Advances in understanding the specificity function of transporters by machine learning

• DOI: 10.1016/j.compbiomed.2021.104893
• Publication Date: 2021-09-24T18:16:00Z
• Authors: Esmaeil Ebrahimie; Fatemeh Zamansani; Ibrahim O. Alanazi; Essa M. Sabi; Manouchehr Khazandi; Faezeh Ebrahimi; Manijeh Mohammadi...; Mansour Ebrahimi
• Keywords: modelling; pattern discovery; Machine Learning; 0303 health sciences; 03 medical and health sciences; machine learning; transporter; prediction; protein function

Understanding the underlying molecular mechanism of transporter activity is one of the major discussions in structural biology. A transporter can exclusively transport one ion (specific transporter) or multiple ions (general transporter). This study compared categorical and numerical features of general and specific calcium transporters using machine learning and attribute weighting models. To this end, 444 protein features, such as the frequency of dipeptides, organism, and subcellular location, were extracted for general (n = 103) and specific calcium transporters (n = 238). Aliphatic index, subcellular location, organism, Ile-Leu frequency, Glycine frequency, hydrophobic frequency, and specific dipeptides such as Ile-Leu, Phe-Val, and Tyr-Gln were the key features in differentiating general from specific calcium transporters. Calcium transporters in the cell outer membranes were specific, while the inner ones were general; additionally, when the hydrophobic frequency or Aliphatic index is increased, the calcium transporter act as a general transporter. Random Forest with accuracy criterion showed the highest accuracy (88.88% ±5.75%) and high AUC (0.964 ± 0.020), based on 5-fold cross-validation. Decision Tree with accuracy criterion was able to predict the specificity of calcium transporter irrespective of the organism and subcellular location. This study demonstrates the precise classification of transporter function based on sequence-derived physicochemical features.