Mechanisms and inhibition of Porcupine-mediated Wnt acylation

Liu, Yang; Qi, Xiaofeng; Donnelly, Linda; Elghobashi-Meinhardt, Nadia; Long, Tao; Zhou, Rich W; Sun, Yingyuan; Wang, Boyuan; Li, Xiaochun

doi:10.1038/s41586-022-04952-2

Cited by 57 publications

(58 citation statements)

References 65 publications

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“…Fig.2New 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.…”

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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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confidence: 99%

TMbed: transmembrane proteins predicted through language model embeddings

Bernhofer

Rost

2022

BMC Bioinformatics

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“…The overall structure of MBOAT7 is similar to other MBOATs 9,10,12,14,17,18 . Two TM bundles (TM1-5, TM9-11) tilt away from each other, generating space in the center for the catalytic chamber.…”

Section: Cryo-em Study Of Human Mboat7 Reveals Characteristic Structu...mentioning

confidence: 76%

“…MBOATs form a large class of ER enzymes (11 in humans) that acylate small molecules, lipids, or proteins 8 . The structures of several MBOAT enzymes have been determined by cryoelectron microscopy (cryo-EM) [9][10][11][12][13][14][15][16][17][18] . Some MBOATs, such as porcupine and hedgehog acyltransferase, acylate polypeptides in the ER lumen 10,11,18 .…”

Section: Introductionmentioning

confidence: 99%

“…The structures of several MBOAT enzymes have been determined by cryoelectron microscopy (cryo-EM) [9][10][11][12][13][14][15][16][17][18] . Some MBOATs, such as porcupine and hedgehog acyltransferase, acylate polypeptides in the ER lumen 10,11,18 . Other MBOATs catalyze neutral lipid synthesis (e.g., DGAT1 and ACAT1/ACAT2) and act as multimers with a catalytic histidine residue that is buried deeply within the membrane [12][13][14][15][16] .…”

Section: Introductionmentioning

confidence: 99%

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The structure, catalytic mechanism, and inhibitor identification of phosphatidylinositol remodeling MBOAT7

Wang

Lee

Sui

et al. 2022

Preprint

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Cells remodel glycerophospholipid acyl chains via the Lands cycle to adjust membrane properties. Membrane-bound O-acyltransferase (MBOAT) 7 acylates lyso-phosphatidylinositol (lyso-PI) with arachidonyl-CoA. MBOAT7 mutations cause brain developmental disorders, and reduced expression is linked to fatty liver disease. Further, increased MBOAT7 expression is linked to hepatocellular and renal cancers. The mechanistic basis of MBOAT7 catalysis and substrate selectivity are unknown. Here, we report the structure and a model for the catalytic mechanism of human MBOAT7. Arachidonyl-CoA and lyso-PI access the catalytic center through a twisted tunnel from the cytosol and lumenal sides, respectively. N-Terminal residues on the ER lumenal side determine phospholipid headgroup selectivity: swapping them between MBOATs 1, 5, and 7 converts enzyme specificity for different lyso-phospholipids. Finally, the MBOAT7 structure and virtual screening enabled identification of small-molecule inhibitors that may serve as lead compounds for pharmacologic development.

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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

View full text Add to dashboard Cite

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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Mechanisms and inhibition of Porcupine-mediated Wnt acylation

Cited by 57 publications

References 65 publications

TMbed: transmembrane proteins predicted through language model embeddings

TMbed: transmembrane proteins predicted through language model embeddings

The structure, catalytic mechanism, and inhibitor identification of phosphatidylinositol remodeling MBOAT7

TMbed – Transmembrane proteins predicted through Language Model embeddings

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