WO2013097058A1 - Procédé d'identification du protéome - Google Patents

Procédé d'identification du protéome Download PDF

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Publication number
WO2013097058A1
WO2013097058A1 PCT/CN2011/002240 CN2011002240W WO2013097058A1 WO 2013097058 A1 WO2013097058 A1 WO 2013097058A1 CN 2011002240 W CN2011002240 W CN 2011002240W WO 2013097058 A1 WO2013097058 A1 WO 2013097058A1
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spectrum
string
library
experimental
spectral
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PCT/CN2011/002240
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English (en)
Chinese (zh)
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杜朝钦
闻博
汪建
王俊
杨焕明
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深圳华大基因研究院
深圳华大基因科技有限公司
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Priority to PCT/CN2011/002240 priority Critical patent/WO2013097058A1/fr
Publication of WO2013097058A1 publication Critical patent/WO2013097058A1/fr

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/10—Signal processing, e.g. from mass spectrometry [MS] or from PCR

Definitions

  • the present invention relates to the field of protein mass spectrometry data analysis, and in particular to a method for identifying a proteome based on a search of a spectral database. Background technique
  • the spectral database search method by comparing the mass image in the spectral library with the experimentally obtained mass spectrum, according to the similarity, it is judged whether the two mass spectra are generated by fragmentation of the same peptide. They are derived from the same peptide, and the experimental quality is identified based on the sequence information of the peptides in the map. It can be seen that in the spectral database search, the similarity calculation between the grammatical map in the spectral library and the experimentally obtained mass spectrometry is a key problem. Choosing an inappropriate similarity calculation method may lead to an error in the identification result. Once a false positive occurs, choosing a suitable similarity calculation method can reduce false positives.
  • the spectral database is a spectrum corresponding to the peptides identified by database search, de novo assembly, etc. in the previous proteome identification according to signal intensity, recurrence rate, signal peak, and theoretical mass spectrometry constructed according to the fragmentation.
  • Set of correct spectra obtained after screening corresponding conditions Due to the difference in the experiment ⁇ , the same ⁇ " ⁇ the fragmentation in different experiments is different; the background noise peaks of different instruments are different. Therefore, in the spectral database search, in the spectrum library The information does not fully reflect the true fragmentation of the peptide.
  • the information in the spectral library mainly has the following problems: 1) Because in the process of constructing the language library, the spectrum with significant differences in the multiple chromatograms corresponding to the same peptide segment will be removed, thus causing the peptide segment The non-3 ⁇ 4 fragmentation form is excluded from the spectrum library; 2) in the combination of the spectra of the same peptide, the peaks with low frequency appearing in multiple maps will be removed, resulting in the missing information of this part of the ions. 3) The peak with high frequency but no spectral icon is retained, which provides information on the peaks that cannot be predicted by the simple theoretical fragmentation model, but its principle of production is unknown, whether it is reproducible and stable.
  • the present invention employs a new similarity calculation method for spectral library search to identify proteomes, which provides a corrected cumulus string: spectral and experimental crosstalk similarity scores, the correction formula is as follows:
  • the invention provides a method for searching a proteome by a spectral library search, comprising the following steps:
  • the pretreatment method is determined according to the processing strategy of the spectral database ⁇ process spectrum For example, when searching the spectrum library of the NIST database, the retention peak is higher than the highest intensity of the spectrum by two thousandths. When searching the GPM spectrum database spectrum, the 20 peaks with the highest intensity are retained to achieve similarity. Sexual calculation methods can utilize different spectral database;
  • the mass-to-nuclear ratio of the above experimental series is J ⁇ 3 ⁇ 4, and the candidate is found in the set mass-to-nuclear ratio error interval (also called the parent ion error interval or the precursor ion error interval).
  • Spectral library string diagram In the spectral library string ⁇ spectrum, the mass-to-nuclear ratio of the above experimental series is J ⁇ 3 ⁇ 4, and the candidate is found in the set mass-to-nuclear ratio error interval (also called the parent ion error interval or the precursor ion error interval).
  • Ltqd— score e VSt 'S lq corrected spectral library string ⁇ spectrum and experimental string ⁇
  • the spectral similarity score is the largest candidate spectrum.
  • the peptide represented by the library spectrum is the experimental identification result.
  • the degree of similarity between the direct matching between the two is considered, and the degree of similarity between the theoretical two-spectrum and the above two passes is considered.
  • the above similarity scores are corrected.
  • the advantage of this is that the information of the theoretical Pan is utilized, and the adverse effect of the spectral specificity on the peptide search and identification of the peptide is reduced under the condition of ensuring the utilization of the unknown peak information of the spectral library spectrum, and the identification rate of the spectrum is improved.
  • the most ⁇ HM ⁇ Il is the similarity between the spectrum library spectrum and the experimental string spectrum S lq
  • the exponential part of the e-index is the theoretical string spectrum and the experimental string W ⁇ Bo and the spectrum library
  • the similarity calculation method used in the present invention can perform corresponding reward scoring processing and punitive scoring processing in the above two cases, reducing the error rate of the proteomic group in the spectral library, and reducing the spectrum library identification proteome method. Leak identification caused by experimental differences.
  • the present invention preprocesses the spectrum, and the preprocessing adopts a peak retention strategy consistent with the spectral library construction process; by the above preprocessing, the method of the present invention does not depend on a specific spectral database.
  • the similarity scores are used to evaluate the similarity of the spectrum and improve Mass spectrometry was used to identify the accuracy of the proteome.
  • the method of calculating similarity between two pairs is a specific implementation, and these implementation methods can have different variants.
  • Figure 1-A is a schematic diagram of the spectrum matching between the spectrum database and the experimental string ⁇ i spectrum.
  • the upper part of the figure is the experimental string diagram, the lower part of the figure is the spectrum library string ⁇ spectrum diagram;
  • Figure 1-B is the diagram 1- A partial enlargement.
  • Figure 2-A is a schematic diagram of the matching of the theoretical string spectrum and the experimental string spectrum.
  • the upper part of the figure is the experimental string diagram, the lower part of the figure is the theoretical string spectrum diagram;
  • Figure 2-B is a partial enlarged view of the 2-A diagram.
  • Figure 3-A is a schematic diagram of the theoretical string spectrum and the spectral library spectrum spectrum matching.
  • the upper part of the figure is the spectrum library string spectrum, the lower part of the figure is the theoretical string spectrum chart;
  • Figure 3-B is the figure 3-A Partially enlarged view.
  • the horizontal axis is the m/z value, ie the mass-to-core ratio;
  • the vertical axis represents the relative intensity of the signal, the highest peak is set to 100, and the intensity of the other peaks is set to the relative value.
  • Fig. 4 is a graph showing the relationship between the false discovery rate and the number of the identified spectra in the similarity calculation method used in the present invention.
  • the horizontal axis is the false positive rate
  • the vertical axis is the number of correctly identified spectra.
  • Figure 5 is a schematic representation of the six series of fragment ions possible after peptide cleavage. detailed description
  • the experimental mass spectrum can be obtained by performing an MS/MS experiment (string ⁇ spectrum experiment) on an experimental sample. Specifically, after the peptide is ionized, the peptide has an nucleophilic ratio, and after a «: spectrum, the ratio of the nuclei of the peptide ion (ie, the ratio of the parent ion to the core) and the signal intensity are obtained. It is then cleaved by collision-induced dissociation (CID). Under the low energy CID cleavage method, the peptide will usually break at the peptide bond 3. As shown in Figure 5, the N-terminal &, b, c ion, C-terminal x, y, z ion. At the same time, in mass spectrometry experiments, in addition to the above predictable ions, there are unpredictable intermediate ions, physical and chemical noises. The above various ions constitute an experimental texture.
  • the map denoising process is performed before the Pantu similarity calculation is performed.
  • the daughter ions of the highest intensity in the experimental mass spectrum are retained according to the type of spectral library employed.
  • the spectral database can be downloaded from a public database, such as the NIST database http:// peptide.nist.gov/> GPM " ⁇ Gallery ftp: //ftp. thegpm.org/projects/ xhunter/libs;
  • the protein database search method can be used to identify the results and build the library.
  • the build tool has the TPP software system (USA), download address:
  • the software's spectral library construction strategy is consistent with the IST database;
  • the database format is the binary file splib, and the corresponding text format file sptxt.
  • a database built by TPP is used, and the database format is sptxt.
  • the construction can be performed by a person skilled in the art based on conventional knowledge.
  • the molecular dynamics model can be employed; the series of ions produced by fragmentation of the peptide fragments are theoretically calculated.
  • the principle of series ion generation is shown in Figure 5.
  • the low energy CID fragmentation mode used by the massifier The types of ions theoretically produced are a, b, y; those skilled in the art can determine the type of ions to be considered in the construction of the theoretical map according to the type of mass spectrometer used and the experimental scheme.
  • each of the sub-ions in the experimental string spectrum and the spectral library string spectrum are separated from each other. Proton nuclear ratio and ionic strength.
  • the collection of child ions forms a string ⁇ spectrum, as shown in Figure 1-3.
  • the matching process of any two strings ⁇ spectra is essentially on two strings, looking for sub-ions with equal mass-to-nuclear ratio and similar intensity.
  • the type of the daughter ion is not known (see the theoretical fragmentation method for the product ion type); and all ion types of the theoretical tandem mass map. They are all known.
  • the upper and lower parts are each a tandem mass spectrum.
  • the mass i-peaks at the same abscissa in the figure are the ion peaks on the two maps.
  • the height of the peak is the intensity of the detected ion ions. Since the theoretical string spectrum is theoretically calculated, there is no intensity information, and all peak intensities of the theoretical tandem mass spectrum are uniform values, for example, 100%.
  • the labeled ion peak intensity refers to the intensity of the sub-ion in the spectral library spectrum and the experimental mass spectrum.
  • the candidate string ⁇ is a string in which the parent ion mass ratio and the parent ion mass ratio of the experimental string are smaller than the set threshold in the case of the same charge: ⁇ , a diagram, for example: If
  • ⁇ Am, then the spectral library tandem mass spectrogram is a candidate string diagram of the experimental string ⁇ language graph, where Am is the mass-to-core ratio tolerance, and MQ is the experimental string diagram parent ionic mass ratio , ML is the spectrum library string ⁇ language map parent ionic mass ratio. In some embodiments of the invention, Am 1.2. Those skilled in the art can adjust the value of ⁇ m according to the accuracy of the mass or the like.
  • I T Q the intensity of the peaks in the real string and the theoretical crosstalk diagram
  • I TQ i the intensity of the peak in the spectrum of the ⁇ series and the theoretical string ⁇ : the i-th in the graph; ⁇ : the peak of the spectrum in the spectrum of the spectrum library and the theoretical string Wt ⁇ "Intensity; iTLi: the intensity of the peak in the spectral library spectrum that is consistent with the i-th in the theoretical string spectrum;
  • Real ⁇ string W ⁇ speaks the peak intensity of the jth ion
  • n the number of spectral ions in the experimental string can be marked
  • N the total number of experimental string ions
  • m can be labeled spectrum library string number of protons
  • the experimental string spectrum and spectral library string similarity calculation can be an optimized spectral vector dot product (SDP) method, for example, the specific calculation formula is as follows: among them: S: spectral library string ⁇ spectrum, and the experimental vector string ⁇ map composed of spectral vector dimension; I Qi: real ⁇ string spectrum i-th daughter ion peak intensity;
  • ILJ Spectral library spectrum
  • each peak has a mass-to-nuclear ratio (mz) and intensity;
  • each mz value is a dimension in a multidimensional vector, and the intensity is a projection of the spectral vector in this dimension;
  • the graph information is converted into a mathematical form of a multidimensional vector; the comparison of the two spectra is converted into a calculation between two vectors, which is the spectral vector dot product.
  • the two-dimensional vector A is a vector of xy (1, 2)
  • the two-dimensional vector B is a yz vector (3, 1)
  • ⁇ , ⁇ vectors are compared, It is unified into a 3-dimensional xyz vector ⁇ (1,2,0), ⁇ (0,3,1).
  • the specificity of the corrected spectral library string and the experimental string similarity is as follows:
  • the method includes the peak spectrum screening and intensity transformation of the spectrum of the experimental series according to the spectrum library construction method used.
  • the peptide corresponding to the Ltqd-score candidate string spectrum library is the experimental identification result.
  • Embodiment 1 is a diagrammatic representation of Embodiment 1:
  • the actual map is from the string spectrum experiment of 18 protein standard samples; the mass spectrometer type Thermo LTQ-FT (linear ion well-Fourier transform) quality data; data can be obtained from Website https: ⁇ regis-web.systemsbiolog .net//PublicDatasets/ Download and get;
  • VGVNGFGR The theoretical spectrum of VGVNGFGR is shown in the upper part of Figure 2-A and the upper part of Figure 3-A.
  • VRVGGFGN is similar and will not be listed here;
  • the sub-ion allows a maximum error interval of 0.6da;
  • the experimental map identified the peptide VRVGGFGN peptide charge 2+.
  • the identification result of the embodiment is analyzed by using a bait library method to evaluate the effectiveness and correctness of the present invention
  • the bait library construction method Firstly, the peptide sequence of the spectrum library spectrum is randomly disturbed, and then the secondary spectrum rearrangement is performed, thereby obtaining the bait library (references: Hemy Lam, Eric W. Deutsch, and Ruedi Aebersold) 'Artificial Decoy Spectral Libraries for False Discovery Rate Estimation in Spectral Library Searching in Proteomics, J. Proteome Res. 2010, 9, 605-610).
  • the tool Spectrast in the TPP software system is used to construct the i-show library;
  • False positive calculation method Sort the identification results of all the experimental images of one experiment according to the score; calculate the number N of the spectrum identifications higher than the score threshold at a given sub-threshold, wherein the number n matching the bait library,
  • the false positive rate FDRi ⁇ is 2*n/N, (Reference: same as the previous paragraph)
  • the false positive rate of the identification result and the number of the identification results under different score thresholds are calculated; the relationship between the false positive rate and the number of the identified spectrum is shown in Fig. 4.
  • the curve in Figure 4 is also referred to as the ROC curve (subject operating characteristic curve); the closer the ROC curve is to the upper left corner, the higher the accuracy of the results.
  • a 5% false positive rate is also referred to as the ROC curve (subject operating characteristic curve); the closer the ROC curve is to the upper left corner, the higher the accuracy of the results.
  • Peptides belonging to only 18 standard proteins 179 Only the 18 standard proteins, the number of peptides commonly used in experiments (proteins mixed in experiments, such as specific cleavage enzymes, keratin, etc.): 4;
  • Embodiment 2 is a diagrammatic representation of Embodiment 1:
  • the experimental sample is a human cell line protein sample; the mass spectrometry experiment uses Thermo Scientific's LTQ Orbitrap electric field orbitrap cyclotron resonance combination "if ⁇ ; to obtain an experimental spectrum; the spectral library spectrum is downloaded from NIST. Database http: ⁇ peptide.nist.gov/; Library name: human 2008_04_18_it.sptxt;
  • the number of identical peptides identified was: 8664; the rate was approximately 75% (generally, the consensus rate identified by different search engines based on stringer technology was around 70%).

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Abstract

La présente invention concerne un procédé permettant l'identification du protéome en effectuant des recherches dans des bibliothèques de spectrogrammes. Le procédé selon l'invention permet d'obtenir un score corrigé de similarité entre la spectrométrie de masse en tandem des bibliothèques de spectrogrammes et la spectrométrie de masse en tandem expérimentale en calculant le score de similarité entre la spectrométrie de masse en tandem des bibliothèques de spectrogrammes et la spectrométrie de masse en tandem expérimentale, entre la spectrométrie de masse en tandem théorique et la spectrométrie de masse en tandem des bibliothèques de spectrogrammes, et entre la spectrométrie de masse en tandem théorique et la spectrométrie de masse en tandem expérimentale. Le fragment peptidique représenté par un spectrogramme choisi dans la bibliothèque de spectrogrammes et présentant le score de similarité corrigé le plus élevé entre la spectrométrie de masse en tandem des bibliothèques de spectrogrammes et la spectrométrie de masse en tandem expérimentale constitue le résultat d'identification du spectrogramme expérimental.
PCT/CN2011/002240 2011-12-31 2011-12-31 Procédé d'identification du protéome WO2013097058A1 (fr)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111883214A (zh) * 2019-07-05 2020-11-03 深圳数字生命研究院 构建诱饵库、构建目标-诱饵库、代谢组fdr鉴定的方法及装置

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2004008371A1 (fr) * 2002-07-10 2004-01-22 Institut Suisse De Bioinformatique Procede d'identification de peptides et de proteines
EP1542002A1 (fr) * 2002-09-05 2005-06-15 National Institute of Advanced Industrial Science and Technology Methode d'identification automatique de biopolymeres
CN101871945A (zh) * 2010-06-13 2010-10-27 中国科学院计算技术研究所 谱库的生成方法和串联质谱谱图鉴定方法

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2004008371A1 (fr) * 2002-07-10 2004-01-22 Institut Suisse De Bioinformatique Procede d'identification de peptides et de proteines
EP1542002A1 (fr) * 2002-09-05 2005-06-15 National Institute of Advanced Industrial Science and Technology Methode d'identification automatique de biopolymeres
CN101871945A (zh) * 2010-06-13 2010-10-27 中国科学院计算技术研究所 谱库的生成方法和串联质谱谱图鉴定方法

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
LAM, H. ET AL.: "Development and validation of a spectral library searching method for peptide indentification from MS/MS.", PROTEOMICS, vol. 7, no. ISSUE, March 2007 (2007-03-01), pages 655 - 667 *
MA, JIE: "Research and application of quality control methods for identification of peptide fragments of proteomes", CHINA DOCTORAL DISSERTATIONS FULL-TEXT DATABASE (BASIC SCIENCES), 15 January 2011 (2011-01-15), pages 13 AND 21 *
NESVIZHSKII, A.I. ET AL.: "Analysis and validation of proteomic data generated by tandem mass spectrometry.", NATURE METHODS, vol. 4, no. 10, October 2007 (2007-10-01), pages 787 - 789 *
QIAO, YANTAO ET AL.: "Review on Protein Identification Approaches Based on Tandem Mass Spectrometry", JOURNAL OF FRONTIERS OF COMPUTER SCIENCE & TECHNOLOGY, vol. 4, no. 2, February 2010 (2010-02-01), pages 102 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111883214A (zh) * 2019-07-05 2020-11-03 深圳数字生命研究院 构建诱饵库、构建目标-诱饵库、代谢组fdr鉴定的方法及装置
CN111883214B (zh) * 2019-07-05 2023-06-16 深圳数字生命研究院 构建诱饵库、构建目标-诱饵库、代谢组fdr鉴定的方法及装置

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