AI Model Predicts Protein Shapes from Infrared Light Data

This patent describes a computer method that uses an artificial intelligence model to predict the detailed 3D shapes of proteins within a food ingredient by analyzing how the ingredient absorbs infrared light.

What it covers

The patent outlines a computer-based method for predicting the secondary structure of a specific protein in an ingredient. First, it collects two types of digital data: Fourier Transform Infrared Spectroscopy (FTIR) spectra from many ingredients and their known protein secondary structures (Claim 1). The FTIR data is then transformed into a 'quantized' format. This prepared data trains an artificial intelligence (AI) model. Once trained, the AI model receives the FTIR spectrum of a new, specific ingredient and predicts its protein secondary structure. For example, a food company could use this to quickly determine if a new plant-based protein ingredient has the desired alpha-helix or beta-sheet structures for a specific texture or function in a product.

What it doesn't cover

• —Does not cover predicting protein secondary structure without using FTIR spectra as input data.
• —Does not cover methods that predict protein secondary structure without using an artificial intelligence model.
• —Does not cover AI models that do not include multiple sub-models like Partial Least Squares or XGBoost, as specified in Claim 3.
• —Does not cover predicting protein tertiary or quaternary structures, only secondary structures like alpha-helix and beta-sheet.
• —Does not cover methods that do not involve a training phase using a dataset of known FTIR spectra and corresponding secondary structures.

The clever bit

The novelty lies in combining specific machine learning techniques, including multiple sub-models like XGBoost and Lasso regression within a 'stacking ensemble regressor,' to accurately interpret complex FTIR spectra and predict protein secondary structures. This approach allows for a more robust and precise prediction than simpler models.

Why it matters

Understanding protein secondary structure is crucial in fields like food science, pharmaceuticals, and materials science, as it directly influences a protein's function, stability, and interaction with other molecules. This patent offers a rapid, non-destructive way to gain this insight, potentially speeding up research and development cycles. For instance, in developing plant-based foods, knowing the protein structure helps engineers formulate products with specific textures, like the chewiness of meat alternatives.

Real-world examples

• 1.Quality control in food manufacturing for protein-rich ingredients
• 2.Development of new plant-based protein products
• 3.Characterization of protein ingredients for nutritional supplements
• 4.Research into protein functionality in biopharmaceuticals