ICFinder: ion channel identification and ion permeation residue prediction using protein language models

Abstract

Ion channel dysfunction underlies many diseases (e.g., arrhythmias, epilepsy, cystic fibrosis), and uncharacterized channels may also contribute to pathology. Identifying such channels and their residues directly contacting the permeation pathway (i.e., ion permeation residues) is key to elucidating transport mechanisms and developing targeted therapies. Leveraging the protein language model ESM-2 and curated datasets, we developed BLAPE and CLAPE frameworks for high-throughput ion channel identification and permeation residue prediction. Our models outperformed existing methods, with 33%-171% improvements in Matthews correlation coefficients (MCC) across different datasets. Analysis of amino acid composition revealed enrichment for weakly polar residues among ion permeation sites. Case studies on four diverse ion channels highlighted that CLAPE consistently outperforms existing predictors and remains applicable to proteins lacking experimental structures, while also complementing structure-based pipelines such as AlphaFold3. We further applied our models to UniRef50 to predict potential ion channels, and made these results publicly available through the ICFinder webserver (https://tianlab-tsinghua.cn/icfinder/), providing a ready-to-use resource for the research community. All source code is available at https://github.com/JueWangTHU/ICFinder.