Free-Energy Ribosome Model for Protein Yield Optimization
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Solution Overview
Problem
Current methods for heterologous protein synthesis in organisms like Escherichia coli often result in low protein yield due to factors beyond codon bias and rare tRNA availability, including ribosome pausing and misfolding, which are not effectively addressed by existing optimization techniques.
Innovation Solution
A free-energy based model of translation elongation that predicts and optimizes protein yield by determining ribosome wait time and displacement, using tRNA abundance, ribosome displacement magnitude, and the force from mRNA-ribosome interactions to modify codons and improve protein synthesis efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If codon adaptation index optimization is used to improve protein yield, then protein synthesis efficiency is improved, but ribosome pausing and misfolding issues persist
Solution Approach 1:
The patent changes the optimization parameters from traditional CAI-based codon selection to a physics-based model incorporating ribosome displacement magnitude, wait time cycles, and free energy calculations. This fundamental parameter change enables simultaneous optimization of both productivity and reliability by addressing ribosome pausing and misfolding mechanisms directly.
Solution Approach 2:
The patent replaces the traditional biological/empirical optimization approach (CAI) with a biophysical model that uses free energy calculations, ribosome displacement mechanics, and thermal effects. This substitution allows for a more fundamental understanding and control of translation dynamics, resolving issues that empirical methods cannot address.
2Speed
If ribosome wait time is reduced to increase translation speed, then protein synthesis rate is improved, but ribosome displacement and pausing increase
Solution Approach 1:
The patent applies dynamics by modeling ribosome displacement as a dynamic process influenced by thermal effects, free energy changes, and interaction forces. The ribosome is treated not as a static machine but as a dynamic system that fluctuates and pauses based on thermodynamic conditions, allowing for optimized translation speed that accounts for inherent temporal variations.
Solution Approach 2:
The patent incorporates phase transition concepts through free energy calculations and thermal effects in the ribosome-mRNA interaction model. By considering thermal fluctuations and energy barriers, the model predicts ribosome pausing and displacement events that occur during translation, enabling optimization of translation speed while accounting for these phase-like transitions in the translation process.
3Productivity
If traditional CAI optimization is applied to heterologous genes, then codon bias is improved, but protein aggregation and misfolding remain problematic
Solution Approach 1:
The patent replaces empirical CAI optimization with a biophysical model that calculates free energy changes, ribosome displacement forces, and thermal effects during translation. This fundamental substitution enables prediction and prevention of protein aggregation by modeling the physical conditions that lead to misfolding, rather than relying on indirect codon bias corrections.
Solution Approach 2:
The patent implements feedback mechanisms through iterative optimization where the model predicts translation dynamics, protein folding outcomes, and aggregation tendencies. The optimization process uses feedback from these predictions to adjust codon selection and gene design, continuously improving protein yield while reducing aggregation and misfolding events.
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 model significantly increases protein yield and reduces aggregation by optimizing codon usage to minimize ribosome wait time and displacement, outperforming traditional methods like codon adaptation index optimization.
Implementation Method 1
the periodic signal corresponds to 'in-frame' ribosome translocation during elongation... interactions between the ribosome and the mRNA... force from binding between the mRNA and a 3′ terminal rRNA tail
Data Source
AI summary
The presently disclosed subject matter provides a free-energy based model of translation elongation to predict and optimize heterologous gene expression. The model and software allow for the prediction and optimization of genes for increased or decreased protein yield and for increased or decreased protein aggregation.