AI-Assisted Screening & Variant Prediction

Screening Challenges

Traditional directed evolution generates more variants than any laboratory can practically evaluate:

These challenges are not solved by simply screening faster. They require a computational pre-filter that identifies promising variants before they reach the bench.

AI-Driven Prediction Platform

Integrated Screening Workflow

Supported Engineering Goals

Related Screening Services

To complement AI-assisted variant prediction, we provide high-throughput enzyme screening, activity assays, stability screening, substrate profiling, and biochemical characterization services for experimental validation of prioritized enzyme variants.

• Enzyme Screening
• Enzyme Activity Measurement
• Substrate Screening and Identification

Example Use Scenario

Computational Design of Enantioselective Epoxide Hydrolases

Figure 1. The developed CASCO framework for redesign of catalytic selectivity by integration of computational enzyme design and HTMI-MD. The computational design includes the introduction of steric hindrance to prevent unwanted substrate binding modes. (Wijma et al., 2015)

This study presents CASCO (catalytic selectivity by computational design), a computational strategy for engineering highly enantioselective epoxide hydrolases. The approach combines rational mutation design, steric control of substrate binding, and high-throughput molecular dynamics simulations to guide substrate orientation toward desired reaction pathways. Using limonene epoxide hydrolase as a model enzyme, researchers designed small mutant libraries capable of selectively producing highly enantioenriched (S,S)-diols and (R,R)-diols. Remarkably, highly stereoselective enzyme variants were identified after screening only 37 mutants experimentally. The results demonstrate that computational enzyme design can greatly reduce experimental workload while efficiently generating biocatalysts with tailored enantioselectivity for synthetic chemistry and industrial biocatalysis.

FAQs

• Q: Does AI replace experimental screening?

  A: No. AI prioritizes which variants to screen, dramatically reducing the number requiring experimental evaluation. The remaining candidates still require wet-lab validation, but the screening burden is typically reduced by 90% or more while maintaining or improving hit rates.

• Q: How accurate are predictions?

  A: Accuracy varies by enzyme family, property target, and available training data. For well-characterized enzyme classes, top-ranked predictions achieve experimental confirmation rates of 70–85%. For novel families, predictions are more exploratory but still substantially outperform random selection. All predictions include calibrated confidence scores.

• Q: What input data is required?

  A: Variant sequences are sufficient for sequence-based predictors. Parent enzyme structures or homology models improve accuracy for structure-aware scoring. Historical screening data from related projects, if available, further enhance prediction quality through transfer learning.

• Q: Can this integrate with your library design service?

  A: Yes. Libraries designed through AI-Guided Mutant Library Design are formatted for direct input into the screening prediction platform. The combination creates a seamless pipeline from intelligent design to intelligent screening.

• Q: What if predictions fail?

  A: Prediction failures are analyzed to identify model limitations and update training data. This feedback loop is integral to our service. Clients receive transparent reporting of prediction accuracy, including both successes and failures, to inform model improvement.

• Q: How quickly can screening priorities be generated?

  A: Scoring of libraries up to 10⁴ variants completes within days. Libraries of 10⁵–10⁶ variants require 1–2 weeks. Results are delivered as ranked lists with filtering tools for custom prioritization.