High-throughput Screening and Experimental Validation

Creative Biogene's AI-assisted high-throughput screening service combines the predictive power of AI models with the advantages of high-throughput screening, helping researchers efficiently identify potential high-activity enzyme mutations, accelerating enzyme optimization, and ultimately improving the success rate of enzyme engineering projects.

Limitations of Traditional High-Throughput Screening

Although traditional High-throughput screening (HTS) is widely used in enzyme engineering, it has significant limitations. Firstly, the screening process often relies on the specific characteristics and initial sequences of the enzymes, resulting in a highly uncertain screening cycle and starting point selection. Secondly, due to the unclear relationship between sequence, structure, and catalytic performance, the screening process requires substantial time and resources. This is particularly problematic when dealing with non-natural reactions, where it may be impossible to find an initial reactive enzyme mutant or sequence as a starting point for directed evolution. Lastly, this method remains a "black box" operation, lacking a deep understanding of the mutation mechanisms, further limiting optimization efficiency and accuracy.

AI-Assisted High-Throughput Screening Service

Creative Biogene's AI-assisted High-throughput Screening service integrates AI technology with advanced computational algorithms, significantly enhancing the efficiency and accuracy of enzyme engineering. By utilizing molecular mechanics (MM), quantum mechanics (QM), and multi-scale QM/MM molecular simulation techniques, we can predict and explore a broader mutation space, providing precise experimental guidance. These computational tools not only reduce trial-and-error costs but also overcome the limitations of traditional high-throughput screening, particularly in designing enzymes for non-natural reactions.

Figure 1 illustrates how the integration of ultra-high-throughput droplet screening, second-generation sequencing, and deep learning facilitates the implementation, validation, and assessment of targeted evolution strategies. (doi: 10.1021/acs.chemrev.2c00910) Figure 1. By integrating results from ultra-high-throughput droplet screening with second-generation sequencing and deep learning, targeted evolution strategies can be implemented, validated, and assessed. (Gantz M, et al., 2023)

Wet Lab Validation Service

Wet lab validation plays a crucial role in Creative Biogene's AI-assisted high-throughput screening service. Firstly, it ensures the practical feasibility of AI predictions by experimentally verifying the accuracy of AI models in predicting enzyme performance. Wet lab validation provides real data on enzyme performance, which can be fed back into the AI models to further optimize them and enhance their predictive capabilities. Additionally, wet lab validation helps identify potential issues in AI-designed enzymes, such as stability and activity problems, allowing for improved enzyme design strategies. By incorporating wet lab validation, our service not only enhances the reliability of AI models but also accelerates the overall enzyme optimization process, ensuring that the final engineered enzymes perform exceptionally well in practical applications.

Detailed Service Process Analysis

1. Requirement Analysis and Planning:

Initial Consultation and Objective Confirmation: Conduct preliminary meetings with clients to thoroughly understand their research or application requirements, objectives, and background.
Project Planning: Develop a personalized enzyme optimization plan integrating AI algorithms and advanced computational methods based on client needs and goals. Define key project milestones and timelines.

2. Computational Simulation and Prediction:

Selection of Simulation Techniques: Utilize AI-driven technologies to predict and explore enzyme mutation spaces. Evaluate the impact of various mutations on enzyme activity under specific reaction conditions.

3. High-Throughput Screening and Mutation Identification:

Experimental Design: Employ AI-assisted design of high-throughput screening experiments, utilizing techniques such as Fluorescence-activated cell sorting (FACS) for intracellular or cell membrane target product screening, and Droplet-based microfluidic sorting (DMFS) for high-throughput screening of extracellular enzymes and metabolic products.

4. Wet Lab Validation and Data Analysis:

Selection of Candidate Variants: Based on high-throughput screening and AI simulation results, select the most promising enzyme variants for wet lab validation.
Experimental Validation: Perform wet lab experiments guided by AI models to measure enzyme activity, stability, and adaptability.
Data Collection and Analysis: Collect experimental data and conduct in-depth analysis using AI algorithms to validate simulation accuracy and identify key factors influencing enzyme performance.

5. Results Interpretation and Report Submission:

Interpretation of Results: Based on AI simulations and experimental data analysis, interpret the success of enzyme optimization strategies and relevant findings.
Report Writing: Prepare comprehensive reports outlining project progress, experimental results, and AI-driven optimal enzyme design strategies. Ensure clear communication of experimental data and recommendations.

6. Follow-Up Support and Optimization Recommendations:

Technical Support: Provide ongoing AI technical support to address implementation challenges and issues.
Optimization Recommendations: Offer suggestions to further enhance enzyme performance and stability based on experimental and AI simulation results, supporting clients in subsequent research and application development.

Discover new opportunities and quicken your success journey with our all-inclusive service package. Get in touch with us right now to find out how our AI-assisted strategy may turn your journey toward enzyme optimization into a driver of innovation and discoveries.

* For research use only. Not intended for any clinical use.

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