<p>I agree with @DanielGeiszler's explanation. Maybe some illustration may help.</p>
<p>Software typically use a scoring function to separate the true and false identifications. In the illustration below, all peptides to the right of the cut off line are the one to keep (meaning within 1% FDR)
<a href="https://i.sstatic.net/xcEIb0iI.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/xcEIb0iI.png" alt="enter image description here" /></a>
Now using a large fragment ion threshold will artificially increase the scores of the false peptides (it may affect the true peptides as well, but to a much lesser degree). The distribution will now look like below, where the false peptides' curve shifted to the right. To maintain the same FDR cut off, the line needs to be drawn to the right as well, thus less confident peptide identification.
<a href="https://i.sstatic.net/0kAbF4UC.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/0kAbF4UC.png" alt="enter image description here" /></a></p>

CC BY-SA 4.0
2024-10-30T18:32:52.550Z
2024-10-30T18:32:52.550Z
John Tran
<p>If your data is high mass accuracy, there isn’t a benefit to using low mass accuracy in your search. You should be hitting nearly all of the true peptide fragment ions with your tight tolerances.</p>
<p>When you use larger mass tolerances, you’re going to match more fragment ions for peptides that are incorrect matches. The result is that incorrect peptide hits score higher while correct peptides don’t increase their scores, so it becomes harder to separate your false peptide hits from your true peptide hits. Then, when you go to filter to a given FDR, this manifests as fewer true peptides passing FDR filtering.</p>
<p>MSFragger (within FragPipe) implements a procedure to optimize your fragment tolerances described briefly here: <a href="https://www.nature.com/articles/s41467-020-17921-y" rel="nofollow noreferrer">https://www.nature.com/articles/s41467-020-17921-y</a> .</p>
<p>Full disclosure: I’m an author on the above paper.</p>

CC BY-SA 4.0
2024-04-22T14:49:59.227Z
Thanks for the answer. "Then, when you go to filter to a given FDR, this manifests as fewer true peptides passing FDR filtering" I didn't fully understand this text. Could you elaborate a bit?
Science123
CC BY-SA 4.0
2024-04-20T18:35:46.460Z
2024-04-20T18:36:25.937Z
2024-04-20T18:36:25.937Z
DanielGeiszler
<p>Proteomics newbie here. I am using proteome discoverer software to analyze the fragmentation of a chemically modified single protein. In my understanding, when we set the Fragment mass tolerance to larger value, we should observe more peptide coverage/modification compared with stricter mass tolerance (for example 1Da vs 0.02 Da).I played with the fragment mass tolerance a bit keeping all other parameters constant. To my surprise, I observed better peptide coverage and newer modifications with lower mass tolerance compared to larger value. How this is possible?</p>

<proteins><proteomics>
Fragment mass tolerance in proteomics analysis
# Fragment mass tolerance in proteomics analysis

(Asked by: username_0 on 2024-04-09)

Proteomics newbie here. I am using proteome discoverer software to analyze the fragmentation of a chemically modified single protein. In my understanding, when we set the Fragment mass tolerance to larger value, we should observe more peptide coverage/modification compared with stricter mass tolerance (for example 1Da vs 0.02 Da).I played with the fragment mass tolerance a bit keeping all other parameters constant. To my surprise, I observed better peptide coverage and newer modifications with lower mass tolerance compared to larger value. How this is possible?

**Tags:** proteins, proteomics

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## 2 answers

### Answer by username_1 on 2024-10-30. Score: 2

I agree with @DanielGeiszler's explanation. Maybe some illustration may help.

Software typically use a scoring function to separate the true and false identifications. In the illustration below, all peptides to the right of the cut off line are the one to keep (meaning within 1% FDR)
[![enter image description here](https://i.sstatic.net/xcEIb0iI.png)](https://i.sstatic.net/xcEIb0iI.png)
Now using a large fragment ion threshold will artificially increase the scores of the false peptides (it may affect the true peptides as well, but to a much lesser degree). The distribution will now look like below, where the false peptides' curve shifted to the right. To maintain the same FDR cut off, the line needs to be drawn to the right as well, thus less confident peptide identification.
[![enter image description here](https://i.sstatic.net/0kAbF4UC.png)](https://i.sstatic.net/0kAbF4UC.png)


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### Answer by username_2 on 2024-04-20. Score: 1

If your data is high mass accuracy, there isn’t a benefit to using low mass accuracy in your search. You should be hitting nearly all of the true peptide fragment ions with your tight tolerances.

When you use larger mass tolerances, you’re going to match more fragment ions for peptides that are incorrect matches. The result is that incorrect peptide hits score higher while correct peptides don’t increase their scores, so it becomes harder to separate your false peptide hits from your true peptide hits. Then, when you go to filter to a given FDR, this manifests as fewer true peptides passing FDR filtering.

MSFragger (within FragPipe) implements a procedure to optimize your fragment tolerances described briefly here: <https://www.nature.com/articles/s41467-020-17921-y> .

Full disclosure: I’m an author on the above paper.

> **Comments:**
> **username_0**: Thanks for the answer. "Then, when you go to filter to a given FDR, this manifests as fewer true peptides passing FDR filtering" I didn't fully understand this text. Could you elaborate a bit?  

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