A maximum-likelihood approach to absolute protein quantification in mass spectrometry

Abstract

Label-free absolute protein quantification refers to a process of estimating protein abundances in complex biological samples based on the data acquired from a liquid chromatography mass spectrometry (LC-MS) analysis. Most approaches to label-free quantification rely on measuring peak areas from an extracted-ion chromatogram. However, because of the differences in physicochemical properties associated with different peptide ions, observed peak areas in a single experiment are determined not only by peptide abundances, but also the intrinsic biases of analytical platforms. Therefore, accurate modeling of these biases provides an opportunity to developing new computational methods for precise absolute protein quantification. In this work, we developed a new algorithm for absolute quantification of proteins. The approach is based on the concept of peptide response rate, which characterizes the peptide-specific signal detection bias in an LC-MS experiment. We argue that peptide response rate is an intrinsic and reproducible property of peptide ions that can be reliably predicted using non-linear regression and features extracted from the sequence of the parent protein. Protein abundances are estimated using a maximum likelihood model in which the observed peak areas of peptide ions are adjusted using predicted peptide response rates. We evaluate our approach on a large LC-MS dataset as well as simulated data and provide evidence that the accuracy of absolute protein quantification is improved when peptide-specific response rates are taken into account.