MCU Graphs for Rapid Complex Molecule Substructure Identification
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Solution Overview
Problem
Current metabolite identification (MetID) approaches are inadequate for large molecules like therapeutic proteins and peptides, as they face computational complexity due to atom-based algorithms, inability to deconvolute monoisotopic peaks, and lack of consideration for distinct metabolic processes, leading to inefficient and inaccurate characterization of metabolites.
Innovation Solution
A system and method utilizing minimum cleavable unit (MCU) graphs and line graphs to represent molecules, enabling efficient identification and characterization of metabolites by generating a line graph data structure and traversing it to identify induced connected subgraphs, which are then used to determine molecular weights and biotransformations required to form substructures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If atom-based algorithms are used for metabolite identification, then identification accuracy for small molecules is improved, but computational complexity increases significantly for large biomolecules
Solution Approach 1:
The patent segments the molecule representation from atom-based to residue-based units. Instead of tracking individual atoms (which creates computational complexity for large biomolecules), the system divides the molecule into smaller functional residues or building blocks. This segmentation maintains identification accuracy by preserving chemically relevant information while dramatically reducing the computational search space for metabolite enumeration.
2Measurement precision
If conventional small molecule MetID software is used, then characterization of small molecule metabolites is improved, but compatibility with large biomolecules is lost
Solution Approach 1:
The patent creates a universal metabolite identification system that works across multiple scales of molecular complexity. By developing a residue-based representation and enumeration algorithm that can handle both small molecules and large biomolecules uniformly, the system achieves multi-functionality. The same computational framework adapts to different molecule sizes and types, eliminating the need for separate specialized software for small molecules versus large biomolecules.
3Measurement precision
If exhaustive metabolite enumeration is performed, then complete metabolite identification is improved, but processing time increases excessively
Solution Approach 1:
The patent performs preliminary action by pre-generating and storing the residue composition database and metabolite enumeration rules before actual metabolite identification. This preprocessing creates a ready-to-use framework of possible metabolites and their corresponding mass spectra characteristics. During actual analysis, the system quickly matches experimental data against this pre-computed database rather than enumerating metabolites from scratch, dramatically reducing processing time while maintaining completeness.
Data Source
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AI summary
Embodiments of the present invention provide a computer-implemented system and method for generating and searching a database containing all of the potential substructures (e.g., metabolites) of a chosen complex molecule based on minimum cleavable units (MCUs) of the chosen complex molecule, wherein each record in the generated database suitably defines the molecular weight and physical arrangement of each substructure. Embodiments of the invention also provide a user interface and a search engine for searching the database based on a query molecular weight (or query molecular weight range) to identify all of the substructures having a total molecular weight matching the query molecular weight or range. Embodiments of the invention are also capable of transmitting to a display device operated by an end user a description and/or a graphical representation of every identified substructure of the chosen complex molecule.