The cryo-EM structure of the S-layer deinoxanthin-binding complex of Deinococcus radiodurans informs properties of its environmental interactions

Farci, Domenica; Haniewicz, Patrycja; Sanctis, Daniele de; Iesu, Luca; Kereı̈che, Sami; Winterhalter, Mathias; Piano, Dario

doi:10.1016/j.jbc.2022.102031

Cited by 17 publications

(43 citation statements)

References 37 publications

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“…2 New membrane proteins. PDB structures for probable flagellin 1 (Q9YAN8; 7TXI [ 73 ]), protein-serine O-palmitoleoyltransferase porcupine (Q9H237; 7URD [ 74 ]), choline transporter-like protein 1 (Q8WWI5; 7WWB [ 75 ]), S-layer protein SlpA (Q9RRB6; 7ZGY [ 76 ]), and membrane protein (P0DTC5; 8CTK [ 77 ]). Transmembrane segments (dark purple) predicted by TMbed; membrane boundaries (dotted lines: red = outside, blue = inside) predicted by the PPM [ 45 ] web server.…”

Section: Resultsmentioning

confidence: 99%

TMbed: transmembrane proteins predicted through language model embeddings

Bernhofer

Rost

2022

BMC Bioinformatics

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Background Despite the immense importance of transmembrane proteins (TMP) for molecular biology and medicine, experimental 3D structures for TMPs remain about 4–5 times underrepresented compared to non-TMPs. Today’s top methods such as AlphaFold2 accurately predict 3D structures for many TMPs, but annotating transmembrane regions remains a limiting step for proteome-wide predictions. Results Here, we present TMbed, a novel method inputting embeddings from protein Language Models (pLMs, here ProtT5), to predict for each residue one of four classes: transmembrane helix (TMH), transmembrane strand (TMB), signal peptide, or other. TMbed completes predictions for entire proteomes within hours on a single consumer-grade desktop machine at performance levels similar or better than methods, which are using evolutionary information from multiple sequence alignments (MSAs) of protein families. On the per-protein level, TMbed correctly identified 94 ± 8% of the beta barrel TMPs (53 of 57) and 98 ± 1% of the alpha helical TMPs (557 of 571) in a non-redundant data set, at false positive rates well below 1% (erred on 30 of 5654 non-membrane proteins). On the per-segment level, TMbed correctly placed, on average, 9 of 10 transmembrane segments within five residues of the experimental observation. Our method can handle sequences of up to 4200 residues on standard graphics cards used in desktop PCs (e.g., NVIDIA GeForce RTX 3060). Conclusions Based on embeddings from pLMs and two novel filters (Gaussian and Viterbi), TMbed predicts alpha helical and beta barrel TMPs at least as accurately as any other method but at lower false positive rates. Given the few false positives and its outstanding speed, TMbed might be ideal to sieve through millions of 3D structures soon to be predicted, e.g., by AlphaFold2.

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Section: Resultsmentioning

confidence: 99%

TMbed: transmembrane proteins predicted through language model embeddings

Bernhofer

Rost

2022

BMC Bioinformatics

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“…PDB structures for probable flagellin 1 (Q9YAN8; 7TXI (75)), protein-serine O-palmitoleoyltransferase porcupine (Q9H237; 7URD (76)), choline transporter-like protein 1 (Q8WWI5; 7WWB (77)), S-layer protein SlpA (Q9RRB6; 7ZGY (78)), and membrane protein (P0DTC5; 8CTK (79)). Transmembrane segments (dark purple) predicted by TMbed; membrane boundaries (dotted lines: red=outside, blue=inside) predicted by the PPM (45) web server.…”

Section: Resultsmentioning

confidence: 99%

TMbed – Transmembrane proteins predicted through Language Model embeddings

Bernhofer

Rost

2022

Preprint

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Background: Despite the immense importance of transmembrane proteins (TMP) for molecular biology and medicine, experimental 3D structures for TMPs remain about 4-5 times underrepresented compared to non-TMPs. Today's top methods can accurately predict structures for many, but the annotations of the transmembrane regions remains a limiting step for proteome-wide predictions. Results: Here, we present a novel method, dubbed TMbed. Inputting embeddings from protein Language Models (in particular ProtT5), TMbed completes predictions of alpha helical and beta barrel TMPs for entire proteomes within hours on a single consumer-grade desktop machine at performance levels similar or better than methods, which are using evolutionary information (extracted from family alignments). On the per-protein level, TMbed correctly identified 61 of the 65 beta-barrel TMPs (94±7%) and 579 of the 593 alpha-helical TMPs (98±1%) in a non-redundant data set, at false positive rates well below 1% (erred on 31 of 5859 non-membrane proteins). On the per-segment level, TMbed correctly placed, on average, 9 of 10 transmembrane segments within five residues of the experimental observation. Although limited by GPU memory, our method can handle sequence of up to 4200 residues on standard graphics cards used in common desktop PCs (e.g., NVIDIA GeForce RTX 3060). Conclusions: TMbed accurately predicts alpha helical and beta barrel TMPs. Utilizing protein Language Models and GPU acceleration it can predict the human in less than an hour. Availability: Our code, method, and data sets are freely available in the GitHub repository, https://github.com/BernhoferM/TMbed

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“…Our structure places several past studies into context, confirming that the D. radiodurans S-layer is made solely of the HPI protein. A recent model of the D. radiodurans S-layer proposes that the S-layer incorporates another protein called SlpA (42–44). This, however, is incompatible with our data and also with previous genetic (39), biochemical (45), and EM studies (8, 9, 11–13, 20–22), which have shown that SlpA is a PG-OM tether (39, 45) and that the S-layer is exclusively made up of the HPI protein (8, 9, 11–13, 20–22).…”

Section: Discussionmentioning

confidence: 99%

Interdigitated immunoglobulin arrays form the hyperstable surface layer of the extremophilic bacterium Deinococcus radiodurans

Kuegelgen

Dorst

Yamashita

et al. 2022

Preprint

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Deinococcus radiodurans is an atypical diderm bacterium with a remarkable ability to tolerate various environmental stresses, partly because of its complex cell envelope encapsulated within a hyperstable surface layer (S-layer). Despite decades of research into this cell envelope, atomic structural details of the S-layer have remained obscure. In this study, we report the electron cryomicroscopy structure of the D. radiodurans S-layer, showing how it is formed by the Hexagonally Packed Intermediate-layer (HPI) protein arranged in a planar hexagonal lattice. The HPI protein forms an array of immunoglobulin-like folds within the S-layer, with each monomer extending into the adjoining hexamer, leading to a highly interconnected, stable, sheet-like arrangement. Using electron cryotomography and subtomogram averaging from focused ion beam-milled D. radiodurans cells, we obtained a structure of the cellular S-layer, showing how this HPI S-layer coats native membranes on the surface of cells. Our S-layer structure from the diderm bacterium D. radiodurans shows similarities to immunoglobulin-like domain-containing S-layers from monoderm bacteria and archaea, highlighting shared traits in cell surface organization across different domains of life, with connotations on the evolution of immunoglobulin-based molecular recognition systems in eukaryotes.

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The cryo-EM structure of the S-layer deinoxanthin-binding complex of Deinococcus radiodurans informs properties of its environmental interactions

Cited by 17 publications

References 37 publications

TMbed: transmembrane proteins predicted through language model embeddings

TMbed: transmembrane proteins predicted through language model embeddings

TMbed – Transmembrane proteins predicted through Language Model embeddings

Interdigitated immunoglobulin arrays form the hyperstable surface layer of the extremophilic bacterium Deinococcus radiodurans

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