PreprintCited by 4Protein Structure and DynamicsMachine Learning in BioinformaticsEnzyme Structure and Function

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

TMVisDB: Annotation and 3D-visualization of transmembrane proteins

Open access
Olenyi, Tobias, Marquet, Céline, Grekova, Anastasia, Houri, Leen, Heinzinger, Michael, Dallago, Christian, Rost, Burkhard·2024·Journal of Molecular Biology

Preprint — published as 10.1016/j.jmb.2025.168997 · Paper page

Abstract

Since the rise of cellular life, transmembrane proteins (TMPs) have been crucial to various cellular processes through their central role as gates and gatekeepers. Despite their importance, experimental high-resolution structures for TMPs remain underrepresented due to experimental challenges. Given its performance leap, structure predictions have begun to close the gap. However, identifying the membrane regions and topology in three-dimensional structure files on a large scale still requires additional in silico predictions. Here, we introduce TMVisDB to sieve through millions of predicted structures for TMPs. This resource enables both browsing through 46 million predicted TMPs and visualizing them along with their topological annotations without having to tap into costly predictions of the AlphaFold3-style. TMVisDB joins AlphaFoldDB structure predictions and transmembrane topology predictions from the protein language model (pLM) based method TMbed . We showcase the utility of TMVisDB for the analysis of proteins through two use cases, namely the B-lymphocyte antigen CD 20 ( Homo sapiens ) and the cellulose synthase ( Novosphingobium sp. P6W ). We demonstrate the value of TMVisDB for large-scale analyses through findings pertaining to all TMPs predicted for the human proteome. TMVisDB is freely available at https://tmvisdb.rostlab.org

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1101/2024.11.22.624323

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 53

  1. 10.1186/s12859-022-04873-x · DOI
  2. Tunyasuvunakool K , Adler J , Wu Z , Green T , Zielinski M , Zidek A , et al. Highly accurate protein structure prediction for the human proteome. Nature. 2021. · DOI · Paper page
  3. 10.1016/j.sbi.2023.102594 · DOI
  4. 10.1093/nar/gkaa1100 · DOI · Paper page
  5. 10.1093/NAR/GKAA1038 · DOI · Paper page
  6. 10.1038/S41586-024-07487-W · DOI · Paper page
  7. 10.1038/s41592-021-01360-8 · DOI
  8. Lee JH , Yadollahpour P , Watkins A , Frey NC , Leaver-Fay A , Ra S , et al. EquiFold: Protein Structure Prediction with a Novel Coarse-Grained Structure Representation. bioRxiv; 2022. · DOI
  9. 10.1038/S41592-022-01488-1 · DOI · Paper page
  10. Li F , Egea PF , Vecchio AJ , Asial I , Gupta M , Paulino J , et al. Highlighting membrane protein structure and function: A celebration of the Protein Data Bank. Journal of Biological Chemistry. 2021;296. · DOI
  11. 10.1002/pro.4318 · DOI
  12. 10.1093/nar/gkr703 · DOI
  13. 2017, TMPL: a database of experimental and theoretical transmembrane protein models positioned in the lipid bilayer, Database, 2017, bax022
  14. 10.1093/nar/gky1047 · DOI
  15. 10.3390/ijms23168877 · DOI · Paper page
  16. 10.1002/pmic.200900258 · DOI
  17. 10.1038/nrd.2016.230 · DOI · Paper page
  18. 10.1093/NAR/GKAB1061 · DOI · Paper page
  19. 10.1093/bioinformatics/bty1057 · DOI · Paper page
  20. 10.1038/s41592-021-01101-x · DOI · Paper page
  21. 10.1038/s41592-019-0437-4 · DOI · Paper page
  22. Alley EC , Khimulya G , Biswas S , AlQuraishi M , Church GM . Unified rational protein engineering with sequence-based deep representation learning. Nat Methods. 2019:1–8. · DOI
  23. Bepler T , Berger B. Learning protein sequence embeddings using information from structure. Seventh International Conference on Learning Representations. 2019/02/22 ed 2019.
  24. 10.1186/s12859-019-3220-8 · DOI · Paper page
  25. 10.1073/pnas.2016239118 · DOI · Paper page
  26. 2021, ProtTrans: Towards Cracking the Language of Lifes Code Through Self-Supervised Deep Learning and High Performance Computing, IEEE Trans Pattern Anal Mach Intell, 44, 7112
  27. 10.1007/00439-021-02411-y · DOI
  28. 10.1038/s41598-021-03431-4 · DOI
  29. Meier J , Rao R , Verkuil R , Liu J , Sercu T , Rives A. Language models enable zero-shot prediction of the effects of mutations on protein function. bioRxiv. 2021:2021.07.09.450648. · DOI · Paper page
  30. Weissenow K , Rost B. Protein language models the new universal key? Current Opinion Structural Biology. 2024. · DOI
  31. 10.1038/s41587-021-01179-w · DOI · Paper page
  32. 10.1093/bioinformatics/bty937 · DOI
  33. 2021, Light attention predicts protein location from the language of life, Bioinformatics Advances, 1, vbab035, 10.1093/bioadv/vbab035 · DOI · Paper page
  34. 10.1038/s41598-020-80786-0 · DOI
  35. Dobson L , Szekeres LI , Gerdán C , Langó T , Zeke A , Tusnády GE . TmAlphaFold database: membrane localization and evaluation of AlphaFold2 predicted alpha-helical transmembrane protein structures. Nucleic Acids Research. 2022:gkac928. · DOI
  36. 10.1002/prot.24749 · DOI
  37. 10.1093/nar/gkr703 · DOI
  38. 10.1093/nar/gks1169 · DOI
  39. 10.1038/s41587 · DOI
  40. 10.1093/nar/gku1119 · DOI
  41. 10.1126/science.aaz9356 · DOI
  42. 10.1089/hyb.1983.2.17 · DOI
  43. 10.1083/jcb.121.5.1121 · DOI
  44. 10.5114/ceji.2014.42135 · DOI
  45. 10.1038/s41467-019-12279-2 · DOI
  46. 10.1016/j.febslet.2007.06.052 · DOI
  47. 10.1016/j.bbamcr.2015.10.009 · DOI
  48. Olenyi T , Marquet C , Heinzinger M , Kröger B , Nikolova T , Bernhofer M , et al. LambdaPP: Fast and accessible protein-specific phenotype predictions. bioRxiv; 2022. · DOI · Paper page
  49. Kempen Mv , Kim SS , Tumescheit C , Mirdita M , Gilchrist CLM , Söding J , et al. Foldseek: fast and accurate protein structure search. bioRxiv; 2022.
  50. 10.1007/s00018-021-04112-1 · DOI · Paper page
  51. Evans R , O’Neill M , Pritzel A , Antropova N , Senior A , Green T , et al. Protein complex prediction with AlphaFold-Multimer. bioRxiv; 2022. · DOI · Paper page
  52. 10.1038/nature13319 · DOI · Paper page
  53. 10.1016/j.jmb.2010.01.042 · DOI

Metadata

Authors
Olenyi, T., Marquet, C., Grekova, A., Houri, L., Heinzinger, M., Dallago, C., & Rost, B.
Affiliations
  • Technical University of Munich (DE)
  • European Molecular Biology Laboratory (DE)
Corresponding author
Olenyi, Tobias
Venue
Journal of Molecular Biology
Publisher
openRxiv
Published
2024-11-23
Volume
437
Issue
15
Pages
168997-168997
ISSN
0022-2836 (ISSN-L)
OpenAlex
W4407367450
Cited by
4
References
1
Citations by year
2026: 1 · 2025: 3
FWCI
1.21
Type
journal article
Language
en
Open access
Locations
  • Journal of Molecular Biology (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
Bayerisches Staatsministerium für Bildung und Kultus, Wissenschaft und Kunst
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
Keywords
transmembrane proteins, membrane protein structure prediction, transmembrane topology prediction, AlphaFold2, 3D visualization, protein databases, CD20, cellulose synthase, human proteome, protein language models
MeSH terms
Glucosyltransferases, Humans, Membrane Proteins, Models, Molecular, Protein Conformation, Software, Computational Biology, Databases, Protein, Molecular Sequence Annotation

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 ›

Are you an author, or do you see a problem with this page? Request a review