CD ComputaBio proudly presents its cutting-edge AI-based service for the prediction of enzyme kinetic parameters. Our service seamlessly integrates state-of-the-art artificial intelligence (AI) technologies with the field of enzymology, enabling us to accurately and efficiently predict and analyze enzyme kinetic parameters. Through the utilization of AI, we empower researchers and professionals in the field of biotechnology to delve into the intricate details of enzymatic reactions, thereby advancing areas such as drug design, biocatalysis, and metabolic engineering.
What is Enzyme Kinetics?
Enzyme kinetics is the study of the chemical reactions rates catalyzed by enzymes. In enzyme kinetics, reaction rates are measured and the effects of changing reaction conditions are studied. Studying the kinetics of an enzyme in this way can reveal the enzyme's catalytic mechanism, its role in metabolism, how its activity is controlled, and how drugs or modifiers (inhibitors or activators) affect the rate.
Advantages of AI Methods
Engaging our AI prediction of enzyme kinetic parameters service offers numerous advantages:
- Enhanced Accuracy: AI-driven predictive models yield highly accurate estimations of enzyme kinetic parameters, reducing the need for extensive trial-and-error experimentation.
- Time-Efficient: Our service accelerates the prediction of enzyme kinetic parameters, saving time in the research and development process.
- Cost-Effective: By optimizing experimental design and focusing efforts on the most promising enzymatic conditions, researchers can save costs associated with excessive experimentation.
- Improved Understanding: Accurate predictions offer a deeper understanding of enzymatic reactions, facilitating the development of targeted enzymatic processes and optimized biocatalysts.
Our Classifiers
The listed classifiers at CD ComputaBio are used for the classification or clustering of enzyme sequences or structural data.
| Our Classifiers |
Applications/Key Features |
| Support Vector Machine (SVM) |
Binary classification and data with very high dimensions |
| K-Nearest-Neighbor (k-NN) |
Multi-class problems, selected Hyperparamerter remains the same, non-parametric algorithm |
| Decision Tree (DT) |
Visual representation of output, can utilize numerical and categorical features |
| Random Forest (RF) |
Classification/Regression |
| Recurrent Neural Network (RNN) |
Non-linear systems |
| Convolutional Neural Network (CNN) |
Classification/Regression |
Our Services
Enzyme Kinetic Models Building
We utilize AI technology to build predictive models for enzyme kinetic parameters.
- Michaelis–menten kinetic model of a double-substrate reaction
- Michaelis–menten kinetic model of a single-substrate reaction
Kinetic Parameter Prediction
Our service provides accurate predictions for various enzyme kinetic parameters, including Michaelis-Menten constants (KM), turnover numbers (Kcat), and catalytic efficiencies.
Comprehensive Analysis Reports
We offer detailed reports that summarize the predicted enzyme kinetic parameters, providing critical insights for experimental design and optimization.
Workflow of Our Services
Our AI-driven prediction of enzyme kinetic parameters service follows a well-structured and comprehensive process as follows:
Data Collection and Preprocessing: Gathering diverse enzymatic datasets and preparing the data for subsequent analysis, including cleaning, normalization, and feature engineering.
Model Training: Utilizing machine learning algorithms to build predictive models based on the preprocessed enzymatic data, employing cross-validation and optimization techniques for model selection.
Model Validation and Refinement: Rigorous validation of predictive models to ensure accuracy and generalizability, followed by iterative refinement to enhance prediction performance.
Enzyme Kinetic Parameter Prediction: Applying the trained models to predict enzyme kinetic parameters, leading to the generation of comprehensive reports and analyses.
At CD ComputaBio, we understand the paramount importance of enzyme engineering in driving innovation and making advancements in various industries. Our computational biology team has extensive experience in the research of building predictive models for enzyme. If you are interested in our services or have any questions, please feel free to contact us.
References:
- Li F, Yuan L, Lu H, et al. Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction[J]. Nature Catalysis, 2022, 5(8): 662-672.
- Rocha R A, Speight R E, Scott C. Engineering enzyme properties for improved biocatalytic processes in batch and continuous flow[J]. Organic Process Research & Development, 2022, 26(7): 1914-1924.
- Foster C J, Wang L, Dinh H V, et al. Building kinetic models for metabolic engineering[J]. Current Opinion in Biotechnology, 2021, 67: 35-41.
