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Journal Articles
2025
1.
Han K; Song S; Pak M; Kim C; Ri C; Conte A D; Piovesan D
PredIDR: Accurate prediction of protein intrinsic disorder regions using deep convolutional neural network Journal Article
In: International Journal of Biological Macromolecules, vol. 284, 2025, (Cited by: 0; Open Access).
Abstract | Altmetric | Dimensions | PlumX | Links:
@article{SCOPUS_ID:85210122158,
title = {PredIDR: Accurate prediction of protein intrinsic disorder regions using deep convolutional neural network},
author = {Kun-Sop Han and Se-Ryong Song and Myong-hyon Pak and Chol-Song Kim and Chol-Pyok Ri and Alessio Del Conte and Damiano Piovesan},
url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85210122158&origin=inward},
doi = {10.1016/j.ijbiomac.2024.137665},
year = {2025},
date = {2025-01-01},
journal = {International Journal of Biological Macromolecules},
volume = {284},
publisher = {Elsevier B.V.},
abstract = {© 2024 Elsevier B.V.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/.},
note = {Cited by: 0; Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
© 2024 Elsevier B.V.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/.
2024
2.
Aspromonte M C; Nugnes M V; Quaglia F; Bouharoua A; Tosatto S C E; Piovesan D; Sagris V; Promponas V J; Chasapi A; Fichó E; Balatti G E; Parisi G; Buitrón M G; Erdos G; Pajkos M; Dosztányi Z; Dobson L; Conte A D; Clementel D; Salladini E; Leonardi E; Kordevani F; Ghafouri H; Ku L G T; Monzon A M; Ferrari C; Kálmán Z; Nilsson J F; Santos J; Pintado-Grima C; Ventura S; Ács V; Pancsa R; Kulik M G; Andrade-Navarro M A; Pereira P J B; Longhi S; Mercier P L; Bergier J; Tompa P; Lazar T
DisProt in 2024: improving function annotation of intrinsically disordered proteins Journal Article
In: Nucleic Acids Research, vol. 52, no. D1, pp. D434-D441, 2024, (Cited by: 20; Open Access).
Abstract | Altmetric | Dimensions | PlumX | Links:
@article{SCOPUS_ID:85176208048,
title = {DisProt in 2024: improving function annotation of intrinsically disordered proteins},
author = {Maria Cristina Aspromonte and Maria Victoria Nugnes and Federica Quaglia and Adel Bouharoua and Silvio C. E. Tosatto and Damiano Piovesan and Vasileios Sagris and Vasilis J. Promponas and Anastasia Chasapi and Erzsébet Fichó and Galo E. Balatti and Gustavo Parisi and Martín González Buitrón and Gabor Erdos and Matyas Pajkos and Zsuzsanna Dosztányi and Laszlo Dobson and Alessio Del Conte and Damiano Clementel and Edoardo Salladini and Emanuela Leonardi and Fatemeh Kordevani and Hamidreza Ghafouri and Luiggi G. Tenorio Ku and Alexander Miguel Monzon and Carlo Ferrari and Zsófia Kálmán and Juliet F. Nilsson and Jaime Santos and Carlos Pintado-Grima and Salvador Ventura and Veronika Ács and Rita Pancsa and Mariane Goncalves Kulik and Miguel A. Andrade-Navarro and Pedro José Barbosa Pereira and Sonia Longhi and Philippe Le Mercier and Julian Bergier and Peter Tompa and Tamas Lazar},
url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85176208048&origin=inward},
doi = {10.1093/nar/gkad928},
year = {2024},
date = {2024-01-01},
journal = {Nucleic Acids Research},
volume = {52},
number = {D1},
pages = {D434-D441},
publisher = {Oxford University Press},
abstract = {© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.DisProt (URL: https://disprot.org) is the gold standard database for intrinsically disordered proteins and regions, providing valuable information about their functions. The latest version of DisProt brings significant advancements, including a broader representation of functions and an enhanced curation process. These improvements aim to increase both the quality of annotations and their coverage at the sequence level. Higher coverage has been achieved by adopting additional evidence codes. Quality of annotations has been improved by systematically applying Minimum Information About Disorder Experiments (MIADE) principles and reporting all the details of the experimental setup that could potentially influence the structural state of a protein. The DisProt database now includes new thematic datasets and has expanded the adoption of Gene Ontology terms, resulting in an extensive functional repertoire which is automatically propagated to UniProtKB. Finally, we show that DisProt’s curated annotations strongly correlate with disorder predictions inferred from AlphaFold2 pLDDT (predicted Local Distance Difference Test) confidence scores. This comparison highlights the utility of DisProt in explaining apparent uncertainty of certain well-defined predicted structures, which often correspond to folding-upon-binding fragments. Overall, DisProt serves as a comprehensive resource, combining experimental evidence of disorder information to enhance our understanding of intrinsically disordered proteins and their functional implications.},
note = {Cited by: 20; Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.DisProt (URL: https://disprot.org) is the gold standard database for intrinsically disordered proteins and regions, providing valuable information about their functions. The latest version of DisProt brings significant advancements, including a broader representation of functions and an enhanced curation process. These improvements aim to increase both the quality of annotations and their coverage at the sequence level. Higher coverage has been achieved by adopting additional evidence codes. Quality of annotations has been improved by systematically applying Minimum Information About Disorder Experiments (MIADE) principles and reporting all the details of the experimental setup that could potentially influence the structural state of a protein. The DisProt database now includes new thematic datasets and has expanded the adoption of Gene Ontology terms, resulting in an extensive functional repertoire which is automatically propagated to UniProtKB. Finally, we show that DisProt’s curated annotations strongly correlate with disorder predictions inferred from AlphaFold2 pLDDT (predicted Local Distance Difference Test) confidence scores. This comparison highlights the utility of DisProt in explaining apparent uncertainty of certain well-defined predicted structures, which often correspond to folding-upon-binding fragments. Overall, DisProt serves as a comprehensive resource, combining experimental evidence of disorder information to enhance our understanding of intrinsically disordered proteins and their functional implications.
3.
Conte A D; Camagni G F; Clementel D; Minervini G; Monzon A M; Ferrari C; Piovesan D; Tosatto S C E
RING 4.0: Faster residue interaction networks with novel interaction types across over 35,000 different chemical structures Journal Article
In: Nucleic Acids Research, vol. 52, no. W1, pp. W306-W312, 2024, (Cited by: 6; Open Access).
Abstract | Altmetric | Dimensions | PlumX | Links:
@article{SCOPUS_ID:85197788039,
title = {RING 4.0: Faster residue interaction networks with novel interaction types across over 35,000 different chemical structures},
author = {Alessio Del Conte and Giorgia F Camagni and Damiano Clementel and Giovanni Minervini and Alexander Miguel Monzon and Carlo Ferrari and Damiano Piovesan and Silvio C. E Tosatto},
url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85197788039&origin=inward},
doi = {10.1093/nar/gkae337},
year = {2024},
date = {2024-01-01},
journal = {Nucleic Acids Research},
volume = {52},
number = {W1},
pages = {W306-W312},
publisher = {Oxford University Press},
abstract = {© 2024 The Author(s). Published by Oxford University Press on behalf of Nucleic Acids Research.Residue interaction networks (RINs) are a valuable approach for representing contacts in protein structures. RINs have been widely used in various research areas, including the analysis of mutation effects, domain-domain communication, catalytic activity, and molecular dynamics simulations. The RING server is a powerful tool to calculate non-covalent molecular interactions based on geometrical parameters, providing high-quality and reliable results. Here, we introduce RING 4.0, which includes significant enhancements for identifying both covalent and non-covalent bonds in protein structures. It now encompasses seven different interaction types, with the addition of π-hydrogen, halogen bonds and metal ion coordination sites. The definitions of all available bond types have also been refined and RING can now process the complete PDB chemical component dictionary (over 35000 different molecules) which provides atom names and covalent connectivity information for all known ligands. Optimization of the software has improved execution time by an order of magnitude. The RING web server has been redesigned to provide a more engaging and interactive user experience, incorporating new visualization tools. Users can now visualize all types of interactions simultaneously in the structure viewer and network component. The web server, including extensive help and tutorials, is available from URL: https://ring.biocomputingup.it/.},
note = {Cited by: 6; Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
© 2024 The Author(s). Published by Oxford University Press on behalf of Nucleic Acids Research.Residue interaction networks (RINs) are a valuable approach for representing contacts in protein structures. RINs have been widely used in various research areas, including the analysis of mutation effects, domain-domain communication, catalytic activity, and molecular dynamics simulations. The RING server is a powerful tool to calculate non-covalent molecular interactions based on geometrical parameters, providing high-quality and reliable results. Here, we introduce RING 4.0, which includes significant enhancements for identifying both covalent and non-covalent bonds in protein structures. It now encompasses seven different interaction types, with the addition of π-hydrogen, halogen bonds and metal ion coordination sites. The definitions of all available bond types have also been refined and RING can now process the complete PDB chemical component dictionary (over 35000 different molecules) which provides atom names and covalent connectivity information for all known ligands. Optimization of the software has improved execution time by an order of magnitude. The RING web server has been redesigned to provide a more engaging and interactive user experience, incorporating new visualization tools. Users can now visualize all types of interactions simultaneously in the structure viewer and network component. The web server, including extensive help and tutorials, is available from URL: https://ring.biocomputingup.it/.
4.
Piovesan D; Zago D; Joshi P; Kaluza M C D P; Mehdiabadi M; Ramola R; Monzon A M; Reade W; Friedberg I; Radivojac P; Tosatto S C E
CAFA-evaluator: a Python tool for benchmarking ontological classification methods Journal Article
In: Bioinformatics Advances, vol. 4, no. 1, 2024, (Cited by: 3; Open Access).
Abstract | Altmetric | Dimensions | PlumX | Links:
@article{SCOPUS_ID:85188993912,
title = {CAFA-evaluator: a Python tool for benchmarking ontological classification methods},
author = {Damiano Piovesan and Davide Zago and Parnal Joshi and M. Clara De Paolis Kaluza and Mahta Mehdiabadi and Rashika Ramola and Alexander Miguel Monzon and Walter Reade and Iddo Friedberg and Predrag Radivojac and Silvio C. E. Tosatto},
url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85188993912&origin=inward},
doi = {10.1093/bioadv/vbae043},
year = {2024},
date = {2024-01-01},
journal = {Bioinformatics Advances},
volume = {4},
number = {1},
publisher = {Oxford University Press},
abstract = {© 2024 The Author(s). Published by Oxford University Press.We present CAFA-evaluator, a powerful Python program designed to evaluate the performance of prediction methods on targets with hierarchical concept dependencies. It generalizes multi-label evaluation to modern ontologies where the prediction targets are drawn from a directed acyclic graph and achieves high efficiency by leveraging matrix computation and topological sorting. The program requirements include a small number of standard Python libraries, making CAFA-evaluator easy to maintain. The code replicates the Critical Assessment of protein Function Annotation (CAFA) benchmarking, which evaluates predictions of the consistent subgraphs in Gene Ontology. Owing to its reliability and accuracy, the organizers have selected CAFA-evaluator as the official CAFA evaluation software.},
note = {Cited by: 3; Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
© 2024 The Author(s). Published by Oxford University Press.We present CAFA-evaluator, a powerful Python program designed to evaluate the performance of prediction methods on targets with hierarchical concept dependencies. It generalizes multi-label evaluation to modern ontologies where the prediction targets are drawn from a directed acyclic graph and achieves high efficiency by leveraging matrix computation and topological sorting. The program requirements include a small number of standard Python libraries, making CAFA-evaluator easy to maintain. The code replicates the Critical Assessment of protein Function Annotation (CAFA) benchmarking, which evaluates predictions of the consistent subgraphs in Gene Ontology. Owing to its reliability and accuracy, the organizers have selected CAFA-evaluator as the official CAFA evaluation software.
5.
Mozaffari S; Arrías P N; Clementel D; Piovesan D; Ferrari C; Tosatto S C E; Monzon A M
STRPsearch: fast detection of structured tandem repeat proteins Journal Article
In: Bioinformatics, vol. 40, no. 12, 2024, (Cited by: 0; Open Access).
Abstract | Altmetric | Dimensions | PlumX | Links:
@article{SCOPUS_ID:85211966531,
title = {STRPsearch: fast detection of structured tandem repeat proteins},
author = {Soroush Mozaffari and Paula Nazarena Arrías and Damiano Clementel and Damiano Piovesan and Carlo Ferrari and Silvio C. E. Tosatto and Alexander Miguel Monzon},
url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85211966531&origin=inward},
doi = {10.1093/bioinformatics/btae690},
year = {2024},
date = {2024-01-01},
journal = {Bioinformatics},
volume = {40},
number = {12},
publisher = {Oxford University Press},
abstract = {© The Author(s) 2024.Motivation: Structured Tandem Repeats Proteins (STRPs) constitute a subclass of tandem repeats characterized by repetitive structural motifs. These proteins exhibit distinct secondary structures that form repetitive tertiary arrangements, often resulting in large molecular assemblies. Despite highly variable sequences, STRPs can perform important and diverse biological functions, maintaining a consistent structure with a variable number of repeat units. With the advent of protein structure prediction methods, millions of 3D models of proteins are now publicly available. 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 remains the most accurate method for detecting and classifying STRPs, making it impracticable to annotate millions of structures. Results: We introduce STRPsearch, a novel tool for the rapid identification, classification, and mapping of STRPs. Leveraging manually curated entries from RepeatsDB as the known conformational space of STRPs, STRPsearch uses the latest advances in structural alignment for a fast and accurate detection of repeated structural motifs in proteins, followed by an innovative approach to map units and insertions through the generation of TM-score profiles. STRPsearch is highly scalable, efficiently processing large datasets, and can be applied to both experimental structures and predicted models. In addition, it demonstrates superior performance compared to existing tools, offering researchers a reliable and comprehensive solution for STRP analysis across diverse proteomes.},
note = {Cited by: 0; Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
© The Author(s) 2024.Motivation: Structured Tandem Repeats Proteins (STRPs) constitute a subclass of tandem repeats characterized by repetitive structural motifs. These proteins exhibit distinct secondary structures that form repetitive tertiary arrangements, often resulting in large molecular assemblies. Despite highly variable sequences, STRPs can perform important and diverse biological functions, maintaining a consistent structure with a variable number of repeat units. With the advent of protein structure prediction methods, millions of 3D models of proteins are now publicly available. 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 remains the most accurate method for detecting and classifying STRPs, making it impracticable to annotate millions of structures. Results: We introduce STRPsearch, a novel tool for the rapid identification, classification, and mapping of STRPs. Leveraging manually curated entries from RepeatsDB as the known conformational space of STRPs, STRPsearch uses the latest advances in structural alignment for a fast and accurate detection of repeated structural motifs in proteins, followed by an innovative approach to map units and insertions through the generation of TM-score profiles. STRPsearch is highly scalable, efficiently processing large datasets, and can be applied to both experimental structures and predicted models. In addition, it demonstrates superior performance compared to existing tools, offering researchers a reliable and comprehensive solution for STRP analysis across diverse proteomes.