PaxDb, a database of protein abundance averages across all three domains of life

qPCA: a scalable assay to measure the perturbation of protein-protein interactions in living cells

One of the most important challenges in systems biology is to understand how cells respond to genetic and environmental perturbations. Here we show that the yeast DHFR-PCA, coupled with high-resolution growth profiling (DHFR-qPCA), is a straightforward assay to study the modulation of protein-protein interactions (PPIs) in vivo as a response to genetic, metabolic and drug perturbations. Using the canonical Protein Kinase A (PKA) pathway as a test system, we show that changes in PKA activity can be measured in living cells as a modulation of the interaction between its regulatory (Bcy1) and catalytic (Tpk1 and Tpk2) subunits in response to changes in carbon metabolism, caffeine and methyl methanesulfonate (MMS) treatments and to modifications in the dosage of its enzymatic regulators, the phosphodiesterases. Our results show that the DHFR-qPCA is easily implementable and amenable to high-throughput. The DHFR-qPCA will pave the way to the study of the effects of drug, genetic and environmental perturbations on in vivo PPI networks, thus allowing the exploration of new spaces of the eukaryotic interactome.

Transcriptional divergence plays a role in the rewiring of protein interaction networks after gene duplication

Gene duplication plays a key role in the evolution of protein-protein interaction (PPI) networks. After a gene duplication event, paralogous proteins may diverge through the gain and loss of PPIs. This divergence can be explained by two non-exclusive mechanisms. First, mutations may accumulate in the coding sequences of the paralogs and affect their protein sequences, which can modify, for instance, their binding interfaces and thus their interaction specificity. Second, mutations may accumulate in the non-coding region of the genes and affect their regulatory sequences. The resulting changes in expression profiles can lead to paralogous proteins being differentially expressed and occurring in the cell with different sets of potential interaction partners. These changes could also alter splicing regulation and lead to the inclusion or exclusion of alternative exons. The evolutionary role of these regulatory mechanisms remains largely unexplored. We use bioinformatics analyses of existing PPI data and proteome-wide PPI screening to show that the divergence of transcriptional regulation between paralogs plays a significant role in determining their PPI specificity. Because many gene duplication events are followed by rapid changes in transcriptional regulation, our results suggest that PPI networks may be rewired by gene duplication, without the need for protein to diverge in their binding specificities. This article is part of a Special Issue entitled: From protein structures to clinical applications.

Independent effects of protein core size and expression on residue-level structure-evolution relationships

Recently, we demonstrated that yeast protein evolutionary rate at the level of individual amino acid residues scales linearly with degree of solvent accessibility. This residue-level structure-evolution relationship is sensitive to protein core size: surface residues from large-core proteins evolve much faster than those from small-core proteins, while buried residues are equally constrained independent of protein core size. In this work, we investigate the joint effects of protein core size and expression on the residue-level structure-evolution relationship. At the whole-protein level, protein expression is a much more dominant determinant of protein evolutionary rate than protein core size. In contrast, at the residue level, protein core size and expression both have major impacts on protein structure-evolution relationships. In addition, protein core size and expression influence residue-level structure-evolution relationships in qualitatively different ways. Protein core size preferentially affects the non-synonymous substitution rates of surface residues compared to buried residues, and has little influence on synonymous substitution rates. In comparison, protein expression uniformly affects all residues independent of degree of solvent accessibility, and affects both non-synonymous and synonymous substitution rates. Protein core size and expression exert largely independent effects on protein evolution at the residue level, and can combine to produce dramatic changes in the slope of the linear relationship between residue evolutionary rate and solvent accessibility. Our residue-level findings demonstrate that protein core size and expression are both important, yet qualitatively different, determinants of protein evolution. These results underscore the complementary nature of residue-level and whole-protein analysis of protein evolution.

Computational framework for analysis of prey-prey associations in interaction proteomics identifies novel human protein-protein interactions and networks

Large-scale protein-protein interaction data sets have been generated for several species including yeast and human and have enabled the identification, quantification, and prediction of cellular molecular networks. Affinity purification-mass spectrometry (AP-MS) is the preeminent methodology for large-scale analysis of protein complexes, performed by immunopurifying a specific "bait" protein and its associated "prey" proteins. The analysis and interpretation of AP-MS data sets is, however, not straightforward. In addition, although yeast AP-MS data sets are relatively comprehensive, current human AP-MS data sets only sparsely cover the human interactome. Here we develop a framework for analysis of AP-MS data sets that addresses the issues of noise, missing data, and sparsity of coverage in the context of a current, real world human AP-MS data set. Our goal is to extend and increase the density of the known human interactome by integrating bait-prey and cocomplexed preys (prey-prey associations) into networks. Our framework incorporates a score for each identified protein, as well as elements of signal processing to improve the confidence of identified protein-protein interactions. We identify many protein networks enriched in known biological processes and functions. In addition, we show that integrated bait-prey and prey-prey interactions can be used to refine network topology and extend known protein networks.

UniProtKB amid the turmoil of plant proteomics research

The UniProt KnowledgeBase (UniProtKB) provides a single, centralized, authoritative resource for protein sequences and functional information. The majority of its records is based on automatic translation of coding sequences (CDS) provided by submitters at the time of initial deposition to the nucleotide sequence databases (INSDC). This article will give a general overview of the current situation, with some specific illustrations extracted from our annotation of Arabidopsis and rice proteomes. More and more frequently, only the raw sequence of a complete genome is deposited to the nucleotide sequence databases and the gene model predictions and annotations are kept in separate, specialized model organism databases (MODs). In order to be able to provide the complete proteome of model organisms, UniProtKB had to implement pipelines for import of protein sequences from Ensembl and EnsemblGenomes. A single genome can be the target of several unrelated sequencing projects and the final assembly and gene model predictions may diverge quite significantly. In addition, several cultivars of the same species are often sequenced - 1001 Arabidopsis cultivars are currently under way - and the resulting proteomes are far from being identical. Therefore, one challenge for UniProtKB is to store and organize these data in a convenient way and to clearly defined reference proteomes that should be made available to users. Manual annotation is one of the landmarks of the Swiss-Prot section of UniProtKB. Besides adding functional annotation, curators are checking, and often correcting, gene model predictions. For plants, this task is limited to Arabidopsis thaliana and Oryza sativa subsp. japonica. Proteomics data providing experimental evidences confirming the existence of proteins or identifying sequence features such as post-translational modifications are also imported into UniProtKB records and the knowledgebase is cross-referenced to numerous proteomics resource.