Biology subjects

Freiberger, M. I.

Publications and source records attributed to Freiberger, M. I..

9 recordsLinked to original sources

bioRxiv2025

ProteoCast: a web server to predict, validate, and interpret missense variant effects

Understanding how mutations affect protein function remains critical yet challenging, particularly for variants in clinical databases lacking experimental characterisation and for intrinsically disordered regions. Current computational approaches often operate as black boxes, providing predictions without sufficient transparency or quality assessment of the underlying data. Here we present ProteoCast, a user-friendly web server that predicts variant effects through evolutionary constraint analysis and structural context integration. ProteoCast provides a three-tier variant classification (impactful, mild, neutral) to help prioritise mutations for clinical interpretation and experimental validation. It incorporates multiple sequence alignment quality controls to ensure prediction reliability and flag positions with insufficient evolutionary information. Beyond single-variant classification, ProteoCast employs a novel segmentation approach based on mutational sensitivity to identify functional linear peptides in disordered regions. Interactive visualisations guide users through results interpretation, from variant-level predictions to protein-wide functional landscapes. Evaluation on 63,000 ClinVar variants demonstrates 77% sensitivity and 87% specificity for pathogenicity prediction, with performance maintained across species (85% accuracy on Drosophila lethal mutations). ProteoCast successfully identifies twice as many functional motifs in intrinsically disordered regions compared to conservation-based phylogenetic methods. Predictions can be tuned to specific conformations, such as bound forms in protein complexes, for improved accuracy and interpretability. With its transparent, unsupervised methodology and computational efficiency (minutes per protein), ProteoCast democratises access to variant effect prediction and functional site discovery for the broader research community. The web server is freely available at: https://proteocast.ijm.fr/. HighlightsO_LIProteoCast web server democratises variant effect prediction through a fast, transparent platform combining evolutionary analysis and structural context C_LIO_LIThree-tier variant classification enables prioritisation for clinical interpretation and genome editing experiments C_LIO_LINovel mutational sensitivity segmentation approach identifies functional peptides in disordered regions C_LIO_LIInteractive visualisations and input alignment quality controls guide interpretation C_LI

bioinformatics↗

bioRxiv2025

Proteome-wide prediction of the Functional Impact of Missense Variants with ProteoCast

Dissecting the functional impact of genetic mutations is essential to advancing our understanding of genotype-phenotype relationships and identifying therapeutic targets. Despite progress in sequencing and genome editing technologies, proteome-wide mutation effect prediction remains challenging. Here we show that evolutionary information alone enables accurate prediction of mutation effects across entire proteomes. ProteoCast is a scalable and interpretable computational method that leverages protein sequence conservation to classify genetic variants and identify functionally important protein sites. We apply ProteoCast to the complete Drosophila melanogaster proteome (22,000 isoforms, 300 million mutations) and validate it against nearly 400,000 natural and experimental variants. It correctly classifies 85% of known lethal mutations as functionally impactful versus 13-18% of population variants. ProteoCast-guided genome editing experiments confirm these predictions. Moreover, ProteoCast successfully identifies functionally important protein modification sites and binding motifs. ProteoCast provides a publicly available resource and deployable pipeline for studying gene function and mutations in any organism.

evolutionary biology↗

bioRxiv2023

Predicting the dynamic interaction between intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) participate in various biological processes. Interactions involving IDPs are usually dynamic and affected by their inherent conformation fluctuations. Comprehensive characterization of these interactions based on current techniques is challenging. Here, we present GSALIDP, a GraphSAGE-LSTM Network to capture the dynamic nature of IDP-involved interactions and predict their behaviors. This framework models multiple conformations of IDP as a dynamic graph which can effectively describe the fluctuation of its flexible conformation. The dynamic interaction between IDPs is studied and the datasets of IDP conformations and their interactions are obtained through atomistic molecular dynamic (MD) simulations. Residues of IDP are encoded through a series of features, including their frustration. GSALIDP can effectively predict the interaction sites of IDP and the contact residue pairs between IDPs. Its performance in predicting IDP interaction is on par with or even better than the conventional models in predicting the interaction of structural proteins. To the best of our knowledge, this is the first model to extend the protein interaction prediction to IDP-involved interactions.

molecular biology↗

bioRxiv2023

Frustraevo: A Web Server To Localize And Quantify The Conservation Of Local Energetic Frustration In Protein Families

According to the Principle of Minimal Frustration, folded proteins can have only a minimal number of strong energetic conflicts in their native states. However, not all interactions are energetically optimized for folding but some remain in energetic conflict, i.e. they are highly frustrated. This remaining local energetic frustration has been shown to be statistically correlated with distinct functional aspects such as protein-protein interaction sites, allosterism and catalysis. Fuelled by the recent breakthroughs in efficient protein structure prediction that have made available good quality models for most proteins, we have developed a strategy to calculate local energetic frustration within large protein families and quantify its conservation over evolutionary time. Based on this evolutionary information we can identify how stability and functional constraints have appeared at the common ancestor of the family and have been maintained over the course of evolution. Here, we present FrustraEvo, a web server tool to calculate and quantify the conservation of local energetic frustration in protein families. The webserver is freely available at URL: https://frustraevo.qb.fcen.uba.ar

bioinformatics↗

bioRxiv2023

The Evolution of Local Energetic Frustration in Protein Families

Energetic local frustration offers a biophysical perspective to interpret the effects of sequence variability on protein families. Here we present a methodology to analyze local frustration patterns within protein families that allows us to uncover constraints related to stability and function, and identify differential frustration patterns in families with a common ancestry. We have analyzed these signals in very well studied cases such as PDZ, SH3, and {beta} globins and RAS families. Recent advances in protein structure prediction make it possible to analyze a vast majority of the protein space. An automatic and unsupervised proteome-wide analysis on the SARS-CoV-2 virus demonstrates the potential of our approach to enhance our understanding of the natural phenotypic diversity of protein families beyond single protein instances. We have applied our method to modify biophysical properties of natural proteins based on their family properties, as well as perform unsupervised analysis of large datasets to shed light on the physicochemical signatures of poorly characterized proteins such as emergent pathogens.

bioinformatics↗

bioRxiv2022

FrustraPocket: A protein-ligand binding site predictor using energetic local frustration

Proteins are evolved polymers that minimize their free energy upon folding to their native states. Still, many folded proteins display energetic conflict between residues in various regions that can be identified as highly frustrated, and these have been shown to be related to several physiological functions. Here we show that small-ligand binding sites are typically enriched in locally frustrated interactions in the unbound state. We built a tool using a simple machine learning algorithm named FrustraPocket that combines the notion of small-molecule binding pockets and the localization of clusters of highly frustrated interactions to identify potential protein-ligand binding sites solely from the unbound forms. Availability and implementation (github)https://github.com/CamilaClemente/FrustraPocket/ Docker containerhttps://hub.docker.com/r/proteinphysiologylab/frustrapocket

bioinformatics↗

bioRxiv2020

FrustratometeR: an R-package to compute Local frustration in protein structures, point mutants and MD simulations

Once folded, natural protein molecules have few energetic conflicts within their polypeptide chains. Many protein structures do however contain regions where energetic conflicts remain after folding, i. e. they have highly frustrated regions. These regions, kept in place over evolutionary and physiological timescales, are related to several functional aspects of natural proteins such as protein-protein interactions, small ligand recognition, catalytic sites and allostery. Here we present FrustratometeR, an R package that easily computes local energetic frustration on a personal computer or a cluster. This package facilitates large scale analysis of local frustration, point mutants and molecular dynamics (MD) trajectories, allowing straightforward integration of local frustration analysis into pipelines for protein structural analysis. Contactgonzalo.parra@embl.de Availability and implementationhttps://github.com/proteinphysiologylab/frustratometeR

bioinformatics↗

bioRxiv2020

Frustration in protein complexes leads to interaction versatility

Disordered proteins can fold into a well-defined structure upon binding but these complexes are often fuzzy: the originally disordered partner adopts different binding modes when bound to different partners. Here we perform a systematic analysis of 160 proteins that form fuzzy complexes and demonstrate that the disordered partner displays a high degree of frustration in both the free and bound states. Although the folding of disordered regions upon binding reduces frustration relative to that of the unbound state, the interactions at the binding interface do not become fully optimized. In addition, we show that sub-optimal interactions lead to alternative frustration patterns in the complexes with different partners. These results demonstrate that disordered proteins do not always achieve fully optimal interactions in their complexes and their residual frustration leads to interaction versatility with different partners.

bioinformatics↗

bioRxiv2019

Large Ankyrin repeat proteins are formed with similar and energetically favorable units

Ankyrin containing proteins are one of the most abundant repeat protein families present in all extant organisms. They are made with tandem copies of similar amino acid stretches that fold into elongated architectures. Here, we build and curated a dataset of 200 thousand proteins that contain 1,2 million Ankyrin regions and characterize the abundance, structure and energetics of the repetitive regions in natural proteins. We found that there is a continuous roughly exponential variety of array lengths with an exceptional frequency at 24 repeats. We describe that individual repeats are seldom interrupted with long insertions and accept few deletions, consistently with the know tertiary structures. We found that longer arrays are made up of repeats that are more similar to each other than shorter arrays, and display more favourable folding energy, hinting at their evolutionary origin. The array distributions show that there is a physical upper limit to the size of an array of Ankyrin repeats of about 120 copies, consistent with the limit found in nature. Analysis of the identity patterns within the arrays suggest that they may have originated by sequential copies of more than one Ankyrin unit. Author summaryRepeat proteins are coded in tandem copies of similar amino acid stretches. We built and curated a large dataset of Ankyrin containing proteins, one of the most abundant families of repeat proteins, and characterized the structure of the arrays formed by the repetitions. We found that large arrays are constructed with repetitions that are more similar to each other than shorter arrays. Also, the largest the array, the more energetically favourable its folding energy is. We speculate about the mechanistic origin of large arrays and hint into their evolutionary dynamics.

bioinformatics↗