Sequencing Cyclic Peptides by Shayan Abtahi
Sequencing Cyclic Peptides
Shayan Abtahi
What is the Problem?
Non ribosomal peptides can’t be found by looking for DNA region encoding it
Have to use mass spectrometer to sequence the peptide
Cyclic peptide so there are a lot of possibilities
Can’t realistically brute force
Noise in actual mass spectrometer data
How to Solve it
Score the theoretical spectrum of a peptide against experimental data
Algorithm maximizes the score of potential peptides
Input includes the number of potential peptides under consideration
Starts with generating potential peptides for each length and removing
inconsistent ones
Starts with an empty peptide, and generates all potential 1-mers, then 2-mers etc… removing
minimally scoring ones at each step
High level steps
Expand function - generates list of k+1-mers
Trim function - Scores each peptide as if it were linear and retains the top N,
where N is an input, scoring peptides
Results
It worked
Was ridiculously slow
So ridiculously slow I couldn’t actually finish checking tyrocidine B1
(VKLFPWFNQY) because my laptop is old garbage
But the output was correct until I had a memory error at around 9-mers
Non-ribosomal peptides are challenging to sequence due to the need for mass spectrometry and the complexity of cyclic peptides. An algorithm comparing theoretical and experimental spectra can help, but may be slow on older devices as seen in a case studying tyrocidine B1.
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Presentation Transcript
Sequencing Cyclic Peptides Shayan Abtahi
What is the Problem? Non ribosomal peptides can t be found by looking for DNA region encoding it Have to use mass spectrometer to sequence the peptide Cyclic peptide so there are a lot of possibilities Can t realistically brute force Noise in actual mass spectrometer data
How to Solve it Score the theoretical spectrum of a peptide against experimental data Algorithm maximizes the score of potential peptides Input includes the number of potential peptides under consideration Starts with generating potential peptides for each length and removing inconsistent ones Starts with an empty peptide, and generates all potential 1-mers, then 2-mers etc removing minimally scoring ones at each step
High level steps Expand function - generates list of k+1-mers Trim function - Scores each peptide as if it were linear and retains the top N, where N is an input, scoring peptides
Results It worked Was ridiculously slow So ridiculously slow I couldn t actually finish checking tyrocidine B1 (VKLFPWFNQY) because my laptop is old garbage But the output was correct until I had a memory error at around 9-mers
