Having difficulties with your experiment? Need guidance on maintenance tips and tricks?

We are dedicated to your success. View our expert recommendations for commonly encountered inquiries.

View the relevant questions below:

Beginning your experiment? Visit 

Getting started

Troubleshooting

Why did I identify so few proteoforms overall?

There are several possible reasons for this. One common issue the user defined database. Database selection/creation is a critical step in Top-Down data analysis. Foremost the database must include the organisms in the sample or closely related ones. Equally important is the inclusion of PTMs in the database. A protein database made from a FASTA file will not include any PTMs and the result of a search will only return the unmodified protein. In order to screen for modified proteoforms it is recommended that the database be constructed from Uniprot .xml or Uniprot .txt file files, or download from the database warehouse mentioned above (use complex for most comprehensive searches.)

Alternatively, users can reduce the confidence required to return a result. In the event that the MS2 data quality is poor it is likely that any PrSMs achieved are lower scoring and may be filtered out by default.

Lastly ensure that all parameters in the ProSight Software match the data collection parameters (e.g. activation method, resolving power, etc.)

For a protein of interest I see multiple proteoforms in my data. Why does ProSight only identify some of them?

Some modifications may not be included in the PTM annotation in the database and may need to be added manually. Additionally, it is common to find a few abundant proteoforms and several low abundance proteoforms. Low abundance proteoforms often do not yield sufficient fragment ion signal to make a confident PrSM. In this case it may be beneficial to use additional scan averages when fragmenting weak or low abundant proteoforms to improve the fragment ion signal to noise ratio.

What is the P-Score?

The P Score is a statistical model of MS/MS matching based on the Poisson Distribution.

If an event is expected to occur λ times, then the probability of it occurring k times is given by the Poisson distribution:

Gen P score Formula

If f is the total number of observed fragments, and n is the number of matching fragments, we may estimate the probability of a random protein match given f and n:

P Score Formula

where x is the mean probability that a mass of an observed fragment ion will randomly match one from a generic protein.

Given the probability of a random protein match with a particular n, f, and Ma, we can now estimate the probability of getting a match at least as good as we have by random chance:

P Score Formula

where p is the p-value assuming our Poisson model – what we call the P Score.

What is E-Value?

The E value is a Bonferroni corrected P Score. Bonferroni correction is a very conservative probability correction factor used to correct for multiple comparisons.

Need more information? Contact us ›

For Research Use Only. Not for use in diagnostic procedures.

CMD SchemaApp code