A large‐scale evolutionary and structural analysis of CLC channels and transporters
Open accessPublished version of a preprint
Abstract
The CLC family of membrane proteins consists of chloride channels and anion/proton antiporters. How the same fold accommodates two distinct mechanisms remains poorly understood, and the small set of experimental structures provides limited insight. Here we show that it is possible to scale up CLC structural information using AlphaFold2 predictions and combine this with an ensemble‐based machine learning approach to identify subtle structural differences associated with each mechanistic class. We first carried out a phylogenetic analysis on CLC sequences to infer 569 channels and 1051 transporter homologs of eukaryotic origin that were previously unidentified. We then examined AlphaFold2's ability to detect subtle differences among experimentally solved CLC structures using distance difference matrices and validated the use of these models in our study. Next, we trained a random forest classifier on channel versus transporter grouped distance data, generating a structure‐based predictor for CLC subtypes. Shapley value analysis was then carried out to calculate importance values, allowing the identification of changes in helix‐pair distances most strongly associated with the classifier's ability to predict CLC subtype. These differences are summarized by three concerted changes found in channels: reduced distance between dimerization interface helices and the C‐terminal half of the protein, expansion of the anion transport pathway along the membrane, and insertion of an interfacial helix between αJ and αK. These changes overlap with observations from experimental structures, showing that this approach expands structural information across sequence space. This establishes a framework for large‐scale structural analysis when experimental data is limited and may be useful for the study of other protein families.
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Transporter engineering in microbial cell factories
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Self-citationSequence and structural alignments reveal insights into ANKLE2 evolution and function
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- Authors
- Mishra, A., Díaz Vázquez, G., & Robertson, J. L.
- Affiliations
- Washington University in St. Louis (US)
- Department of Biochemistry & Molecular Biophysics Washington University School of Medicine St. Louis Missouri USA
- Corresponding author
- Robertson, Janice L.
- Venue
- Protein Science
- Publisher
- Wiley
- Published
- 2025-11-22
- Volume
- 34
- Issue
- 12
- Pages
- e70389-e70389
- ISSN
- 0961-8368 (ISSN-L)
- 1469-896X
- PMC
- PMC12639529
- OpenAlex
- W4416540132
- Cited by
- 3
- References
- 62
- Citations by year
- 2026: 3
- FWCI
- 0.90
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Protein Science (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion)
- Funders
- National Institute of General Medical Sciences (R01GM120260)
- National Institute of Neurological Disorders and Stroke (R03NS133680)
- National Heart, Lung, and Blood Institute (T32HL007081)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Ion channel regulation and function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- chloride channels, protein structure prediction, AlphaFold2, phylogenetic analysis, structural classification, Shapley value, protein evolution
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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