ArticleProceedings of the National Academy of Sciences of the United States of America2024
Correlating enzymatic reactivity for different substrates using transferable data-driven collective variables.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Methods for the establishment of enzymatic mechanisms - from QM to ML.Chemical science · 2026Review
- Targeting the intrinsically disordered AR-NTD through a machine learning-based enhanced sampling workflow.Nature communications · 2026Article
- A Transferable and Robust Computational Framework for Class A GPCR Activation Free Energies.The journal of physical chemistry letters · 2026Article
- MDIntrinsicDimension: Dimensionality-Based Analysis of Collective Motions in Macromolecules from Molecular Dynamics Trajectories.Journal of chemical information and modeling · 2026Article
- Mechanism of Polyester Hydrolysis by Marine Bacterium PE-H Enzyme: an Atomistic and Thermodynamic Characterization.Journal of chemical information and modeling · 2026Article
- The role of fluctuations in the nucleation process.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications.Chemical reviews · 2026Review
- Adiabatic-Bias Molecular Dynamics Simulations Reveal the Impact of Mutations on Muscarinic Antagonist Unbinding Kinetics.Journal of chemical information and modeling · 2025Article
- Correlating enzymatic reactivity for different substrates using transferable data-driven collective variables.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
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Authors and funding
5 authors.
Funding
Abstract
Machine learning (ML) is transforming the investigation of complex biological processes. In enzymatic catalysis, one significant challenge is identifying the reactive conformations (RC) of the enzyme:substrate complex where the substrate assumes a precise arrangement in the active site necessary to initiate a reaction. Traditional methods are hindered by the complexity of the multidimensional free energy landscape involved in the transition from nonreactive to reactive conformations. Here, we applied ML techniques to address this challenge, focusing on human pancreatic α-amylase, a crucial enzyme in type-II diabetes treatment. Using ML-based collective variables (CVs), we correlated the probability of being in a RC with the experimental catalytic activity of several malto-oligosaccharide substrates. Our findings demonstrate a remarkable transferability of these CVs across various compounds, significantly streamlining the modeling process and reducing both computational demand and manual intervention in setting up simulations for new substrates. This approach not only advances our understanding of enzymatic processes but also holds substantial potential for accelerating drug discovery by enabling rapid and accurate evaluation of drug efficacy across different generations of inhibitors.
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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.