IonFusion: Multimodal Fusion for Hierarchical Classification and Functional Annotation of Ion Channels and Transporters
- 1School of Computer Science and Engineering, Central South University, Changsha 410083, China
Abstract
Ion channels and transporters are diverse membrane proteins that regulate ion flux across biological membranes and play essential roles in cellular signaling, homeostasis, and physiological processes. Accurate classification and functional annotation of these proteins are important for characterizing membrane protein function, interpreting pathogenic mutations, and identifying therapeutic targets. However, classification of ion channels and transporters is complicated by similar fine-grained classes, hierarchical labels, and functional determinants spanning sequence motifs, domains, and structural features. We present IonFusion, which uses sequence-conditioned cross-attention to aggregate domain-derived and structure-aware representations. To model the hierarchical organization of classes of ion channels and transporters, IonFusion employs a coarse-to-fine autoregressive classifier linking five coarse-grained classes to nine fine-grained classes. For GO annotation, IonFusion combines multimodal predictions with DIAMOND homology scores. Through comprehensive benchmarks, IonFusion achieved 0.988 accuracy and 0.860 macro-F1, outperforming eight baselines. Across three GO ontologies and sequence-identity thresholds of ≤10%, ≤30%, and ≤50%, it achieved the highest Fmax in all nine settings (mean 0.732). Ablations supported contributions from each modality and both prediction components. Attention-based and targeted perturbation analyses highlighted a conserved, class-associated motif in potassium channels. IonFusion provides a multimodal computational framework for fine-grained classification of ion channels and transporters and GO annotation within the evaluated label spaces. By integrating complementary multimodal representations with hierarchical prediction and homology-based fusion, IonFusion improves the discrimination of closely related ion channel and transporter classes and enhances GO annotation performance across different sequence-identity thresholds.
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BibTeX
@article{zheng2026ionfusion,
title = {{IonFusion: Multimodal Fusion for Hierarchical Classification and Functional Annotation of Ion Channels and Transporters}},
author = {Ruiqing Zheng and Wentao Yu and Qiurong Yang and Zelin Zhao and Wenxuan He and Shubiao Qu and Taozhe Li and Wenkang Wang},
journal = {Molecules},
year = {2026},
month = oct,
doi = {10.3390/molecules31203679},
url = {https://www.mdpi.com/1420-3049/31/20/3679},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - IonFusion: Multimodal Fusion for Hierarchical Classification and Functional Annotation of Ion Channels and Transporters
AU - Ruiqing Zheng
AU - Wentao Yu
AU - Qiurong Yang
AU - Zelin Zhao
AU - Wenxuan He
AU - Shubiao Qu
AU - Taozhe Li
AU - Wenkang Wang
PY - 2026
DA - 2026/10/14
JO - Molecules
DO - 10.3390/molecules31203679
UR - https://www.mdpi.com/1420-3049/31/20/3679
AB - Ion channels and transporters are diverse membrane proteins that regulate ion flux across biological membranes and play essential roles in cellular signaling, homeostasis, and physiological processes. Accurate classification and functional annotation of these proteins are important for characterizing membrane protein function, interpreting pathogenic mutations, and identifying therapeutic targets. However, classification of ion channels and transporters is complicated by similar fine-grained classes, hierarchical labels, and functional determinants spanning sequence motifs, domains, and structural features. We present IonFusion, which uses sequence-conditioned cross-attention to aggregate domain-derived and structure-aware representations. To model the hierarchical organization of classes of ion channels and transporters, IonFusion employs a coarse-to-fine autoregressive classifier linking five coarse-grained classes to nine fine-grained classes. For GO annotation, IonFusion combines multimodal predictions with DIAMOND homology scores. Through comprehensive benchmarks, IonFusion achieved 0.988 accuracy and 0.860 macro-F1, outperforming eight baselines. Across three GO ontologies and sequence-identity thresholds of ≤10%, ≤30%, and ≤50%, it achieved the highest Fmax in all nine settings (mean 0.732). Ablations supported contributions from each modality and both prediction components. Attention-based and targeted perturbation analyses highlighted a conserved, class-associated motif in potassium channels. IonFusion provides a multimodal computational framework for fine-grained classification of ion channels and transporters and GO annotation within the evaluated label spaces. By integrating complementary multimodal representations with hierarchical prediction and homology-based fusion, IonFusion improves the discrimination of closely related ion channel and transporter classes and enhances GO annotation performance across different sequence-identity thresholds.
SN - 1420-3049
ER -