Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/101280
Title: | SPOTONE: Hot Spots on Protein Complexes with Extremely Randomized Trees via Sequence-Only Features | Authors: | Preto, A. J. Moreira, Irina S. |
Keywords: | big-data; hot-spots; machine learning; protein–protein complexes; structural biology | Issue Date: | 1-Oct-2020 | Serial title, monograph or event: | International Journal of Molecular Sciences | Volume: | 21 | Issue: | 19 | 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. | URI: | https://hdl.handle.net/10316/101280 | ISSN: | 1422-0067 | DOI: | 10.3390/ijms21197281 | Rights: | openAccess |
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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