Absolute quantification of peptide products in in vitro digestions: A computational approach using Bayesian inference

by Sarah Henze
Doctoral thesis
Date of Examination:2022-07-04
Date of issue:2023-06-29
Advisor:Dr. Juliane Liepe
Referee:Dr. Juliane Liepe
Referee:Prof. Dr. Peter Sollich
Persistent Address: http://dx.doi.org/10.53846/goediss-9972

 

 

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Abstract

English

Insight into enzyme specificities and dynamics is central to understanding biochemical processes. The peptide products generated by purified proteases in in vitro digestions are often identified by mass spectrometry measurements. However, these provide only relative quantification and to obtain absolute quantities, laborious titration of synthetic peptide equivalents is required. Our aim is to develop a method to convert MS ion signals to concentrations for many peptide products computationally without further experimental effort. To achieve this, a conversion parameter for each digestion product needs to be estimated. We present an algorithm named Quantifiation of Peptides using Bayesian inference (QPuB), which works on the principle of mass conservation. It employs Bayesian statistical inference in an adaptive, population-based Markov chain Monte Carlo sampling scheme to estimate the conversion factors. This approach allows to quantify the underlying uncertainty in the form of full posterior distributions of the estimated parameters. We calibrated the algorithm on synthetic noise-free datasets mimicking the dynamics of real proteases. For low-informative data causing parameter non-identifiability, we propose strategies to enable successful inference. We show that QPuB is able to infer the conversion factors for up to 45 peptides with high accuracy and precision. Although the algorithm still requires further development, we believe that QPuB could become a useful quantification tool to the field of peptidomics.
Keywords: Label-free quantification; absolute quantification; in vitro digestion; peptidomics; mass spectrometry; Bayesian inference; Markov chain Monte Carlo; Differential Evolution
 
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