@article{Mehdiabadi2025b,

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},

doi = {10.1002/prot.70045},

issn = {1097-0134},

year  = {2025},

date = {2025-08-26},

journal = {Proteins},

publisher = {Wiley},

abstract = {ABSTRACTIntrinsic 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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Erdős2025,

title = {AIUPred – Binding: Energy Embedding to Identify Disordered Binding Regions},

author = {Gábor Erdős and Norbert Deutsch and Zsuzsanna Dosztányi},

doi = {10.1016/j.jmb.2025.169071},

issn = {0022-2836},

year  = {2025},

date = {2025-08-01},

journal = {Journal of Molecular Biology},

volume = {437},

number = {15},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Mehdiabadi2025,

title = {MobiDB-lite 4.0: faster prediction of intrinsic protein disorder and structural compactness},

author = {Mahta Mehdiabadi and Matthias Blum and Giulio Tesei and Sören von Bülow and Kresten Lindorff-Larsen and Silvio C E Tosatto and Damiano Piovesan},

editor = {Jianlin Cheng},

doi = {10.1093/bioinformatics/btaf297},

issn = {1367-4811},

year  = {2025},

date = {2025-05-06},

volume = {41},

number = {5},

publisher = {Oxford University Press (OUP)},

abstract = {Abstract

               

                  Motivation

                  In recent years, many disorder predictors have been developed to identify intrinsically disordered regions (IDRs) in proteins, achieving high accuracy. However, it may be difficult to interpret differences in predictions across methods. Consensus methods offer a simple solution, highlighting reliable predictions while filtering out uncertain positions. Here, we present a new version of MobiDB-lite, a consensus method designed to predict long IDRs and classify them based on compositional biases and conformational properties.

               

               

                  Results

                  MobiDB-lite 4.0 pipeline was optimized to be ten times faster than the previous version. It now provides compactness annotations based on predicted apparent scaling exponent. The newly added features and disorder subclassifications allow the users to get a comprehensive insight into the protein’s function and characteristics. MobiDB-lite 4.0 is integrated into the MobiDB and DisProt databases. A version without the compactness predictor is integrated into InterProScan, propagating MobiDB-lite annotations to UniProtKB.

               

               

                  Availability and implementation

                  The MobiDB-lite 4.0 source code and a Docker container are available from the GitHub repository: https://github.com/BioComputingUP/MobiDB-lite.

               },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Piovesan2024,

title = {MOBIDB in 2025: integrating ensemble properties and function annotations for intrinsically disordered proteins},

author = {Damiano Piovesan and Alessio Del Conte and Mahta Mehdiabadi and Maria Cristina Aspromonte and Matthias Blum and Giulio Tesei and Sören von Bülow and Kresten Lindorff-Larsen and Silvio C E Tosatto},

doi = {10.1093/nar/gkae969},

issn = {1362-4962},

year  = {2025},

date = {2025-01-06},

volume = {53},

number = {D1},

pages = {D495--D503},

publisher = {Oxford University Press (OUP)},

abstract = {Abstract

               The MobiDB database (URL: https://mobidb.org/) aims to provide structural and functional information about intrinsic protein disorder, aggregating annotations from the literature, experimental data, and predictions for all known protein sequences. Here, we describe the improvements made to our resource to capture more information, simplify access to the aggregated data, and increase documentation of all MobiDB features. Compared to the previous release, all underlying pipeline modules were updated. The prediction module is ten times faster and can detect if a predicted disordered region is structurally extended or compact. The PDB component is now able to process large cryo-EM structures extending the number of processed entries. The entry page has been restyled to highlight functional aspects of disorder and all graphical modules have been completely reimplemented for better flexibility and faster rendering. The server has been improved to optimise bulk downloads. Annotation provenance has been standardised by adopting ECO terms. Finally, we propagated disorder function (IDPO and GO terms) from the DisProt database exploiting sequence similarity and protein embeddings. These improvements, along with the addition of comprehensive training material, offer a more intuitive interface and novel functional knowledge about intrinsic disorder.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DelConte2024,

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},

editor = {Toma Tebaldi},

doi = {10.1093/bioadv/vbaf112},

issn = {2635-0041},

year  = {2024},

date = {2024-12-26},

volume = {5},

number = {1},

publisher = {Oxford University Press (OUP)},

abstract = {Abstract

               

                  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.

               

               

                  Availability and implementation

                  https://github.com/BioComputingUP/DRMAAtic, https://drmaatic.biocomputingup.it/.

               },

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Aspromonte2024,

title = {Searching and Using MobiDB Resource 6 to Explore Predictions and Annotations for Intrinsically Disordered Proteins},

author = {Maria Cristina Aspromonte and Federica Quaglia and Alexander Miguel Monzon and Damiano Clementel and Alessio Del Conte and Damiano Piovesan and Silvio C. E. Tosatto},

doi = {10.1002/cpz1.70077},

issn = {2691-1299},

year  = {2024},

date = {2024-12-24},

journal = {Current Protocols},

volume = {4},

number = {12},

publisher = {Wiley},

abstract = {<jats:title>Abstract</jats:title><jats:p>Intrinsically disordered proteins (IDPs) make up around 30% of eukaryotic proteomes and play a crucial role in cellular processes and in pathological conditions such as neurodegenerative disorders and cancers. However, IDPs exhibit dynamic conformational ensembles and are often involved in the formation of biomolecular condensates. Understanding the function of IDPs is critical to research in many areas of science. MobiDB is a unique resource that serves as a comprehensive knowledgebase of IDPs and intrinsically disordered regions (IDRs), combining disorder annotations from experimental evidence and predictions for a broad range of protein sequences. Over the past decade, MobiDB has evolved with a focus on expanding annotation coverage, standardizing annotation provenance, and enhancing database accessibility. The latest MobiDB, version 6, released in July 2024, includes significant improvements, such as the integration of AlphaFoldDB predictions and a new homology transfer pipeline that has substantially increased the number of entries with high‐quality annotations. The user interface has also been updated, highlighting annotation features, clarifying the entry page, and providing an immediate overview of disorder, binding, and disorder functions information in the protein sequence. This protocol guides the user through applications of the MobiDB, including disorder prediction, curated data analysis, and exploration of interaction data. This guide covers how to perform a search in MobiDB annotations using the web interface and the MobiDB REST API for programmatic access. The protocols use a step‐by‐step walkthrough using the human growth hormone receptor to demonstrate MobiDB's functions for visualization and interpretation of protein disorder data. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC.</jats:p><jats:p><jats:bold>Basic Protocol 1</jats:bold>: Searching MobiDB query formats</jats:p><jats:p><jats:bold>Basic Protocol 2</jats:bold>: Searching MobiDB selected datasets and selected proteomes</jats:p><jats:p><jats:bold>Basic Protocol 3</jats:bold>: Performing a search on the Statistics page in MobiDB</jats:p><jats:p><jats:bold>Support Protocol</jats:bold>: Programmatic access with MobiDB REST API</jats:p><jats:p><jats:bold>Basic Protocol 4</jats:bold>: Visualizing and interpreting a MobiDB Entry: The GHR use case</jats:p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}