ArticleACS central science2022
Accurate Models of Substrate Preferences of Post-Translational Modification Enzymes from a Combination of mRNA Display and Deep Learning.
Article in ACS central science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advancements in the Application of Ribosomally Synthesized and Post-Translationally Modified Peptides (RiPPs).Biomolecules · 2024Pooled it
- A Framework for the In Vivo Production of Extensively Engineered Thiopeptides.ACS synthetic biology · 2026Article
- mRNA Display for Identifying Peptide Substrates of Enzymes.Biochemistry · 2026Review
- Direct Selection of Functional De Novo Macrocycles for Activation of On-Cellulo Insulin Receptor.Angewandte Chemie (International ed. in English) · 2026Article
- Systematic Profiling of Peptide Substrate Specificity in N-Terminal Processing by Methionine Aminopeptidase Using mRNA Display and an Unnatural Methionine Analogue.ACS chemical biology · 2026Article
- Machine learning-driven prediction of substrates for enzymes introducing or removing protein post-translational modifications.Communications chemistry · 2025Article
- Computational approaches to enzymatic reaction assignment: a review of methods, validations, and future directions.Briefings in bioinformatics · 2025Review
- Integrated proteome, phospho-proteome and malonyl-proteome revealed a molecular alteration of breast cancer.Scientific reports · 2025Article
- LassoESM a tailored language model for enhanced lasso peptide property prediction.Nature communications · 2025Article
- Using extension-based mRNA display to design antibody-like proteinogenic peptides for human PD-L1.Protein science : a publication of the Protein Society · 2025Article
- Uncovering Enzyme-Specific Post-Translational Modifications: An Overview of Current Methods.Proteomes · 2025Review
- Characterizing and engineering post-translational modifications with high-throughput cell-free expression.Nature communications · 2025Article
- Assessing Substrate Scope of the Cyclodehydratase LynD by mRNA Display-Enabled Machine Learning Models.Biochemistry · 2025Article
- Substrate recognition by a peptide-aminoacyl-tRNA ligase.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Ligand-Enabled Selective Coupling of MIDA Boronates to Dehydroalanine-Containing Peptides and Proteins.Journal of the American Chemical Society · 2025Article
- Substrate prediction for RiPP biosynthetic enzymesDigital discovery · 2025Article
- Kinetic Analysis of Cyclization by the Substrate-Tolerant Lanthipeptide Synthetase ProcM.ACS catalysis · 2024Article
- Cell-Free Systems: Ideal Platforms for Accelerating the Discovery and Production of Peptide-Based Antibiotics.International journal of molecular sciences · 2024Review
- Cell-free biosynthesis and engineering of ribosomally synthesized lanthipeptides.Nature communications · 2024Article
- Cheminformatics-Guided Cell-Free Exploration of Peptide Natural Products.Journal of the American Chemical Society · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.