Enzyme Engineering via Electrostatic Barrier Prediction
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Solution Overview
Problem
Current enzyme engineering processes, such as directed evolution, are slow and expensive due to the astronomical number of possibilities in enzyme sequences, limiting the rate at which enzymes can be optimized for sustainable processes, and existing methods struggle to predict the impact of mutations outside the active site on catalytic activity.
Innovation Solution
A methodology combining molecular dynamics and quantum mechanics to predict enzyme catalytic activity by estimating the electrostatic component of the activation barrier, allowing for the rapid evaluation of candidate mutant enzymes and the identification of synergistic epistatic effects across multiple sites, including those outside the active site, using machine learning models trained on large datasets generated by these predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If directed evolution is used to optimize enzyme catalytic activity, then enzyme properties can be improved, but the process becomes extremely time-consuming and resource-intensive due to the astronomical number of possible mutations
Solution Approach 1:
The patent applies preliminary action by using machine learning models to predict beneficial mutations before experimental validation. The system trains ML models on existing enzyme data to identify promising mutant variants, allowing researchers to prioritize and test only the most likely successful candidates rather than randomly screening all possible mutations.
Solution Approach 2:
The patent uses computational copying by creating virtual representations of enzyme variants through molecular dynamics simulations and ML predictions. Instead of physically creating and testing every possible mutant, the system generates computational models that predict the behavior and activity of potential mutants, reducing the need for extensive physical experimentation.
2Manufacturing precision
If exhaustive screening of all possible mutations is performed, then the best enzyme variants can be identified, but the complexity and cost of the process becomes unmanageable
Solution Approach 1:
The patent applies local quality by focusing computational and experimental resources on specific regions of the enzyme that are most likely to influence catalytic activity. The ML models identify key residues and active site regions that contribute most to enzyme function, allowing targeted mutagenesis and screening of only those critical areas rather than the entire enzyme structure.
Solution Approach 2:
The patent uses parameter changes by systematically varying specific mutations and observing their effects on enzyme activity through ML predictions and targeted experiments. The system changes individual amino acid parameters at key positions, predicts the outcome, and iteratively optimizes based on these controlled parameter changes rather than exhaustive random screening.
3Productivity
If multiple mutations are introduced simultaneously to explore synergistic effects, then enzyme performance can be significantly improved, but the number of possible combinations becomes experimentally intractable
Solution Approach 1:
The patent applies preliminary action by using ML models to predict which combinations of multiple mutations are most likely to produce synergistic effects. The system analyzes interactions between different mutations computationally before experimental validation, allowing researchers to test only the most promising multi-mutation combinations rather than all possible combinations.
Solution Approach 2:
The patent uses computational ML models as intermediaries between individual mutation analysis and multi-mutation combination testing. The ML models predict how multiple mutations will interact and combine their effects, serving as a virtual mediator that guides the selection of multi-mutation variants for experimental testing without requiring exhaustive screening of all combinations.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly accelerates the enzyme engineering process by enabling the rapid identification of high-performing enzyme variants and the discovery of novel enzyme variants, reducing the need for extensive screening and improving enzyme turnover numbers, thereby enhancing the efficiency and effectiveness of enzyme optimization.
Implementation Method 1
a region of the enzyme (QM region) comprising at least part of the active site and a substrate of the enzyme is optimised with a quantum mechanics method
Implementation Method 2
performing a molecular dynamics simulation with the candidate mutant enzyme and a substrate of the enzyme to obtain a plurality of conformations each associated with a set of atomic coordinates
Implementation Method 3
estimating the electrostatic component of the activation barrier (ΔΔG‡Q20) for each of the plurality of conformations of the candidate mutant enzyme
Data Source
AI summary
The present invention relates to computer-implemented methods for predicting catalytic activity for a candidate mutant enzyme comprising estimating the electrostatic component of the activation barrier for each of a plurality of conformations of each candidate mutant enzyme, for predicting catalytic activity for a candidate mutant enzyme using a machine learning model trained using data obtained using such methods, for providing a site directed mutagenesis potential map for an enzyme using the described methods, and for identifying a candidate enzyme with improved catalytic activity using the described methods. Related systems and computer-readable media are also described.