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

Authors :
Preto, A. J.
Moreira, Irina S.
Source :
International Journal of Molecular Sciences. Oct2020, Vol. 21 Issue 19, p7281. 1p.
Publication Year :
2020

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, only requiring the user to submit a FASTA file with one or more protein sequences. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
16616596
Volume :
21
Issue :
19
Database :
Academic Search Index
Journal :
International Journal of Molecular Sciences
Publication Type :
Academic Journal
Accession number :
146415385
Full Text :
https://doi.org/10.3390/ijms21197281