High-throughput Isolation and Characterization of Untagged Membrane Protein Complexes: Outer Membrane Complexes of <i>Desulfovibrio vulgaris</i>

Cell membranes represent the “front line” of cellular defense and the interface between a cell and its environment. To determine the range of proteins and protein complexes that are present in the cell membranes of a target organism, we have utilized a “tagless” process for the system-wide isolation and identification of native membrane protein complexes. As an initial subject for study, we have chosen the Gram-negative sulfate-reducing bacterium <i>Desulfovibrio vulgaris</i>. With this tagless methodology, we have identified about two-thirds of the outer membrane- associated proteins anticipated. Approximately three-fourths of these appear to form homomeric complexes. Statistical and machine-learning methods used to analyze data compiled over multiple experiments revealed networks of additional protein–protein interactions providing insight into heteromeric contacts made between proteins across this region of the cell. Taken together, these results establish a <i>D. vulgaris</i> outer membrane protein data set that will be essential for the detection and characterization of environment-driven changes in the outer membrane proteome and in the modeling of stress response pathways. The workflow utilized here should be effective for the global characterization of membrane protein complexes in a wide range of organisms

High-throughput Isolation and Characterization of Untagged Membrane Protein Complexes: Outer Membrane Complexes of <i>Desulfovibrio vulgaris</i>

Cell membranes represent the “front line” of cellular defense and the interface between a cell and its environment. To determine the range of proteins and protein complexes that are present in the cell membranes of a target organism, we have utilized a “tagless” process for the system-wide isolation and identification of native membrane protein complexes. As an initial subject for study, we have chosen the Gram-negative sulfate-reducing bacterium <i>Desulfovibrio vulgaris</i>. With this tagless methodology, we have identified about two-thirds of the outer membrane- associated proteins anticipated. Approximately three-fourths of these appear to form homomeric complexes. Statistical and machine-learning methods used to analyze data compiled over multiple experiments revealed networks of additional protein–protein interactions providing insight into heteromeric contacts made between proteins across this region of the cell. Taken together, these results establish a <i>D. vulgaris</i> outer membrane protein data set that will be essential for the detection and characterization of environment-driven changes in the outer membrane proteome and in the modeling of stress response pathways. The workflow utilized here should be effective for the global characterization of membrane protein complexes in a wide range of organisms

Schematic overview of the computational and experimental contributions of COMBREX and its users, and the interrelationships of these contributions.

<p>Data and results specific to COMBREX are shown in boxes. External data imported into COMBREX are also shown, with arrows indicating entry points into the cycle. Methodology employed by COMBREX and its users is shown in blue type, as it is used to generate data. Not shown are two critical contributions to COMBREX: genome and cluster data imported from NCBI RefSeq and ProtClustDB, respectively, and NIH funding, which enables the grants that COMBREX issues to experimental laboratories.</p

Definitions of COMBREX functional status symbols and fractions of microbial genes in COMBREX in each status category.

<p>Experimentally characterized proteins are <i>green</i>. (Those in the <i>green</i> set that have been manually curated by the GSDB are also marked with a gold “G.”) Proteins with functional predictions but no experimental evidence are <i>blue</i>. Proteins with no available functional predictions are <i>black</i>.</p

Additional file 1 of An expanded evaluation of protein function prediction methods shows an improvement in accuracy

A document containing a subset of CAFA2 analyses that are equivalent to those provided about the CAFA1 experiment in the CAFA1 supplement. (PDF 11100 kb