Comparative analysis of carboxysome shell proteins

Carboxysomes are metabolic modules for CO2 fixation that are found in all cyanobacteria and some chemoautotrophic bacteria. They comprise a semi-permeable proteinaceous shell that encapsulates ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) and carbonic anhydrase. Structural studies are revealing the integral role of the shell protein paralogs to carboxysome form and function. The shell proteins are composed of two domain classes: those with the bacterial microcompartment (BMC; Pfam00936) domain, which oligomerize to form (pseudo)hexamers, and those with the CcmL/EutN (Pfam03319) domain which form pentamers in carboxysomes. These two shell protein types are proposed to be the basis for the carboxysome’s icosahedral geometry. The shell proteins are also thought to allow the flux of metabolites across the shell through the presence of the small pore formed by their hexameric/pentameric symmetry axes. In this review, we describe bioinformatic and structural analyses that highlight the important primary, tertiary, and quaternary structural features of these conserved shell subunits. In the future, further understanding of these molecular building blocks may provide the basis for enhancing CO2 fixation in other organisms or creating novel biological nanostructures

Depletion of somatic mutations in splicing-associated sequences in cancer genomes

Abstract Background An important goal of cancer genomics is to identify systematically cancer-causing mutations. A common approach is to identify sites with high ratios of non-synonymous to synonymous mutations; however, if synonymous mutations are under purifying selection, this methodology leads to identification of false-positive mutations. Here, using synonymous somatic mutations (SSMs) identified in over 4000 tumours across 15 different cancer types, we sought to test this assumption by focusing on coding regions required for splicing. Results Exon flanks, which are enriched for sequences required for splicing fidelity, have ~ 17% lower SSM density compared to exonic cores, even after excluding canonical splice sites. While it is impossible to eliminate a mutation bias of unknown cause, multiple lines of evidence support a purifying selection model above a mutational bias explanation. The flank/core difference is not explained by skewed nucleotide content, replication timing, nucleosome occupancy or deficiency in mismatch repair. The depletion is not seen in tumour suppressors, consistent with their role in positive tumour selection, but is otherwise observed in cancer-associated and non-cancer genes, both essential and non-essential. Consistent with a role in splicing modulation, exonic splice enhancers have a lower SSM density before and after controlling for nucleotide composition; moreover, flanks at the 5’ end of the exons have significantly lower SSM density than at the 3’ end. Conclusions These results suggest that the observable mutational spectrum of cancer genomes is not simply a product of various mutational processes and positive selection, but might also be shaped by negative selection

Phylogenetic Distribution and Evolutionary History of Bacterial DEAD-Box Proteins

DEAD-box proteins are found in all domains of life and participate in almost all cellular processes that involve RNA. The presence of DEAD and Helicase_C conserved domains distinguish these proteins. DEAD-box proteins exhibit RNA-dependent ATPase activity in vitro, and several also show RNA helicase activity. In this study, we analyzed the distribution and architecture of DEAD-box proteins among bacterial genomes to gain insight into the evolutionary pathways that have shaped their history. We identified 1,848 unique DEAD-box proteins from 563 bacterial genomes. Bacterial genomes can possess a single copy DEAD-box gene, or up to 12 copies of the gene, such as in Shewanella. The alignment of 1,208 sequences allowed us to perform a robust analysis of the hallmark motifs of DEAD-box proteins and determine the residues that occur at high frequency, some of which were previously overlooked. Bacterial DEAD-box proteins do not generally contain a conserved C-terminal domain, with the exception of some members that possess a DbpA RNA-binding domain (RBD). Phylogenetic analysis showed a separation of DbpA-RBD-containing and DbpA-RBD-lacking sequences and revealed a group of DEAD-box protein genes that expanded mainly in the Proteobacteria. Analysis of DEAD-box proteins from Firmicutes and γ-Proteobacteria, was used to deduce orthologous relationships of the well-studied DEAD-box proteins from Escherichia coli and Bacillus subtilis. These analyses suggest that DbpA-RBD is an ancestral domain that most likely emerged as a specialized domain of the RNA-dependent ATPases. Moreover, these data revealed numerous events of gene family expansion and reduction following speciation

Triangle network motifs predict complexes by complementing high-error interactomes with structural information

BackgroundA lot of high-throughput studies produce protein-protein interaction networks (PPINs) with many errors and missing information. Even for genome-wide approaches, there is often a low overlap between PPINs produced by different studies. Second-level neighbors separated by two protein-protein interactions (PPIs) were previously used for predicting protein function and finding complexes in high-error PPINs. We retrieve second level neighbors in PPINs, and complement these with structural domain-domain interactions (SDDIs) representing binding evidence on proteins, forming PPI-SDDI-PPI triangles.ResultsWe find low overlap between PPINs, SDDIs and known complexes, all well below 10%. We evaluate the overlap of PPI-SDDI-PPI triangles with known complexes from Munich Information center for Protein Sequences (MIPS). PPI-SDDI-PPI triangles have ~20 times higher overlap with MIPS complexes than using second-level neighbors in PPINs without SDDIs. The biological interpretation for triangles is that a SDDI causes two proteins to be observed with common interaction partners in high-throughput experiments. The relatively few SDDIs overlapping with PPINs are part of highly connected SDDI components, and are more likely to be detected in experimental studies. We demonstrate the utility of PPI-SDDI-PPI triangles by reconstructing myosin-actin processes in the nucleus, cytoplasm, and cytoskeleton, which were not obvious in the original PPIN. Using other complementary datatypes in place of SDDIs to form triangles, such as PubMed co-occurrences or threading information, results in a similar ability to find protein complexes.ConclusionGiven high-error PPINs with missing information, triangles of mixed datatypes are a promising direction for finding protein complexes. Integrating PPINs with SDDIs improves finding complexes. Structural SDDIs partially explain the high functional similarity of second-level neighbors in PPINs. We estimate that relatively little structural information would be sufficient for finding complexes involving most of the proteins and interactions in a typical PPIN

Measuring competitive self-focus perspective taking, submissive compassion and compassion goals.

Research in the last 20 years has provided good evidence that developing compassion-focused motives for self and others has a range of benefits. However, people can behave in prosocial ways for different reasons, not all of which are genuinely care focused. This paper reports research comparing submissive compassion (being helpful to be liked) to “genuine” compassion in relation to domains of empathy and perspective taking. We developed a new short (5 item) self-report scale (the competitive perspective taking scale) to explore how people might use perspective taking for self-focused reasons. We investigated its association with validated empathy and compassion measures.N/