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Open AccessArticle

SPOTONE: Hot Spots on Protein Complexes with Extremely Randomized Trees via Sequence-Only Features

1
CNC—Center for Neuroscience and Cell Biology, University of Coimbra, 3004-504 Coimbra, Portugal
2
Department of Life Sciences, Center for Neuroscience and Cell Biology, Coimbra University, 3000-456 Coimbra, Portugal
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2020, 21(19), 7281; https://doi.org/10.3390/ijms21197281
Received: 10 August 2020 / Revised: 26 September 2020 / Accepted: 30 September 2020 / Published: 1 October 2020
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Drug Development)
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, only requiring the user to submit a FASTA file with one or more protein sequences. View Full-Text
Keywords: big-data; hot-spots; machine learning; protein–protein complexes; structural biology big-data; hot-spots; machine learning; protein–protein complexes; structural biology
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Preto, A.J.; Moreira, I.S. SPOTONE: Hot Spots on Protein Complexes with Extremely Randomized Trees via Sequence-Only Features. Int. J. Mol. Sci. 2020, 21, 7281.

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