Expression Patterns of Protein Kinases Correlate with Gene Architecture and Evolutionary Rates

Protein kinase (PK) genes comprise the third largest superfamily that occupy ∼2% of the human genome. They encode regulatory enzymes that control a vast variety of cellular processes through phosphorylation of their protein substrates. Expression of PK genes is subject to complex transcriptional regulation which is not fully understood.Our comparative analysis demonstrates that genomic organization of regulatory PK genes differs from organization of other protein coding genes. PK genes occupy larger genomic loci, have longer introns, spacer regions, and encode larger proteins. The primary transcript length of PK genes, similar to other protein coding genes, inversely correlates with gene expression level and expression breadth, which is likely due to the necessity to reduce metabolic costs of transcription for abundant messages. On average, PK genes evolve slower than other protein coding genes. Breadth of PK expression negatively correlates with rate of non-synonymous substitutions in protein coding regions. This rate is lower for high expression and ubiquitous PKs, relative to low expression PKs, and correlates with divergence in untranslated regions. Conversely, rate of silent mutations is uniform in different PK groups, indicating that differing rates of non-synonymous substitutions reflect variations in selective pressure. Brain and testis employ a considerable number of tissue-specific PKs, indicating high complexity of phosphorylation-dependent regulatory network in these organs. There are considerable differences in genomic organization between PKs up-regulated in the testis and brain. PK genes up-regulated in the highly proliferative testicular tissue are fast evolving and small, with short introns and transcribed regions. In contrast, genes up-regulated in the minimally proliferative nervous tissue carry long introns, extended transcribed regions, and evolve slowly.PK genomic architecture, the size of gene functional domains and evolutionary rates correlate with the pattern of gene expression. Structure and evolutionary divergence of tissue-specific PK genes is related to the proliferative activity of the tissue where these genes are predominantly expressed. Our data provide evidence that physiological requirements for transcription intensity, ubiquitous expression, and tissue-specific regulation shape gene structure and affect rates of evolution

From protein sequences to 3D-structures and beyond: the example of the UniProt Knowledgebase

With the dramatic increase in the volume of experimental results in every domain of life sciences, assembling pertinent data and combining information from different fields has become a challenge. Information is dispersed over numerous specialized databases and is presented in many different formats. Rapid access to experiment-based information about well-characterized proteins helps predict the function of uncharacterized proteins identified by large-scale sequencing. In this context, universal knowledgebases play essential roles in providing access to data from complementary types of experiments and serving as hubs with cross-references to many specialized databases. This review outlines how the value of experimental data is optimized by combining high-quality protein sequences with complementary experimental results, including information derived from protein 3D-structures, using as an example the UniProt knowledgebase (UniProtKB) and the tools and links provided on its website (http://www.uniprot.org/). It also evokes precautions that are necessary for successful predictions and extrapolations

Ongoing and future developments at the Universal Protein Resource.

International audienceThe primary mission of Universal Protein Resource (UniProt) is to support biological research by maintaining a stable, comprehensive, fully classified, richly and accurately annotated protein sequence knowledgebase, with extensive cross-references and querying interfaces freely accessible to the scientific community. UniProt is produced by the UniProt Consortium which consists of groups from the European Bioinformatics Institute (EBI), the Swiss Institute of Bioinformatics (SIB) and the Protein Information Resource (PIR). UniProt is comprised of four major components, each optimized for different uses: the UniProt Archive, the UniProt Knowledgebase, the UniProt Reference Clusters and the UniProt Metagenomic and Environmental Sequence Database. UniProt is updated and distributed every 4 weeks and can be accessed online for searches or download at http://www.uniprot.org