Cryo-EM structure of arabinosyltransferase EmbB from Mycobacterium smegmatis

Tan, Yong Zi; Rodrigues, J.; Keener, James E.; Zheng, Ruixiang Blake; Brunton, Richard; Kloss, Brian; Giacometti, Sabrina I.; Rosario, Ana Lucia; Zhang, Lei; Niederweis, Michael; Clarke, Oliver; Lowary, Todd L.; Marty, Michael T.; Archer, Margarida; Potter, Clinton S.; Carragher, Bridget; Mancia, Filippo

doi:10.1038/s41467-020-17202-8

Cited by 15 publications

(8 citation statements)

References 87 publications

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“…Similarly, 5 families have the highest positive FAS score against GT-C clusters and are predicted to adopt the GT-C fold (Supplementary Table 5). In agreement with our predictions, cryo-EM based structures of representative bacterial Embs of the GT53 family have revealed a GT-C fold 44 and recent structural predictions on the human TMTCs of family GT105 have suggested that they adopt a GT-C fold . In addition to these 2 families, we predict that PigVs (GT76), bacterial arabinofuranosyltransferases AftBs (GT89), and dpy-19 mannosyltransferases (GT98) also adopt a GT-C fold.…”

Section: Identifying Families With Novel Gt Folds Using the Convolutional Autoencoder Modelsupporting

confidence: 91%

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Mapping the glycosyltransferase fold landscape using deep learning

Taujale¹,

Zhou²,

Yeung³

et al. 2021

Preprint

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Glycosyltransferases (GTs) play fundamental roles in nearly all cellular processes through 10 the biosynthesis of complex carbohydrates and glycosylation of diverse protein and small 11 molecule substrates. The extensive structural and functional diversification of GTs presents a 12 major challenge in mapping the relationships connecting sequence, structure, fold and function 13 using traditional bioinformatics approaches. Here, we present a convolutional neural network 14 with attention (CNN-attention) based deep learning model that leverages simple secondary 15 structure representations generated from primary sequences to provide GT fold prediction with 16 high accuracy. The model learned distinguishing features free of primary sequence alignment 17 constraints and, unlike other models, is highly interpretable and helped identify common 18 secondary structural features shared by divergent families. The model delineated sequence and 19 structural features characteristic of individual fold types, while classifying them into distinct 20 clusters that group evolutionarily divergent families based on shared secondary structural 21 features. We further extend our model to classify GT families of unknown folds and variants of 22 known folds. By identifying families that are likely to adopt novel folds such as GT91, GT96 and 23 GT97, our studies identify targets for future structural studies and expand the GT fold landscape.

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Section: Identifying Families With Novel Gt Folds Using the Convolutional Autoencoder Modelsupporting

confidence: 91%

“…In addition to these 2 families, we predict that PigVs (GT76), bacterial arabinofuranosyltransferases AftBs (GT89), and dpy-19 mannosyltransferases (GT98) also adopt a GT-C fold. In addition, all of these 5 families utilize lipid-linked sugar donor substrates similar to other known GT-C fold enzymes 29,44,46 .…”

Section: Resultsmentioning

confidence: 99%

Mapping the glycosyltransferase fold landscape using deep learning

Taujale¹,

Zhou²,

Yeung³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Similarly, five families have the highest positive FAS score against GT-C clusters and are predicted to adopt the GT-C fold (Supplementary Table 5). In agreement with our predictions, cryo-EM-based structures of representative bacterial Embs of the GT53 family have revealed a GT-C fold 44 and recent structural predictions on the human TMTCs of family GT105 have suggested that they adopt a GT-C fold 45 . In addition to these two families, we predict that PigVs (GT76), bacterial arabinofuranosyltransferases AftBs (GT89), and dpy-19 mannosyltransferases (GT98) also adopt a GT-C fold.…”

Section: Bsupporting

confidence: 90%

“…In addition to these two families, we predict that PigVs (GT76), bacterial arabinofuranosyltransferases AftBs (GT89), and dpy-19 mannosyltransferases (GT98) also adopt a GT-C fold. In addition, all of these five families utilize lipid-linked sugar donor substrates similar to other known GT-C fold enzymes 29,44,46 .…”

Section: Bmentioning

confidence: 99%

Mapping the glycosyltransferase fold landscape using interpretable deep learning

et al. 2021

View full text Add to dashboard Cite

Glycosyltransferases (GTs) play fundamental roles in nearly all cellular processes through the biosynthesis of complex carbohydrates and glycosylation of diverse protein and small molecule substrates. The extensive structural and functional diversification of GTs presents a major challenge in mapping the relationships connecting sequence, structure, fold and function using traditional bioinformatics approaches. Here, we present a convolutional neural network with attention (CNN-attention) based deep learning model that leverages simple secondary structure representations generated from primary sequences to provide GT fold prediction with high accuracy. The model learns distinguishing secondary structure features free of primary sequence alignment constraints and is highly interpretable. It delineates sequence and structural features characteristic of individual fold types, while classifying them into distinct clusters that group evolutionarily divergent families based on shared secondary structural features. We further extend our model to classify GT families of unknown folds and variants of known folds. By identifying families that are likely to adopt novel folds such as GT91, GT96 and GT97, our studies expand the GT fold landscape and prioritize targets for future structural studies.

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“…It would be interesting to further explore what other enzymes it could target and map its detailed mode of action. Recently the structure of Arabinofuranosyltransferase (AftD) and arabinosyltransferase EmbB from mycobacteria was reported ( Tan et al., 2020a , 2020b ). Exploring other enzymes from this pathway using similar studies would help us understand the mode of action of EMB more clearly.…”

Section: A Brief Overview Of the Current Treatment Regimenmentioning

confidence: 99%

Tuberculosis: Past, present and future of the treatment and drug discovery research

Bendre

Peters

Kumar

2021

Current Research in Pharmacology and Drug Discovery

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Tuberculosis (TB) is an infectious disease caused by the bacterium Mycobacterium tuberculosis . Despite decades of research driving advancements in drug development and discovery against TB, it still leads among the causes of deaths due to infectious diseases. We are yet to develop an effective treatment course or a vaccine that could help us eradicate TB. Some key issues being prolonged treatment courses, inadequate drug intake, and the high dropout rate of patients during the treatment course. Hence, we require drugs that could accelerate the elimination of bacteria, shortening the treatment duration. It is high time we evaluate the probable lacunas in research holding us back in coming up with a treatment regime and/or a vaccine that would help control TB spread. Years of dedicated and focused research provide us with a lead molecule that goes through several tests, trials, and modifications to transform into a ‘drug’. The transformation from lead molecule to ‘drug’ is governed by several factors determining its success or failure. In the present review, we have discussed drugs that are part of the currently approved treatment regimen, their limitations, vaccine candidates under trials, and current issues in research that need to be addressed. While we are waiting for the path-breaking treatment for TB, these factors should be considered during the ongoing quest for novel yet effective anti-tubercular. If these issues are addressed, we could hope to develop a more effective treatment that would cure multi/extremely drug-resistant TB and help us meet the WHO's targets for controlling the global TB pandemic within the prescribed timeline.

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Cryo-EM structure of arabinosyltransferase EmbB from Mycobacterium smegmatis

Cited by 15 publications

References 87 publications

Mapping the glycosyltransferase fold landscape using deep learning

Mapping the glycosyltransferase fold landscape using deep learning

Mapping the glycosyltransferase fold landscape using interpretable deep learning

Tuberculosis: Past, present and future of the treatment and drug discovery research

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