Jan Gorodkin

SRPDB (Signal Recognition Particle Database)

Signal recognition particle (SRP) is a stable cytoplasmic ribonucleoprotein complex that serves to translocate secretory proteins across membranes during translation. The SRP Database (SRPDB) provides compilations of SRP components, ordered alphabetically and phylogenetically. Alignments emphasize phylogenetically-supported base pairs in SRP RNA and conserved residues in the proteins. Data are provided in various formats including a column arrangement for improved access and simplified computational usability. Included are motifs for identification of new sequences, SRP RNA secondary structure diagrams, 3-D models and links to high-resolution structures. This release includes 11 new SRP RNA sequences (total of 129), two protein SRP9 sequences (total of seven), two protein SRP14 sequences (total of 10), two protein SRP19 sequences (total of 16), 10 new SRP54 (ffh) sequences (total of 66), two protein SRP68 sequences (total of seven) and two protein SRP72 sequences (total of nine). Seven sequences of the SRP receptor alpha-subunit and its FtsY homolog (total of 51) are new. Also considered are ss-subunit of SRP receptor, Flhf, Hbsu, CaM kinase II and cpSRP43. Access to SRPDB is at http://psyche.uthct. edu/dbs/SRPDB/SRPDB.html and the European mirror http://www.medkem. gu.se/dbs/SRPDB/SRPDB.html

The tmRDB and SRPDB resources.

Maintained at the University of Texas Health Science Center at Tyler, Texas, the tmRNA database (tmRDB) is accessible at the URL with mirror sites located at Auburn University, Auburn, Alabama () and the Royal Veterinary and Agricultural University, Denmark (). The signal recognition particle database (SRPDB) at is mirrored at and the University of Goteborg (). The databases assist in investigations of the tmRNP (a ribonucleoprotein complex which liberates stalled bacterial ribosomes) and the SRP (a particle which recognizes signal sequences and directs secretory proteins to cell membranes). The curated tmRNA and SRP RNA alignments consider base pairs supported by comparative sequence analysis. Also shown are alignments of the tmRNA-associated proteins SmpB, ribosomal protein S1, alanyl-tRNA synthetase and Elongation Factor Tu, as well as the SRP proteins SRP9, SRP14, SRP19, SRP21, SRP54 (Ffh), SRP68, SRP72, cpSRP43, Flhf, SRP receptor (alpha) and SRP receptor (beta). All alignments can be easily examined using a new exploratory browser. The databases provide links to high-resolution structures and serve as depositories for structures obtained by molecular modeling.

Semi-automated update and cleanup of structural RNA alignment databases

UNLABELLED We have developed a series of programs which assist in maintenance of structural RNA databases. A main program BLASTs the RNA database against GenBank and automatically extends and realigns the sequences to include the entire range of the RNA query sequences. After manual update of the database, other programs can examine base pair consistency and phylogenetic support. The output can be applied iteratively to refine the structural alignment of the RNA database. Using these tools, the number of potential misannotations per sequence was reduced from 20 to 3 in the Signal Recognition Particle RNA database. AVAILABILITY A quick-server and programs are available at http://www.bioinf.au.dk/rnadbtool/

Recognition of environmental and genetic effects on barley phenolic fingerprints by neural networks

Through computational analysis of high-performance liquid chromatography (HPLC) traces we find correlations between secondary metabolites and growth conditions of six varieties of barley. Using artificial neural networks, it was possible to classify chromatograms for which the varieties were fertilized by nitrogen and treated by fungicide. For each variety of barley we could also differentiate it from the others. Surprisingly, all these classification tasks could be solved successfully by a simple network with no hidden units. When adding to the methodology pruning of the network weights, we were able to reduce the set of peaks in the chromatograms and obtain a necessary subset from which the growth conditions and differentiation may be decided. In some instances, more complex networks with hidden units could lead to a further reduction of the number of peaks used. In most cases, far more than half of the peaks are redundant. We find that it requires fewer information-rich peaks to perform the variety differentiation tasks than to recognize any of the growth conditions. Analysis of the network weights reveals correlations between weighted combinations of peaks.

Orbits of Boolean functions

Abstract The group of congruences and permutations of the two-colored N -dimensional Boolean cube is considered. The total number of orbits generated by these automorphisms are shown to scale as 2 2 n (2 n+1 N when N tends to infinity. The probability that a randomly chosen function will belong to an orbit containing the maximum possible number of elements, 2 N +1 N , approaches one as N goes to infinity. Simulations for N ⩽ 6 are in agreement with the scaling predictions.