AIMC Topic: Enzymes

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Artificial intelligence driven platform for rapid catalytic performance assessment of nanozymes.

Scientific reports Apr 17, 2025
Traditional methods for synthesizing nanozymes are often time-consuming and complex, hindering efficiency. Artificial intelligence (AI) has the potential to simplify these processes, but there are very few dedicated nanozyme databases available, limi...
Artificial Intelligence Databases, Factual Nanostructures Enzymes Kinetics Catalysis
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Advancing Enzyme-Based Detoxification Prediction with ToxZyme: An Ensemble Machine Learning Approach.

Toxins Apr 1, 2025
The aaccurate prediction of enzymes with environment detoxification functions is crucial, not only to achieve a better understanding of bioremediation strategies, but also to alleviate environmental pollution. In the present study, a novel machine le...
Inactivation, Metabolic Deep Learning Neural Networks, Computer Machine Learning Enzymes Biodegradation, Environmental
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A Deep Retrieval-Enhanced Meta-Learning Framework for Enzyme Optimum pH Prediction.

Journal of chemical information and modeling Mar 24, 2025
The potential of hydrogen (pH) influences the function of the enzyme. Measuring or predicting the optimal pH (pH) at which enzymes exhibit maximal catalytic activity is crucial for enzyme design and application. The rapid development of enzyme mining...
Hydrogen-Ion Concentration Enzymes Deep Learning Algorithms
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Robust enzyme discovery and engineering with deep learning using CataPro.

Nature communications Mar 20, 2025
Accurate prediction of enzyme kinetic parameters is crucial for enzyme exploration and modification. Existing models face the problem of either low accuracy or poor generalization ability due to overfitting. In this work, we first developed unbiased ...
Protein Engineering Enzymes Kinetics Deep Learning
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TopEC: prediction of Enzyme Commission classes by 3D graph neural networks and localized 3D protein descriptor.

Nature communications Mar 20, 2025
Tools available for inferring enzyme function from general sequence, fold, or evolutionary information are generally successful. However, they can lead to misclassification if a deviation in local structural features influences the function. Here, we...
Protein Conformation Binding Sites Computational Biology Graph Neural Networks Enzymes Neural Networks, Computer Algorithms Software
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Artificial Intelligence Technology Assists Enzyme Prediction and Rational Design.

Journal of agricultural and food chemistry Mar 11, 2025
Since the structure of enzymes determines their function, elucidating the structure of enzymes lays a solid foundation for deciphering their catalytic mechanism and enabling rational design. The development of artificial intelligence (AI) has sparked...
Artificial Intelligence Algorithms Enzymes Protein Engineering
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CatPred: a comprehensive framework for deep learning in vitro enzyme kinetic parameters.

Nature communications Feb 28, 2025
Estimation of enzymatic activities still heavily relies on experimental assays, which can be cost and time-intensive. We present CatPred, a deep learning framework for predicting in vitro enzyme kinetic parameters, including turnover numbers (k), Mic...
Kinetics Uncertainty Deep Learning Enzymes
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A novel interpretability framework for enzyme turnover number prediction boosted by pre-trained enzyme embeddings and adaptive gate network.

Methods (San Diego, Calif.) Feb 26, 2025
It is a vital step to identify the enzyme turnover number (kcat) in synthetic biology and early-stage drug discovery. Recently, deep learning methods have achieved inspiring process to predict kcat with the development of multi-species enzyme-substra...
Substrate Specificity Algorithms Enzymes Kinetics Drug Discovery Synthetic Biology Deep Learning Neural Networks, Computer
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EC2Vec: A Machine Learning Method to Embed Enzyme Commission (EC) Numbers into Vector Representations.

Journal of chemical information and modeling Feb 21, 2025
Enzyme commission (EC) numbers play a vital role in classifying enzymes and understanding their functions in enzyme-related research. Although accurate and informative encoding of EC numbers is essential for enhancing the effectiveness of machine lea...
Machine Learning Enzymes Computational Biology
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Tailoring industrial enzymes for thermostability and activity evolution by the machine learning-based iCASE strategy.

Nature communications Jan 11, 2025
The pursuit of obtaining enzymes with high activity and stability remains a grail in enzyme evolution due to the stability-activity trade-off. Here, we develop an isothermal compressibility-assisted dynamic squeezing index perturbation engineering (i...
Directed Molecular Evolution Machine Learning Evolution, Molecular Enzymes Enzyme Stability Protein Engineering
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