Trans-SILAC: sorting out the non-cell-autonomous proteome.

Non-cell-autonomous proteins are incorporated into cells that form tight contacts or are invaded by bacteria, but identifying the full repertoire of transferred proteins has been a challenge. Here we introduce a quantitative proteomics approach to sort out non-cell-autonomous proteins synthesized by other cells or intracellular pathogens. Our approach combines stable-isotope labeling of amino acids in cell culture (SILAC), high-purity cell sorting and bioinformatics analysis to identify the repertoire of relevant non-cell-autonomous proteins. This 'trans-SILAC' method allowed us to discover many proteins transferred from human B to natural killer cells and to measure biosynthesis rates of Salmonella enterica proteins in infected human cells. Trans-SILAC should be a useful method to examine protein exchange between different cells of multicellular organisms or pathogen and host.

Quality assessment of protein model-structures using evolutionary conservation.

MOTIVATION: Programs that evaluate the quality of a protein structural model are important both for validating the structure determination procedure and for guiding the model-building process. Such programs are based on properties of native structures that are generally not expected for faulty models. One such property, which is rarely used for automatic structure quality assessment, is the tendency for conserved residues to be located at the structural core and for variable residues to be located at the surface. RESULTS: We present ConQuass, a novel quality assessment program based on the consistency between the model structure and the protein's conservation pattern. We show that it can identify problematic structural models, and that the scores it assigns to the server models in CASP8 correlate with the similarity of the models to the native structure. We also show that when the conservation information is reliable, the method's performance is comparable and complementary to that of the other single-structure quality assessment methods that participated in CASP8 and that do not use additional structural information from homologs. AVAILABILITY: A perl implementation of the method, as well as the various perl and R scripts used for the analysis are available at http://bental.tau.ac.il/ConQuass/. CONTACT: nirb@tauex.tau.ac.il SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Co-evolving residues in membrane proteins.

MOTIVATION: The analysis of co-evolving residues has been exhaustively evaluated for the prediction of intramolecular amino acid contacts in soluble proteins. Although a variety of different methods for the detection of these co-evolving residues have been developed, the fraction of correctly predicted contacts remained insufficient for their reliable application in the construction of structural models. Membrane proteins, which constitute between one-fourth and one-third of all proteins in an organism, were only considered in few individual case studies. RESULTS: We present the first general study of correlated mutations in alpha-helical membrane proteins. Using seven different prediction algorithms, we extracted co-evolving residues for 14 membrane proteins having a solved 3D structure. On average, distances between correlated pairs of residues lying on different transmembrane segments were found to be significantly smaller compared to a random prediction. Covariation of residues was frequently found in direct sequence neighborhood to helix-helix contacts. Based on the results obtained from individual prediction methods, we constructed a consensus prediction for every protein in the dataset that combines obtained correlations from different prediction algorithms and simultaneously removes likely false positives. Using this consensus prediction, 53% of all predicted residue pairs were found within one helix turn of an observed helix-helix contact. Based on the combination of co-evolving residues detected with the four best prediction algorithms, interacting helices could be predicted with a specificity of 83% and sensitivity of 42%. AVAILABILITY: http://webclu.bio.wzw.tum.de/helixcorr/