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ProteoEM resolves molecular trace ambiguity in single-molecule affinity mapping by replacing hard identity classification with an expectation-maximization framework that probabilistically apportions ambiguous traces across candidate proteoforms. Addressing imperfect, nonspecific probe binding is critical because discrete thresholding introduces massive quantification distortion when individual binding traces match multiple protein identities. In simulations, the method accurately reconstructed true molecular abundances and corrected for observation yields, maintaining robustness against moderate calibration error where standard hard-assignment approaches failed.
Single-molecule protein mapping breaks down under discrete classification, but importing RNA-seq-style probabilistic deconvolution accurately recovers true proteoform abundances directly from ambiguous, noisy binding traces.
Single-molecule affinity mapping enables molecular-level measurement of proteins and proteoforms, but imperfect and nonspecific probe binding makes individual affinity traces compatible with multiple molecular identities. Accurate abundance estimation therefore requires apportionment of ambiguous traces by weight rather than assignment to a single candidate. We developed ProteoEM, an expectation-maximization framework for weighted proteoform quantification, inspired by transcript abundance estimation methods for RNA sequencing and released as an open-source Python package. ProteoEM evaluates each molecule against every candidate using fixed, pre-calibrated probe-response rates held separate from the abundance estimate, while retaining the full likelihood of the observed affinity features. The framework estimates proteoform abundances, reports indistinguishable proteoforms as groups when measurements cannot separate them, and accounts for differential observation yields to distinguish the composition of observed molecules from that of the source sample. In simulations, ProteoEM accurately recovered the underlying molecular composition where approaches that reduce each trace to a hard yes/no call introduced substantial errors. ProteoEM's performance was insensitive to a moderate, uniform calibration error but was biased by informative missing data and by proteoforms absent from the reference. When observation yields were known, it also recovered source-sample composition from observed molecular counts. ProteoEM provides an open-source, reproducible framework for quantitative analysis of single-molecule affinity measurements, and these results motivate validation on experimental molecule-level data.