Mass Spectrometry and Proteomics - Lecture 5 - Matthias Trost Newcastle University

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1 Mass Spectrometry and Proteomics - Lecture 5 - Matthias Trost Newcastle University matthias.trost@ncl.ac.uk

2 Previously Proteomics Sample prep 144

3 Lecture 5 Quantitation techniques Search Algorithms Proteomics software 145

4 Current limitations of MS-based Proteomics Cellular proteins span a wide range of expression and current mass spectrometric technologies typically sample only a fraction of all the proteins present in a sample. Due to limited data quality, only a fraction of all identified proteins can also be reliably quantified. Bantscheff et al, Anal Bioanal Chem,

5 Limitations of Proteomics concentration of proteins in plasma Anderson & Anderson, MCP,

6 Quantitation techniques Label-free Ion intensity Spectral counting Chemical isotopic labeling ICAT itraq/tmt mtraq Formaldehyde label Enzymatic label Metabolic isotopic labeling SILAC 15 N 148

7 The three different spectral sources of quantitative information Wilm, Proteomics,

8 Quantitation methods Isotope label (SILAC, ICAT, demethyl label etc) Fragmentation-based label (itraq) Label-free X Da MS MS/MS 150

9 Quantitation strategies Bantscheff et al, Anal Bioanal Chem,

10 Characteristics of quantitative MS methods Bantscheff et al, Anal Bioanal Chem,

11 Label-free quantitation Condition A Condition B MS/MS MASCOT identification driven peptide assignment Peak detection (in triplicate) Peak detection (in triplicate) Hierarchical clustering 153

12 Label-free proteomics RLEIpSPDpSpSPER Cond. A Cond. B Stdev Cond. A Stdev Cond. B Ratio Cond. A/Cond. B 0.49 Advantages and Disadvantages + Lower complexity + Lower cost + Primary tissue possible (+) Repetitions increase identification rates - High LC-reproducibility necessary - Good clustering dependent on high mass accuracy - Several peptides for reliable quantitation required 154

13 Another label-free quantitation: Spectral counting The number of spectra matched to peptides from a protein is used as a surrogate measure of protein abundance. As the sampling of peptides in a mass spectrometer is usually depending on the peptides intensities, spectral counting has a reasonable statistical significance. Spectral counting is cheaper, easier to implement and does not require highly reproducible data. It requires however still thorough computational and statistical analysis. Modern mass specs are getting to sensitive and fast for this quantitation. 155

14 Isobaric tag for relative and absolute quantitation (TMT or itraq) Reacts with N-termini and other primary amines of peptides. Uses a reporter group for quantification that can be identified in MS/MS spectra. Another labeled group serves as a balancer

15 Isobaric tag for relative and absolute quantitation (TMT or itraq) Quantification is done in MS/MS mode (low intensity!) Once labeled with TMT or itraq, the 4/6/8/10 individual samples are pooled for further processing and analysis. During subsequent MS/MS of the peptides, each isobaric tag produces a unique reporter ion that identifies which samples the peptide originated and its relative abundance. Gingras et al, Nat Rev Mol Cell Biol,

16 Isobaric tag for relative and absolute quantitation (itraq or TMT) + Up to 11 samples (11-plex) can be quantified at the same time. + Saves instrument time. - Quite expensive. - Low dynamic range. - Can not be performed in most ion-trap instruments as they do not reach this low mass range. - Non-changing peptides are favored to be identified. - large mass addition to peptides - high ratios are suppressed by coeluting other peptides. 158

17 Ratio compression in TMT experiments Ow, J Prot Res, 2009 Ting et al, Nature Methods,

18 Reducing ratio compression by using Synchronous Precursor Selection (SPS) 160

19 Formaldehyde/dimethyl label Chen et al, Anal Chem, 2003; Boersema et al, Proteomics, 2008 Samples are labeled with heavy and light formaldehyde on their primary amines (N-termini, Lys) relatively cheap and simple. can be used on virtually any sample. quite large mass difference between samples. Problematic retention time shifts in long LC runs due to Deuterium. 161

20 Formaldehyde/dimethyl label Chen et al, Anal Chem,

21 Enzymatic isotope label Further disadvantage: Introduction of 18 O at acidic side chains often incomplete incorporation of the label Miyagi et al, Mass Spec Rev,

22 Stable isotope labeling with amino acids in cell culture (SILAC) Cells are grown with normal and heavy isotope amino acids. + The isotopically labeled peptides are chemically (almost) identical (Retention time etc) + The different samples are mixed at a very early step during sample preparation. - labeled amino acids (Lys/Arg) might be metabolized to other amino acids - Expensive for large amounts of cells. - Not for primary tissue. - Increases complexity of the sample. commons.wikimedia.org - Some cell types do not grow well in dialysed serum. 164

23 Neutron encoding (NeuCode) SILAC Makes use of the subtle mass differences caused by nuclear binding energy variation in stable isotopes ( mass defect ). For example, labelling with lysine with 2 H 8 ( Da) and Lysine with 13 C 6 and 15 N 2 ( Da). Can only be resolved with very high resolution >200,000. In a low-resolution (<15,000) MS/MS scan, peaks are overlaying and indistinguishable, thus both peaks add to the intensity. Theoretically, up to 39 isotopologues of Lysine are possible. Herbert et al, Nature Methods 2013 Rose et al, Anal Chem,

24 Neutron encoding (NeuCode) SILAC (a) Mass calculations of the 39 isotopologues for a +8-Da lysine. Shown in solid black are the isotopologues used for the experiments presented here. (b) Theoretical calculations depicting the percentage of peptides that are resolved (full width at 1% maximum peak height) when spaced 12, 18 or 36 mda apart for resolving powers (R) of 15,000 1,000,000. (c) Top, MS1 scan collected with typical 30,000 resolving power. Center, a selected precursor with m/z at 827 collected with 30,000 resolving power (black) and the signal recorded in a highresolution MS1 scan (480,000 resolving power). Herbert et al, Nature Methods

25 Protein Identification Either de novo (thus no database) or from genomic data. When genomic data is available, the software performs an in silico digestion of the whole database using the specific protease. The mass of the peptide and the MS/MS spectrum are compared to the theoretical mass and the spectrum. 167

26 Search Engines Good search engines take common rules (high peaks after P) into account. The engines calculates a score from the number of matched peaks compared to peaks present in spectrum. This score is usually linked to a probability. Lately, search engines using spectral libraries have emerged. They are much faster and more accurate. However, good spectra for each peptide are required and ideally acquired in different kinds of instruments. 168

27 Peptide ID & matching For large scale proteomics, identification of peptides becomes a complex matching problem

28 Peptide ID & matching For large scale proteomics, identification of peptides becomes a complex matching problem

29 Database Fragmentation in silico Proteome UniProt Digestion in silico Peptide A Mass Peptide B Mass Peptide A Fragment Masses Peptide B Fragment Masses

30 Database Search Corresponding MS 2 data Observed Mass 1000 ± Da Intensity The Database Search 1. MS 1 filter 2. MS 2 scoring 3. Probabilistic analysis m/z

31 Database Search MS1 filter Peptide A Mass Peptide B Mass Observed Mass 1000 ± Da Peptide C Mass Peptide D Mass Peptide E Mass

32 Database Search MS1 filter Peptide A Mass Peptide B Mass Observed Mass 1000 ± Da Peptide C Mass Peptide D Mass Peptide E Mass

33 Database Search theoretical MS/MS spectra Peptide B Mass Peptide C Mass Peptide D Mass Score Observed Spectra Observed Mass 1000 ± Da

34 Database Search scoring Theoretical spectra Observed spectra Score Peptide Evidence: Peptide C Mass Observed Mass ± Da 80

35 Search constraints Classic Peptide/precursor mass accuracy MS/MS/fragment mass accuracy Fixed and variable modifications Enzyme (specificity) Instrument/type of ions generated Proposed Retention time 177

36 Commonly used Search Engines Mascot Sequest OMSSA X!Tandem Andromeda (within MaxQuant) 178

37 Decoy/target strategy to determine FDR 179

38 Decoy/target strategy to determine FDR probability that the match of score 10 is incorrect ~ 90% probability that a match of score 100 is incorrect ~ 0 PEP = # hits decoy database # a given score

39 Decoy/target strategy to determine FDR >Ubiquitin MQIFVKTLTGKTITLEVEPSDTIENVKAKIQD KEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKE STLHLVLRLRGG >Ubiquitin MQIFVK Target Database MQIFVK Decoy Database VFIQMK

40 False-Discovery Rate Peptide/protein identification by mass spectrometry is a statistical analysis with false-negatives and falsepositives. False-discovery rate (FDR) is estimated by searching the data against a combined forward and reversed database. The number of hits from the reversed database is thought equivalent with false hits in the forward database. Please note that the FDR is on the identification level only, not on the quantitation level. Commonly accepted FDRs are <1%. 182

41 Considerations We accept that a very small proportion of peptide identifications (usually set to 1%) will likely be false discoveries Hence, having multiple supporting peptides per protein is important for confident identification and quantitation

42 Considerations FDR estimation is challenging using small databases or when most of the database is identified. Always use bigger databases (for example include human with bacterial database)

43 Considerations Choose your PTMs wisely Too many PTMs lead to combinatorial explosion and long database search times Common chemical modifications Deamidation (NQ) Gln PyroGlu Oxidation (M) Carbamidomethylation (C) Acetyl (N-terminus)

44 Considerations The vast majority of MS identification and quantitation is performed on peptides; information on proteins is through inference The peptide to protein relationship is a many to many match VFIQMK VFIQMK TLSDYNIQK ESTLHLVLR EGIPPDQQR MQIFVK Protein A Protein B Protein C Protein A Protein B Protein C

45 Considerations Assigning non-unique peptides: Occam s Razor Accept the simplest explanation that fits the observations Non-unique peptides are assigned to proteins that have the most unique peptides VFIQMK VFIQMK TLSDYNIQK ESTLHLVLR EGIPPDQQR MQIFVK Protein A Protein B Protein C Protein A Protein B Protein C

46 Check your data: histograms Evaluate distribution of data Normalise data Calculate standard deviation to set cutoffs

47 Check your data: scatter plots Intensities vs intensities Reproducibility

48 Check your data: volcano plots Evaluate experimental reproducibility (0.05 is usual p-value cutoff) Appropriate fold change cutoff depends on standard deviation

49 Databases UniProt databases are the standard for mouse, human and most other organisms. They should be ideally non-redundant. Can/should contain splice variants. Database should not be too small (problem for bacteria) as FDR calculation might be wrong. A common set of contaminants (keratin, BSA, milk proteins ) should be added to the searched database. 191

50 Software for MS ID and Quant Software Platforms MaxQuant Trans Proteomic Pipeline (TPP) Proteome Discoverer PEAKS Scaffold ID only Mascot Sequest OMSSA Morpheus SRM/Targeted Skyline TMT quantitation COMPASS MaxQuant Proteome Discoverer de novo sequencing PEAKS

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