In Silico Protein Quality Scoring for Microorganism Selection
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Solution Overview
Problem
Current methods for determining the nutritional quality of proteins are labor-intensive and costly, relying heavily on animal-based digestibility tests, which also raise animal welfare concerns, and there is a need for more sustainable and efficient ways to produce high-quality protein sources to meet the increasing demand for high-protein diets.
Innovation Solution
An in silico method that calculates an organism's protein nutritional quality score by accessing a genomic library, creating an adjusted relative abundance proteomic library, and using computational algorithms, such as machine learning, to select organisms with high protein nutritional quality scores for producing high-quality protein ingredients through fermentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If animal-based digestibility tests are used to determine protein nutritional quality, then measurement precision is improved, but loss of time and cost increase significantly
Solution Approach 1:
The patent creates in silico copies of animal-based digestibility tests through computational algorithms. These digital models replicate the functionality of physical animal tests without requiring actual animal subjects, thereby maintaining measurement precision while eliminating time-consuming and costly biological experiments.
Solution Approach 2:
The patent replaces the mechanical/biological system of animal-based digestibility testing with an information-based computational system. By substituting physical biological experiments with in silico algorithms, the method maintains scientific rigor while dramatically reducing time and resource requirements.
2Measurement precision
If animal-based digestibility tests are used to determine protein nutritional quality, then measurement precision is improved, but cost increases significantly
Solution Approach 1:
The patent creates digital replicas of expensive animal-based testing procedures through computational models. These in silico copies provide the same measurement precision as physical animal tests but at a fraction of the cost, eliminating the need for maintaining animal facilities and conducting complex biological experiments.
Solution Approach 2:
The patent substitutes the costly mechanical and biological infrastructure of animal testing with affordable computational algorithms. This replacement maintains scientific accuracy while making protein quality assessment economically viable for widespread application in food product development.
3Measurement precision
If animal-based digestibility tests are used, then protein nutritional quality can be determined, but animal welfare concerns arise
Solution Approach 1:
The patent creates virtual models that replicate animal digestive processes without involving actual animals. These in silico simulations provide the same nutritional quality assessment capabilities while completely eliminating harm to animal welfare, as the computational models process protein data without requiring biological subjects.
Solution Approach 2:
The patent replaces the biological system involving animal subjects with an artificial computational system. This substitution maintains the ability to measure protein nutritional quality while removing all animal welfare concerns, as the in silico algorithms process biochemical data without involving living organisms.
4Measurement precision
If traditional protein production methods are used, then high-quality protein can be produced, but resource consumption increases
Solution Approach 1:
The patent performs preliminary in silico screening and selection of microorganisms before actual fermentation production. By using computational algorithms to predict protein nutritional quality and identify optimal strains in advance, the method prevents resource waste on unsuccessful production attempts and ensures high-quality protein output from the outset.
Solution Approach 2:
The patent enables microorganisms to be selected and optimized based on their inherent genomic and proteomic characteristics without requiring extensive external resource input. The in silico methods allow the system to self-evaluate and identify optimal protein producers through computational analysis of biological data, reducing the need for resource-intensive trial-and-error experimentation.
Data Source
AI summary
Provided are in silico methods for utilizing an algorithm and machine learning model to compute a protein nutritional quality score for an organism from the organism's genome and to select an organism as a source of protein based on a computed protein nutritional quality score.