PEP-PREDNa+: A web server for prediction of highly specific peptides targeting voltage-gated Na+ channels using machine learning techniques. (June 2022)

• Record Type: Journal Article
• Authors: Herrera-Bravo, Jesús; Farías, Jorge G.; Contreras, Fernanda Parraguez; Herrera-Belén, Lisandra; Beltrán, Jorge F.
• Is Part Of: Computers in biology and medicine. Volume 145(2022)
• Publication Date: 2022-06
• Subjects: Machine learning -- Peptide -- Toxin -- Channel -- Sodium -- Server; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.105414
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Voltage-gated sodium channel activity has long been associated with several diseases including epilepsy, chronic pain, cardiovascular diseases, cancers, immune system, neuromuscular and respiratory disorders. The strong participation of these channels in the development of diseases makes them excellent promising therapeutic targets. Voltage-gated Na + channel blocking peptides come from a wide source of organisms such as venoms. However, the in vitro and in vivo identification and validation of these peptides are time-consuming and resource-intensive. In this work, we developed a bioinformatics tool called PEP-PRED Na + for the highly specific prediction of voltage-gated Na + channel blocking peptides. PEP-PRED Na+ is based on the random forest algorithm, which presented excellent performance measures during the cross-validation (sensitivity = 0.81, accuracy = 0.83, precision = 0.85, F-score = 0.83, specificity = 0.86, and Matthew's correlation coefficient = 0.67) and testing (sensitivity = 0.88, accuracy = 0.92, precision = 0.96, F-score = 0.91, specificity = 0.96, and Matthew's correlation coefficient = 0.84) phases. The PEP-PRED Na + tool could be very useful in accelerating and reducing the costs of the discovery of new voltage-gated Na + channel blocking peptides with therapeutic potential. Graphical abstract: Image 1 Highlights: The Nav activity is linked to several diseases. We assessed several machine learning algorithms for the prediction of Nav blocking peptides. PEP-PRED Na+ is a robust tool based on the random forest classifier. This tool can be useful for discovering new Nav blocking peptides.