Combining classifiers for improved classification of proteins from sequence or structure

Background  

Predicting a protein's structural or functional class from its amino acid sequence or structure is a fundamental problem in computational biology. Recently, there has been considerable interest in using discriminative learning algorithms, in particular support vector machines (SVMs), for classification of proteins. However, because sufficiently many positive examples are required to train such classifiers, all SVM-based methods are hampered by limited coverage.     

Proteome profiling of breast tumors by gel electrophoresis and nanoscale electrospray ionization mass spectrometry

We have conducted proteome-wide analysis of fresh surgery specimens derived from breast cancer patients, using an approach that integrates size-based intact protein fractionation, nanoscale liquid separation of peptides, electrospray ion trap mass spectrometry, and bioinformatics. Through this approach, we have acquired a large amount of peptide fragmentation spectra from size-resolved fractions of the proteomes of several breast tumors, tissue peripheral to the tumor, and samples from patients undergoing noncancer surgery. Label-free quantitation was used to generate protein abundance maps for each proteome and perform comparative analyses. The mass spectrometry data revealed distinct qualitative and quantitative patterns distinguishing the tumors from healthy tissue as well as differences between metastatic and non-metastatic human breast cancers including many established and potential novel candidate protein biomarkers. Selected proteins were evaluated by Western blotting using tumors grouped according to histological grade, size, and receptor expression but differing in nodal status. Immunohistochemical analysis of a wide panel of breast tumors was conducted to assess expression in different types of breast cancers and the cellular distribution of the candidate proteins. These experiments provided further insights and an independent validation of the data obtained by mass spectrometry and revealed the potential of this approach for establishing multimodal markers for early metastasis, therapy outcomes, prognosis, and diagnosis in the future.     

Association Rate Constants of Ras-Effector Interactions Are Evolutionarily Conserved

Evolutionary conservation of protein interaction properties has been shown to be a valuable indication for functional importance. Here we use homology interface modeling of 10 Ras-effector complexes by selecting ortholog proteins from 12 organisms representing the major eukaryotic branches, except plants. We find that with increasing divergence time the sequence similarity decreases with respect to the human protein, but the affinities and association rate constants are conserved as predicted by the protein design algorithm, FoldX. In parallel we have done computer simulations on a minimal network based on Ras-effector interactions, and our results indicate that in the absence of negative feedback, changes in kinetics that result in similar binding constants have strong consequences on network behavior. This, together with the previous results, suggests an important biological role, not only for equilibrium binding constants but also for kinetics in signaling processes involving Ras-effector interactions. Our findings are important to take into consideration in system biology approaches and simulations of biological networks.     

Structure-based design of novel groups for use in the P1 position of thrombin inhibitor scaffolds. Part 2: N-acetamidoimidazoles

Guided by X-ray crystallography of thrombin-inhibitor complexes and molecular modeling, alkylation of the N1 nitrogen of the imidazole P1 ligand of the pyridinoneacetamide thrombin inhibitor 1 with various acetamide moieties furnished inhibitors with significantly improved thrombin potency, trypsin selectivity, functional in vitro anticoagulant potency and in vivo antithrombotic efficacy. In the pyrazinoneacetamide series, oral bioavailability was also improved.     

Staying alive: bacterial inhibition of apoptosis during infection

The ability of bacterial pathogens to inhibit apoptosis in eukaryotic cells during infection is an emerging theme in the study of bacterial pathogenesis. Prevention of apoptosis provides a survival advantage because it enables the bacteria to replicate inside host cells. Bacterial pathogens have evolved several ways to prevent apoptosis by protecting the mitochondria and preventing cytochrome c release, by activating cell survival pathways, or by preventing caspase activation. This review summarizes the most recent work on bacterial anti-apoptotic strategies and suggests new research that is necessary to advance the field.     

Restricted lateral mobility of plasma membrane CD4 impairs HIV-1 envelope glycoprotein mediated fusion

We investigated the effect of receptor mobility on HIV-1 envelope glycoprotein (Env)-triggered fusion using B16 mouse melanoma cells that are engineered to express CD4 and CXCR4 or CCR5. These engineered cells are resistant to fusion mediated CD4-dependent HIV-1 envelope glycoprotein. Receptor mobility was measured by fluorescence recovery after photobleaching (FRAP) using either fluorescently-labeled antibodies or transient expression of GFP-tagged receptors in the cells. No significant differences between B16 and NIH3T3 (fusion-permissive) cells were seen in lateral mobility of CCR5 or lipid probes. By contrast CD4 mobility in B16 cells was about seven-fold reduced compared to its mobility in fusion-permissive NIH3T3 cells. However, a CD4 mutant (RA5) that localizes to non-raft membrane microdomains exhibited a three-fold increased mobility in B16 cells as compared with WT-CD4. Interestingly, the B16 cells expressing the RA5 mutant (but not the wild type CD4) and coreceptors supported HIV-1 Env-mediated fusion. Our data demonstrate that the lateral mobility of CD4 is an important determinant of HIV-1 fusion/entry.