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Identification and classification of ion channels across the tree of life provide functional insights into understudied CALHM channels.

Rahil Taujale | Sung Jin Park | Nathan Gravel | Saber Soleymani | Rayna Carter | Kennady Boyd | Sarah I Keuning | Zheng Ruan | Wei Lü | Natarajan Kannan
eLife | 2026

The ion channel (IC) genes encoded in the human genome play fundamental roles in cellular functions and disease, and are one of the largest classes of druggable proteins. However, limited knowledge of the diverse molecular and cellular functions carried out by ICs presents a major bottleneck in developing selective chemical probes for modulating their functions in disease states. The wealth of sequence data available on ICs from diverse organisms provides a valuable source of untapped information for illuminating the unique modes of channel regulation and functional specialization. However, the extensive diversification of IC sequences and the lack of a unified resource present a challenge in effectively using existing data for IC research. Here, we perform integrative mining of available sequence, structure, and functional data on 419 human ICs across disparate sources, including extensive literature mining by leveraging advances in LLMs to annotate and curate the full complement of the 'channelome'. We employ a well-established orthology inference approach to identify and extend the IC orthologs across diverse organisms to above 48,000. We show that the depth of conservation and taxonomic representation of IC sequences can further be translated to functional similarities by clustering them into functionally relevant groups, which can be used for downstream functional prediction on understudied members. We demonstrate this by delineating co-conserved patterns characteristic of the understudied family of the calcium homeostasis modulator (CALHM) family of ICs. Through mutational analysis of co-conserved residues altered in human diseases and electrophysiological studies, we show that these evolutionarily constrained residues play an important role in channel gating functions. Thus, by providing new tools and resources for performing large comparative analyses on ICs, this study addresses the unique needs of the IC community and provides the groundwork for accelerating the functional characterization of dark channels for therapeutic intervention.

Pubmed ID: 42149114

Associated grants

  • Agency: NINDS NIH HHS, United States
    Id: R00 NS128258
  • Agency: NINDS NIH HHS, United States
    Id: 5R00NS128258
  • Agency: NIH HHS, United States
    Id: U01CA271376

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This is a list of tools and resources that we have found mentioned in this publication.


Pfam (tool)

RRID:SCR_004726

A database of protein families, each represented by multiple sequence alignments and hidden Markov models (HMMs). Users can analyze protein sequences for Pfam matches, view Pfam family annotation and alignments, see groups of related families, look at the domain organization of a protein sequence, find the domains on a PDB structure, and query Pfam by keywords. There are two components to Pfam: Pfam-A and Pfam-B. Pfam-A entries are high quality, manually curated families that may automatically generate a supplement using the ADDA database. These automatically generated entries are called Pfam-B. Although of lower quality, Pfam-B families can be useful for identifying functionally conserved regions when no Pfam-A entries are found. Pfam also generates higher-level groupings of related families, known as clans (collections of Pfam-A entries which are related by similarity of sequence, structure or profile-HMM).

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PubMed (tool)

RRID:SCR_004846

Public bibliographic database that provides access to citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites. PubMed citations and abstracts include fields of biomedicine and health, covering portions of life sciences, behavioral sciences, chemical sciences, and bioengineering. Provides access to additional relevant web sites and links to other NCBI molecular biology resources. Publishers of journals can submit their citations to NCBI and then provide access to full-text of articles at journal web sites using LinkOut.

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SMART (tool)

RRID:SCR_005026

Software tool for identification and annotation of genetically mobile domains and analysis of domain architectures.

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SciPy (tool)

RRID:SCR_008058

A Python-based environment of open-source software for mathematics, science, and engineering. The core packages of SciPy include: NumPy, a base N-dimensional array package; SciPy Library, a fundamental library for scientific computing; and IPython, an enhanced interactive console.

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National Institutes of Health (tool)

RRID:SCR_011417

NIH is the nations medical research agency - making important medical discoveries that improve health and save lives. The National Institutes of Health (NIH), a part of the U.S. Department of Health and Human Services, is the primary Federal agency for conducting and supporting medical research. Helping to lead the way toward important medical discoveries that improve peoples health and save lives, NIH scientists investigate ways to prevent disease as well as the causes, treatments, and even cures for common and rare diseases. NIH research impacts: * child and teen health, * men's health, * minority health, * seniors' health, * women's health, and * wellness and lifestyle issues. Composed of 27 Institutes and Centers, the NIH provides leadership and financial support to researchers in every state and throughout the world.

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KEGG (tool)

RRID:SCR_012773

Integrated database resource consisting of 16 main databases, broadly categorized into systems information, genomic information, and chemical information. In particular, gene catalogs in completely sequenced genomes are linked to higher-level systemic functions of cell, organism, and ecosystem. Analysis tools are also available. KEGG may be used as reference knowledge base for biological interpretation of large-scale datasets generated by sequencing and other high-throughput experimental technologies.

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National Institute of Neurological Disorders and Stroke (tool)

RRID:SCR_013116

National institute whose mission is to reduce the burden of neurological disease - a burden borne by every age group, by every segment of society, by people all over the world. To accomplish this goal the NINDS supports and conducts basic, translational, and clinical research on the normal and diseased nervous system. The Institute also fosters the training of investigators in the basic and clinical neurosciences, and seeks better understanding, diagnosis, treatment, and prevention of neurological disorders. NINDS: * Conducts, fosters, coordinates, and guides research on the causes, prevention, diagnosis, and treatment of neurological disorders and stroke, and supports basic research in related scientific areas. * Provides grants-in-aid to public and private institutions and individuals in fields related to its areas of interest, including research project, program project, and research center grants. * Operates a program of contracts for the funding of research and research support efforts in selected areas of institute need. * Provides individual and institutional fellowships to increase scientific expertise in neurological fields. * Conducts a diversified program of intramural and collaborative research in its own laboratories, branches, and clinics. * Collects and disseminates research information related to neurological disorders.

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SnapGene (tool)

RRID:SCR_015052

Molecular biology software for visualizing and documenting gene constructs for InFusion cloning, Gibson assembly, restriction cloning, PCR, and mutagenesis.

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Microsoft Excel (tool)

RRID:SCR_016137

Software application with data analysis tools and spreadsheet templates to track and visualize data. It is used to manage and process data.

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SIGMA (tool)

RRID:SCR_028585

Software pipeline for automated identification of targeted cell types from multiple heterogeneous single-cell transcriptomic datasets (formats supported: .h5ad). It is designed to simplify and accelerate integrative large-scale single-cell analyses.

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