Interactome-Wide Prediction of Protein-Protein Binding Sites Reveals Effects of Protein Sequence Variation in <em>Arabidopsis thaliana</em>

<div><p>The specificity of protein-protein interactions is encoded in those parts of the sequence that compose the binding interface. Therefore, understanding how changes in protein sequence influence interaction specificity, and possibly the phenotype, requires knowing the location of binding sites in those sequences. However, large-scale detection of protein interfaces remains a challenge. Here, we present a sequence- and interactome-based approach to mine interaction motifs from the recently published <em>Arabidopsis thaliana</em> interactome. The resultant proteome-wide predictions are available via <a href="http://www.ab.wur.nl/sliderbio">www.ab.wur.nl/sliderbio</a> and set the stage for further investigations of protein-protein binding sites. To assess our method, we first show that, by using <em>a priori</em> information calculated from protein sequences, such as evolutionary conservation and residue surface accessibility, we improve the performance of interface prediction compared to using only interactome data. Next, we present evidence for the functional importance of the predicted sites, which are under stronger selective pressure than the rest of protein sequence. We also observe a tendency for compensatory mutations in the binding sites of interacting proteins. Subsequently, we interrogated the interactome data to formulate testable hypotheses for the molecular mechanisms underlying effects of protein sequence mutations. Examples include proteins relevant for various developmental processes. Finally, we observed, by analysing pairs of paralogs, a correlation between functional divergence and sequence divergence in interaction sites. This analysis suggests that large-scale prediction of binding sites can cast light on evolutionary processes that shape protein-protein interaction networks.</p> </div

Putative molecular mechanisms underlying effects of amino acid mutagenesis.

<p>A, C and E show the interacting partners of the proteins ZTL, CXIP1 and SHY2, respectively (interactions shown as dashed lines are not covered in the Arabidopsis Interactome data). B, D and F show a schematic representation of the sequences of the three proteins, including predicted binding sites (coloured box, using same colour as the proteins predicted to bind to it), mutagenesis sites (triangles for experimental mutagenesis sites, circles for naturally occurring sequence variants) and their positions, and residue surface accessibility (RSA) and conservation (bar plots) as predicted based on the sequence. A–B, in the protein ZTL, alanine mutagenesis of the residues 200 and 213 independently eliminate the interaction with ASK1; for ZTL, the stretch of residues from 208 to 220 is predicted as interaction site for binding with ASK2 and ASK4. This leads to the hypothesis that mutation on ZTP, specifically on the residue Leu213, would not only disrupt its interaction with ASK1, but also with other SKP1-like proteins, such as ASK2 and ASK4. C–D, In CXIP1, alanine mutagenesis of two highly conserved motifs (residues from 133 to 137; and residues from 97 to 100) leads to loss of ability to activate CAX1. For CXIP1, the stretch of residues from 125 to 136 was predicted as binding site, which overlaps the mutated motif SNWPT. The interaction of CXIP1 and the other interacting partners identified in the Arabidopsis interactome, i.e. AT5G09830, AT3G50780, AT1G70410 and TCP13 (AT3G02150), may also be mediated by the same motif. E–F, in the sequence of SHY2, three motifs were predicted as binding sites. The first (residues from 59 to 69; represented in grey) overlaps the position of two naturally occurring mutations (residues 67 and 69) and is predicted to be responsible for binding of TOPLESS (TPL, AT5G27030). A second motif (residues from 180 to 187; represented in brown) is predicted to be responsible for the interactions of SHY2 with six other IAA proteins. This leads to the hypothesis that two known mutations disrupt the interaction of SHY2 with TPL, but the same mutations do not impede its interaction with other IAA proteins.</p

Overall performance of the SLIDERBio algorithm in different datasets.

<p>(A–C) Coverage of protein-protein interfaces and Accuracy of predicted motifs. Each dot represents the result of SLIDERBio using one of the 180 tested sets of parameters, for (A) human, (B) yeast and (C) Arabidopsis structurally mapped subsets. The grey arrows indicate the dot corresponding to the result of the previous SLIDER algorithm. (D–F), Correlation of the performance for each of the SLIDERBio parameter settings is compared among datasets of different species: (D) human vs. yeast; (E) human vs. Arabidopsis; and (F) yeast vs. Arabidopsis. Pearson Correlation Coefficient (PCC) is indicated.</p

Overall description of the predicted binding sites in the Arabidopsis interactome.

<p>(A) Network representation of the Arabidopsis interactome and predicted interaction sites. The vertices and edges in black represent, respectively, the 985 proteins and the 1498 interactions to which predicted motifs are mapped. (B) Degree distributions from the complete protein-protein interaction dataset (grey) and from the subset with only proteins and interactions that have a predicted motif (black). A and B suggest that our method is not biased to predict motifs that can be mapped only to proteins with high degree (<i>i.e.</i> number of interactions); moreover, the proteins with predicted motifs are distributed in different positions in the network. (C) Percentage of residues in the interfaces, either in the predicted interfaces or those observed in the structurally mapped dataset. Standard deviation is indicated.</p

Functionally annotated protein sites that coincide with predicted interaction sites.

<p>Functionally annotated protein sites that coincide with predicted interaction sites.</p

Plasma Biomarkers and Identification of Resilient Metabolic Disruptions in Patients With Venous Thromboembolism Using a Metabolic Systems Approach

International audienceObjective: Deep vein thrombosis and pulmonary embolism referred as venous thromboembolism (VTE) are a common cause of morbidity and mortality. Plasma from healthy controls or individuals who have experienced a VTE were analyzed using metabolomics to characterize biomarkers and metabolic systems of patients with VTE.Approach and Results: Polar metabolite and lipidomic profiles from plasma collected 3 months after an incident VTE were obtained using liquid chromatography mass spectrometry. Fasting-state plasma samples from 42 patients with VTE and 42 healthy controls were measured. Plasma metabolomic profiling identified 512 metabolites forming 62 biological clusters. Multivariate analysis revealed a panel of 21 metabolites altogether capable of predicting VTE status with an area under the curve of 0.92 (P=0.00174, selectivity=0.857, sensitivity=0.971). Multiblock systems analysis revealed 25 of the 62 functional biological groups as significantly affected in the VTE group (P<0.05 to control). Complementary correlation network analysis of the dysregulated functions highlighted a subset of the lipidome composed mainly of n-3 long-chain polyunsaturated fatty acids within the predominant triglycerides as a potential regulator of the post-VTE event biological response, possibly controlling oxidative and inflammatory defence systems, and metabolic disorder associated dysregulations. Of interest was microbiota metabolites including trimethylamine N-oxide that remained associated to post incident VTE patients, highlighting a possible involvement of gut microbiota on VTE risk and relapse.Conclusions: These findings show promise for the elucidation of underlying mechanisms and the design of a diagnostic test to assess the likely efficacy of clinical care in patients with VTE

Network of flowering time integrator genes.

<p>Green indicates expression in leaf tissue, blue in meristem tissue. Red arrows represent repression, blue arrows activation. Most interactions were taken as given based on literature information, but for regulation of <i>LFY</i> by AGL24 and SOC1, different ways of combining the two inputs were tested (indicated by the light blue arrows). Dashed arrow represents FT transport. Junction symbol next to <i>AP1</i> indicates cooperativity predicted for regulation of <i>AP1</i> by LFY. As indicated, <i>AP1</i> expression is used as a marker for the moment of the floral transition. This network was used to fit expression time-course data and to predict the effect of perturbations. Gene names are given in full in the text.</p