CaBind_MCNN: Identifying Potential Calcium Channel Blocker Targets by Predicting Calcium-Binding Sites in Ion Channels and Ion Transporters Using Protein Language Models and Multiscale Feature Extraction
Year: 2025
Type: article
Abstract: High Resolution Image Download MS PowerPoint Slide Calcium ions (Ca 2+ ) are crucial for various physiological processes, including neurotransmission and cardiac function. Dysregulation of Ca 2+ homeostasis can lead to serious health conditions such as cardiac arrhythmias and hypertension. Ion channels and transporters play a vital role in maintaining cellular Ca 2+ balance by facilitating Ca 2+ transport across cell membranes. Accurate prediction of Ca 2+ binding sites within these proteins is essential for understanding their function and identifying potential therapeutic targets, particularly for developing novel calcium channel blockers (CCBs). This study introduces CaBind_MCNN, an innovative computational model that leverages pretrained protein language models (PLMs) and a multiscale feature extraction approach to predict Ca 2+ binding sites in ion channels and transporter proteins. Our method integrates embeddings from the ProtTrans PLM with a convolutional neural network (CNN)-based multiwindow scanning approach, capable of capturing diverse sequence features relevant to Ca 2+ binding. The model, trained on a curated data set of 27 calcium-binding protein sequences, achieves high accuracy with an area under the curve (AUC) of 0.9886, significantly outperforming some existing methods. These results demonstrate the potential of CaBind_MCNN to enhance drug discovery efforts by identifying potential CCB targets and advancing the development of novel therapies for calcium-related disorders.
Authors: Yan-Yun Chang, Yu-Chen Liu, Wei-En Jhang, Sin-Siang Wei, Yu-Yen Ou
Institution: Yuan Ze University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $4,500
Award: G6462130056
OpenAlex ID: W4407259374