Computational Peptide Sequence Construction via Pattern Segmentation
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Solution Overview
Problem
Current methods for generating synthetic peptide sequences lack transparency and efficiency in producing sequences with desired functionalities, particularly for targeted and untargeted applications, and require improved computational techniques for accurate and speedy synthesis.
Innovation Solution
A computational method that identifies candidate sequence building blocks from known functional and non-functional peptide sequences, selects qualified blocks based on threshold requirements, and assembles them to generate synthetic peptide sequences, using a Multilayer Vector System model to create functional peptides with tunable accuracy and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional computational methods are used to generate peptide sequences, then sequence generation can be performed, but the methods lack transparency and efficiency in producing sequences with desired functionalities
Solution Approach 1:
The computational method is segmented into distinct functional modules: a database module storing known functional peptide sequences, a pattern recognition module that analyzes sequences to identify functional patterns, and a sequence generation module that constructs new sequences based on identified patterns. This segmentation improves reliability by ensuring each module performs its specific function accurately while maintaining overall system transparency.
Solution Approach 2:
The method performs preliminary analysis of known functional peptide sequences to identify and store functional patterns in a database before generating new sequences. This preliminary action ensures that the pattern recognition module has pre-processed reference data available, improving the accuracy and reliability of subsequent sequence generation without adding complexity during the actual synthesis phase.
2Reliability
If comprehensive database analysis is performed to identify functional patterns, then sequence functionality improves, but computational time and processing requirements increase
Solution Approach 1:
The system performs comprehensive database analysis in advance to identify and store functional patterns in a pre-processed database. This preliminary action consolidates the computationally intensive pattern recognition work before sequence generation, ensuring high sequence functionality while reducing processing time during actual peptide synthesis operations.
Solution Approach 2:
The method creates a database of copied and stored functional patterns from known peptide sequences. Instead of re-analyzing entire sequences each time, the system copies identified functional patterns into a database for rapid retrieval and application, maintaining sequence functionality while significantly reducing computational processing time for new sequence generation.
3Measurement precision
If pattern recognition procedures are applied to analyze peptide sequences, then functional patterns are identified, but the process lacks transparency in decision-making
Solution Approach 1:
The system incorporates feedback mechanisms where the pattern recognition module not only identifies patterns but also provides information about the matching process and confidence levels. This feedback loop maintains measurement precision by allowing verification of pattern matches while preserving computational transparency through visible decision-tracing capabilities that show how patterns were identified and applied.
4Manufacturing precision
If all combinations of amino acids are analyzed to conform to functional patterns, then sequence accuracy improves, but the quantity of data to be processed increases significantly
Solution Approach 1:
The method extracts only the essential functional patterns from comprehensive peptide sequence databases, separating critical functional elements from redundant data. This extraction process maintains peptide sequence accuracy by focusing on the most important pattern features while significantly reducing the volume of data that needs to be processed and stored in the database.
Solution Approach 2:
Instead of analyzing all possible amino acid combinations exhaustively, the system applies partial action by focusing analysis on the most critical pattern positions and features that determine functionality. This approach maintains manufacturing precision by concentrating computational resources on the most influential sequence elements while reducing overall data processing requirements.
Data Source
AI summary
A computational method for constructing a synthetic peptide sequence is disclosed. The method of the present invention includes the steps of (i) identifying a candidate sequence building block set comprising candidate sequence building blocks from a base set comprising known functional peptide sequences and optionally known non-functional peptide sequences; (ii) selecting a qualified sequence building block set comprising qualified sequence building blocks from said candidate sequence building block set; said qualified sequence building blocks satisfying a threshold requirement and (iii) assembling said qualified sequence building blocks to generate a synthetic peptide sequence. A synthetic peptide sequence and a functional synthetic peptide are also described.