Mass Deconvolution in Peptide Analysis

Deconvolution converts a set of multiply charged m/z peaks into an estimated neutral molecular mass.

Inputs

Software considers:

• charge-state spacing
• isotope patterns
• m/z range
• signal intensity
• adducts
• baseline
• mass tolerance

Processing choices

Different algorithms may produce different outputs when spectra contain noise, overlapping species, or broad envelopes.

Common errors

• incorrect charge assignment
• merging two species
• treating adducts as separate proteins
• using the wrong mass convention
• excluding weak charge states
• over-smoothing

Verification

The deconvoluted result should be traceable back to the raw spectrum and supported by coherent charge states.

Frequently asked questions

Is deconvolution raw data?

No. It is processed data derived from raw m/z signals.

Can software invent a mass from noise?

Poor settings can generate artifacts.

Should the observed mass exactly equal theoretical mass?

A tolerance is expected based on instrument performance and mass convention.

Can mixed species produce one broad mass?

Yes, especially when resolution is insufficient.

Key takeaways

Deconvolution is a powerful interpretation step, but it must be controlled, reviewed, and linked to the underlying spectrum.

References

1. Aebersold R, Mann M. Mass-spectrometric exploration of proteome structure and function. Nature. 2016.
2. Gross JH. Mass Spectrometry: A Textbook. Springer.

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TSMS Labs educational disclaimer: For laboratory research and educational purposes only. Not for human consumption. This content is not medical, clinical, or regulatory advice.