wTSA-CRAFT: an open-access web server for rapid analysis of thermal shift assay experiments

Motivation

The automated data processing provided by the TSA-CRAFT tool enables now to reach high throughput speed analysis of thermal shift assays. While the software is powerful and freely available, it still requires installation process and command line efforts that could be discouraging.

Results

To simplify the procedure, we decided to make it available and easy to use by implementing it with a graphical interface via a web server, enabling a cross-platform usage from any web browsers. We developed a web server embedded version of the TSA-CRAFT tool, enabling a user-friendly graphical interface for formatting and submission of the input file and visualization of the selected thermal denaturation profiles. We describe a typical case study of buffer condition optimization of the biologically relevant APH(3')-IIb bacterial protein in a 96 deep-well thermal shift analysis screening.

Availability and implementation

wTSA-CRAFT is freely accessible for noncommercial usage at https://bioserv.cbs.cnrs.fr/TSA_CRAFT.

SLiMAn: An Integrative Web Server for Exploring Short Linear Motif-Mediated Interactions in Interactomes

Cells express thousands of macromolecules, and their functioning relies on multiple networks of intermolecular interactions. These interactions can be experimentally determined at different spatial and temporal resolutions. But, physical interfaces are not often delineated directly, especially in high-throughput experiments. A large fraction of protein-protein interactions involves domain and so-called SLiMs (for Short Linear Motifs). Often, SLiMs lie in disordered regions or loops. Their small size, limited sequence conservation, and loosely folded nature prevent straightforward detection. SLiMAn (Short Linear Motif Analysis), a new web server, is provided to help thorough analysis of interactomics data. From a list of putative interactants (e.g., output from an interactomics study), SLiMs (from ELM) and SLiM-recognition domains (from Pfam) are extracted, and putative pairings are displayed. Predicted results can be filtered using motif E-values, IUPred2 scores, or BioGRID interaction matches. When structural templates are available, a given SLiM and its recognition domain can be modeled using SCWRL. We illustrate here the use of SLiMAn on distinct examples, including one real-case study. We oversee wide-range applications for SLiMAn in the context of the massive analysis of protein-protein interactions. This new web server is made freely available at https://sliman.cbs.cnrs.fr.

Proteo3Dnet: a web server for the integration of structural information with interactomics data

Proteo3Dnet is a web server dedicated to the analysis of mass spectrometry interactomics experiments. Given a flat list of proteins, its aim is to organize it in terms of structural interactions to provide a clearer overview of the data. This is achieved using three means: (i) the search for interologs with resolved structure available in the protein data bank, including cross-species remote homology search, (ii) the search for possibly weaker interactions mediated through Short Linear Motifs as predicted by ELM-a unique feature of Proteo3Dnet, (iii) the search for protein-protein interactions physically validated in the BioGRID database. The server then compiles this information and returns a graph of the identified interactions and details about the different searches. The graph can be interactively explored to understand the way the core complexes identified could interact. It can also suggest undetected partners to the experimentalists, or specific cases of conditionally exclusive binding. The interest of Proteo3Dnet, previously demonstrated for the difficult cases of the proteasome and pragmin complexes data is, here, illustrated in the context of yeast precursors to the small ribosomal subunits and the smaller interactome of 14-3-3zeta frequent interactors. The Proteo3Dnet web server is accessible at http://bioserv.rpbs.univ-paris-diderot.fr/services/Proteo3Dnet/.

Profilage in silico des inhibiteurs de protéine kinases

Les protéine kinases ont été rapidement identifiées comme favorisant l’apparition de cancers, à travers leur implication dans la régulation du développement et du cycle cellulaire. Il y a une vingtaine d’années, la mise sur le marché des premiers traitements par inhibiteur de protéine kinase, ouvrait la voie vers de nouvelles solutions médicamenteuses plus ciblées contre le cancer. Depuis, nombreuses sont les données structurales et fonctionnelles acquises sur ces cibles thérapeutiques. Les techniques informatiques ont elles aussi évolué, notamment les méthodes d’apprentissage automatique. En tirant parti de la grande quantité d’informations disponibles aujourd’hui, ces méthodes devraient permettre prochainement la prédiction fine de l’interaction d’un inhibiteur donné avec chaque protéine kinase humaine et donc, à terme, la construction d’outils de profilage de leurs inhibiteurs spécifiques. Cette approche intégrative devrait aider la découverte de solutions thérapeutiques anti-cancéreuses plus efficaces et plus sûres.

Probing Protein Interaction Networks by Combining MS-Based Proteomics and Structural Data Integration

Protein-protein interactions play a major role in the molecular machinery of life, and various techniques such as AP-MS are dedicated to their identification. However, those techniques return lists of proteins devoid of organizational structure, not detailing which proteins interact with which others. Proposing a hierarchical view of the interactions between the members of the flat list becomes highly tedious for large data sets when done by hand. To help hierarchize this data, we introduce a new bioinformatics protocol that integrates information of the multimeric protein 3D structures available in the Protein Data Bank using remote homology detection, as well as information related to Short Linear Motifs and interaction data from the BioGRID. We illustrate on two unrelated use-cases of different complexity how our approach can be useful to decipher the network of interactions hidden in the list of input proteins, and how it provides added value compared to state-of-the-art resources such as Interactome3D or STRING. Particularly, we show the added value of using homology detection to distinguish between orthologs and paralogs, and to distinguish between core obligate and more facultative interactions. We also demonstrate the potential of considering interactions occurring through Short Linear Motifs.