@article{SCOPUS_ID:105027748200,

title = {DisProt in 2026: enhancing intrinsically disordered proteins accessibility, deposition, and annotation},

author = {Maria Victoria Nugnes and Kamel Eddine Adel Bouhraoua and Mehdi Zoubiri and Rita Pancsa and Erzsébet Fichó and Alexander M Monzon and Ana M Melo and Edoardo Salladini and Emanuela Leonardi and Federica Quaglia and Daniyal Nasiribavil and Hamidreza Ghafouri and Gobeill Julien and Emilie Pasche and Patrick Ruch and Paul Van Rijen and László Dobson and Marco Schiavina and Trinidad Cordero and Zsófia E Kálmán and Ximena Castro and Valentín Iglesias and István Reményi and Mahta Mehdiabadi and Gábor Erdős and Zsuzsanna Dosztányi and Peter Tompa and Damiano Piovesan and Silvio C. E Tosatto and Maria Cristina Aspromonte},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-105027748200&origin=inward},

doi = {10.1093/nar/gkaf1175},

year  = {2026},

date = {2026-01-01},

journal = {Nucleic Acids Research},

volume = {54},

number = {D1},

pages = {D383-D392},

publisher = {Oxford University Press},

abstract = {© 2025 The Author(s). Published by Oxford University Press.DisProt (https://disprot.org/) is an open database integrating experimental evidence on intrinsically disordered proteins (IDPs), intrinsically disordered regions (IDRs), and their functions. Over the past two years, the database has grown over 20%, now comprising 3201 IDPs and 13 347 pieces of evidence, including over 1500 new structural state annotations and >1300 new function annotations. DisProt has systematically adopted the Minimum Information About Disorder Experiments (MIADE) guidelines, more than doubling annotations with experimental details and improving the interpretability of disorder-related experiments. The website has evolved into a hybrid knowledgebase and deposition system, introducing a Deposition Page that allows direct submissions by external users. Through BLAST-based homology propagation in MobiDB, DisProt disorder regions and linear interacting peptides have been extended from hundreds to hundreds of thousands of proteins across >11 000 organisms. This new release marks a paradigm shift by integrating computational predictions as valid evidence and introducing major updates and restructuring of the IDP Ontology, enhancing accuracy, interoperability, and semantic clarity. DisProt continues to support community engagement through training resources together with DisTriage, an AI-based literature triage tool, providing curators with regularly updated lists of prioritized publications.},

note = {Cited by: 4; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:105034187048,

title = {Toward a unified framework for determining conformational ensembles of disordered proteins},

author = {Hamidreza Ghafouri and Pavel Kadeřávek and Ana M. Melo and Maria Cristina Aspromonte and Pau Bernadó and Juan Cortés and Zsuzsanna Dosztányi and Gábor Erdős and Michael Feig and Giacomo Janson and Kresten Lindorff-Larsen and Frans A. A. Mulder and Peter Nagy and Richard Pestell and Damiano Piovesan and Marco Schiavina and Benjamin Schuler and Nathalie Sibille and Giulio Tesei and Peter Tompa and Michele Vendruscolo and Jiri Vondrasek and Wim Vranken and Lukas Zidek and Silvio C. E. Tosatto and Alexander Miguel Monzon},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-105034187048&origin=inward},

doi = {10.1038/s41592-026-03003-2},

year  = {2026},

date = {2026-01-01},

journal = {Nature Methods},

volume = {23},

number = {4},

pages = {705-719},

publisher = {Nature Research},

abstract = {© Springer Nature America, Inc. 2026.Disordered proteins play essential roles in myriad cellular processes, yet their structural characterization remains a major challenge due to their dynamic and heterogeneous nature. Here we present a community-driven initiative to address this problem by advocating a unified framework for determining conformational ensembles of disordered proteins. Our aim is to integrate state-of-the-art experimental techniques with advanced computational methods, including knowledge-based sampling, enhanced molecular dynamics and machine learning models. The modular framework comprises three interconnected components: experimental data acquisition, computational ensemble generation and validation. The systematic development of this framework will ensure the accurate and reproducible determination of conformational ensembles of disordered proteins. We highlight the open challenges necessary to achieve this goal, including force-field accuracy, efficient sampling, and environmental dependence, advocating for collaborative benchmarking and standardized protocols.},

note = {Cited by: 0; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:105014118997,

title = {Critical Assessment of Protein Intrinsic Disorder Round 3 - Predicting Disorder in the Era of Protein Language Models},

author = {Mahta Mehdiabadi and Alessio Del Conte and Maria Victoria Nugnes and Maria Cristina Aspromonte and Silvio C. E. Tosatto and Damiano Piovesan},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-105014118997&origin=inward},

doi = {10.1002/prot.70045},

year  = {2026},

date = {2026-01-01},

journal = {Proteins: Structure, Function and Bioinformatics},

volume = {94},

number = {1},

pages = {414-424},

publisher = {John Wiley and Sons Inc},

abstract = {© 2025 The Author(s). PROTEINS: Structure, Function, and Bioinformatics published by Wiley Periodicals LLC.Intrinsic disorder (ID) in proteins is a complex phenomenon, encompassing a continuum from entirely disordered regions to structured domains with flexible segments. The absence of a ground truth for all forms of disorder, combined with the possibility of structural transitions between ordered and disordered states under specific conditions, makes accurate prediction of ID especially challenging. The Critical Assessment of Protein Intrinsic Disorder (CAID) evaluates ID prediction methods using diverse benchmarks derived from DisProt, a manually curated database of experimentally validated annotations. This paper presents findings from the third round (CAID3), in which 24 new methods were assessed along with the predictors from previous rounds. Compared to CAID2, the top-performing methods in CAID3 demonstrated significant gains in average precision: over 31% improvement in predicting linker regions, and 15% in disorder prediction. This round introduces a new binding sub-challenge focused on identifying binding regions within known IDR boundaries. The results indicate that this task remains challenging, highlighting the potential for improvement. The top-performing methods in CAID3 are mostly new and commonly used embeddings from protein language models (pLMs), underscoring the growing impact of pLMs in tackling the complexities of disordered proteins and advancing ID prediction.},

note = {Cited by: 4; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:105026115828,

title = {Modeling intrinsically disordered regions from AlphaFold2 to AlphaFold3},

author = {Mahta Mehdiabadi and Silvio C. E. Tosatto and Damiano Piovesan},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-105026115828&origin=inward},

doi = {10.1002/pro.70426},

year  = {2026},

date = {2026-01-01},

journal = {Protein Science},

volume = {35},

number = {1},

publisher = {John Wiley and Sons Inc},

abstract = {© 2025 The Protein Society.AlphaFold2 has demonstrated a remarkable success in predicting the structures of globular proteins and folded domains with near-experimental accuracy. However, it typically represents intrinsically disordered regions (IDRs), protein segments that lack a stable 3D structure under physiological conditions, as long extended loops that appear to float around the structured core. While AlphaFold2's static prediction cannot capture the conformational heterogeneity and the dynamic nature of IDRs, it performs well in predicting IDRs from sequence. AlphaFold3 introduces significant architectural and training modifications over its predecessor, including the use of cross-distillation aimed at reducing structural hallucinations in disordered regions. In this study, we look into how these models differ in representing IDRs. We evaluate the performance of AlphaFold3 and AlphaFold2 on disorder prediction, using the CAID3 benchmark. Our analysis shows that AlphaFold3 does not outperform AlphaFold2 in this benchmark. We observe that solvent accessibility remains a robust and consistent proxy for predicting intrinsic disorder across both models. However, changes in the predicted secondary structure content and pLDDT scores lead to different interpretations of disorder. Overall, our findings suggest that AlphaFold2 remains the preferred choice for identifying intrinsically disordered regions, as it avoids structural hallucinations while providing predictions comparable to those of AlphaFold3.},

note = {Cited by: 0; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:105008238034,

title = {DRMAAtic: Dramatically improve your cluster potential},

author = {Alessio Del Conte and Hamidreza Ghafouri and Damiano Clementel and Ivan Mičetić and Damiano Piovesan and Silvio C. E Tosatto and Alexander Miguel Monzon},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-105008238034&origin=inward},

doi = {10.1093/bioadv/vbaf112},

year  = {2025},

date = {2025-01-01},

journal = {Bioinformatics Advances},

volume = {5},

number = {1},

publisher = {Oxford University Press},

abstract = {© 2025 The Author(s).Motivation The accessibility and usability of high-performance computing (HPC) resources remain significant challenges in bioinformatics, particularly for researchers lacking extensive technical expertise. While Distributed Resource Managers (DRMs) optimize resource utilization, the complexities of interfacing with these systems often hinder broader adoption. DRMAAtic addresses these challenges by integrating the Distributed Resource Management Application API (DRMAA) with a user-friendly RESTful interface, simplifying job management across diverse HPC environments. This framework empowers researchers to submit, monitor, and retrieve computational jobs securely and efficiently, without requiring deep knowledge of underlying cluster configurations. Results We present DRMAAtic, a flexible and scalable tool that bridges the gap between web interfaces and HPC infrastructures. Built on the Django REST Framework, DRMAAtic supports seamless job submission and management via HTTP calls. Its modular architecture enables integration with any DRM supporting DRMAA APIs and offers robust features such as role-based access control, throttling mechanisms, and dependency management. Successful applications of DRMAAtic include the RING web server for protein structure analysis, the CAID Prediction Portal for disorder and binding predictions, and the Protein Ensemble Database deposition server. These deployments demonstrate DRMAAtic's potential to enhance computational workflows, improve resource efficiency, and facilitate open science in life sciences.},

note = {Cited by: 0; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}