Protein domain | EPFL Graph Search

In molecular biology, a protein domain is a region of a protein's polypeptide chain that is self-stabilizing and that folds independently from the rest. Each domain forms a compact folded three-dimensional structure. Many proteins consist of several domains, and a domain may appear in a variety of different proteins. Molecular evolution uses domains as building blocks and these may be recombined in different arrangements to create proteins with different functions. In general, domains vary in length from between about 50 amino acids up to 250 amino acids in length. The shortest domains, such as zinc fingers, are stabilized by metal ions or disulfide bridges. Domains often form functional units, such as the calcium-binding EF hand domain of calmodulin. Because they are independently stable, domains can be "swapped" by genetic engineering between one protein and another to make chimeric proteins. Background The concept of the domain was first proposed in 1973 by Wetlaufer aft

Protein Domain Prediction and Alternative Splicing Histories

Yann Christinat

Modularity is at the core of bioinformatics. Here we focused on two different building blocks: protein domains and exons. While protein domains enable us to study the protein‚s functions, exons allow us to understand the evolution of the former. However, ab initio prediction of protein domains performs poorly on multi-domain proteins and we addressed this problem in the first part of the dissertation. In a second part, we focused on the evolution of alternative splicing which, despite its importance in higher eukaryotes, has not seen much work. Based on the observation from Wheelan et al. that domain sizes have a narrow distribution and are independent of the number of domains in a protein, we proposed a theoretical distribution for the size of a domain and, by extension, a probability distribution for the length of a protein given its number of domains. Using these distributions, we designed an ab initio predictor for domain boundaries. Given a set of potential boundaries, our method detects the most likely subset. A set of potential boundaries was obtained through three existing features and a novel one based on PSIBLAST alignments. The resulting software, DomML, outperformed all tested methods on multi-domain proteins, the most demanding category. We then developed a consensus method based on DomML and compared it to a standard majority vote method. Our consensus performed well with low and medium quality inputs but could not outperform a standard consensus method in the presence of a high-quality feature. Nonetheless, the predicted number of domains was correct in most cases – a performance that could not be achieved with a majority vote. In the second part of the dissertation, we addressed the lack of evolutionary models for alternative splicing. We presented a model of transcript evolution that distinguishes the evolution of the gene structure – exons and introns – and the evolution of the splicing patterns. Our model depicts the transcript phylogeny as a forest of transcript trees, each representing the evolution of an ancestral transcript. The reconstruction of transcript phylogenies is a complex problem. We designed thus a first algorithm, validated the concept on two gene families, MAG and PAX6, then created a faster heuristic and bundled it in a tool: TrEvoR. We selected two applications to demonstrate the usefulness of TrEvoR and transcript phylogenies. On the ASPic database, we showed that transcript phylogenies can refine the accuracy of transcriptome reconstruction methods from ESTs. On the same set of genes (805 gene families, 7 species), we then demonstrated the feasibility of large-scale functional studies with TrEvoR.

EPFL2012

PANDITplus: toward better integration of evolutionary view on molecular sequences with supplementary bioinformatics resources

Slavica Dimitrieva Janeva

Recent comparative genomic and other large-scale bioinformatics studies increasingly have been using gene annotations, functional classifications, and complimentary data from the emerging “-omics” disciplines. Indeed, such analyses have better chances to uncover hidden patterns in complex multidimensional and heterogeneous biological systems data. On the other hand, inferences from such studies are extremely sensitive to data samples and quality, and are more difficult to compare or replicate owing to differences in supplementary data sources at times not publicly available. As a contribution toward the unification and integration of good quality data from heterogeneous bioinformatics resources, we present here an integrated data bank PANDITplus. It is built as an extension of PANDIT, the database of PFAM alignments and phylogenetic trees for known protein domains and families spanning lineages from the three domains of life. PANDITplus is a relational database containing information on functional categories, metabolic pathways, protein–protein interactions, disease associations, gene expression, three-dimensional structure, as well as estimates from evolutionary analyses of selective pressures. User-friendly interface enables customized queries and fast data access. We recommend PANDITplus as a common bioinformatics platform for testing evolutionary hypotheses, which go beyond the mere inferences from molecular data by incorporating supplementary gene information. Equally, PANDITplus provides an excellent resource for the development, testing, and comparison of statistical models of substitution and probabilistic dependencies between a molecular sequence and its various attributes. The database may be accessed via http://www.panditplus.org.

2010

Unraveling Patterns of Site-to-Site Synonymous Rates Variation and Associated Gene Properties of Protein Domains and Families

Slavica Dimitrieva Janeva

In protein-coding genes, synonymous mutations are often thought not to affect fitness and therefore are not subject to natural selection. Yet increasingly, cases of non-neutral evolution at certain synonymous sites were reported over the last decade. To evaluate the extent and the nature of site-specific selection on synonymous codons, we computed the site-to-site synonymous rate variation (SRV) and identified gene properties that make SRV more likely in a large database of protein-coding gene families and protein domains. To our knowledge, this is the first study that explores the determinants and patterns of the SRV in real data. We show that the SRV is widespread in the evolution of protein-coding sequences, putting in doubt the validity of the synonymous rate as a standard neutral proxy. While protein domains rarely undergo adaptive evolution, the SRV appears to play important role in optimizing the domain function at the level of DNA. In contrast, protein families are more likely to evolve by positive selection, but are less likely to exhibit SRV. Stronger SRV was detected in genes with stronger codon bias and tRNA reusage, those coding for proteins with larger number of interactions or forming larger number of structures, located in intracellular components and those involved in typically conserved complex processes and functions. Genes with extreme SRV show higher expression levels in nearly all tissues. This indicates that codon bias in a gene, which often correlates with gene expression, may often be a site-specific phenomenon regulating the speed of translation along the sequence, consistent with the co-translational folding hypothesis. Strikingly, genes with SRV were strongly overrepresented for metabolic pathways and those associated with several genetic diseases, particularly cancers and diabetes.

Public Library of Science2014

Unraveling Patterns of Site-to-Site Synonymous Rates Variation and Associated Gene Properties of Protein Domains and Families

Slavica Dimitrieva Janeva

Public Library of Science2014

Protein Domain Prediction and Alternative Splicing Histories

Yann Christinat

EPFL2012