  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.
