ArticleCommunications chemistry2020
Unraveling the energetic significance of chemical events in enzyme catalysis via machine-learning based regression approach.
Article in Communications chemistry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed, 21 citations in OpenAlex.
- Enhancing Conformational Sampling for Intrinsically Disordered and Ordered Proteins by Variational Autoencoder.International journal of molecular sciences · 2023Article
- Unveiling the structural features that regulate carbapenem deacylation in KPC-2 through QM/MM and interpretable machine learning.Physical chemistry chemical physics : PCCP · 2023Article
- Trends inComputational and structural biotechnology journal · 2023Review
- ADMETboost: a web server for accurate ADMET prediction.Journal of molecular modeling · 2022Article
- Mechanistic Insights into Enzyme Catalysis from Explaining Machine-Learned Quantum Mechanical and Molecular Mechanical Minimum Energy Pathways.ACS physical chemistry Au · 2022Article
- QM/MM modeling of class A β-lactamases reveals distinct acylation pathways for ampicillin and cefalexin.Organic & biomolecular chemistry · 2021Article
- UMAP as a Dimensionality Reduction Tool for Molecular Dynamics Simulations of Biomacromolecules: A Comparison Study.The journal of physical chemistry. B · 2021Article
- Explore Protein Conformational Space With Variational Autoencoder.Frontiers in molecular biosciences · 2021Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
The bacterial enzyme class of β-lactamases are involved in benzylpenicillin acylation reactions, which are currently being revisited using hybrid quantum mechanical molecular mechanical (QM/MM) chain-of-states pathway optimizations. Minimum energy pathways are sampled by reoptimizing pathway geometry under different representative protein environments obtained through constrained molecular dynamics simulations. Predictive potential energy surface models in the reaction space are trained with machine-learning regression techniques. Herein, using TEM-1/benzylpenicillin acylation reaction as the model system, we introduce two model-independent criteria for delineating the energetic contributions and correlations in the predicted reaction space. Both methods are demonstrated to effectively quantify the energetic contribution of each chemical process and identify the rate limiting step of enzymatic reaction with high degrees of freedom. The consistency of the current workflow is tested under seven levels of quantum chemistry theory and three non-linear machine-learning regression models. The proposed approaches are validated to provide qualitative compliance with experimental mutagenesis studies.
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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.