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Borza, R.

Publications and source records attributed to Borza, R..

4 recordsLinked to original sources

Disentangling the CHAOS of intrinsic disorder in human proteins

Most proteins consist of both folded domains and Intrinsically Disordered Regions (IDRs). However, the widespread occurrence of intrinsic disorder in human proteins, along with its characteristics, is often overlooked by the broader communities of structural and molecular biologists. Building on the MobiDB database of intrinsic disorder in proteins, here we develop a comprehensive dataset (Comprehensive analysis of Human proteins And their disOrdered Segments - CHAOS). We implement internally consistent definitions of disordered regions, and annotate general characteristics such as cellular location, essentiality, post-translational modifications, and predicted pathogenicity. Further, we cross-reference to structure predictions from AlphaFold. We find that most human proteins contain at least one disordered region, predominantly located at the protein termini. IDRs are less hydrophobic, enriched in post-translational modifications, and mutations in IDRs are predicted to be less pathogenic than in non-IDRs. Additionally, we discovered that proteins residing in different cellular locations possess distinct disorder profiles. Finally, the predicted AlphaFold models of proteins in CHAOS suggest that disordered regions and proteins are often predicted to adopt secondary structure. Hereby we enhance the visibility and understanding of intrinsic disorder in human proteins. Key messagesO_LIFour out of five human proteins contain one or more intrinsically disordered regions (IDRs). C_LIO_LIHalf of the IDRs are located at protein termini, but three quarters of all human proteins contain a terminal IDR. C_LIO_LIThe amount and location of disordered regions differs throughout cellular compartments. C_LIO_LIOne in five missense mutations in IDRs are likely pathogenic. C_LIO_LIAlphaFold predicts secondary structure elements within intrinsically disordered regions and fully disordered proteins. C_LI

bioinformatics↗

AlphaBridge: tools for the analysis of predicted macromolecular complexes

Artificial intelligence (AI)-powered protein structure prediction has transformed how scientists explore macromolecular function. AI-based prediction of macromolecular complexes is increasingly used for evaluating the likelihood of proteins forming complexes with other proteins, nucleic acids, lipids, sugars, or small-molecule ligands. Efficient tools are needed to evaluate these predicted models. We introduce an approach based on combining the confidence metrics of AlphaFold3 to enable clustering of sequence motifs participating in binary interactions and subsequently in 3D interfaces of complexes. Interaction interfaces within confidence limits are finally visualised in 2D using chord diagrams and network graphs. The analysis and visualisation are implemented in a web tool, which links them with interactive graphics and summary tables of predicted interfaces and intermolecular interactions, including confidence scores. Finally, we demonstrate real-life examples of how AlphaBridge is used for providing an efficient way to assess and validate predicted protein complexes and interfaces. The reproducible, objective and automated procedures we present provide a straightforward critical assessment of structure prediction of biomolecular complexes, that should be consulted before conducting more resource-intensive analyses.

bioinformatics↗

Autotaxin facilitates selective LPA receptor signaling

Autotaxin (ATX; ENPP2) produces the lipid mediator lysophosphatidic acid (LPA) that signals through disparate EDG (LPA1-3) and P2Y (LPA4-6) G protein-coupled receptors. ATX/LPA promote several (patho)physiological processes, including in pulmonary fibrosis, thus serving as attractive drug targets. However, it remains unclear if clinical outcome depends on how different ATX inhibitors modulate the ATX/LPA signaling axis. Here, we show that inhibitors binding to the ATX "tunnel" specifically abrogate key aspects of ATX/LPA signaling. We find that the tunnel is essential for signaling efficacy and dictates cellular responses independent of ATX catalytic activity, with a preference for activation of P2Y LPA receptors. These responses are abrogated by tunnel-binding inhibitors, such as ziritaxestat, but not by inhibitors that exclusively target the active site, as shown in primary lung fibroblasts and a murine model of radiation-induced pulmonary fibrosis. Our results uncover a receptor-selective signaling mechanism for ATX, implying clinical benefit for tunnel-targeting ATX inhibitors. HighlightsATX is a dual-function protein acting as an LPA-producing enzyme and LPA chaperone. Structural integrity of the ATX tunnel is essential to mediate signaling functions. ATX-bound LPA signals preferentially via P2Y family LPA receptors. Occupancy of the ATX tunnel is crucial for ziritaxestat to exert inhibition in vivo.

cell biology↗

Subcellular localization of the immunoregulatory protein MACIR by TNPO1 is mediated by a C terminal PY motif

Expression of the macrophage immunometabolism regulator gene (MACIR) is associated with severity of autoimmune disease pathology and the regulation of macrophage biology through unknown mechanisms. The 206 amino acid protein lacks homology to any characterized protein sequence and is a disordered protein according to structure prediction algorithms. Here we identify specific interactions of MACIR using a fragment complementation-based affinity pull down of cellular proteins prepared with a membrane solubilization buffer. Quantitative mass spectrometry showed enrichment of nuclear and mitochondrial proteins and of 63 significant interacting proteins, binding to the nuclear transport receptor TNPO1 and trafficking proteins UNC119 homolog A and B were validated by immunoprecipitation. Analysis of mutations in two candidate recognition motifs in the MACIR amino acid sequence confirmed TNPO1 binds via a PY-NLS motif (aa98-117). Characterizing nuclear MACIR activity in macrophage and fibroblasts is a priority with respect to developing strategies for treatment of autoimmune disease.

biochemistry↗