WO2012126451A2

WO2012126451A2 - Method for identifying in particular unknown substances using mass spectrometry

Info

Publication number: WO2012126451A2
Application number: PCT/DE2012/000177
Authority: WO
Inventors: Sebastian BÖCKER; Kerstin Scheubert; Florian Rasche; Franziska Hufsky
Original assignee: Boecker Sebastian; Kerstin Scheubert; Florian Rasche; Franziska Hufsky
Priority date: 2011-03-18
Filing date: 2012-02-15
Publication date: 2012-09-27
Also published as: DE102011014805A1; WO2012126451A3

Abstract

It was the object to provide an analysis method, which enables, for all substance classes, in particular also for pesticides and alcohols, identification of the substance to be evaluated with a high safety and reliability. According to the invention, a plurality of mass-spectrometric fragmentation spectra of the substance to be identified are recorded and on this basis a fragmentation graph is determined by calculating the possible molecular formulas for the measured masses, determining direct derivation relationships between said molecular formulas, assessing both the direct and the indirect derivation relationships on the basis of the mass intensities of the peaks in the spectrum and ascertaining from said assessments the best possible fragmentation graph for comparison with reference data. The invention is used in particular in biology, pharmacology and chemistry to determine the structure and/or substance class and/or the chemical properties of unknown substances found.

Description

Method for identifying in particular unknown substances by mass spectrometry

The invention relates to a method for identifying preferably unknown substances by mass spectrometry in order to determine their structure and / or substance classes and / or their chemical properties.

Mass spectrometry is one of the currently most common methods for the analysis of preferably unknown substances (for example, J. H. Gross: Mass Spectrometry: A Textbook, Springer Verlag Berlin, 2004).

By mass spectrometry, the molecular mass of the examined substance can be determined exactly. Furthermore, it is possible to fragment a substance in the mass spectrometer one or more times, i. H. to break their chemical bonds. The masses of the resulting fragments are then measured again. This results in one or more fragmentation spectra (also called daughter ion spectra).

However, it is problematic, in particular for unknown chemical compounds, to identify the structure and / or the substance classes and / or chemical properties of this compound, since only masses can be determined by mass spectrometry.

Many drugs, as well as other chemicals used in industry and research, are produced in their original form by animals and have been discovered by chance or through a very elaborate search. Most substances produced by living things are still completely unknown to research.

The method presented here can significantly simplify the systematic search for potential drugs by z. B. the classes of all occurring in a biological sample small substances (lighter than 1500 daltons) identified. After that only have to even those compounds are examined in more detail, which belong to relevant for the field of application substance classes.

The substance identification of pharmaceuticals and natural products is therefore particularly interesting because of the great relevance of these substances for medicine and pharmaceutical and biological research. Natural substances are all substances that are found in the living and inanimate nature, so especially in plants and animals, but z. B. also found in fossil deposits. This includes, for example, all metabolic products resulting from chemical or enzymatic reactions, as well as the degradation products of artificially added to nature substances such. For example, drugs or environmental toxins. Although natural products are likely to be the main field of application, the process presented is not limited to these. An application is also possible in other areas of chemistry, for example in materials science.

Since natural substances are usually present as mixtures (eg cell extract, environmental sample), mass spectrometry is often preceded by a separation process in order to separate the substances to be identified for mass spectrometry analysis. This separation method is usually gas or liquid chromatography or capillary electrophoresis (for example U. Roessner, C. Wagner, J. Kopka, R. Tretheway, L. Willmitzer: Technical advance: simultaneous analysis of metabolites in potato by gas chromatography-mass spectrometry , Plant J, 2000, 23, 131-142).

For certain classes of substances, in particular biopolymers (such as, for example, peptides and glycans), there are methods to identify them by means of a fragmentation spectrum (for example A. Frank, P. Pevzner: PepNovo: de novo peptide sequencing via probabilistic network modeling, Anal , 2005, 15, 964-973 and D, Goldberg, M. Bern, B. Li, C. Lebrilla: Automatic Determination of O-Glycan Structure from Fragmentation Spectra, J Proteome Res., 2006, 5, 1429-1434). , However, this approach requires that the substance class of the substance to be identified (eg recordable peptide or glycan) is known. An application of these methods to classes of substances that are not polymers is not possible. It is known (for example, R. Mistrik: XCalibur High Chem: Mass Frontier Software, High Chem / Thermo Finninn, Manual 2001) to compare fragmentation patterns, which are determined by mass spectrometric analysis, with idealized patterns, so-called rules, obtained manually from reference data. Such a comparison would, of course, be automatable, but assuming that the corresponding rules were established for the substance under investigation, it is therefore in no way applicable to unknown substances. Moreover, these rule-based approaches can not handle erroneous data, rendering them unusable in practice (K. Klagkou, F. Pullen, M. Harrison, A. Organ, A. Firth & GJ Langley: Approaches to Automated Interpretation and Prediction of electrospray tandem mass spectra of non-peptidic combinatorial compounds, Rapid Commun Mass Spectrom, 2003, 17, 163-1668).

For the special case that a fragmentation spectrum created under identical measurement conditions already exists identically in a reference database, it would be possible to find the examined substance by means of computational comparison by searching the spectrum in the reference database and to identify in this way (L. Vogt, T Gröger & R. Zimmermann: Automated Compound Classification for ambient aerosol sample separations using comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry, J Chromatogr A, 2007, 1 150, 2-12, DE 103 58 366 B4, US Pat. No. 6,624,408 B1, US 2003 023 66 36 A1, US Pat. No. 6,747,272 B2).

This method does not work for completely unknown substances, as a reference spectrum of the substance must be present. In addition, fragmentation spectra depend in part strongly on external parameters and therefore differ from laboratory to laboratory. Direct comparisons between spectra are not meaningful in this case, therefore a search for an identical reference spectrum present under comparable conditions is possible in only a few applications. In order to circumvent this latter disadvantage, it is also known to search in a database for fragment ions which are stored there to defined fragmentation patterns (US Pat. No. 7,197,402 B2). These ions must then either have a known, unambiguous structure or fragmentation spectra of these ions must be measured with additional mass spectrometric analysis. These spectra (MS ⁿ ) resulting from multiple fragmentation should, as stated, be more comparable than the abovementioned simple ¹ fragmentation spectra.

This method can contribute to the partial identification even if the fragmentation spectrum of a daughter is stored in the database. However, the application of the method remains limited to such exact matches of daughter ion spectra in the database. Therefore, the method requires very many daughter ion spectra already analyzed for a large number of molecules. Measuring them and analyzing them manually is an enormous effort. The method does not provide information that goes beyond the existence of an exact hit of a daughter ion spectrum.

If substances are to be identified for which reference data or comparison or identification rules are not or not completely available, smaller molecules must still be assessed on the basis of their fragmentation patterns, ie, it must be investigated in an extensive manner to what extent comparable similarities to known structures are found which could or could at least support a determination of class of substance, chemical properties, or perhaps even molecular structure (Shi P., Q. He, Y. Song, H. Qu, and Y. Cheng: Characterization and identification of isomeric flavonoid O-. Actly, Cly. in negative electrospray ionization by ion trap mass spectrometry and time-of-flight mass spectrometry, Anal, Chim. Acta, 2007, 598, 1 10-118). However, this assessment is subjective, lengthy and based on human intuition. It therefore does not represent an objective and rapid substance identification and rather builds on high knowledge and extensive experience of experts ahead of this area. Nevertheless, the hit rate in practice is not very high, even for smaller molecules. The method can not be automated for the aforementioned reasons. For larger molecules, this method would be virtually unusable, in particular because of the high demands on the reviewer and the expected low hit rate.

In 2008 Böcker and Rasche (S. Böcker & F. Rasche: Towards de novo Identification of metabolites by analyzing tandem mass spectra, Bioinformatics, 2008, 24, 149-155) introduced a mathematical formalization of the concept of fragmentation patterns. They use graphene to represent the fragmentation pattern of a substance. A graph is to be understood to mean a set of objects, usually referred to as nodes, and a set of pairs of the elements of this set, usually referred to as edges. This set of pairs represents the relationships between the objects. In this case, the fragments of the substance are represented as nodes and the fragmentation reactions as edges. Since the structure of the examined substance is not known, the nodes are labeled with the sum formulas of the fragments, and the edges with the sum formulas of the neutral losses. These fragmentation graphs are used to determine the molecular formula of an unknown substance. However, molecular formulas themselves are not sufficient to identify a substance and also do not allow any conclusions about the substance class of the substance being investigated.

It is also known to use fragmentation graphs for the identification of unknown substances (DE 10 2009 0058 45 AI). However, this method is highly dependent on the quality of the underlying graphs of fragmentation patterns. A good quality of Fragmentierunggraphen is not given in particular for pesticides and alcohols so far, and thus a reliable identification of the unknown substances, especially of pesticides and alcohols, is not possible. Further, in a particular biological or medical field, aliquoting of trees per se for comparison of RNA structures is known (T. Jiang, L. Wang & K. Zhang: Alignment of trees: an alternative to tree edit, Theor. Comput. Sci., Elsevier Science Publishers Ltd., 1995, 143, 137-148). Here, the labeled nodes of the trees to be compared are superimposed so that the labels differ as little as possible. Structurally, the trees must be the same, only so-called gap nodes may be inserted into the branches of the tree representation, if necessary. About applications of this method, especially in mass spectrometric investigations of substances to identify them or to determine their class and / or chemical properties, nothing has become known.

The invention is based on the object to provide an evaluation, which allows for all classes, especially for pesticides and alcohols, an identification of the substance to be evaluated with high reliability and reliability, even if the class of the substance is unknown.

This object is achieved by a method for identifying in particular unknown substances by mass spectrometry, which consists of the following steps:

a) recording at least two mass spectrometric fragmentation spectra (daughter ion spectra) of the substance to be identified,

b) determining a spectrum graph of this substance from the at least two mass spectrometric fragmentation spectra, by calculating the possible sum formulas for the measured masses, determining direct lineage relationships between these sum formulas, and evaluating both the direct and indirect lineage relationships on the basis of the mass intensities of the peaks in the spectrum,

c) Determine the best possible fragmentation graph of the substance by taking the ones available in the spectrum graph Relationships that are selected with the inclusion of the most probable relationships, and

d) comparing the data of the complete or partial fragmentation graph with reference data to identify the substance by its structure and / or class and / or chemical properties.

Surprisingly, it has been found that not only substances with high reliability can be determined with this method, with which the fragmentation graph from the at least two mass spectrometric fragmentation spectra is determined taking into account not only direct but also indirect ancestral relationships between said sum formulas. if at all, were difficult or only with low reliability to determine, for example, pesticides and alcohols, but that the safety and reliability of the fragmentation graph in the determination of all examined substance classes is generally increased, which significantly improves the evaluation and thus the substance determination. It is not necessary to measure an MS spectrum from each peak of the MS ² spectrum and an MS ⁴ spectrum from each peak of the MS ³ spectra, but two MS spectra are sufficient for the proposed method. A huge advantage of the invention is that a reliable identification of the substances to be evaluated is made possible, even if the class of the substance is unknown.

The method can be applied in particular to metabolic intermediates, antibiotics, toxins, insecticides, messengers, flavonoids, phenols, xanthones, propanoids, stilbenes, terpenes, steroids, carotenoids, lipids and alkaloids, but is not limited to these substance classes.

The invention will be explained in more detail below with reference to an embodiment shown in the drawing. Show it:

Fig. 1: Fragmentation spectra of phenylalanine, consisting of an MS ² spectrum (166 Da) and an MS ³ spectrum (120 Da), and specific MS ³ evaluation of the peaks

Fig. 2: Evaluation of the direct lineage relationships in the

Spectral graph of phenylalanine, where the nodes correspond to the fragments measured in mass spectrometry, the boxes to the peaks and the edges to the neutral losses

Fig. 3: Options for the evaluation of indirect

Lineage relationships, where dashed peaks represent non-spectrum peaks

Fig. 4: Evaluation of the indirect lineage relationships in the

Spectral graph of phenylalanine, where the nodes correspond to the fragments measured in mass spectrometry, the boxes to the peaks, and the edges to the parameters relevant for the evaluation (G \, C2 or 03)

Fig. 5: Evaluation of the indirect lineage relationships in the

Spectrum graph of phenylalanine (detail), with additional contradictory rating edge

Fig. 6: Evaluation of the indirect lineage relationships in the

Spectral graph of phenylalanine (detail), resolving the contradiction by copying and redrawing the contradictory parts

Fig. 7: Fragmentation graph of phenylalanine, representation again by nodes (fragments) and by edges (neutral losses) a) Creation of a spectral graph with direct and indirect lineage relationships:

The determination of a spectrum graph with direct and indirect lineage relationships is described below by way of example with the substance phenylalanine. The typical application is an unknown substance of unknown structure and function. Then, with the help of the method, assumptions can be made about the structure.

Phenylalanine was studied by multiple mass spectrometry. The fragmentation was carried out with known collision induced dissociation (CID). However, it is also possible to use other fragmentation methods (for example, in-source fragmentation, post-source decay, ETD, ECD, HCD or PQD). If the substance to be determined is part of a mixture, it would be useful to perform a separation using gas or liquid chromatography.

After determining the mass-to-charge ratios and associated peak peaking intensities (see Figure 1), for each mass-to-charge ratio, all possible sum formulas within the range of mass tolerance are determined ( See also S. Böcker & Z. Liptäk, A Fast and Simple Algorithm for the Money Changing Problem, Algorithmica, 2007, 48, 413-432).

These sum formulas are used to construct the fragmentation graph (see Fig. 2): An edge-weighted, knot-stained graph is constructed in which there is a knot for each possible molecular formula. Two nodes are joined together by a directed edge if the sum formula of the child node is a sub-formula of that of the parent node. Edges represent neutral losses due to fragmentation. They point to direct lineage relationships. All sum formulas that belong to the same peak have the same color. The weights w (u, v) of the edges are determined as a function of peak intensities, mass deviations between predicted molecules and actual peak masses, chemical properties, collision energies of fragmentation and neutral losses (see also S. Böcker & F. Rasche: Towards de novo identification of metabolites by analyzing tandem mass spectra, Bioinformatics, 2008, 24, 149-155). Due to the at least two mass spectra, one can additionally define indirect lineage relationships. Three examples are shown in Fig. 3:

1 . If a peak i also appears in a spectrum with a tracer ion peak p _k , this indicates that the fragment belonging to i is derived directly or indirectly from the fragment belonging to p _k . Therefore, such direct or indirect lineage relationships in the fragmentation graph get a positive score with the parameter σι> 0.

2. If one peak P _k occurs in one spectrum, but not another peak pi, this indicates that the fragment belonging to i does not derive either directly or indirectly from the fragment which belongs to p _k . Therefore, such direct or indirect lineage relationships in the fragmentation graph get a negative rating with the parameter σ ₂ <0,

, Given two spectra with different collision energies and two peaks p _k and pi with mass (pi) <mass pk). If the higher collision energy spectrum contains only pi, but the lower collision energy spectrum has both peaks, this indicates that the fragment belonging to pi is neither directly nor indirectly derived from the fragment belonging to p _k . Therefore, such direct or indirect lineage relationships in the fragmentation graph get a negative score with the parameter σ3 <0.

These indirect lineages are plotted by additional indirect edges with weights w ⁺ (c (u), c (v)) in addition to the direct edges with weights w (u, v) in the spectrum graph. All the above-mentioned indirect lineage relationships of phenylalanine are visualized in Fig. 1.

Fig. 4 shows the corresponding spectrum graph with indirect lineage relationships (for clarity, the direct lineages of Fig. 3 were neglected and only the indirect lineages were drawn). Fig. 5 shows a more complex case: Assuming that a third fragmentation spectrum shows that the peaks at 103.1 Da can not have originated from the peak at 120.1 Da (evaluation with σ2 or σ ₃ ). Then one possible explanation is that the peaks at 103.1 Da are due to two different fragments of equal mass. This situation is modeled by copying all the structures belonging to the peak 103, 1 Da and coloring the copies in a free color (Fig. 6). This allows the subsequently calculated fragmentation graph to contain the peak twice. b) Calculation of the optimal fragmentation graph from a spectrum graph with direct and indirect lineage relationships One way to determine the optimal fragmentation graph considering not only direct but also indirect lineage relationships is to determine the subtree from the fragmentation spectrum with maximum scoring, where each color is maximal once is used. This prevents a peak from being explained by more than one fragment.

So it is an optimization method, in contrast to the method according to US 7,197,402 B2, which operates rule-based.

The evaluation of a sub-tree is formed by the sum of its direct edge weights (edges from Fig. 2) and the sum of its indirect edge weights (edges from Fig. 4). Indirect edge weights are counted if and only if the child node is either a direct descendant or an indirect descendant of the father node.

One way to get an optimal subtree as fast as possible is using an FPT algorithm:

W ^* v, S) =

Where W * (v, S) is the maximum score of a tree with root node v and uses only colors from SQC. In the lower maximum, the trivial cases Sl - {v} and S2 = {v} are excluded.

The initialization takes place with W * (v, c (v)) = 0.

Fig. 7 shows the optimal fragmentation graph of phenylalanine when the parameters are given with σι = 3, σ ₂ = -0.5 and σ3 = -0.5.

The Fragmentienmgsgraphvergleich compared stored reference fragmentation graphs is done in a known per se. From this comparison, the substance or substance class (see DE 10 2009 0058 45 Al) are determined.

Both the determination of the fragmentation graph and the subsequent data comparison are performed computationally.

Claims

claims

1. A method for identifying in particular unknown substances by mass spectrometry, comprising the following steps:

b) determining a spectrum graph of this substance from the at least two mass spectrometric fragmentation spectra, by calculating the possible sum formulas for the measured masses, determining direct lineage relationships between these sum formulas, and evaluating both direct and indirect lineage relationships on the basis of the mass intensities of the peaks in the spectrum; c Determining the best possible fragmentation graph of the substance by selecting from the relationships present in the spectrum graphs the most probable relationships involving the scores, and d) comparing the data of the complete or partial fragmentation graph with reference data to determine the substance by its structure and / or structure Substance class and / or their chemical properties.

2. The method according to claim 1, characterized in that the determination of the fragmentation graph and the subsequent data comparison are performed computationally.

3. The method according to claim 1, characterized in that in the case of a both positive and negative evaluation of a direct or indirect lineage relationship

- the parts of the spectrum graph to which the contradictory assessment relates are doubled,

- a copy of these parts is assigned a color not yet available and - the best possible fragmentation graph is determined from the spectrum graph modified in this way.

A method according to claim 1, characterized in that the fragmentation graph is typically represented by nodes as fragments of the substance and edges as fragmentation reactions (linkage).

A method according to claim 1, characterized in that the Fragmentierungsgraph using a different from the typical presentation with nodes and edges mathematical representation, such. A partial order, a relation, or a hierarchy.