Peptide Fitness Prediction Using Atom Type Composition
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Solution Overview
Problem
Existing methods for determining peptide fitness, particularly in protein engineering, are inefficient and require extensive physical interaction testing, making them costly and data-intensive, and struggle with sparse data issues in predicting peptide interactions.
Innovation Solution
A method using machine learning based on atom type composition analysis of a peptide library, where peptides are classified by their atom type composition, trained on interaction data, and used to predict the fitness of new peptides without physical interaction testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical interaction testing is used to determine peptide fitness, then measurement precision is improved, but productivity deteriorates due to extensive testing requirements
Solution Approach 1:
The patent calculates atom type composition for all peptides in advance and stores it in a database. This preliminary calculation eliminates the need for repeated physical testing, allowing rapid prediction of peptide fitness by simply comparing atom type composition against trained models, thus resolving the contradiction between measurement precision and productivity.
Solution Approach 2:
The patent creates a computational model that copies the essential features of physical interaction testing through atom type composition analysis. Instead of performing actual physical tests for each peptide, the system uses a trained machine learning model that replicates the predictive capability of physical testing, enabling fast and accurate fitness determination.
2Device complexity
If atom type composition analysis is used instead of sequence analysis, then device complexity is reduced, but measurement precision may worsen
Solution Approach 1:
The patent transforms the input parameters from complex amino acid sequences to simplified atom type composition features. By changing the parameter representation from sequence-based to composition-based, the model complexity is reduced while maintaining predictive accuracy through comprehensive atom-type categorization that captures essential interaction features.
Solution Approach 2:
The patent extracts only the essential features needed for fitness prediction by focusing on atom type composition rather than complete sequence information. This extraction of critical features simplifies the modeling process while retaining the key determinants of peptide-target interaction, resolving the contradiction between complexity and precision.
3Reliability
If a comprehensive peptide library is tested physically, then reliability of training data is improved, but loss of substance increases due to material consumption
Solution Approach 1:
The patent performs comprehensive physical testing on a representative training library in advance to establish reliable atom type composition-fitness relationships. This preliminary action creates a robust training dataset that can then be used for predicting the fitness of numerous other peptides without additional physical testing, thus maintaining reliability while minimizing material consumption.
Solution Approach 2:
The trained machine learning model serves multiple functions: it can predict fitness for any peptide with the target, validate new peptide designs, and guide library synthesis. This universal applicability of the trained model eliminates the need for repeated physical testing across different peptide sets, reducing material loss while maintaining reliable predictions.
Data Source
AI summary
A method for determining a fitness value of a new peptide including: generating a library of sample peptides having unique amino acid sequences; measuring the interaction of each sample peptide with the target peptide; classifying each of the sample peptides according to their atom type composition, wherein the atom type composition is based on at least one of: the type of element, number of atoms, role in a functional group, position in within the amino acid, or a combination thereof; training a machine learning system with the sample peptides, the training is based on the measured interaction and the atom type composition; providing to the machine learning system a new peptide, not being part of the library of sample peptides; and, predicting via the machine learning system, the fitness value of the new peptide based on the atom type composition of the new peptide.