Protein Ingredient Selection via Vector Clustering and Precision Fermentation
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Solution Overview
Problem
Current agricultural practices are unsustainable and contribute significantly to environmental issues such as climate change, water scarcity, and deforestation, necessitating the development of sustainable and nutritious food sources to meet the growing global population's demands.
Innovation Solution
A technology that uses predictive modeling and machine learning to identify and develop new protein sources by analyzing structural and functional characteristics of biomolecules, allowing for rapid-throughput production and empirical testing of proteins with desired target functions for use in food products, leveraging natural sources and recombinant expression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional agricultural practices are used to produce food ingredients, then food production can meet current demands, but environmental degradation, greenhouse gas emissions, and resource depletion worsen
Solution Approach 1:
The patent replaces traditional mechanical and chemical agricultural systems with a biological-computational system. Machine learning models predict protein functions from sequence data, and recombinant expression systems produce target proteins in controlled bioreactors, substituting field-based agriculture with laboratory-based molecular manufacturing that has minimal environmental footprint
Solution Approach 2:
The invention changes the fundamental parameters of food ingredient production by working at the molecular level rather than the organism level. By modifying amino acid sequences and expressing recombinant proteins, the system achieves precise control over protein properties (solubility, stability, function) while using minimal land, water, and energy resources compared to traditional agriculture
2Quantity of substance
If the global population grows to meet future demands, then food availability increases, but water and arable land resources become insufficient
Solution Approach 1:
The patent replaces resource-intensive agricultural systems with cellular factories that produce proteins in bioreactors. This substitution eliminates the need for vast arable land and freshwater resources, as recombinant protein expression requires only controlled cultivation of host cells (bacteria, yeast, or mammalian cells) in nutrient media, dramatically reducing water and land consumption per unit of protein produced
Solution Approach 2:
The invention creates a universal production platform that can manufacture any desired protein ingredient by simply changing the expressed gene sequence. This multi-functional system can produce whey proteins, caseins, gelatins, and other food ingredients in a single facility type, eliminating the need for diverse agricultural ecosystems and reducing overall resource requirements
3Adaptability or versatility
If new protein sources are discovered through traditional methods, then novel food ingredients can be identified, but the process is time-consuming and resource-intensive
Solution Approach 1:
The patent performs preliminary computational analysis of protein sequences and structures before any experimental work. Machine learning models predict functional properties, solubility, stability, and other critical parameters in silico, allowing researchers to prioritize only the most promising candidates for expression and testing. This preliminary computational filtering dramatically reduces the time and resources needed for actual protein discovery and characterization
Solution Approach 2:
The invention replaces traditional trial-and-error protein discovery methods with a computational design and prediction system. By using machine learning models trained on known protein structures and functions, the system can rapidly evaluate millions of sequence variants and predict their properties, identifying novel protein sources in days or weeks rather than years of field-based screening
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 enables the discovery of sustainable protein sources that can replace traditional ingredients, reducing environmental impact while enhancing food product properties, such as gelation, antimicrobial activity, and sensory characteristics, thereby addressing the need for resource-efficient and environmentally friendly food production.
Implementation Method 1
a computer system that is adapted for machine learning is trained to group similar proteins together and/or predict whether a protein has a preselected target function
Implementation Method 2
rapid-throughput production of previously uncharacterized proteins
Implementation Method 3
purified to determine if they have the target function and other desirable characteristics
Data Source
AI summary
This disclosure provides a technology for developing alternative protein sources for use in industrial food production. The technology evaluates naturally occurring proteins by a process that is done partly in silico and partly by empirical evaluation. A database is created in which each individual protein is characterized by vector representations of structural and functional features. Clusters of individual proteins are formed by pairwise comparison of each protein's vector representation, adjusting the degree of similarity used to define clusters until a desired number of clusters are obtained. A protein representative is selected from each cluster for evaluation by high-throughput expression and laboratory testing for a particular food function. High scoring representatives identify clusters that can be mined for additional protein candidates. Multiple cycles of the machine learning, database mining, expression and testing yield ingredients suitable for assessment as part of a commercial food product.