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.
Highlights
Hot-Spots (HS) can be defined as amino acid residues that upon alanine mutation generate a change in binding free energy (∆∆Gbinding) higher than 2.0 kcal mol−1, in opposition to Null-Spots (NS), which are unable to meet this threshold
We developed SPOTONE, a HS predictor that only makes use of protein sequence-based features, all of which were calculated with an in-house Python pipeline
The results presented were attained following a Machine Learning (ML) pipeline, depicted in Figure 1, which lays the overall steps involved in dataset preparation and prediction model training and refinement
Summary
Hot-Spots (HS) can be defined as amino acid residues that upon alanine mutation generate a change in binding free energy (∆∆Gbinding) higher than 2.0 kcal mol−1, in opposition to Null-Spots (NS), which are unable to meet this threshold. The threshold of 2.0 kcal mol−1 can vary in the definition of HS, a representative amount of studies on the subject typically use this cut-off [1,2,3,4,5,6]. HS are key elements in Protein–Protein Interactions (PPIs) and, as such, fundamental for a variety of biochemical functions. The disruption of these interactions can alter entire pathways and is of interest to therapeutic approaches [1,7]. These residues are known to be important for protein dimerization [8] and ligand binding [9]. HS tend to be associated with the binding of small ligands, becoming ideal subjects of study on target proteins for drug design approaches [9,10,11]
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