Polypeptide Expression via Codon and RNA Folding Optimization
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Solution Overview
Problem
Current methods for recombinant polypeptide expression in biochemistry and biotechnology face challenges due to low expression yields, poor transcription, and limited understanding of physiochemical parameters influencing expression, leading to inefficient production of recombinant proteins in expression systems.
Innovation Solution
A method involving synonymous substitutions in protein-coding nucleic acid sequences, optimized using a generalized linear multiparameter model, to enhance RNA sequence parameters such as codon frequencies, nucleotide composition, and folding energy, which are linked to a ribosome-binding site, to increase recombinant polypeptide expression levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If synonymous substitutions are made to optimize codon usage and RNA folding energy, then polypeptide expression levels increase, but the complexity of nucleic acid sequence design increases
Solution Approach 1:
The patent applies parameter changes by systematically modifying nucleic acid sequence parameters including codon frequencies, nucleotide base composition, and RNA folding free energy. The generalized linear multiparameter model evaluates multiple sequence parameters simultaneously to identify optimal combinations that maximize polypeptide expression, transforming the design process from trial-and-error to parameter-optimized engineering.
Solution Approach 2:
The patent implements preliminary action by using computational modeling to predict and optimize nucleic acid sequence properties before actual expression experiments. The generalized linear multiparameter model allows researchers to screen and select optimal sequence designs in silico, performing the design optimization work beforehand to guide subsequent experimental validation and minimize iterative testing.
2Quantity of substance
If comprehensive multiparameter optimization is applied to nucleic acid sequences, then expression yield improves, but the time and computational resources required increase
Solution Approach 1:
The patent uses copying by creating computational models that replicate the complex biological expression system in silico. The generalized linear multiparameter model serves as a virtual copy of the expression machinery, allowing researchers to test and optimize sequences computationally before experimental validation, thereby reducing the need for numerous time-consuming wet lab iterations.
Solution Approach 2:
The model enables efficient parameter changes by allowing rapid evaluation of multiple sequence configurations through computational calculation. Rather than physically synthesizing and testing each variant, the system can quickly assess how changes in codon usage, base composition, and folding energy affect predicted expression levels, significantly reducing optimization time.
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 increases the expression levels of recombinant polypeptides by optimizing codon usage and RNA folding energy, improving protein production efficiency and yield in both in vitro and in vivo systems.
Implementation Method 1
the partition-function free-energy of RNA folding calculated by the program RNAstructure with default parameters
Implementation Method 2
in-frame single codon frequencies, in-frame ATA-ATA dicodon frequency
Data Source
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AI summary
The invention is directed to methods and metric suitable for use in modulating the expression of a polypeptide encoded by a nucleic acid sequence. In certain aspects, the invention also relates to methods for introducing modifications in a polypeptide, for example through substitution of one or more nucleic acids in an untranslated sequence or in a coding sequence of a nucleic acid sequence encoding a polypeptide to increase the expression of the polypeptide.