FAQs

• manual how to read the mass spec result v6 20220308.pdf

• What is LC/MSMS?
• What is shotgun proteomics?
• What is # peptides?
• What is a protein group?
• What is Protein Inference?
• What is # unique peptides?
• What is # PSMs?
• What is a missed cleavage?
• What is MS Amanda Score?
• Which modifications can be identified?
• What are the concerns about mapping PTM sites?
• What can I do to increase the sequence coverage of my protein(s) of interest for mapping PTMs?
• What is ptmRS?
• How is the False-Discovery-Rate (FDR) calculated?
• What is the protein area and iBAQ?
• How is the normalization of the protein areas performed?
• What is m/z?
• What is MH+?
• What is search space?
• What is Isolation Interference [%]?
• What are GO terms?
• What is sequence coverage?
• Why are some protein areas written in bold and some not?
• What is match-between-runs (MBR)?
• Why do you use limma?
• What is limma doing? How does limma work?
• I expected one single protein in the band we cut from the gel, but the result shows one hundred proteins. How come?
• I see quite a lot of keratins in my result list of identified proteins. Where do they come from?
• What is the detection limit and how many proteins can be detected in a typical proteomics analysis?
• Is it possible to distinguish between modifications that differ in decimal region of mass?
• I cannot find my protein of interest in the list of identified proteins. Does this mean it is not in the sample?
• I found some interesting and promising candidates (potential members of my protein complex or binding partners of my protein of interest) in the mass spec result. What can I do next as follow-up to confirm those proteins?
• How can I evaluate the quality of my data?
• What can I do if I cannot find the mass spec result files you sent me anymore?
• Can the raw data be accessed too?
• How to overcome the problem of distinct interaction properties?

We routinely search for post-translational modifications such as Phosphorylation (S, T, Y), Acetylation (K), Ubiquitination (K), Methylation (K, R), Dimethylation (K, R), Trimethylation (K). Additionally, we search for modifications that may be introduced during samples preparation, such as oxidation (M) and carbamidomethylation (C) that we generate by reduction and alkylation of cysteine disulfide bonds. We can also search for many other modifications which we have to define in the search parameters, but we cannot search simultaneously for every possible modification.

The higher the sequence coverage of the protein of interest, the greater the statistical chance to detect and identify the peptide carrying a modification group. PTMs often occur in a sub-stoichiometric manner, meaning if the site occupancy of a certain modification is low, then the chance of detecting the modified peptide also decreases.

Protein sequence coverage can be increased by starting the sample preparation with higher protein amounts, by gaining higher purity of the protein of interest, by using multiple enzymes with different specificity for protein digestion or by application of enrichment techniques for specific PTMs, such as TiO₂ for enrichment of phosphorylated peptides.

ptmRS is an algorithm for the assignment of PTM sites in peptides. The program uses MS/MS spectra in conjunction with the respective peptide sequences to calculate site probabilities for all potential modification accessor sites. The ptmRS score is a calculated site probability for a modified residue (localization probability) using site-determining ions of the tandem mass spectrum. The algorithm was developed in our group and we mainly use it to calculate site localization probabilities for phosphorylation sites.

https://www.ncbi.nlm.nih.gov/pubmed/22073976

Adding up the intensity of an eluting peptide over time is a quantitative measure of the peptide’s abundance. It enables relative quantification by comparing the amount of a peptide between samples. As peptides ionization ability is strongly variable, comparisons between different peptides are not possible. Nevertheless, adding up the integrated signal of several peptides belonging to a protein also enables the comparison of the protein amount between samples – relative quantification. The protein area is calculated by summing up the peak areas of the top three peptides and is a semi‐quantitative value reflecting the protein abundance.

Another option is iBAQ: here the sum of intensities of all tryptic peptides for each protein is divided by the number of theoretically observable peptides. Each peptide produces a signal in the MS, whose intensity is measured and the area under the curve is calculated. All peptide areas belonging to one protein are summed up according to certain criteria (including or excluding peptides which are not unique for a certain protein isoform). These protein areas are then divided by the number of theoretical peptides for this protein. This quantification is called iBAQ (Intensity Based Absolute Quantification) and iBAQ values are proportional to the molar quantities of the proteins. The iBAQ algorithm can roughly estimate the relative abundance of the proteins within each sample.

These are estimates which can be used to rank proteins according to their abundance: 1E4 is quite low and represents background signals, noise close to the detection limit, whereas 1E10 is quite high, reflecting very abundant proteins. Often the used proteases, antibodies or also contaminants like keratins show up on top of the most abundant proteins identified in a mass spec run. In a good affinity purification experiment the bait protein should show up among the top 20 hits.

Raw sample areas are normalized to account for different sample amounts loaded onto the MS. This allows comparison of protein amounts over different samples.  Therefore, samples with high total sum are divided by a factor, whereas samples with low total area are multiplied by a linear factor such that all samples finally have the same total area sum after normalization.

A mass spectrometer does not detect the actual molecule mass but rather the ratio between its mass (m) and its charge (z), m/z. All molecules have at least a charge of 1, as a mass spectrometer only detects charged molecules. Molecules that are multiply charged appear lower on the m/z scale.

To determine the actual mass of a molecule from its m/z value, its charge state has to be identified. For this, the molecules isotope pattern is used. http://www.ionsource.com/tutorial/isotopes/slide2.htm

From the m/z difference between a peak and its isotope peaks the charge state is calculated (e.g. difference of 1 Da: z=1; difference of 0,5 Da: z=2, …) and from this the actual mass of the molecule is determined.

MH+ is the peptide mass of a singly charged molecule, which can be calculated from the measured m/z. MH+ is composed of the mass of the molecule M plus the mass of a proton H+ which creates the positive charge and increases the mass by 1 Da. The ion mass detected is therefore one unit higher than the monoisotopic mass of the uncharged molecule.

This number indicates the number for possible candidates of each individual spectrum in the used database. The larger the database and the higher of the number of potential cleavages sites and missed cleavage sites, the higher the search space. The same is true for the number of dynamic modifications.

To create a fragment spectrum, you select a precursor mass (=peptide) for isolation, isolate and fragment the ions within a mass window that you define, and record the product ion masses created.

Ideally, you would isolate and fragment only the precursor ions of a single selected component. However, in practice you isolate the precursor ions within a user-specified window—typically 1 or 2 Daltons around the isolation mass. Co-eluting components with a mass falling into this isolation window are also isolated and fragmented. This process is called co-isolation.

We routinely perform a Gene Ontology Analysis to report Gene Ontologies for Molecular Functions, Cellular Components, Biological Processes as well as protein families (Pfam IDs) of identified proteins.

Sequence coverage is the percentage value of how many amino acids of a protein are identified.

If identified peptides cover 200 residues in a 1.000 amino acids long protein, sequence coverage would be 20%. Sequence coverage can vary from 0 to 100%, as often not all peptides belonging to protein are detected in the sample. This is dependent many factors, such as protein sequence, protein length, abundance in the sample, complexity of the sample, etc.

Identified (covered) amino acids are highlighted in green.

Amino acids that are identified with modifications are highlighted in red.

Black amino acids indicate no identification.

If a protein area is written in bold, it means that this protein has been identified by a certain number of peptides the area of which was subsequently quantified. If a protein area is not written in bold, this protein has not been identified by the search engine directly, but it has been quantified using a method called ´match-between-runs´.

In order to be able to calculate some kind of ratio ('fold change') we perform imputation to empty values. This is also indicated by 'italic' expression of the value.

Ideally, for each MS run there is enough information available to retrieve the peptide sequence from the measured MS/MS spectrum. However, sometimes the available information is not sufficient for peptide identification; either the measured MS/MS spectrum is insufficient to identify the sequence or the peptide was not even fragmented. In this case, the MBR algorithm comes into play.

It determines an “elution profile” of an identified peptide (m/z, z and retention time) in one run of the experiment (e.g. the control), and looks for the same elution profile in another run of the same experiment (e.g. the sample), where the peptide was not identified.

To obtain an identified feature, MS/MS and sequence information from one run are matched within tight mass and retention time windows. For this purpose, the runs are first aligned by their retention times, and then the identification(s) are transferred from the run containing the necessary information to the one(s) with insufficient information. The corresponding peak will be integrated and the respective area is calculated.

limma is a statistical test especially suited when the number of replicates is low, which is the case in most biological experiments. Nevertheless it can not estimate the significance if less than three replicates per condition are available. For more detail see Schwämmle et al.

limma can compute the statistical significance of differentially abudant proteins. Obviously this requires availability of replicates. In contrast to a conventional t-test, limma uses the variance information of all individual proteins to estimate the variance and statistical significance. This is especially unseful in cases where the number of replicates is low and leads to more robust and reliable results.

If one single band was excised from a gel, it is not unlikely that this band contains several proteins. These proteins do not necessarily have the same size. Some of the unexpected proteins may be degradation products, some may carry PTMs. Some proteins that show up may be below the detection level of the staining method used. Mass spec by far exceeds the sensitivity of silver staining or coomassie.

Contaminants like keratins and dermcidin usually come from human contamination and are introduced during sample preparation, staining or cutting. They can origin from air, skin or clothes. We routinely search for a list of known contaminants, in addition to the taxonomy database of the organism used in the experiment, and highlight them in the final result list of identified proteins.

There is no general number to address this question. The number of detectable proteins is dependent on factors such as complexity of the sample, dynamic range of protein concentrations, available material and LC-MS instrumentation being used. The range of detectable proteins in a single mass spec run can vary from a few proteins to several hundred or more than thousand.

The general limit of detection of current state-of-the-art mass spectrometers is in the sub-femtomol and attomol region.

However, the actual detection limit for peptides varies with its so-called “flyability” in the MS machine. A peptide’s flyability is dependent on several properties, such sequence length and composition, ionization efficiency and fragmentation characteristics. Commonly, some peptides of a protein “fly” very well and are therefore highly abundant in the result, while other peptides e.g. are not ionized well and will never be detected.

Yes, with current high mass accuracy mass spectrometers one can easily distinguish modifications that differ in decimal region of mass, such as Acetylation and Trimethylation, which differ by 0.04 Da in mass.

It is extremely difficult to prove that a protein is definitely not present in a sample, rather than just not detected. The failure to detect a protein could be due to low amount/concentration or because it was masked by more abundant proteins. It is also possible that the peptides of your protein of interest could not be uniquely mapped and a close homolog of your protein shows up in the list instead (see also protein grouping).

As next step, best would be to repeat the experiment in replicates to see if we can reproducibly confirm initial results. Triplicates (for experiment and control conditions) are needed for a good statistical foundation and to calculate the fold change and associated p-values. For better discrimination of true interactors and background proteins these values can be visualized as a volcano plot.

We monitor several parameters of LC and mass spec machines, calibrate machines on a regular base, perform quality control runs before, after and in between sample runs and additionally introduce washing steps before, after and in between sample runs to avoid carry-over effects. In the mass spec xlsm result file we provide there is a tab called ´Quality Control´ where several parameters are visualized, such as distribution of mass deviation, number of miscleaved peptides or occurrence of methionine oxidation.

We store all result files on a secure server, where we have access to the data at any time.

So if you cannot find your mass spec result anymore, just contact us and we will send you another copy of the original result file.

We store all the raw data files on a secure server, where we have access to at any time.

So if you wish to have the raw data for yourself or for providing it to a journal, because it was requested by the editors for the reason of transparency, just contact us and we will send you a copy of the original raw data files.

Many proteins are social and talk to each another in groups called protein complexes. Some other partners are rather “unsocial”, with only limited communication (e.g. signal transduction pathways) and merely engaging in transient interactions (i.e. protein interactions that are easily formed and broken).

Partnering proteins can be “glued” together using a tool called cross-linker (e.g. formaldehyde).
As an example, you can work with 0.75 % formaldehyde and in-vitro crosslink proteins on beads in a protein extract. Incubation time could be 10 minutes; if you “over-crosslink” your proteins you will probably increase unspecific protein background.
 
Possible points to consider:

1. Formaldehyde oxidizes to paraformaldehyde if not kept tightly closed. While formaldehyde is a zero-length cross linker, paraformaldehyde is not.
2. Formaldehyde crosslinking -like any other crosslinker- is dependent on the substrate concentration. If you are performing in-vivo crosslinking, make sure that each of the binding partners are expressed at right level. Having controls is the key.
3. Crosslinking is also highly temperature dependent. So, using cold or warm PBS can affect not only the crosslinking of your target complex, but also background non-specific crosslinks.
4. If controls suggest that each of the binding partners are expressed as expected, working formaldehyde concentration may require some optimization.
5. Low concentrations of formaldehyde are mass spec compatible

Please use Methanol-free FA, as the stabilized solution from store can be used for desinfection, but is not appropriate for biochemical or cell biology tasks.