Method and arrangement for the control of measuring systems, corresponding computer program and corresponding computer-readable storage medium

Abstract

Disclosed herein is a method and an arrangement for the control of measuring systems such as a mass spectrometer or nuclear magnetic resonance (NMR) instrument, the control being based on an online data analysis of the current measurements. Depending on the measurement experiment, the combined result of the data analysis can have either a direct influence on the next measurement or result in a dynamically organized sequence of measurements. The measuring systems may be controlled by establishing a database that comprises information on the objects to be measured, the measurement data which can be detected during the measurement experiments using the measuring systems, and information regarding the relationships between or among items of the measurement data.

Claims

1. A method of analyzing biopolymers and/or derivatives thereof using a mass spectrometer system, comprising steps of: providing a database including information about biopolymers and/or derivatives thereof in association with corresponding mass spectroscopy data and relations between or among items of the mass spectroscopy data, the database comprises at least one of mass differences between monomers and/or polymers of the biopolymers and intensity differences; acquiring mass spectroscopy data using the mass spectrometer system, the mass spectroscopy data comprise at least one of mass/charge ratios, signal intensities, physical quantities, and quantities derivable therefrom; evaluating the acquired mass spectroscopy data to derive relations between or among items of the acquired mass spectroscopy data; making at least one inquiry to the database having criteria based on at least one of the acquired mass spectroscopy data and the relations between or among items of the acquired mass spectroscopy data and retrieving information from the database about the biopolymers and/or derivatives thereof, the information including candidates for unelucidated parts of a sequence of at least one of the biopolymers and/or derivatives thereof; and in accordance with the retrieved information about acquired measurement objects, determining precursors for further fragmentations, selecting fragmentation methods, and adjusting fragmentation parameters to the determined unelucidated sequence parts; fragmenting the determined precursors according to the selected fragmentation methods and the selected fragmentation parameters; acquiring mass spectroscopy data for the resulting fragments; and determining at least a portion of the sequence for unelucidated parts of the sequence of at least one of the biopolymers and/or derivatives thereof.

2. The method as claimed in claim 1, wherein the biopolymers and/or their derivatives being built up from monomers, and a subunit of a biopolymer and/or of a derivative, which comprises several monomers, forms a polymer.

3. The method as claimed in claim 1, wherein the biopolymers or derivatives also comprise modified biopolymers or modified derivatives.

4. The method as claimed in claim 1, wherein the monomers also comprise modified monomers.

5. The method as claimed in claim 1, wherein the biopolymers or derivatives are proteins, peptides, DNA, RNA, PNA, LNA, TNA, GNA, polysaccharides, lipids, polyglucosamines, polyhydroxyalkanoates, cutin, suberin or lignin or combinations thereof.

6. The method as claimed in claim 1, wherein the database comprises at least on of: previously calculated measurement data, relations between or among the previously calculated measurement data, information about physical and/or chemical properties of the measurement objects, and information derived from preceding measurements.

7. The method as claimed in claim 2, wherein the database comprises at least one of mass differences between monomers and/or polymers of the biopolymers and intensity differences.

8. The method as claimed in claim 7, wherein the database comprises mass differences between polymers with distances apart of one or more monomers.

9. The method as claimed in claim 1, wherein the step of making at least one inquiry to the database includes determining elucidated parts of a sequence of at least one of the biopolymers.

10. The method as claimed in claim 9, wherein determined elucidated sequence parts are combined with sequence parts obtained in other measurements to determine the completeness of the degree of sequencing.

11. The method as claimed in claim 9, wherein an assignment to a biopolymer group is derived from determined elucidated sequence parts.

12. The method as claimed in claim 1, wherein the at least one inquiry to the database includes criteria based on the masses and mass differences in a fragment sprectrum.

13. The method as claimed in claim 1, wherein from the results of the database inquiry, two masses of biopolymer fragments are assigned to each other and corresponding information on assignment is produced, when at least one entry that corresponds to a mass difference between these two masses is found in the database.

14. The method as claimed in claim 2, wherein the information on assignment further comprises information about one or more monomers, which represent the mass difference between the two masses.

15. The method as claimed in claim 2, wherein the determination of candidates for the unelucidated parts of the sequences of biopolymers includes the application of at least one of methods of graph theory, methods of artificial intelligence, methods for using neural networks, genetic algorithms, and evolution strategies.

16. The method as claimed in claim 2, wherein by evaluating the masses assigned to each other or information on assignment, possible candidates for unelucidated parts of the sequence of the biopolymers are determined, in which at least one linking chain is determined between masses, starting from one mass, which comprises elements to which in each case a mass is assigned, and linkages, to which in each case information about one or more monomers is assigned, which represent the mass difference between the two linked masses, with the at least one linking chain always running in each case in the direction of increasing or decreasing masses, and with possible candidates for unelucidated sequence parts of the biopolymers being indicated by linkages, to which at least two monomers are assigned.

17. The method as claimed in claim 11, further comprising identifying the presence of unelucidated sequence parts by applying at least one of the following criteria: linkages exist, to which at least two monomers are assigned and the lowest mass does not correspond to the mass of an individual monomer, or linkages exist, of the highest mass to the mass of the unfragmented biopolymers, to which at least two monomers are assigned, and the complete elucidation of a sequence is indicated by a linking chain, which consists exclusively of monomers.

18. The method as claimed in claim 1, wherein the measuring system is controlled in such a way that, in relation to the information about acquired measurement objects from a first measuring operation during the measuring experiment, the measuring operation directly following the first measuring operation or a later measuring operation is parameterized.

19. The method as claimed in claim 18, wherein the parameters for a measuring operation during the measuring experiment are selected according to a task list.

20. The method as claimed in claim 1, wherein at least one device parameter of the measuring instrument is varied depending on the candidates determined for unelucidated parts of a sequence of a biopolymer.

21. The method as claimed in claim 20, wherein the variation of the at least one device parameter takes place according to whether the signal representing the unelucidated sequence part is still present.

22. The method as claimed in claim 1, characterized in that at least one device parameter is varied by establishing the device parameters of the next measurement on the basis of the current measurement.

23. The method as claimed in claim 2, wherein the determination of unelucidated sequence parts and the determining precursors, selecting fragmentation methods, and adjusting fragmentation parameters essentially take place in real time during the measuring operation.

24. A mass spectrometer system comprising: a database including information about biopolymers and/or derivatives thereof in association with corresponding mass spectroscopy data and relations between or among items of the mass spectroscopy data, the database comprises at least one of mass differences between monomers and/or polymers of the biopolymers and/or derivatives thereof and intensity differences; at least one mass spectrometer for acquiring mass spectroscopy data, the mass spectroscopy data comprise at least one of mass/charge ratios, signal intensities, physical quantities, and quantities derivable therefrom; a computer readable storage medium having program instructions for performing steps of: evaluating the acquired mass spectroscopy data to derive relations between or among items of the acquired mass spectroscopy data; making at least one inquiry to the database having criteria based on at least one of the acquired mass spectroscopy data and the relations between or among items of the acquired mass spectroscopy data and retrieving information from the database about the biopolymers and/or derivatives thereof, the information including candidates for unelucidated parts of a sequence of at least one of the biopolymers and/or derivatives thereof; in accordance with the retrieved information about biopolymers and/or derivatives thereof, determining precursors for further fragmentations, selecting methods, and adjusting fragmentation parameters to the determined unelucidated sequence parts; fragmenting the determined precursors using the selected fragmentation methods and adjusted fragmentation parameters; acquiring mass spectroscopy data for the resulting fragments; and determining at least a portion of the sequence for the unelucidated parts of the sequence of the at least one of the biopolymers and/or derivatives thereof.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) The invention will be explained in more detail below for an example, referring to the drawings, which show:

(2) FIG. 1 Sketch of an automated and target-oriented measuring instrument control system based on a database-supported online data analysis and real-time feedback of resultant measurement results,

(3) FIG. 2 Example of elucidation of amino acid sequences in biopolymers: Fragment of the various series (a, b, c series (with N-terminus) and x, y, z series (with C-terminus)) for the example of the pentapeptide SEQ ID NO:1,

(4) FIG. 3 Example of elucidation of amino acid sequences in biopolymers: Temporal ion intensity curve of a set of peptides in the mass spectrometer (MS) with preceding separation system (HPLC),

(5) FIG. 4 Example of elucidation of amino acid sequences in biopolymers: Illustration of a rigid measurement sequence: MS overview scan (A) and subsequent fragmentation of the three most intense peaks by IRMPD; MS2(IRMPD464.25) (B), MS2(IRMPD653.36) (C), MS2(IRMPD585.02) (D),

(6) FIG. 5 Example of elucidation of amino acid sequences in biopolymers: Illustration of the determination of masses from measurement data in an evaluation of fragment spectra,

(7) FIG. 6 As an example of elucidation of amino acid sequences in biopolymers: Illustration of the production of a matrix from mass differences,

(8) FIG. 7a As an example of elucidation of amino acid sequences in biopolymers: Illustration of the construction of a graph from results of a measurement request,

(9) FIG. 7b Example of elucidation of amino acid sequences in biopolymers: Abstracted illustration of the DBnovo evaluation method for preparation of statements from the measurement data of a measurement,

(10) FIG. 8 As an example of elucidation of amino acid sequences in biopolymers: Schematic illustration of an example of determination of paths in a graph,

(11) FIG. 9a Example of elucidation of amino acid sequences in biopolymers: Extracted ion traces of three peptides from an HPLC/MS measuring experiment,

(12) FIGS. 9b-d Example of elucidation of amino acid sequences in biopolymers: The increase in sequence coverage after altered measuring modes is shown for each peptide. In total, 10 measurements are required to identify the three peptides unambiguously. The difference from the previously required measurements (45 scans) can be utilized for the identification of additional peptides occurring in this time window.

(13) FIG. 10 Example of elucidation of amino acid sequences in biopolymers: Illustration of an evaluation of a fragment spectrum when using a first fragmentation mode and

(14) FIG. 11 Example of elucidation of amino acid sequences in biopolymers: Illustration of an evaluation of a fragment spectrum when using a second fragmentation mode.

DETAILED DESCRIPTION OF EMBODIMENTS OF INVENTION

(15) In the following, the invention will be explained in more detail with an example of elucidation of amino acid sequences in biopolymers. The invention is not of course restricted to this particular example of embodiment, but also covers other measuring operations, for example structural elucidations such as elucidation of the 3D molecular structure or similar, provided only that the features defined in the independent claims are realized.

(16) Brief Synoptic Description of the Invention:

(17) The invention described here comprises an automated system for controlling measuring instruments, which is based on the online data analysis of the current measurements in each case, for target-oriented intervention in the measuring experiment.

(18) Existing approaches only use measurement data and data relations between these derivable directly from the measurement as indicators, for initiating previously defined measurements. However, these indicators alone are insufficient for extracting statements with respect to a more complex formulated question from results that are derivable and linked in the sense of the evaluation strategy. As well as additional references to already known analyte information, references are also required that have been formed on the basis of simpler and/or on the basis of other relations.

(19) In a measuring experiment, the current measurement in each case is evaluated in such a way that (partial) questions can be answered by statements derivable from the measurement data (in the form of interpreted results and results combined in the sense of the evaluation strategy). The derivation of these statements requires a particular evaluation strategy depending on the type of formulated question and the measuring mode used, sometimes being of very complex form and based on the generation of a variable large number of data relations. In order to obtain statements at run time and intervene in the course of measurement, provided the measurement is still current, i.e. before there is a change in analytes or analyte composition, a rapid method is required for evaluating the data relations.

(20) Depending on the amounts of data arising and the existing complexity of evaluation, a computational algorithm can be very time-consuming and may be unsuitable especially in time-critical measuring experiments. However, if for a measuring experiment all possibilities that arise, i.e. all constructable data relations from all theoretical measured values of the analytes and/or analyte composition to be investigated can be generated computationally and combinatorially, then the complete result space of the evaluation or a part thereof selected for the measuring experiment can be represented by a database. This links individual data relations of the generated result space to information about measurement objects, their properties and/or data relations within the latter. The generated data relations of each measurement can therefore be evaluated by simple data requests to a database that can be generated before the experiment. (This is valid for a set of measuring experiments/particular measuring arrangement).

(21) The invention presented here makes use of the speed of such data requests, and thus offers the possibility of real-time feedback of the derived statements to the next measurement run. This means that the manner in which the control of measurement is influenced at run time can be automated and can also be target-oriented with respect to complex (partial) formulated questions.

(22) The present invention for automated and target-oriented measuring instrument control is applicable in particular, though not exclusively, for the following scenarios with corresponding formulated questions and goals: Scenario 1: Answering questions relating to analyte properties Example: The elucidation of amino acid sequences of peptides/proteins by MS (mass spectrometry) The efficiency of measuring experiments can be increased with respect to the completeness and uniqueness of the amino acid sequences of peptides/proteins, in that after each measurement a statement about structure/sequence elucidation is available and based on unresolved structure/sequence parts, a targeted adjustment of the device parameters—at run time—is performed. Compared with existing approaches, either the number of measurements required for elucidating the amino acid sequences can be greatly reduced or the information content of the sequence information can be greatly increased. Scenario 2: Elucidation of the influence/interrelations of individual or coupled device parameters on the measurement result obtained How, for example, does the fragmentation behavior of a group of biopolymers change, if the time between two supplied energy pulses is altered? By detecting all possible fragment parts and the accompanying changes in intensity of these fragments with change of the device parameters, it is possible to determine any regeoselectivity and sequence specificity that arise. An investigation that brings about a change of the device parameters is possible. Differentiation of whether an effect is intensified or attenuated with increase or decrease of the parameter is determinable. When there is variation of the parameter, this can be used for the evaluation, in order to ignore regions in which an effect is no longer to be noted. “Tuning” of parameters is therefore also possible when using several parameters. New instrument options/measuring methods can therefore be made usable for certain applications and can be optimized for basic functionality. Results from this application can be used for defining standard parameters or parameters for specific cases. Scenario 3: Optimization of device parameters (sets of parameters), in order to obtain a defined measurement result Example: A particular set of device parameters is required for an instrument, giving cleavage of an analyte at a desired/specified position. As in scenario 2, device parameters and sets of device parameters can be varied, in order to determine their influence on the measurement, for example the fragmentation efficiency. If the effects of the device parameters used can only be estimated with difficulty, especially when there is a large number of possible combinations between simultaneously used device parameters of the fragmentation methods, they can be optimized for a desired influence. If we are looking for the optimum sets of parameters for obtaining a favored bond cleavage, then it is possible to analyze, by systematic variation, when the specific fragments for this occur in larger quantity, without undesirable fragments being produced. It is also possible to assess the directions in which the parameters should be varied, in order to approach this goal. Results from this application can be used for defining standard parameters or parameters for specific cases.

(23) An example of automated, target-oriented control of a measuring instrument 100 will be described below, referring to FIG. 1.

(24) Preparation of the measured values and deconvolution thereof take place in step 01. Step 01 comprises in particular the preparation of the measured values from the current measurement into processable measurement data d.sub.1, d.sub.2, d.sub.3. This step can already be performed in parts by the measuring instrument 100.

(25) Step 02 comprises generating the data relations. In this, relations are produced between the resultant measurement data d.sub.1, d.sub.2, d.sub.3, resulting in data relations b.sub.1, b.sub.2, b.sub.3. The nature of the data relations b.sub.1, b.sub.2, b.sub.3 is determined by the chosen evaluation strategy. In an example of an embodiment from the area of mass spectrometry, the measurement data d.sub.1, d.sub.2, d.sub.3 are mass values of biopolymers, in this case amino acids or amino acid sequences, which are related to one another by finding the differences between at least a proportion of the mass values.

(26) Step 03, evaluation of the data relations b.sub.1, b.sub.2, b.sub.3, serves for evaluating the data relations b.sub.1, b.sub.2, b.sub.3 with the aid of directly executed online data requests to an existing or previously calculated database 110.

(27) In step 04, statements a.sub.1, a.sub.2, a.sub.3 are derived. For this, the individual results e.sub.1, e.sub.2, e.sub.3, are derived and combined in such a way that statements a.sub.1, a.sub.2, a.sub.3 can be found for answering (partial) questions. The tracking of these statements a.sub.1, a.sub.2, a.sub.3 requires a particular evaluation strategy depending on how the question is formulated, and is based on the previously produced data relations b.sub.1, b.sub.2, b.sub.3 and their linkages within the database. For the example mentioned of mass spectrometry, a formulated question may for instance relate to sequence regions that have not yet been elucidated. If these unelucidated sequence regions are found, say by determining mass differences, which indicate a sequence of at least two amino acids, a statement a.sub.1, a.sub.2, a.sub.3 derived from the combined individual results can for example comprise stating these unelucidated sequence regions.

(28) In step 05, the statements a.sub.1, a.sub.2, a.sub.3 are compared with the question that is to be achieved. From the evaluation, a derived statement a.sub.1, a.sub.2, a.sub.3 can be derived with respect to a (partial) formulated question. Step 05 is used for coordinating the extracted statements a.sub.1, a.sub.2, a.sub.3 with the set of established partial questions (>target), with the aim of subsequent identification of strategies for continuing the measurement or for varying the device parameters.

(29) In step 06, target-oriented variation of the device parameters p.sub.1, p.sub.z, p.sub.3 takes place, i.e. the derived statements a.sub.1, a.sub.2, a.sub.3 extracted from the current measured values flow directly (by essentially real-time feedback) into the next measurements of the experiment. (see generally: Variation strategy)

(30) Application: Mass Spectrometry

(31) Database-supported Online De-novo Sequencing of Biopolymers

(32) A possible application of the invention described here is the database-supported online-identification of biopolymers (e.g. peptides, proteins, DNA, RNA) using mass spectrometry and target-oriented control of the mass spectrometer.

(33) Mass spectrometry is used in proteome research for elucidating (identifying) primary structures of proteins and peptides, i.e. their amino acid sequences and any modifications that have occurred.

(34) The mass spectrometers used detect mass/charge ratios of the biopolymers being investigated, which are present in ionized forms (ions for short). Each measurement results in a mass spectrum, which compares the mass/charge ratio with the intensity. The resultant Gaussian distributions of the measurement data relating to the ions are called peaks hereinafter. From the distances between the peaks, charges can be determined, from which the masses (m) of the peptides are calculated (deconvolution).

(35) Once all peptide masses have been determined in an overview mass spectrum (MS), in the next step peptide ions are selected and fragmented into smaller fragments for structure elucidation (tandem mass spectrometry, MSn). This results in fragment spectra, in which—depending on the fragmentation method used (CID=Electron Capture Dissociation, IRMPD=Infrared Multiphoton Dissociation, ECD=Collision Induced Dissociation)—specific fragments of the selected precursor ion (peptide) can be observed. These can be divided into various series (a, b, c series or x, y, z series). The various series are presented synoptically in FIG. 2 for the example of the pentapeptide SEQ ID NO:1 and formally are formed by successive and series-specific cleavage of the individual amino acids starting from one end of the peptide (C/N-terminus). Depending on the fragmentation efficiency and hence on parameters such as the normalized collision energy (in CID), laser performance (in IRMPD) or the duration and delay of the effects of electrons (ECD), there is a varying level of coverage of the observable fragment series. Depending on the completeness and overlaps of the fragment series, the complete and unambiguous sequence of the amino acids can be determined.

(36) The measurement setup for the identification of peptide mixtures normally includes a separation system such as for example high-performance liquid chromatography (HPLC), which is coupled directly to a mass spectrometer (e.g. LTQ-FT). As a result, the various peptides enter the mass spectrometer in different time windows and can be analyzed (FIG. 3). Depending on the separation, the time windows in which the peptide is available can vary considerably and moreover they sometimes have large overlaps, leading to so-called time-critical measurements. Accordingly, for each peptide only a few measurements might be available, for identifying these unambiguously. This, and the fact that the prevailing concentrations of the peptides can also vary considerably, make it essential in a measuring experiment to decide at run time which measurements, and when, should be performed for a peptide.

(37) However, such decisions can only be taken on the basis of evaluated mass spectra, as information about the completeness and uniqueness of the amino acid sequence belonging to the particular peptide can only be derived from these.

(38) The measuring process of a mass spectrometer is at present controlled by predefinable measuring methods, which only operate dynamically to a limited extent and normally perform a rigid alternation between the previously defined measuring modes (for example MS, MSn). As an example, the flow chart of a widely used procedure is presented below, and is visualized in FIG. 4 on the basis of measured spectra:

(39) Measuring Mode 1.

(40) Recording of an overview spectrum with all ions that arise (MS)
Measuring Mode 2. Fragmentation of the 1st most intense ion signal from overview spectrum (1) (MS2)
Measuring Mode 3. Fragmentation of the 2nd most intense ion signal from overview spectrum (1) (MS2)
Measuring Mode 4. Fragmentation of the 3rd most intense ion signal from overview spectrum (1) (MS2) . . . Go to measuring mode 1.

(41) The measurement sequence shown as an extract in FIG. 4 is maintained throughout the separation run regardless of the fragmentation efficiency or extractable statements of the individual spectra. Such a procedure inevitably leads to data that are redundant and are unnecessary for a sequencing and moreover neglects peptides of low concentration with low intensities. The additional use of predefinable lists with preferred ion masses and those to be ruled out, and exclusion lists, which already exclude fragmented ions from further measurements during the measuring experiment, can only limit the amount of possible precursor ions.

(42) Without information about the existing measurements (measurement data d.sub.1, d.sub.2, d.sub.3) of the peptides and the associated conclusion (statements a.sub.1, a.sub.2, a.sub.3) about completeness and uniqueness of the investigated amino acid sequences, at run time it is not possible to react optimally to the occurrence of various ions.

(43) Based on the mass difference (Δm) between two peptide fragments of a fragment spectrum, it is possible to make out the quantity of possible amino acids (including their modifications), in which they differ.

(44) The strategy of using a set of mass differences (Δm), in order to reveal the fragment series of a peptide, is called de-novo sequencing and assumes knowledge of the masses of all amino acids and with modified forms that are to be incorporated. If the observed fragment series of the investigated peptide are complete or overlap these sufficiently, the sequence of a peptide can be determined by successive aligning of fragments which in each case differ by just one amino acid (optionally including any modification) (cf. FIG. 2).

(45) One difficulty in de-novo sequencing is incorporating all possible combinations of amino acids and their modified forms in the determination of the mass gap. The computational effort increases with the number of possible modifications so much (exponentially) that application at run time is not possible, especially in the case of time-critical measurements.

(46) The invention described here solves this problem in that the necessary database 110 with all possible combinations of amino acids, including their possible modifications, is produced beforehand as mass-sequence pair. The resultant mass differences (Δm) can therefore be assigned to the possible amino acid forms by simple data requests.

(47) This novel method makes it possible to evaluate the measured values of the fragment spectra through the speed advantage of data requests immediately, so that a selected ion (for example a peptide) is completely identifiable from the available measurement data. We call this method Online De-Novo Sequencing. Based on knowledge about partial identifications already achieved, the measurement parameters (device parameters p.sub.1, p.sub.2, p.sub.3) of the mass spectrometer can be varied deliberately for the rest of the measurement. This feedback of the information obtained to the measuring process in progress permits, especially in the case of time-critical measurements (for example HPLC/MS), target-oriented intervention in the measuring process, while the ions to be investigated are still available.

(48) The mass spectrometer (LTQ-FT) selected for this example of application produces, in pulsed cycles, mass spectra according to a measuring mode that is specified in advance in each case. The cycle time is on average about ˜1 s. We regard this time window as a guide for the evaluation of a mass spectrum and feedback of the results obtained to the future measurements.

(49) The following is a detailed account of the procedure (as sequence of individual steps)—which we call DBnovo hereinafter—for the evaluation of fragment spectra and is supported with the temporal extent of the partial processes determined in various tests for the purpose of classification/clarification of feasibility.

(50) Step 01: Preparation of the Measured Values and Deconvolution

(51) After deconvolution and filtering of the measurement data d.sub.1, d.sub.2, d.sub.3 (peaks), the masses of the fragments occurring are determined and are prepared for further processing (cf. FIG. 5). During this, it is possible to vary the average number of signals per measurement, and determine signals with identical information content, the threshold value for signal-to-noise ratio (i.e. up to what value is a signal deemed to be usable, or is ascribed to the general noise of the measurement) or the like.

(52) Step 02: Production of the Data Relations b1, b2, b3

(53) The extracted masses are related to one another (data relations b.sub.1, b.sub.2, b.sub.3) by finding their mass differences (Δm) and presenting them in a Δm-matrix (cf. FIG. 6). It is possible to vary the number of signals to be compared, allowance for optimization quantities (e.g. prior elimination of duplicates) or the like.

(54) Step 03: Evaluation of the Data Relations b1, b2, b3

(55) For all masses of the fragment peptides of a fragment spectrum and the nonredundant set of mass differences of the associated Δm-matrix, a data request is sent to the previously produced database 110.

(56) From the results of the data request, an undirected graph 700 is constructed (cf. FIG. 7). In this, a node is produced for each mass. All mass differences for which an entry exists in the database 110 are defined as edges between the associated nodes and are provided with the result of the inquiry. Nodes without relations are ignored. For evaluating the data relations b1, b2, b3, it is possible to vary the number of requested mass gaps per fragment spectrum (size of the matrix), the number of entries of the requested database, previous optimizations (for example elimination of unusable mass gaps (e.g. number of amino acid correspondences found is too large, so that no usable information can be obtained)) or the like.

(57) Step 04: Derivation of Statements a.sub.1, a.sub.2, a.sub.3

(58) Statements a.sub.1, a.sub.2, a.sub.3 are connected with the finding of paths through the graphs 700 produced in step 03 (cf. FIG. 8). In a typical embodiment, the node with the lowest mass forms the starting point. All paths that run along the edges to nodes in each case of higher mass are sought, until the node that represents the mass of the peptide is reached. The resultant paths correspond to possible parts of fragment series of the peptide investigated. (Alternatively it is also possible to search starting from the node with the highest mass. The algorithm is then modified correspondingly in an obvious manner, for example by going along the edges to nodes of lower mass in each case.)

(59) If exclusively unambiguous amino acid forms can be assigned to the starting node and all edges, an unambiguous and complete fragment series is produced and consequently the sequence of the peptide. Nonunambiguous edges show, in contrast, sequence regions of the peptide which must be investigated further for identification in the subsequent measurements. During derivation of statements a.sub.1, a.sub.2, a.sub.3 it is possible to vary the size (number of nodes) of the resultant graph, the degree of linkage and the unambiguity of the resultant graph, the completeness of the mass gaps found, or the like.

(60) Step 05: Alignment of Statement a.sub.1, a.sub.2, a.sub.3 and Setting the Target

(61) From the derived statements a.sub.1, a.sub.2, a.sub.3 of the preceding evaluation, i.e. the identification of the amino acid sequence of the investigated peptide achieved by online de-novo sequencing, it is established how completely and unambiguously the imposed (partial) target is reached. During this it is possible for example to vary the degree of sequence coverage or the like.

(62) Step 06: Target-oriented Variation of the Device Parameters, Measuring Mode

(63) From the previously derived statements a.sub.1, a.sub.2, a.sub.3, the existing target coverage, and the peptide ions available in the course of the measurement, the device parameters p.sub.1, p.sub.2, p.sub.3, and hence the subsequent course of measurement are determined target-oriented. During this it is possible for example to vary the present signal strength (variation of the signal in the chromatogram) or the like.

(64) The invention is used especially advantageously for example in a time-critical HPLC/MS run. During an HPLC/MS test run (measuring experiment) various peptides of a starting mixture can enter the mass spectrometer simultaneously. The concentration of each peptide passes through a maximum caused by the separation system, until the peptide can no longer be detected (FIG. 9a). The superposition of individual concentration curves makes sequential measurement of the peptides difficult. It is therefore necessary to establish an order of priority, depending on the availability of a peptide. If the need for measurements for each peptide, to identify it unambiguously, is kept as small as possible, the total number of peptides that can be analyzed is increased.

(65) Based on an HPLC/MS test run that had already been executed, each mass spectrum was analyzed for the extent to which the fragmentation of the peptide already carried out led to the identification. In this case it would have been possible to use each further measurement in the same measuring mode for the sequencing of additional peptides.

(66) In the case of a nonunambiguous sequencing of a peptide, measurements with varied measuring mode on the corresponding peptide were used. In the case of complete and unambiguous sequencing or attainment of the maximum possible measurements, the fragmentation of the peptide was stopped and the (partial) sequence extracted to date and the increase in sequence coverage (compared with existing methods) was the output. This is shown in FIGS. 9b-c for the example of three peptides, which appear in a time window of about 1.5 min.

(67) Another example is the evaluation of a fragment spectrum of an MeCAT-Lu modified peptide P3. IRMPD was used as the fragmentation method. Large parts of the sequence are not unambiguous (upper marking b series, lower marking y series).

(68) According to a different measuring mode with ECD as fragmentation method, the substance was measured again and the resulting mass spectrum was evaluated. The peptide is now completely and unambiguously identified by an almost complete c series (upper markings) and some fragments of the z series (lower markings) except for the order of two amino acids on the N-terminus.

(69) Tests with the existing, previously defined measuring methods have shown that the resultant mass spectra give rise to a pool of unnecessary measurements. This includes for example sets of identical measurements (redundant data), unnecessary measurements of a statement already known at the current time point and a set of unusable measurements. This results in measurements being “wasted” and part sequences possibly only being identified nonunambiguously and incompletely.

(70) In comparison, the resultant mass spectra were first evaluated offline with DBnovo in the imposed time window (is), in order to show, by means of the resultant statements, the sequence regions of the peptides that are still required for complete sequence coverage.

(71) By choosing a target-oriented altered measuring mode from a set of established basic modes, it was demonstrated that with repeat fragmentation of the total peptide (MS.sup.2) or of a selected fragment (MS3), a complete and unambiguous sequencing could be achieved.

(72) It is therefore possible, by use of the invention, to force the identification through situation-specific alteration of the measuring process and through recognition (of the existence) of a completely and unambiguously elucidated sequence, to direct the focus contemporaneously onto another peptide.

(73) The invention is not restricted in its embodiment to the preferred examples presented above. Rather, a number of variants is conceivable, which make use of the arrangement according to the invention and the method according to the invention even with fundamentally different forms of execution.