Electrostatics-Guided Protein Variant Prioritization with TKSA-MC

Overview

This offering provides a high-throughput computational workflow based on TKSA-MC (Titration-KMC with Monte Carlo), designed to prioritize enzyme variants before synthesis and screening. The method evaluates residue-level electrostatic contributions to native-state stability while simultaneously sampling coupled protonation states. By identifying destabilizing charged residues and ranking candidate substitutions, it enables researchers to narrow large sequence libraries to a focused set of variants that are most likely to maintain folding and stability while functional sites are engineered.

This is particularly valuable for herbicide targets and detoxification enzymes, where mutations near binding pockets or catalytic regions often destabilize the protein. The workflow helps preserve structural integrity and expression, reducing the number of constructs that need to be experimentally tested.

Technical specifications

Key features:

• Physics-based electrostatic analysis of protein structures, including coupled protonation state sampling across varying pH and temperature conditions
• Residue-level identification of favorable and unfavorable electrostatic interactions
• Evaluation of single and combinatorial mutations for compensatory or stabilizing effects
• Integration of structural, active-site, conservation, and ligand-interaction constraints to protect function
• Output of ranked mutation sets, electrostatic stability profiles, and design rationale
• Reproducible computational workflow that can be extended to multiple protein targets

The method is designed to be used as a pre-screening step, reducing the number of variants that need to be expressed and tested by focusing on those with the highest probability of success.

Technology readiness level

TKSA-MC has been experimentally validated in enzyme engineering studies, including the design of variants with improved thermotolerance and catalytic performance. The method is published and has demonstrated utility in generating stable, functional variants. It is currently offered as a computational service for target-specific analysis, with a staged collaboration model that includes initial structure analysis, variant prioritization, and refinement based on experimental results. The workflow is ready for application to new protein targets, with further validation planned through collaborative projects.