Site-specific nonenzymatic peptide S/O–glutamylation reveals the extent of substrate promiscuity in glutamate elimination domains

Vinogradov, Alexander A.; Suga, Hiroaki

doi:10.26434/chemrxiv-2021-z44z2

Cited by 4 publications

(4 citation statements)

References 71 publications

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“…Chemical structure assignments of the resulting products were guided by mass shifts, HPLC retention times, and the available substrate specificity data for Laz enzymes (Supporting Information S2.5). 34,45,46 All tested precursors underwent efficient maturation to thiopeptides as judged by the formation of leader-NH 2 , which was the major reaction product in every case (Figure 2c). Modification of 11 constructs yielded a single major thiopeptide product, despite the presence of potentially modifiable Ser/Thr/Cys residues in the random regions of 13 peptides.…”

Section: ■ Results and Discussionmentioning

confidence: 95%

De Novo Discovery of Thiopeptide Pseudo-natural Products Acting as Potent and Selective TNIK Kinase Inhibitors

et al. 2022

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Bioengineering of ribosomally synthesized and post-translationally modified peptides (RiPPs) is an emerging approach to explore the diversity of pseudo-natural product structures for drug discovery purposes. However, despite the initial advances in this area, bioactivity reprogramming of multienzyme RiPP biosynthetic pathways remains a major challenge. Here, we report a platform for de novo discovery of functional thiopeptides based on reengineered biosynthesis of lactazole A, a RiPP natural product assembled by five biosynthetic enzymes. The platform combines in vitro biosynthesis of lactazole-like thiopeptides and mRNA display to prepare and screen large (≥1012) combinatorial libraries of pseudo-natural products. We demonstrate the utility of the developed protocols in an affinity selection against Traf2- and NCK-interacting kinase (TNIK), a protein involved in several cancers, which yielded a plethora of candidate thiopeptides. Of the 11 synthesized compounds, 9 had high affinities for the target kinase (best K D = 1.2 nM) and 10 inhibited its enzymatic activity (best K i = 3 nM). X-ray structural analysis of the TNIK/thiopeptide interaction revealed the unique mode of substrate-competitive inhibition exhibited by two of the discovered compounds. The thiopeptides internalized to the cytosol of HEK293H cells as efficiently as the known cell-penetrating peptide Tat (4–6 μM). Accordingly, the most potent compound, TP15, inhibited TNIK in HCT116 cells. Altogether, our platform enables the exploration of pseudo-natural thiopeptides with favorable pharmacological properties in drug discovery applications.

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Section: ■ Results and Discussionmentioning

confidence: 95%

De Novo Discovery of Thiopeptide Pseudo-natural Products Acting as Potent and Selective TNIK Kinase Inhibitors

et al. 2022

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“…In the second step, the elimination domain (LazF) catalyzes a retro-Michael reaction in the Ser(OGlu) intermediate to yield the Dhacontaining product. 56 Although preliminary enzyme characterization indicated that LazBF prefers a Trp residue in position +1…”

Section: ■ Results and Discussionmentioning

confidence: 99%

Accurate Models of Substrate Preferences of Post-Translational Modification Enzymes from a Combination of mRNA Display and Deep Learning

et al. 2022

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Promiscuous post-translational modification (PTM) enzymes often display nonobvious substrate preferences by acting on diverse yet well-defined sets of peptides and/or proteins. Understanding of substrate fitness landscapes for PTM enzymes is important in many areas of contemporary science, including natural product biosynthesis, molecular biology, and biotechnology. Here, we report an integrated platform for accurate profiling of substrate preferences for PTM enzymes. The platform features (i) a combination of mRNA display with next-generation sequencing as an ultrahigh throughput technique for data acquisition and (ii) deep learning for data analysis. The high accuracy (>0.99 in each of two studies) of the resulting deep learning models enables comprehensive analysis of enzymatic substrate preferences. The models can quantify fitness across sequence space, map modification sites, and identify important amino acids in the substrate. To benchmark the platform, we performed profiling of a Ser dehydratase (LazBF) and a Cys/Ser cyclodehydratase (LazDEF), two enzymes from the lactazole biosynthesis pathway. In both studies, our results point to complex enzymatic preferences, which, particularly for LazBF, cannot be reduced to a set of simple rules. The ability of the constructed models to dissect such complexity suggests that the developed platform can facilitate a wider study of PTM enzymes.

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“…In the second step, the elimination domain (LazF) catalyzes a retro-Michael reaction in the Ser(OGlu) intermediate to yield the Dha-containing product. 56 Although preliminary enzyme characterization indicated that LazBF prefers a Trp residue in position +1 relative to the modification site, the enzyme also displayed more elaborate preferences which eluded generalization. 47,53 Here, we have sought to develop an mRNA display/deep learning-based platform for comprehensive profiling of LazBF substrate fitness landscapes.…”

Section: Development Of the Mrna Display Scheme For Lazbf Profilingmentioning

confidence: 99%

Accurate models of substrate preferences of post-translational modification enzymes from a combination of mRNA display and deep learning

Vinogradov

Chang

Onaka

et al. 2022

Preprint

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Promiscuous post-translational modification (PTM) enzymes often display non-obvious substrate preferences by acting on diverse yet well-defined sets of peptides and/or proteins. Thorough understanding of substrate fitness landscapes for promiscuous PTM enzymes is important because they play key roles in many areas of contemporary science, including natural product biosynthesis, molecular biology and biotechnology. Here, we report the development of an integrated platform for accurate profiling of substrate preferences for PTM enzymes. The platform features a combination of i) mRNA display with next generation sequencing as an ultrahigh throughput technique for data acquisition and ii) deep learning for data analysis. The high accuracy (>0.99 in each of two studies) and generalizability of the resulting deep learning models enables comprehensive analysis of enzymatic substrate preferences. The models can be utilized to quantify fitness across sequence space, map modification sites, and identify important amino acids in the substrate. To benchmark the platform, we perform substrate specificity profiling of a Ser dehydratase (LazBF) and a Cys/Ser cyclodehydratase (LazDEF), two enzymes from the lactazole biosynthesis pathway. In both studies, our results point to highly complex enzymatic preferences, which, particularly for LazBF, cannot be reduced to a set of simple rules. The ability of the constructed models to dissect and analyze such complexity suggests that the developed platform can facilitate the wider study of PTM enzymes.

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Site-specific nonenzymatic peptide S/O–glutamylation reveals the extent of substrate promiscuity in glutamate elimination domains

Cited by 4 publications

References 71 publications

De Novo Discovery of Thiopeptide Pseudo-natural Products Acting as Potent and Selective TNIK Kinase Inhibitors

De Novo Discovery of Thiopeptide Pseudo-natural Products Acting as Potent and Selective TNIK Kinase Inhibitors

Accurate Models of Substrate Preferences of Post-Translational Modification Enzymes from a Combination of mRNA Display and Deep Learning

Accurate models of substrate preferences of post-translational modification enzymes from a combination of mRNA display and deep learning

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