Leveraging a meta-learning approach to advance the accuracy of Na<inf>v</inf> blocking peptides prediction
Issued Date
2024-12-01
Resource Type
eISSN
20452322
Scopus ID
2-s2.0-85185969042
Journal Title
Scientific Reports
Volume
14
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Scientific Reports Vol.14 No.1 (2024)
Suggested Citation
Shoombuatong W., Homdee N., Schaduangrat N., Chumnanpuen P. Leveraging a meta-learning approach to advance the accuracy of Na<inf>v</inf> blocking peptides prediction. Scientific Reports Vol.14 No.1 (2024). doi:10.1038/s41598-024-55160-z Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/97462
Title
Leveraging a meta-learning approach to advance the accuracy of Na<inf>v</inf> blocking peptides prediction
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
The voltage-gated sodium (Nav) channel is a crucial molecular component responsible for initiating and propagating action potentials. While the α subunit, forming the channel pore, plays a central role in this function, the complete physiological function of Nav channels relies on crucial interactions between the α subunit and auxiliary proteins, known as protein–protein interactions (PPI). Nav blocking peptides (NaBPs) have been recognized as a promising and alternative therapeutic agent for pain and itch. Although traditional experimental methods can precisely determine the effect and activity of NaBPs, they remain time-consuming and costly. Hence, machine learning (ML)-based methods that are capable of accurately contributing in silico prediction of NaBPs are highly desirable. In this study, we develop an innovative meta-learning-based NaBP prediction method (MetaNaBP). MetaNaBP generates new feature representations by employing a wide range of sequence-based feature descriptors that cover multiple perspectives, in combination with powerful ML algorithms. Then, these feature representations were optimized to identify informative features using a two-step feature selection method. Finally, the selected informative features were applied to develop the final meta-predictor. To the best of our knowledge, MetaNaBP is the first meta-predictor for NaBP prediction. Experimental results demonstrated that MetaNaBP achieved an accuracy of 0.948 and a Matthews correlation coefficient of 0.898 over the independent test dataset, which were 5.79% and 11.76% higher than the existing method. In addition, the discriminative power of our feature representations surpassed that of conventional feature descriptors over both the training and independent test datasets. We anticipate that MetaNaBP will be exploited for the large-scale prediction and analysis of NaBPs to narrow down the potential NaBPs.
