A novel framework to quantify uncertainty in peptide-tandem mass spectrum matches with application to nanobody peptide identification

TL;DR

This paper introduces a new Bayesian framework for peptide-spectrum matching in nanobody proteomics, addressing violations of standard assumptions and improving accuracy in identifying nanobody peptides from mass spectrometry data.

Contribution

The authors develop a novel Bayesian model selection approach that accounts for all sources of error in peptide-spectrum matching, specifically tailored for nanobody peptides.

Findings

Improved peptide-spectrum matching accuracy on nanobody data

Demonstrated robustness of the method with simulated and real datasets

Provided the first rigorous description of MS/MS spectrum noise

Abstract

Nanobodies are small antibody fragments derived from camelids that selectively bind to antigens. These proteins have marked physicochemical properties that support advanced therapeutics, including treatments for SARS-CoV-2. To realize their potential, bottom-up proteomics via liquid chromatography-tandem mass spectrometry (LC-MS/MS) has been proposed to identify antigen-specific nanobodies at the proteome scale, where a critical component of this pipeline is matching nanobody peptides to their begotten tandem mass spectra. While peptide-spectrum matching is a well-studied problem, we show the sequence similarity between nanobody peptides violates key assumptions necessary to infer nanobody peptide-spectrum matches (PSMs) with the standard target-decoy paradigm, and prove these violations beget inflated error rates. To address these issues, we then develop a novel framework and method that treats peptide-spectrum matching as a Bayesian model selection problem with an incomplete model space, which are, to our knowledge, the first to account for all sources of PSM error without relying on the aforementioned assumptions. In addition to illustrating our method's improved performance on simulated and real nanobody data, our work demonstrates how to leverage novel retention time and spectrum prediction tools to develop accurate and discriminating data-generating models, and, to our knowledge, provides the first rigorous description of MS/MS spectrum noise.