Protein interactions in human genetic diseases

A method is presented to identify residues that form part of an interaction interface, leading to the prediction that 1,428 OMIM mutations are related to an interaction defect

HMM Logos for visualization of protein families

BACKGROUND: Profile Hidden Markov Models (pHMMs) are a widely used tool for protein family research. Up to now, however, there exists no method to visualize all of their central aspects graphically in an intuitively understandable way. RESULTS: We present a visualization method that incorporates both emission and transition probabilities of the pHMM, thus extending sequence logos introduced by Schneider and Stephens. For each emitting state of the pHMM, we display a stack of letters. The stack height is determined by the deviation of the position's letter emission frequencies from the background frequencies. The stack width visualizes both the probability of reaching the state (the hitting probability) and the expected number of letters the state emits during a pass through the model (the state's expected contribution). A web interface offering online creation of HMM Logos and the corresponding source code can be found at the Logos web server of the Max Planck Institute for Molecular Genetics . CONCLUSIONS: We demonstrate that HMM Logos can be a useful tool for the biologist: We use them to highlight differences between two homologous subfamilies of GTPases, Rab and Ras, and we show that they are able to indicate structural elements of Ras

Dosage Sensitivity Shapes the Evolution of Copy-Number Varied Regions

Dosage sensitivity is an important evolutionary force which impacts on gene dispensability and duplicability. The newly available data on human copy-number variation (CNV) allow an analysis of the most recent and ongoing evolution. Provided that heterozygous gene deletions and duplications actually change gene dosage, we expect to observe negative selection against CNVs encompassing dosage sensitive genes. In this study, we make use of several sources of population genetic data to identify selection on structural variations of dosage sensitive genes. We show that CNVs can directly affect expression levels of contained genes. We find that genes encoding members of protein complexes exhibit limited expression variation and overlap significantly with a manually derived set of dosage sensitive genes. We show that complexes and other dosage sensitive genes are underrepresented in CNV regions, with a particular bias against frequent variations and duplications. These results suggest that dosage sensitivity is a significant force of negative selection on regions of copy-number variation

Pfam: clans, web tools and services

Pfam is a database of protein families that currently contains 7973 entries (release 18.0). A recent development in Pfam has enabled the grouping of related families into clans. Pfam clans are described in detail, together with the new associated web pages. Improvements to the range of Pfam web tools and the first set of Pfam web services that allow programmatic access to the database and associated tools are also presented. Pfam is available on the web in the UK (http://www.sanger.ac.uk/Software/Pfam/), the USA (http://pfam.wustl.edu/), France (http://pfam.jouy.inra.fr/) and Sweden (http://pfam.cgb.ki.se/)

Reuse of structural domain–domain interactions in protein networks

<p>Abstract</p> <p>Background</p> <p>Protein interactions are thought to be largely mediated by interactions between structural domains. Databases such as <it>i</it>Pfam relate interactions in protein structures to known domain families. Here, we investigate how the domain interactions from the <it>i</it>Pfam database are distributed in protein interactions taken from the HPRD, MPact, BioGRID, DIP and IntAct databases.</p> <p>Results</p> <p>We find that known structural domain interactions can only explain a subset of 4–19% of the available protein interactions, nevertheless this fraction is still significantly bigger than expected by chance. There is a correlation between the frequency of a domain interaction and the connectivity of the proteins it occurs in. Furthermore, a large proportion of protein interactions can be attributed to a small number of domain interactions. We conclude that many, but not all, domain interactions constitute reusable modules of molecular recognition. A substantial proportion of domain interactions are conserved between <it>E. coli</it>, <it>S. cerevisiae </it>and <it>H. sapiens</it>. These domains are related to essential cellular functions, suggesting that many domain interactions were already present in the last universal common ancestor.</p> <p>Conclusion</p> <p>Our results support the concept of domain interactions as reusable, conserved building blocks of protein interactions, but also highlight the limitations currently imposed by the small number of available protein structures.</p

Tuning transcription factor availability through acetylation-mediated genomic redistribution

It is widely assumed that decreasing transcription factor DNA-binding affinity reduces transcription initiation by diminishing occupancy of sequence-specific regulatory elements. However, in vivo transcription factors find their binding sites while confronted with a large excess of low-affinity degenerate motifs. Here, using the melanoma lineage survival oncogene MITF as a model, we show that low-affinity binding sites act as a competitive reservoir in vivo from which transcription factors are released by mitogen-activated protein kinase (MAPK)-stimulated acetylation to promote increased occupancy of their regulatory elements. Consequently, a low-DNA-binding-affinity acetylation-mimetic MITF mutation supports melanocyte development and drives tumorigenesis, whereas a high-affinity non-acetylatable mutant does not. The results reveal a paradoxical acetylation-mediated molecular clutch that tunes transcription factor availability via genome-wide redistribution and couples BRAF to tumorigenesis. Our results further suggest that p300/CREB-binding protein-mediated transcription factor acetylation may represent a common mechanism to control transcription factor availability

Mutational signature distribution varies with DNA replication timing and strand asymmetry

Abstract Background DNA replication plays an important role in mutagenesis, yet little is known about how it interacts with other mutagenic processes. Here, we use somatic mutation signatures—each representing a mutagenic process—derived from 3056 patients spanning 19 cancer types to quantify the strand asymmetry of mutational signatures around replication origins and between early and late replicating regions. Results We observe that most of the detected mutational signatures are significantly correlated with the timing or direction of DNA replication. The properties of these associations are distinct for different signatures and shed new light on several mutagenic processes. For example, our results suggest that oxidative damage to the nucleotide pool substantially contributes to the mutational landscape of esophageal adenocarcinoma. Conclusions Together, our results indicate an interaction between DNA replication, the associated damage repair, and most mutagenic processes

A network representation of the CORUM database.

<p>Nodes represent complexes and are ordered by number of unique components (shown as number next to groups). Edges denote shared components between complexes. The number of shared components is reflected in the colour (from yellow (few) to red (many) shared components) as well as in the line width. The large, highly overlapping complexes in the first row are mainly modules of the ribosome (6 out of 12) and spliceosome (3 out of 12). Other large complexes include RNA polymerase, respiratory chain complex and the proteasome. The group of complexes with only 1 member are homo-dimers.</p

Distribution of average Pearson correlation coefficients between all members of known proteins complexes as defined in CORUM (black), and randomly sampled proteins (white, N = 10).

<p>Expression data was taken from the Human Gene Expression Atlas (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0009474#s2" target="_blank">Methods</a>).</p