ArticleNature communications2018
Discovering de novo peptide substrates for enzymes using machine learning.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 82 citations in OpenAlex.
- Precision Pediatric Cancer Nanomedicine: Advancing Personalized Nano Therapies to Reduce Non-Communicable Diseases Through AI-Driven 3D-Printed Drugs.International journal of nanomedicine · 2026Review
- Decoding heterogeneous electrocatalysts for acidic oxygen evolution: mechanisms, rational design and AI acceleration.National science review · 2025Review
- Uncovering Enzyme-Specific Post-Translational Modifications: An Overview of Current Methods.Proteomes · 2025Review
- Deep-ProBind: binding protein prediction with transformer-based deep learning model.BMC bioinformatics · 2025Article
- ACVPICPred: Inhibitory activity prediction of anti-coronavirus peptides based on artificial neural network.Computational and structural biotechnology journal · 2024Article
- Cross-Species Applications of Peptide Substrate Reporters to Quantitative Measurements of Kinase Activity.ACS measurement science au · 2024Article
- Active learning streamlines development of high performance catalysts for higher alcohol synthesis.Nature communications · 2024Article
- Microbial synthesis of pyrroloquinoline quinone.World journal of microbiology & biotechnology · 2023Review
- "Freedom of design" in chemical compound space: towards rationalChemical science · 2023Article
- A Customized Bayesian Algorithm to Optimize Enzyme-Catalyzed Reactions.ACS sustainable chemistry & engineering · 2023Article
- Article
- Anti-Cancer Peptides: Status and Future Prospects.Molecules (Basel, Switzerland) · 2023Review
- Assessing sequence-based protein-protein interaction predictors for use in therapeutic peptide engineering.Scientific reports · 2022Article
- Protein-protein interface analysis of the non-ribosomal peptide synthetase peptidyl carrier protein and enzymatic domains.Synthetic and systems biotechnology · 2022Review
- Development of Anticancer Peptides Using Artificial Intelligence and Combinational Therapy for Cancer Therapeutics.Pharmaceutics · 2022Review
- Structure-based prediction of HDAC6 substrates validated by enzymatic assay reveals determinants of promiscuity and detects new potential substrates.Scientific reports · 2022Article
- PDL1Binder: Identifying programmed cell death ligand 1 binding peptides by incorporating next-generation phage display data and different peptide descriptors.Frontiers in microbiology · 2022Article
- Design of Protein Segments and Peptides for Binding to Protein Targets.Biodesign research · 2022Review
- Discovery of phosphotyrosine-binding oligopeptides with supramolecular target selectivity.Chemical science · 2021Article
- Synthase-Selective Exploration of a Tunicate Microbiome by Activity-Guided Single-Cell Genomics.ACS chemical biology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The discovery of peptide substrates for enzymes with exclusive, selective activities is a central goal in chemical biology. In this paper, we develop a hybrid computational and biochemical method to rapidly optimize peptides for specific, orthogonal biochemical functions. The method is an iterative machine learning process by which experimental data is deposited into a mathematical algorithm that selects potential peptide substrates to be tested experimentally. Once tested, the algorithm uses the experimental data to refine future selections. This process is repeated until a suitable set of de novo peptide substrates are discovered. We employed this technology to discover orthogonal peptide substrates for 4'-phosphopantetheinyl transferase, an enzyme class that covalently modifies proteins. In this manner, we have demonstrated that machine learning can be leveraged to guide peptide optimization for specific biochemical functions not immediately accessible by biological screening techniques, such as phage display and random mutagenesis.
Indexed as
Identifiers
What OpenQuestion holds
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.