ReviewJACS Au2023
AlphaFold2 and Deep Learning for Elucidating Enzyme Conformational Flexibility and Its Application for Design.
Review in JACS Au, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
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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.
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Who cites it
25 citing papers in PubMed.
- Granaticin biosynthesis and engineering: mechanistic understanding, production bottlenecks, and computational opportunities.World journal of microbiology & biotechnology · 2026Review
- Scaffold-Lab: Critical evaluation and ranking of protein backbone generation methods in a unified framework.PLoS computational biology · 2026Article
- Machine Learning Prediction of Laccase-Catalyzed Oxidation of Aromatic Compounds Using Curated Enzyme-Specific Datasets.Journal of computational chemistry · 2026Article
- Protein structural dynamics in covalent drug design: insights from irreversible and reversible covalent inhibitors.RSC chemical biology · 2026Review
- AI-Driven Enzyme Engineering: Emerging Models and Next-Generation Biotechnological Applications.Molecules (Basel, Switzerland) · 2025Review
- Directed evolution of a beta-lactamase samples a wide variety of conformational states.Protein science : a publication of the Protein Society · 2025Article
- Of Revolutions and Roadblocks: The Emerging Role of Machine Learning in Biocatalysis.ACS central science · 2025Review
- Approaches for regulating enzyme activities: Recent advances in experiment and computation.Current opinion in structural biology · 2025Review
- Structure Prediction of Alternate Frame Folding Systems with AlphaFold3.Journal of chemical information and modeling · 2025Article
- Substrate Activation Efficiency in Active Sites of Hydrolases Determined by QM/MM Molecular Dynamics and Neural Networks.International journal of molecular sciences · 2025Article
- Reversible Substrate-Specific Photocontrol of the Chemotherapeutic Asparaginase(-Glutaminase) fromACS catalysis · 2025Article
- Decoding allosteric landscapes: computational methodologies for enzyme modulation and drug discovery.RSC chemical biology · 2025Review
- Multiscale Differential Geometry Learning for Protein Flexibility Analysis.Journal of computational chemistry · 2025Article
- Bridging prediction and reality: Comprehensive analysis of experimental and AlphaFold 2 full-length nuclear receptor structures.Computational and structural biotechnology journal · 2025Article
- Identification and Optimization of more Efficient Olivetolic Acid Synthases.Cannabis and cannabinoid research · 2024Article
- A novel shark VNAR antibody-based immunotoxin targeting TROP-2 for cancer therapy.Acta pharmaceutica Sinica. B · 2024Article
- Harnessing conformational dynamics in enzyme catalysis to achieve nature-like catalytic efficiencies: the shortest path map tool for computational enzyme redesign.Faraday discussions · 2024Article
- AlphaFold two years on: Validation and impact.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Integration of a Randomized Sequence Scanning Approach in AlphaFold2 and Local Frustration Profiling of Conformational States Enable Interpretable Atomistic Characterization of Conformational Ensembles and Detection of Hidden Allosteric States in the ABL1 Protein Kinase.Journal of chemical theory and computation · 2024Article
- Active site remodeling in tumor-relevant IDH1 mutants drives distinct kinetic features and potential resistance mechanisms.Nature communications · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The recent success of AlphaFold2 (AF2) and other deep learning (DL) tools in accurately predicting the folded three-dimensional (3D) structure of proteins and enzymes has revolutionized the structural biology and protein design fields. The 3D structure indeed reveals key information on the arrangement of the catalytic machinery of enzymes and which structural elements gate the active site pocket. However, comprehending enzymatic activity requires a detailed knowledge of the chemical steps involved along the catalytic cycle and the exploration of the multiple thermally accessible conformations that enzymes adopt when in solution. In this Perspective, some of the recent studies showing the potential of AF2 in elucidating the conformational landscape of enzymes are provided. Selected examples of the key developments of AF2-based and DL methods for protein design are discussed, as well as a few enzyme design cases. These studies show the potential of AF2 and DL for allowing the routine computational design of efficient enzymes.
Identifiers
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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.