ProSeqViewer: an interactive, responsive and efficient TypeScript library for visualization of sequences and alignments in web applications

E3 ubiquitin ligases recognize substrates through their short linear motifs termed degrons. While degron-signaling has been a subject of extensive study, resources for its systematic screening are limited. To bridge this gap, we developed DEGRONOPEDIA, a web server that searches for degrons and maps them to nearby residues that can undergo ubiquitination and disordered regions, which may act as protein unfolding seeds. Along with an evolutionary assessment of degron conservation, the server also reports on post-translational modifications and mutations that may modulate degron availability. Acknowledging the prevalence of degrons at protein termini, DEGRONOPEDIA incorporates machine learning to assess N-/C-terminal stability, supplemented by simulations of proteolysis to identify degrons in newly formed termini. An experimental validation of a predicted C-terminal destabilizing motif, coupled with the confirmation of a post-proteolytic degron in another case, exemplifies its practical application. DEGRONOPEDIA can be freely accessed at degronopedia.com.

Section: Methodsmentioning

confidence: 99%

DEGRONOPEDIA: a web server for proteome-wide inspection of degrons

Szulc,

Stefaniak,

Piechota

et al. 2024

“…As in the previous version, the central component of the entry page is the feature viewer ( 48 ), which shows the type and position of the annotated regions. The feature viewer propagates user actions (region selections and clicks) to the sequence and structure viewers, which are instances of ProSeqViewer ( 49 ) and Mol* ( 50 ) plugins, respectively. Selected regions (or tracks) are highlighted synchronously in all the viewers.…”

Section: Methodsmentioning

confidence: 99%

MobiDB: 10 years of intrinsically disordered proteins

Piovesan

Conte

Clementel

et al. 2022

Self Cite

103

The MobiDB database (URL: https://mobidb.org/) is a knowledge base of intrinsically disordered proteins. MobiDB aggregates disorder annotations derived from the literature and from experimental evidence along with predictions for all known protein sequences. MobiDB generates new knowledge and captures the functional significance of disordered regions by processing and combining complementary sources of information. Since its first release 10 years ago, the MobiDB database has evolved in order to improve the quality and coverage of protein disorder annotations and its accessibility. MobiDB has now reached its maturity in terms of data standardization and visualization. Here, we present a new release which focuses on the optimization of user experience and database content. The major advances compared to the previous version are the integration of AlphaFoldDB predictions and the re-implementation of the homology transfer pipeline, which expands manually curated annotations by two orders of magnitude. Finally, the entry page has been restyled in order to provide an overview of the available annotations along with two separate views that highlight structural disorder evidence and functions associated with different binding modes.

“…The FuzPred server provides information on some sequence features that may be relevant to regulate binding characteristics. In this panel, the sequence corresponding to the UniProt code or the sequence provided is shown ( 25 ). Below the experimentally observed fuzzy regions, with validated functional impacts are displayed as derived from the FuzDB, the database of fuzzy interactions ( 4 ).…”

Section: Prediction Of the Binding Modes Of Proteinsmentioning

confidence: 99%

FuzPred: a web server for the sequence-based prediction of the context-dependent binding modes of proteins

Hatos

Teixeira

Barrera-Vilarmau

et al. 2023

Self Cite

Proteins form complex interactions in the cellular environment to carry out their functions. They exhibit a wide range of binding modes depending on the cellular conditions, which result in a variety of ordered or disordered assemblies. To help rationalise the binding behavior of proteins, the FuzPred server predicts their sequence-based binding modes without specifying their binding partners. The binding mode defines whether the bound state is formed through a disorder-to-order transition resulting in a well-defined conformation, or through a disorder-to-disorder transition where the binding partners remain conformationally heterogeneous. To account for the context-dependent nature of the binding modes, the FuzPred method also estimates the multiplicity of binding modes, the likelihood of sampling multiple binding modes. Protein regions with a high multiplicity of binding modes may serve as regulatory sites or hot-spots for structural transitions in the assembly. To facilitate the interpretation of the predictions, protein regions with different interaction behaviors can be visualised on protein structures generated by AlphaFold. The FuzPred web server (https://fuzpred.bio.unipd.it) thus offers insights into the structural and dynamical changes of proteins upon interactions and contributes to development of structure-function relationships under a variety of cellular conditions.