. Based on these findings, a recommended basic scan range of 300 to 1200m/z would suitably capture the peptide population in shotgun proteomic analyses in Arabidopsis.