protein hot spots
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2020 ◽  
Vol 21 (19) ◽  
pp. 7281
Author(s):  
A. J. Preto ◽  
Irina S. Moreira

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.


2012 ◽  
Vol 415 (2) ◽  
pp. 419-428 ◽  
Author(s):  
Yosef Y. Kuttner ◽  
Stanislav Engel
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2011 ◽  
Vol 133 (28) ◽  
pp. 10740-10743 ◽  
Author(s):  
John L. Kulp ◽  
John L. Kulp ◽  
David L. Pompliano ◽  
Frank Guarnieri
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2011 ◽  
Vol 14 (7-8) ◽  
pp. 360-365 ◽  
Author(s):  
Gerald F. Audette ◽  
Stephanie Lombardo ◽  
Jonathan Dudzik ◽  
Thomas M. Arruda ◽  
Michal Kolinski ◽  
...  
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