Mass Spectrometry Data Analysis for Unknown Substance Structure Deduction
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Solution Overview
Problem
Existing mass spectrometric data analyzing methods struggle to deduce the structure of unknown substances with unregistered structural changes, particularly in drug metabolites, as they are limited by predefined structural change patterns.
Innovation Solution
A method that creates structural candidates by eliminating parts of a known substance and combining them with additional structural parts, allowing for a higher degree of freedom in structural changes, and ranks these candidates based on probability matching with MSn spectrum data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pattern matching using a mass spectrum database is used to deduce the structure of an unknown substance, then the structure can be deduced for registered substances, but the method fails for unregistered substances such as drug metabolites with structural variations
Solution Approach 1:
The method segments the structural change from a known substance to an unknown substance into two parts: (1) elimination of a first structural part from the known substance, and (2) addition of a second structural part. This segmentation allows systematic exploration of structural candidates without requiring complete registration of all possible metabolites in the database.
Solution Approach 2:
The method performs preliminary action by pre-defining a set of candidate structural parts that can be added to the eliminated portion. This preliminary preparation enables efficient structure deduction by limiting the search space to chemically reasonable candidates, thereby improving both accuracy and coverage for unregistered substances.
2Adaptability or versatility
If all variations of drug metabolites are registered in the database, then complete coverage can be achieved, but it is impractical due to the enormous number of variations
Solution Approach 1:
Instead of storing complete structures of all possible metabolite variations, the method segments the problem into: (1) a known parent substance structure, (2) eliminated structural parts, and (3) candidate additional structural parts. This segmentation dramatically reduces database requirements while maintaining comprehensive coverage capability.
Solution Approach 2:
The method changes the parameter representation from complete molecular structures to structural difference parameters (eliminated parts and added parts). This parameter transformation enables efficient storage and rapid generation of structural candidates without requiring exhaustive database registration.
3Ease of operation
If predefined patterns of structural change are used to create structural candidates, then the deduction process is simplified, but the method cannot handle structural changes that are not registered as patterns
Solution Approach 1:
The method introduces dynamics by allowing flexible combination of eliminated structural parts with candidate additional structural parts. Rather than using fixed predefined patterns, the system dynamically generates structural candidates by combining different eliminated portions with different candidate additions, enabling adaptation to unregistered structural changes while maintaining operational simplicity.
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
Enables the deduction of structural candidates with a higher degree of freedom, effectively handling substances with numerous structural change patterns, such as drug metabolites, by exhaustively creating combinations and evaluating their probability matches.
Implementation Method 1
The selected precursor ion is dissociated by collision induced dissociation (CID) to produce fragment ions.
Data Source
AI summary
Provided is a mass spectrometric data analyzing method for deducing the structure of an unknown substance from data obtained by an MSn analysis, in which a structural candidate having a high degree of freedom for covering a structural change of the known substance can be created. In the mass spectrometric data analyzing method according to the present invention, a candidate of the partial structure of a known substance which is structurally similar to an unknown substance as the target of deduction is created by eliminating a part of the structure of the known substance (Step S1). Previously given candidates of known additional structural parts are individually added to each candidate of the partial structure of the known substance, thus forming various combinations (Step S5). All the structural formulae that can be derived from each combination are created as the structural candidates of the unknown substance (Step S6).