Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/101280
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Preto, A. J. | - |
dc.contributor.author | Moreira, Irina S. | - |
dc.date.accessioned | 2022-08-19T10:01:21Z | - |
dc.date.available | 2022-08-19T10:01:21Z | - |
dc.date.issued | 2020-10-01 | - |
dc.identifier.issn | 1422-0067 | pt |
dc.identifier.uri | https://hdl.handle.net/10316/101280 | - |
dc.description.abstract | Protein Hot-Spots (HS) are experimentally determined amino acids, key to small ligand binding and tend to be structural landmarks on protein-protein interactions. As such, they were extensively approached by structure-based Machine Learning (ML) prediction methods. However, the availability of a much larger array of protein sequences in comparison to determined tree-dimensional structures indicates that a sequence-based HS predictor has the potential to be more useful for the scientific community. Herein, we present SPOTONE, a new ML predictor able to accurately classify protein HS via sequence-only features. This algorithm shows accuracy, AUROC, precision, recall and F1-score of 0.82, 0.83, 0.91, 0.82 and 0.85, respectively, on an independent testing set. The algorithm is deployed within a free-to-use webserver at http://moreiralab.com/resources/spotone, only requiring the user to submit a FASTA file with one or more protein sequences. | pt |
dc.language.iso | eng | pt |
dc.rights | openAccess | pt |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt |
dc.subject | big-data; hot-spots; machine learning; protein–protein complexes; structural biology | pt |
dc.subject.mesh | Amino Acid Sequence | pt |
dc.subject.mesh | Amino Acids | pt |
dc.subject.mesh | Binding Sites | pt |
dc.subject.mesh | Computational Biology | pt |
dc.subject.mesh | Databases, Protein | pt |
dc.subject.mesh | Datasets as Topic | pt |
dc.subject.mesh | Humans | pt |
dc.subject.mesh | Protein Binding | pt |
dc.subject.mesh | Protein Interaction Mapping | pt |
dc.subject.mesh | Proteins | pt |
dc.subject.mesh | Machine Learning | pt |
dc.title | SPOTONE: Hot Spots on Protein Complexes with Extremely Randomized Trees via Sequence-Only Features | pt |
dc.type | article | - |
degois.publication.firstPage | 7281 | pt |
degois.publication.issue | 19 | pt |
degois.publication.title | International Journal of Molecular Sciences | pt |
dc.peerreviewed | yes | pt |
dc.identifier.doi | 10.3390/ijms21197281 | pt |
degois.publication.volume | 21 | pt |
dc.date.embargo | 2020-10-01 | * |
uc.date.periodoEmbargo | 0 | pt |
item.fulltext | Com Texto completo | - |
item.languageiso639-1 | en | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.openairetype | article | - |
item.grantfulltext | open | - |
item.cerifentitytype | Publications | - |
crisitem.author.orcid | 0000-0003-2970-5250 | - |
Appears in Collections: | I&D CNC - Artigos em Revistas Internacionais FCTUC Ciências da Vida - Artigos em Revistas Internacionais |
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File | Description | Size | Format | |
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ijms-21-07281-v2.pdf | 4.93 MB | Adobe PDF | View/Open |
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