Cas Protein Variant Engineering Using Entropy-Based Normal Mode Analysis

Solution Overview

Problem

There is a need for methods to engineer proteins and other macromolecules with improved functional properties, particularly those interacting with nucleic acids, as existing methods do not effectively predict sequence-dependent activity and specificity of CRISPR-Cas systems.

Innovation Solution

A method utilizing normal mode analysis (NMA) to calculate entropy values for macromolecules and their complexes, identifying correlations between entropy and function to engineer variants with improved properties, such as Cas protein variants with specific amino acid substitutions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If normal mode analysis is used to calculate entropy values for macromolecules, then the ability to predict sequence-dependent activity and specificity is improved, but the computational complexity and time required for analysis increases

Engineering Contradiction:

Improveprediction accuracy of sequence-dependent activityVSAvoidcomputational time for entropy calculation

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent performs preliminary normal mode analysis on representative macromolecule structures to establish baseline entropy-function correlations before actual engineering applications. This pre-computed reference data enables faster prediction for new variants without repeating full NMA calculations, thus reducing computational time while maintaining prediction accuracy.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent transforms the complex macromolecular conformational analysis into entropy parameter calculations that can be correlated with functional outcomes. By changing the analysis parameter from detailed atomic trajectories to entropy values, the method achieves predictive power with reduced computational burden.

Inventive Principle:

Principle #35Parameter changes

2Reliability

If macromolecule variants are engineered to improve functional properties such as substrate specificity and nuclease activity, then the effectiveness of CRISPR-Cas systems is improved, but the complexity of protein engineering and characterization increases

Engineering Contradiction:

Improveeffectiveness of CRISPR-Cas systemVSAvoidcomplexity of protein engineering process

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent implements a feedback loop where entropy calculations from NMA are correlated with experimental functional data, and this correlation information feeds back into predicting the function of new variants. This feedback mechanism guides the engineering process, reducing trial-and-error complexity by providing predictive guidance for which variants to test next.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent replaces traditional trial-and-error mechanical protein engineering approaches with a computational prediction system based on entropy-function correlations. This substitution reduces the experimental complexity by filtering promising variants in silico before synthesis and testing, thereby streamlining the overall engineering process.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If existing methods are used for protein engineering without entropy correlation analysis, then the process is simpler and faster, but the ability to predict sequence-dependent activity and specificity is insufficient

Engineering Contradiction:

Improvespeed of protein engineering processVSAvoidprediction capability of sequence-dependent activity

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent performs preliminary normal mode analysis on representative macromolecule structures to establish baseline entropy-function correlations before actual engineering applications. This pre-computed reference data enables faster prediction for new variants without repeating full NMA calculations, thus reducing computational time while maintaining prediction accuracy.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent transforms the complex macromolecular conformational analysis into entropy parameter calculations that can be correlated with functional outcomes. By changing the analysis parameter from detailed atomic trajectories to entropy values, the method achieves predictive power with reduced computational burden.

Inventive Principle:

Principle #35Parameter changes

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

The method enables the prediction and engineering of macromolecule variants with enhanced functional properties, such as improved substrate specificity and nuclease activity, by correlating entropy values with functional outcomes.

Implementation Method 1

Normal mode analysis (NMA) is a computational method that can assess which conformational variations are accessible for a given protein. It relies on the premise that a protein is an oscillating system.

Methodology Applied

Scientific EffectNormal mode analysis: Harmonic Oscillator

Data Source

PatentUS20250215407A1Improved macromolecules and methods for designing same

Publication Date: 2025.07.03 RAMOT AT TEL AVIV UNIVERSITY LTD

AI summary

The present invention is directed to, inter alia, a method for identifying a reference macromolecule for which a variant having improved function can be identified and/or engineered. Further provided is a macromolecule variant, such as, but not limited to Cas protein variant(s), being characterized by having improved function compared to a reference, and a method for designing same.