Accurate detection of very sparse sequence motifs.

Protein sequence alignments are more reliable the shorter the evolutionary distance. Here, we align distantly related proteins using many closely spaced intermediate sequences as stepping stones. Such transitive alignments can be generated between any two proteins in a connected set, whether they are direct or indirect sequence neighbors in the underlying library of pairwise alignments. We have implemented a greedy algorithm, MaxFlow, using a novel consistency score to estimate the relative likelihood of alternative paths of transitive alignment. In contrast to traditional profile models of amino acid preferences, MaxFlow models the probability that two positions are structurally equivalent and retains high information content across large distances in sequence space. Thus, MaxFlow is able to identify sparse and narrow active-site sequence signatures which are embedded in high-entropy sequence segments in the structure based multiple alignment of large diverse enzyme superfamilies. In a challenging benchmark based on the urease superfamily, MaxFlow yields better reliability and double coverage compared to available sequence alignment software. This promises to increase information returns from functional and structural genomics, where reliable sequence alignment is a bottleneck to transferring the functional or structural characterization of model proteins to entire protein superfamilies.     

Targeting of malate synthase 1 to the peroxisomes of Saccharomyces cerevisiae cells depends on growth on oleic acid medium.

The eukaryotic glyoxylate cycle has been previously hypothesized to occur in the peroxisomal compartment, which in the yeast Saccharomyces cerevisiae additionally represents the sole site for fatty acid beta-oxidation. The subcellular location of the key glyoxylate-cycle enzyme malate synthase 1 (Mls1p), an SKL-terminated protein, was examined in yeast cells grown on different carbon sources. Immunoelectron microscopy in combination with cell fractionation showed that Mls1p was abundant in the peroxisomes of cells grown on oleic acid, whereas in ethanol-grown cells Mls1p was primarily cytosolic. This was reinforced using a green fluorescent protein (GFP)-Mls1p reporter, which entered peroxisomes solely in cells grown under oleic acid-medium conditions. Although growth of cells devoid of Mls1p on ethanol or acetate could be fully restored using a cytosolic Mls1p devoid of SKL, this construct could only partially alleviate the requirement for native Mls1p in cells grown on oleic acid. The combined results indicated that Mls1p remained in the cytosol of cells grown on ethanol, and that targeting of Mls1p to the peroxisomes was advantageous to cells grown on oleic acid as a sole carbon source.     

Molecular Evolution of Peptide Ligands with Custom-Tailored Characteristics for Targeting of Glycostructures

As an advanced approach to identify suitable targeting molecules required for various diagnostic and therapeutic interventions, we developed a procedure to devise peptides with customizable features by an iterative computer-assisted optimization strategy. An evolutionary algorithm was utilized to breed peptides in silico and the “fitness” of peptides was determined in an appropriate laboratory in vitro assay. The influence of different evolutional parameters and mechanisms such as mutation rate, crossover probability, gaussian variation and fitness value scaling on the course of this artificial evolutional process was investigated. As a proof of concept peptidic ligands for a model target molecule, the cell surface glycolipid ganglioside G_M1, were identified. Consensus sequences describing local fitness optima were reached from diverse sets of L- and proteolytically stable D lead peptides. Ten rounds of evolutional optimization encompassing a total of just 4400 peptides lead to an increase in affinity of the peptides towards fluorescently labeled ganglioside G_M1 by a factor of 100 for L- and 400 for D-peptides.     

Association Between the Rat Homologue of CO-029, a Metastasis-associated Tetraspanin Molecule and Consumption Coagulopathy

Recently, we have described a panel of metastasis-associated antigens in the rat, i.e., of molecules expressed on metastasizing, but not on nonmetastasizing tumor lines. One of these molecules, recognized by the monoclonal antibody D6.1 and named accordingly D6.1A, was found to be abundantly expressed predominantly on mesenchyme-derived cells. The DNA of the antigen has been isolated and cloned. Surprisingly, the gene product proved to interfere strongly with coagulation.

The 1.182-kb cDNA codes for a 235–amino acid long molecule with a 74.2% homology in the nucleotide and a 70% homology in the amino acid sequence to CO-029, a human tumor-associated molecule. According to the distribution of hydrophobic and hydrophilic amino acids, D6.1A belongs to the tetraspanin superfamily. Western blotting of D6.1A-positive metastasizing tumor lines revealed that the D6.1A, like many tetraspanin molecules, is linked to further membrane molecules, one of which could be identified as α6β1 integrin. Transfection of a low-metastasizing tumor cell line with D6.1A cDNA resulted in increased metastatic potential and provided a clue as to the functional role of D6.1A. We noted massive bleeding around the metastases and, possibly as a consequence, local infarctions predominantly in the mesenteric region and all signs of a consumption coagulopathy. By application of the D6.1 antibody the coagulopathy was counterregulated, though not prevented.

It has been known for many years that tumor growth and progression is frequently accompanied by thrombotic disorders. Our data suggest that the phenomenon could well be associated with the expression of tetraspanin molecules.

     

Functional evaluation of domain-domain interactions and human protein interaction networks

MOTIVATION: Large amounts of protein and domain interaction data are being produced by experimental high-throughput techniques and computational approaches. To gain insight into the value of the provided data, we used our new similarity measure based on the Gene Ontology (GO) to evaluate the molecular functions and biological processes of interacting proteins or domains. The applied measure particularly addresses the frequent annotation of proteins or domains with multiple GO terms. RESULTS: Using our similarity measure, we compare predicted domain-domain and human protein-protein interactions with experimentally derived interactions. The results show that our similarity measure is of significant benefit in quality assessment and confidence ranking of domain and protein networks. We also derive useful confidence score thresholds for dividing domain interaction predictions into subsets of low and high confidence. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.