Biology subjects

Piovesan, D.

Publications and source records attributed to Piovesan, D..

16 recordsLinked to original sources

bioRxiv2026

The Hidden Disorder Divide: Reconciling Benchmark Inconsistencies in Intrinsically Disordered Protein Binding Site Prediction

Computational predictors of protein-binding sites within intrinsically disordered regions (IDRs) show highly inconsistent performance across high-quality benchmark datasets. To understand the origins of these discrepancies, we systematically compared predictors across three independent test sets: two CAID datasets updated with the latest DisProt annotations and a composite dataset (DBs) assembled from DIBS, FuzDB, IDEAL, and MFIB. Predictors trained predominantly on DisProt data achieved substantially higher AUCs on the CAID sets but performed poorly on the DBs. In contrast, predictors trained on older, low-quality PDB-based datasets showed balanced performance across all sets, with a slight preference for DBs. Predictors with mixed training exposure displayed intermediate behavior. Through controlled experiments using identical CNN architectures and feature analysis, we demonstrate that the dominant factor driving these performance differences is the intrinsic disorder propensity of the binding sites themselves. Binding residues in DisProt-based datasets exhibit markedly higher average disorder propensity scores than those in PDB-derived datasets. This previously unrecognized selection bias -- literature studies preferentially characterizing more disordered binding sites, while PDB-derived annotations capture less disordered ones -- effectively splits IDR-protein binding sites into two distinct categories. Predictors optimized on one category therefore generalize poorly to the other. Binding-site length and sequence conservation play only minor or negligible roles in explaining the observed inconsistencies. These findings highlight a critical limitation in current benchmarking practices and training strategies for IDR-binding site prediction, underscoring the need for more balanced and disorder-aware reference datasets. Finally, the diagnostic techniques introduced here could prove valuable beyond the specific application examined in this study. The data and code used to generate all figures and tables are available at: https://github.com/NawarMalhis/HDD

bioinformatics↗

bioRxiv2026

On the state of protein function prediction: a report on the fourth CAFA challenge

BackgroundThe Critical Assessment of Functional Annotation (CAFA) is a community effort held to understand the field of computational protein function prediction. Every three years, since 2010, the organizers initiate an experiment to collect function predictions on a large set of proteins and then evaluate the performance of predicting methods on a subset of proteins that have accumulated experimental annotations between the submission deadline and the evaluation time. CAFA provides an independent and rigorous assessment of the current state of the art, thus leveling the playing field, highlighting successes, revealing bottlenecks, and offering a forum for the exchange of ideas in protein science. Here, we report the results of the fourth CAFA experiment (CAFA4). ResultsCAFA4 featured the participation of 148 methods from 70 research groups on a total of 46,205 unique proteins over a 5-year annotation accumulation phase, the longest in any CAFA. In a comparison across CAFA2-CAFA4 methods, the prediction of Gene Ontology (GO) terms has clearly improved across all three GO aspects and traditional evaluation settings. While not achieving the first rank, several CAFA2 and CAFA3 methods featured in the top ten methods in many evaluations, suggesting that earlier methods still hold relevance. The performance is weaker in the newly introduced "partial knowledge" evaluation category (proteins with experimental annotations before submission deadline that gained additional annotations in the same GO aspect during the annotation accumulation phase), highlighting the need for a new class of methods. The rankings of the methods were stable over the years in traditional evaluation settings, but less so in the new partial knowledge evaluation. Overall, the field continues to progress with some influx of new participants. Sustained efforts will be necessary to substantially advance it.

bioinformatics↗

bioRxiv2026

Advances in protein function prediction from the fifth CAFA challenge

The Critical Assessment of Functional Annotation (CAFA) is a long-standing community effort to independently assess computational methods for protein function prediction, to highlight well-performing methodologies, to identify bottlenecks in the field, and to provide a forum for the dissemination of results and exchange of ideas. In its fifth round (CAFA5) of triennial challenges, a partnership with Kaggle Inc. facilitated participation from a large community of data scientists and computational biologists through a competitive prospective challenge on the crowdsourcing platform. In this work, we present an in-depth analysis of the submitted predictions and report improvements in accuracy over all methods from the previous CAFA challenges. We further introduce a new evaluation setting for proteins with pre-existing (incomplete) annotations and identify the need for methods that better leverage existing annotations to predict those that will be discovered later. Finally, we characterize the prospective evaluation framework by examining performance on a strict set of unpublished annotations and across intermediate database releases. Our results indicate that recent developments in the field, such as the availability of protein language models and accurately predicted 3D structures, as well as the growth of experimental annotations through biocuration, have all contributed to performance improvements. 1

bioinformatics↗

bioRxiv2026

emb2dis: a novel protein disorder prediction tool based on ResNets, dilated convolutions & protein language models

Intrinsically disordered proteins (IDPs) play an important role in a wide range of biological functions and are linked to several diseases. Due to technical difficulties and the high cost of experimental determination of disorder in proteins, combined with the exponential increase of unannotated protein sequences, the development of computational methods for disorder prediction became an active area of research in the last few decades. In this work, we present emb2dis, a deep learning model that uses protein language models (pLMs) to predict disorder from sequence. The emb2dis tool is a pre-trained model that receives as input a protein sequence, calculates its pLM embedding and passes it to a deep learning model. In contrast to existing approaches, emb2dis integrates informative sequence representations with a novel architecture that combines residual networks (ResNets) and dilated convolutions. This design effectively enlarges the receptive field of the convolution operation, enabling the model to better capture an extended context of each amino acid. At the output, emb2dis assigns a disorder propensity score to each residue in the sequence. The model was evaluated on datasets from the latest CAID3 blind benchmark for disorder prediction, where it achieved first place in the Disorder-PDB category, exhibiting strong performance with high AUC and Fmax scores. Additionally, it ranked among the top ten methods on the Disorder-NOX dataset. We provide a freely available web-demo for emb2dis and a source code repository for local installation. Weblink for the toolhttps://sinc.unl.edu.ar/web-demo/emb2dis/ The importance of the emb2dis tool is that it provides a new deep learning approach and significant improvements in the prediction of protein disorder, with a simple web interface and graphical output detailing per-residue disorder.

bioinformatics↗

bioRxiv2025

GeomeTRe: accurate calculation of geometrical descriptors of tandem repeat proteins

Structured tandem repeat proteins (STRPs) are characterized by preserved structural motifs arranged in a modular way. The structural and functional diversity of STRPs makes them particularly important for studying evolution, novel structure and function relationships, and ultimately for designing new synthetic proteins with specific functions. One crucial aspect of their classification is the estimation of geometrical parameters, which can provide better insight into their properties and the relationship between the spatial arrangement of repeated units and protein function. Calculating geometric descriptors for STRPs is challenging because naturally occurring repeats are not perfect and often contain insertions and deletions. Existing tools for predicting structural symmetry work well on simple cases but often fail for most natural proteins. Here we present GeomeTRe, an algorithm that calculates geometrical descriptors such as curvature (yaw), twist (roll), and pitch for a protein structure with known repeat unit positions. The algorithm simulates the movement of consecutive units, identifies rotational axes, and calculates the corresponding Tait-Bryan angles. The parameters of GeomeTRe can enhance STRP annotation and classification by identifying variations in geometric arrangements among different functional groups. The package is fast and suitable for processing large protein structure datasets when repeat region information (e.g., from RepeatsDB) is available. GeomeTRe is available as a Python package; source code and documentation can be found at https://github.com/BioComputingUP/GeomeTRe

bioinformatics↗

bioRxiv2024

PredIDR: Accurate prediction of protein intrinsic disorder regions using deep convolutional neural network

The involvement of protein intrinsic disorder in essential biological processes, it is well known in structural biology. However, experimental methods for detecting intrinsic structural disorder and directly measuring highly dynamic behavior of protein structure are limited. To address this issue, several computational methods to predict intrinsic disorder from protein sequences were developed and their performance is evaluated by the Critical Assessment of protein Intrinsic Disorder (CAID). In this paper, we describe a new computational method, PredIDR, which provides accurate prediction of intrinsically disordered regions in proteins, mimicking experimental X-ray missing residues. Indeed, missing residues in Protein Data Bank (PDB) were used as positive examples to train a deep convolutional neural network which produces two types of output for short and long regions. PredIDR took part in the second round of CAID and was as accurate as the top state-of-the-art IDR prediction methods. PredIDR can be freely used through the CAID Prediction Portal available at https://caid.idpcentral.org/portal or downloaded as a Singularity container from https://biocomputingup.it/shared/caid-predictors/.

bioinformatics↗

bioRxiv2024

STRPsearch: fast detection of structured tandem repeat proteins

MotivationState-of-the-art prediction methods are generating millions of publicly available protein structures. Structured Tandem Repeats Proteins (STRPs) constitute a subclass of tandem repeats characterized by repetitive structural motifs. STRPs exhibit distinct propensities for secondary structure and form regular tertiary structures, often comprising large molecular assemblies. They can perform important and diverse biological functions due to their highly degenerated sequences, which maintain a similar structure while displaying a variable number of repeat units. This suggests a disconnection between structural size and protein function. However, automatic detection of STRPs remains challenging with current state-of-the-art tools due to their lack of accuracy and long execution times, hindering their application on large datasets. In most cases, manual curation is the most accurate method for detecting and classifying them, making it impossible to inspect millions of structures. ResultsWe present STRPsearch, a novel computational tool for rapid identification, classification, and mapping of STRPs. Leveraging the manually curated entries in RepeatsDB as the known conformational space of the STRPs, STRPsearch utilizes the latest advancements in structural alignment techniques for a fast and accurate detection of repeated structural motifs in protein structures, followed by an innovative approach to map units and insertions through the generation of TM-score graphs. STRPsearch can serve researchers in structural bioinformatics and protein science as an efficient and practical tool for analysis and detection of STRPs. Availability and implementationSTRPsearch is coded in Python, all the scripts and the associated documentation are available at https://github.com/BioComputingUP/STRPsearch. Contactalexander.monzon@unipd.it, silvio.tosatto@unipd.it Supplementary informationSupplementary data are available..

bioinformatics↗

bioRxiv2022

3D-Beacons: Decreasing the gap between protein sequences and structures through a federated network of protein structure data resources

While scientists can often infer the biological function of proteins from their 3-dimensional quaternary structures, the gap between the number of known protein sequences and their experimentally determined structures keeps increasing. A potential solution to this problem is presented by ever more sophisticated computational protein modelling approaches. While often powerful on their own, most methods have strengths and weaknesses. Therefore, it benefits researchers to examine models from various model providers and perform comparative analysis to identify what models can best address their specific use cases. To make data from a large array of model providers more easily accessible to the broader scientific community, we established 3D-Beacons, a collaborative initiative to create a federated network with unified data access mechanisms. The 3D-Beacons Network allows researchers to collate coordinate files and metadata for experimentally determined and theoretical protein models from state-of-the-art and specialist model providers and also from the Protein Data Bank.

bioinformatics↗

bioRxiv2022

PDB ProtVista: A reusable and open-source sequence feature viewer

The PDB ProtVista is a reusable and customisable sequence feature viewer that provides intuitive and detailed 2D visualisation of residue-level annotations while supporting interactive communication with 3D viewers. The Protein Data Bank in Europe (PDBe) team develops and maintains PDB ProtVista. Several public web services use it to display structural and functional annotations such as macromolecular interaction interfaces, intrinsic disorder predictions, sequence variants, and sequence conservation. The PDB ProtVista is freely available from https://github.com/PDBeurope/protvista-pdb. We provide extensive documentation and step-by-step user guides on integrating PDB ProtVista with existing web applications and 3D molecular viewers. We also offer examples of displaying the users custom data and functional annotations for PDB and UniProt entries powered by a rich set of PDBe API endpoints.

bioinformatics↗

bioRxiv2022

Intrinsic Protein Disorder, Conditional Folding and AlphaFold2

Intrinsically disordered regions (IDRs) defying the traditional protein structure-function paradigm have been difficult to analyze. AlphaFolds recent breakthrough in predicting protein structures accurately offers a fresh perspective on IDR prediction as assessed on the CAID dataset. Surprisingly, AlphaFold is highly competitive for predicting both IDRs and conditionally folded regions, demonstrating the plasticity of the disorder to structure continuum.

bioinformatics↗

bioRxiv2021

APICURON: a database to credit and acknowledge the work of biocurators

APICURON is an open and freely accessible resource that tracks and credits the work of biocurators across multiple participating knowledgebases. Biocuration is essential to extract knowledge from research data and make it available in a structured and standardized way to the scientific community. However, processing biological data - mainly from literature - requires a huge effort which is difficult to quantify and acknowledge. APICURON collects biocuration events from third party resources and aggregates this information, spotlighting biocurator contributions. APICURON promotes biocurator engagement implementing gamification concepts like badges, medals and leaderboards and at the same time provides a monitoring service for registered resources and for biocurators themselves. APICURON adopts a data model that is flexible enough to represent and track the majority of biocuration activities. Biocurators are identified through their ORCID. The definition of curation events, scoring systems and rules for assigning badges and medals are resource-specific and easily customizable. Registered resources can transfer curation activities on the fly through a secure and robust Application Programming Interface (API). Here we show how simple and effective it is to connect a resource to APICURON describing the DisProt database of intrinsically disordered proteins as a use case. We believe APICURON will provide biological knowledgebases with a service to recognize and credit the effort of their biocurators, monitor their activity and promote curators engagement. Database URL: https://apicuron.org

bioinformatics↗

bioRxiv2020

Critical Assessment of Protein Intrinsic Disorder Prediction

Intrinsically disordered proteins defying the traditional protein structure-function paradigm represent a challenge to study experimentally. As a large part of our knowledge rests on computational predictions, it is crucial for their accuracy to be high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in predicting intrinsically disordered regions in proteins and the subset of disordered residues involved in binding other molecules. A total of 43 methods, 32 for disorder and 11 for binding regions, were evaluated on a dataset of 646 novel manually curated proteins from DisProt. The best methods use deep learning techniques and significantly outperform widely used earlier physicochemical methods across different types of targets. Disordered binding regions remain hard to predict correctly. Depending on the definition used, the top disorder predictor has an FMax of 0.483 (DisProt) or 0.792 (DisProt-PDB). As the top binding predictor only attains an FMax of 0.231, this suggests significant potential for improvement. Intriguingly, computing times among the top performing methods vary by up to four orders of magnitude.

bioinformatics↗

bioRxiv2020

The E3 ubiquitin-protein ligase MDM2 is a novel interactor of the von Hippel-Lindau tumor suppressor

Mutations of the von Hippel-Lindau (pVHL) tumor suppressor are causative of a familiar predisposition to develop different types of cancer. pVHL is mainly known for its role in regulating hypoxia-inducible factor 1- (HIF-1) degradation, thus modulating the hypoxia response. There are different pVHL isoforms, including pVHL30 and pVHL19. However, little is known about isoform-specific functions and protein-protein interactions. Integrating in silico predictions with in vitro and in vivo assays, we describe a novel interaction between pVHL and mouse double minute 2 homolog (MDM2). Importantly, we found that pVHL30, and not pVHL19, forms a complex with MDM2, and that the N-terminal acidic tail of pVHL30 is required for its association with MDM2. Further, we demonstrate that an intrinsically disordered region upstream of the tetramerization domain of MDM2 is responsible for its isoform-specific association with pVHL30. This region is highly conserved in higher mammals, including primates, similarly to what has been already proposed for the N-terminal tail of pVHL30. Finally, we show that overexpression of pVHL30 and MDM2 together reduces cell proliferation, suggesting a synergistic effect of these E3 ubiquitin ligases. Collectively, our data support the idea that pVHL30 plays a role in MDM2 regulation, suggesting a wider interplay among hypoxia sensing and cell cycle regulation.

biochemistry↗

bioRxiv2020

Experimentally determined long intrinsically disordered protein regions are now abundant in the Protein Data Bank

Intrinsically disordered protein regions are commonly defined from missing electron density in X-ray structures. Experimental evidence for long disorder regions (LDRs) of at least 30 residues was so far limited to a less than a thousand manually curated proteins. Here, we describe a comprehensive and large-scale analysis of experimental LDRs for 3,133 unique proteins, demonstrating an increasing coverage of intrinsic disorder in the Protein Data Bank (PDB) in the last decade. The results suggest that long missing residue regions are a good quality source to annotate intrinsically disordered regions and perform functional analysis in large data sets. The consensus approach used to define LDRs allows to evaluate context dependent disorder and provide a common definition at the protein level.

bioinformatics↗

bioRxiv2020

Assessing predictors for new post translational modification sites: a case study on hydroxylation

Post-translational modification (PTM) sites have become popular for predictor development. However, with the exception of phosphorylation and a handful of other examples, PTMs suffer from a limited number of available training examples and their sparsity in protein sequences. Here, proline hydroxylation is taken as an example to compare different methods and evaluate their performance on new experimentally determined sites. As a proxy for an effective experimental design, predictors require both high specificity and sensitivity. However, the self-reported performance is often not indicative of prediction quality and detection of new sites is not guaranteed. We have benchmarked seven published hydroxylation site predictors on two newly constructed independent datasets. The self-reported performance widely overestimates the real accuracy measured on independent datasets. No predictor performs better than random on new examples, indicating the refined models are not sufficiently general to detect new sites. The number of false positives is high and precision low, in particular for non-collagen proteins whose motifs are not conserved. In short, existing predictors for hydroxylation sites do not appear to generalize to new data. Caution is advised when dealing with PTM predictors in the absence of independent evaluations, in particular for unique specific sites such as those involved in signalling. Author SummaryMachine learning methods are extensively used by biologists to design and interpret experiments. Predictors which take the only sequence as input are of particular interest due to the large amount of sequence data available and self-reported performance is often very high. In this work, we evaluated post-translational modification (PTM) predictors for hydroxylation sites and found that they perform no better than random, in strong contrast to performances reported in the original publications. PTMs are chemical amino acids alterations providing the cell with conditional mechanisms to fine tune protein function, thereby regulating complex biological processes such as signalling and cell cycle. Hydroxylation sites are a good PTM test case due to the availability of a range of predictors and an abundance of newly experimentally detected modification sites. Poor performances in our results highlight the overlooked problem of predicting PTMs when best practices are not followed and training data are likely incomplete. Experimentalists should be careful when using PTM predictors blindly and more independent assessments are needed to separate the wheat from the chaff in the field.

bioinformatics↗

bioRxiv2020

KDM6B-dependent chromatin remodelling underpins effective virus-specific CD8+ T cell differentiation.

Naive CD8+ T cell activation results in an autonomous program of cellular proliferation and differentiation. However, the mechanisms that underpin this process are unclear. Here we profiled genome-wide changes in chromatin accessibility, gene transcription and the deposition of a key chromatin modification (H3K27me3) early after naive CD8+ T cell activation. Rapid upregulation of the histone demethylase, KDM6B, prior to first cell division was required for initiating H3K27me3 removal at genes essential for subsequent T cell differentiation and proliferation. Inhibition of KDM6B-dependent H3K27me3 demethylation limited the magnitude of an effective primary virus-specific CD8+ T cell response and the formation of memory CD8+ T cell populations. Accordingly, we define the early spatio-temporal events underpinning early lineage-specific epigenetic reprogramming that is necessary for autonomous CD8+ T cell proliferation and differentiation.

immunology↗