David Juan

Multiple evidence strands suggest that there may be as few as 19 000 human protein-coding genes

Determining the full complement of protein-coding genes is a key goal of genome annotation. The most powerful approach for confirming protein-coding potential is the detection of cellular protein expression through peptide mass spectrometry (MS) experiments. Here, we mapped peptides detected in seven large-scale proteomics studies to almost 60% of the protein-coding genes in the GENCODE annotation of the human genome. We found a strong relationship between detection in proteomics experiments and both gene family age and cross-species conservation. Most of the genes for which we detected peptides were highly conserved. We found peptides for >96% of genes that evolved before bilateria. At the opposite end of the scale, we identified almost no peptides for genes that have appeared since primates, for genes that did not have any protein-like features or for genes with poor cross-species conservation. These results motivated us to describe a set of 2001 potential non-coding genes based on features such as weak conservation, a lack of protein features, or ambiguous annotations from major databases, all of which correlated with low peptide detection across the seven experiments. We identified peptides for just 3% of these genes. We show that many of these genes behave more like non-coding genes than protein-coding genes and suggest that most are unlikely to code for proteins under normal circumstances. We believe that their inclusion in the human protein-coding gene catalogue should be revised as part of the ongoing human genome annotation effort.

Identification of amino acid residues crucial for chemokine receptor dimerization.

Chemokines coordinate leukocyte trafficking by promoting oligomerization and signaling by G protein–coupled receptors; however, it is not known which amino acid residues of the receptors participate in this process. Bioinformatic analysis predicted that Ile52 in transmembrane region-1 (TM1) and Val150 in TM4 of the chemokine receptor CCR5 are key residues in the interaction surface between CCR5 molecules. Mutation of these residues generated nonfunctional receptors that could not dimerize or trigger signaling. In vitro and in vivo studies in human cell lines and primary T cells showed that synthetic peptides containing these residues blocked responses induced by the CCR5 ligand CCL5. Fluorescence resonance energy transfer showed the presence of preformed, ligand-stabilized chemokine receptor oligomers. This is the first description of the residues involved in chemokine receptor dimerization, and indicates a potential target for the modification of chemokine responses.

High-confidence prediction of global interactomes based on genome-wide coevolutionary networks

Interacting or functionally related protein families tend to have similar phylogenetic trees. Based on this observation, techniques have been developed to predict interaction partners. The observed degree of similarity between the phylogenetic trees of two proteins is the result of many different factors besides the actual interaction or functional relationship between them. Such factors influence the performance of interaction predictions. One aspect that can influence this similarity is related to the fact that a given protein interacts with many others, and hence it must adapt to all of them. Accordingly, the interaction or coadaptation signal within its tree is a composite of the influence of all of the interactors. Here, we introduce a new estimator of coevolution to overcome this and other problems. Instead of relying on the individual value of tree similarity between two proteins, we use the whole network of similarities between all of the pairs of proteins within a genome to reassess the similarity of that pair, thereby taking into account its coevolutionary context. We show that this approach offers a substantial improvement in interaction prediction performance, providing a degree of accuracy/coverage comparable with, or in some cases better than, that of experimental techniques. Moreover, important information on the structure, function, and evolution of macromolecular complexes can be inferred with this methodology.

Protein interactions and ligand binding: from protein subfamilies to functional specificity.

The divergence accumulated during the evolution of protein families translates into their internal organization as subfamilies, and it is directly reflected in the characteristic patterns of differentially conserved residues. These specifically conserved positions in protein subfamilies are known as “specificity determining positions” (SDPs). Previous studies have limited their analysis to the study of the relationship between these positions and ligand-binding specificity, demonstrating significant yet limited predictive capacity. We have systematically extended this observation to include the role of differential protein interactions in the segregation of protein subfamilies and explored in detail the structural distribution of SDPs at protein interfaces. Our results show the extensive influence of protein interactions in the evolution of protein families and the widespread association of SDPs with protein interfaces. The combined analysis of SDPs in interfaces and ligand-binding sites provides a more complete picture of the organization of protein families, constituting the necessary framework for a large scale analysis of the evolution of protein function.

The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease

Summary Many common variants have been associated with hematological traits, but identification of causal genes and pathways has proven challenging. We performed a genome-wide association analysis in the UK Biobank and INTERVAL studies, testing 29.5 million genetic variants for association with 36 red cell, white cell, and platelet properties in 173,480 European-ancestry participants. This effort yielded hundreds of low frequency (<5%) and rare (<1%) variants with a strong impact on blood cell phenotypes. Our data highlight general properties of the allelic architecture of complex traits, including the proportion of the heritable component of each blood trait explained by the polygenic signal across different genome regulatory domains. Finally, through Mendelian randomization, we provide evidence of shared genetic pathways linking blood cell indices with complex pathologies, including autoimmune diseases, schizophrenia, and coronary heart disease and evidence suggesting previously reported population associations between blood cell indices and cardiovascular disease may be non-causal.

Scoring docking models with evolutionary information

We have developed methods for the extraction of evolutionary information from multiple sequence alignments for use in the study of the evolution of protein interaction networks and in the prediction of protein interaction. For Rounds 3, 4, and 5 of the CAPRI experiment, we used scores derived from the analysis of multiple sequence alignments to submit predictions for 7 of the 12 targets. Our docking models were generated with Hex and GRAMM, but all our predictions were selected using methods based on multiple sequence alignments and on the available experimental evidence. With this approach, we were able to predict acceptable level models for 4 of the targets, and for a fifth target, we located the residues involved in the binding surface. Here we detail our successes and highlight several of the limitations and problems that we faced while dealing with particular docking cases. Proteins 2005;60:275–280.

Co-evolution and co-adaptation in protein networks

Interacting or functionally related proteins have been repeatedly shown to have similar phylogenetic trees. Two main hypotheses have been proposed to explain this fact. One involves compensatory changes between the two protein families (co‐adaptation). The other states that the tree similarity may be an indirect consequence of the involvement of the two proteins in similar cellular process, which in turn would be reflected by similar evolutionary pressure on the corresponding sequences. There are published data supporting both propositions, and currently the available information is compatible with both hypotheses being true, in an scenario in which both sets of forces are shaping the tree similarity at different levels.

EcID. A database for the inference of functional interactions in E. coli

The EcID database (Escherichia coli Interaction Database) provides a framework for the integration of information on functional interactions extracted from the following sources: EcoCyc (metabolic pathways, protein complexes and regulatory information), KEGG (metabolic pathways), MINT and IntAct (protein interactions). It also includes information on protein complexes from the two E. coli high-throughput pull-down experiments and potential interactions extracted from the literature using the web services associated to the iHOP text-mining system. Additionally, EcID incorporates results of various prediction methods, including two protein interaction prediction methods based on genomic information (Phylogenetic Profiles and Gene Neighbourhoods) and three methods based on the analysis of co-evolution (Mirror Tree, In Silico 2 Hybrid and Context Mirror). EcID associates to each prediction a specifically developed confidence score. The two main features that make EcID different from other systems are the combination of co-evolution-based predictions with the experimental data, and the introduction of E. coli-specific information, such as gene regulation information from EcoCyc. The possibilities offered by the combination of the EcID database information are illustrated with a prediction of potential functions for a group of poorly characterized genes related to yeaG. EcID is available online at http://ecid.bioinfo.cnio.es.

TreeDet: a web server to explore sequence space

The TreeDet (Tree Determinant) Server is the first release of a system designed to integrate results from methods that predict functional sites in protein families. These methods take into account the relation between sequence conservation and evolutionary importance. TreeDet fully analyses the space of protein sequences in either user-uploaded or automatically generated multiple sequence alignments. The methods implemented in the server represent three main classes of methods for the detection of family-dependent conserved positions, a tree-based method, a correlation based method and a method that employs a principal component analyses coupled to a cluster algorithm. An additional method is provided to highlight the reliability of the position in the alignments. The server is available at .

Subfunctionalization via Adaptive Evolution Influenced by Genomic Context: The Case of Histone Chaperones ASF1a and ASF1b

Gene duplication is regarded as the main source of adaptive functional novelty in eukaryotes. Processes such as neo- and subfunctionalization impact the evolution of paralogous proteins where functional divergence is frequently key to retain the gene copies. Here, we examined antisilencing function 1 (ASF1), a conserved eukaryotic H3-H4 histone chaperone, involved in histone dynamics during replication, transcription, and DNA repair. Although yeast feature a single ASF1 protein, two paralogs exist in most vertebrates, termed ASF1a and ASF1b, with distinct cellular roles in mammals. To explain this division of tasks, we integrated evolutionary and comparative genomic analyses with biochemical and structural approaches. First, we show that a duplication event at the ancestor of jawed vertebrates, followed by ASF1a relocation into an intron of the minichromosome maintenance complex component 9 (MCM9) gene at the ancestor of tetrapods, provided a different genomic environment for each paralog with marked differences of GC content and DNA replication timing. Second, we found signatures of positive selection in the N- and C-terminal regions of ASF1a and ASF1b. Third, we demonstrate that regions outside the primary interaction surface are key for the preferential interactions of the human paralogs with distinct H3-H4 chaperones. On the basis of these data, we propose that ASF1 experienced subfunctionalization shaped by the adaptation of the genes to their respective genomic context, reflecting a case of genomic context-driven escape from adaptive conflict.