Structure-function relationships in a molluscan hemocyanin have been investigated by determining the crystal structure of the Rapana thomasiana (gastropod) hemocyanin functional unit RtH2e in deoxygenated form at 3.38 A resolution. This is the first X-ray structure of an unit from the wall of the molluscan hemocyanin cylinder. The crystal structure of RtH2e demonstrates molecular self-assembly of six identical molecules forming a regular hexameric cylinder. This suggests how the functional units are ordered in the wall of the native molluscan hemocyanins. The molecular arrangement is stabilized by specific protomer-to-protomer interactions, which are probably typical for the functional units building the wall of the cylinders. A molecular mechanism for cooperative dioxygen binding in molluscan hemocyanins is proposed on the basis of the molecular interactions between the protomers. In particular, the deoxygenated RtH2e structure reveals a tunnel leading from two opposite sides of the molecule to the active site. The tunnel represents a possible entrance pathway for dioxygen molecules. No such tunnels have been observed in the crystal structure of the oxy-Odg, a functional unit from the Octopus dofleini (cephalopod) hemocyanin in oxygenated form.

To analyze gene regulatory networks, the sequence-dependent DNA/RNA binding affinities of proteins and noncoding RNAs are crucial. Often, these are deduced from sets of sequences enriched in factor binding sites. Two classes of computational approaches exist. The first describe binding motifs by sequence patterns and search the patterns with highest statistical significance for enrichment. The second class uses the more powerful position weight matrices (PWMs). Instead of maximizing the statistical significance of enrichment, they maximize a likelihood. Here we present XXmotif (eXhaustive evaluation of matriX motifs), the first PWM-based motif discovery method that can optimize PWMs by directly minimizing their P-values of enrichment. Optimization requires computing millions of enrichment P-values for thousands of PWMs. For a given PWM, the enrichment P-value is calculated efficiently from the match P-values of all possible motif placements in the input sequences using order statistics. The approach can naturally combine P-values for motif enrichment, conservation, and localization. On ChIP-chip/seq, miRNA knock-down, and coexpression data sets from yeast and metazoans, XXmotif outperformed state-of-the-art tools, both in numbers of correctly identified motifs and in the quality of PWMs. In segmentation modules of D. melanogaster, we detect the known key regulators and several new motifs. In human core promoters, XXmotif reports most previously described and eight novel motifs sharply peaked around the transcription start site, among them an Initiator motif similar to the fly and yeast versions. XXmotif's sensitivity, reliability, and usability will help to leverage the quickly accumulating wealth of functional genomics data.[Supplemental material is available for this article.]The rapid progress in high-throughput sequencing is transforming the way in which we study genomes and their role in regulating cellular and developmental processes. Increasingly, single-locus and single-gene approaches are replaced by genome-wide measurements. Whether it be ChIP-seq (Johnson et al. 2006 (Lieberman-Aiden et al. 2009), most of these experiments need to be analyzed with respect to protein and noncoding RNA (ncRNA) factors that bind to specific sequences in the genome or transcriptome. These binding events are the key to understanding regulatory processes because, unlike epigenetic marks, only the genomic sequence carries information at a density that is sufficient to target factors unambiguously to specific loci or transcripts.Therefore, finding binding motifs for regulatory factors that are expected to be enriched in certain sequences is of central importance in the analysis of most of these types of experiments. This has led to a growing interest in tools for de novo motif finding (Tompa et al. 2005;Sandve et al. 2007). De novo motif discovery methods search for motifs of binding sites that are enriched in a positive sequence set in comparison to a negative sequence set or to a statistical background model derived from su...