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.

Abstract

Calcium ions (Ca²⁺) are crucial for various physiological processes, including neurotransmission and cardiac function. Dysregulation of Ca²⁺ homeostasis can lead to serious health conditions such as cardiac arrhythmias and hypertension. Ion channels and transporters play a vital role in maintaining cellular Ca²⁺ balance by facilitating Ca²⁺ transport across cell membranes. Accurate prediction of Ca²⁺ 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 Ca²⁺ 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 Ca²⁺ 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.