Leveraging a meta-learning approach to advance the accuracy of Na_v blocking peptides prediction.

ABSTRACT: The voltage-gated sodium (Na_v) 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 Na_v channels relies on crucial interactions between the α subunit and auxiliary proteins, known as protein-protein interactions (PPI). Na_v 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 NaBP

SUBMITTER: Shoombuatong W 

PROVIDER: S-EPMC10891130 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature