Assessing the Performance of Mass Spectrometry Search Strategies in Identifying Translational Errors Using PDX Proteomics Data

TL;DR

This study evaluates how well mass spectrometry methods can detect errors in protein translation using cross-species data from patient-derived xenografts.

Contribution

The paper introduces the first benchmark for identifying translational errors using proteomics data and evaluates open and closed search strategies.

Findings

Open search approaches achieve over 65% sensitivity and 70% precision in high-quality samples.

Performance varies significantly across different amino acid substitutions.

Closed searches are limited by mislocalization of post-translational modifications.

Abstract

Translational errors (TEs) result in a mismatch between mRNA codons and the amino acids (AAs) of the corresponding protein. Unlike DNA mutations or RNA editing, where nucleotide sequences can be used to infer AA substitutions, TEs can only be detected at the protein level. Although high-throughput mass spectrometry (MS) proteomics offers the potential to resolve peptide sequences and could theoretically be used to identify TEs, the feasibility of current MS data analysis approaches for this application remains uncertain. Here, we utilize patient-derived xenograft proteomics data, which include both human and mouse peptides with identifiable cross-species AA variations, as a ground truth for benchmarking TE identification methods. By using high-confidence mouse peptides as surrogates for “TE-containing” peptides, we show that current open search approaches can achieve >65% overall sensitivity and >70% overall precision for high-quality samples. The intersection of different search strategies significantly enhances precision, albeit at the expense of reduced sensitivity. Notably, the evaluation metrics vary significantly across individual AA substitutions, suggesting that caution is warranted when detecting or interpreting specific AA substitutions. Moreover, closed searches targeting predefined AA changes exhibit poor precision, with post-translational modification mislocalization identified as a key bottleneck for this application. Overall, our study provides a first-of-its-kind benchmark for MS-based TE discovery and offers guidance for optimizing MS search strategies.

•PDX proteomics data enable rigorous evaluation of mistranslation search strategies.•Open searches can reach >70% precision and >65% sensitivity in high-quality samples.•Identification performance varies across different amino acid substitutions.•Closed searches are limited by PTM mislocalization for translational error IDs.

PDX proteomics data enable rigorous evaluation of mistranslation search strategies.

Open searches can reach >70% precision and >65% sensitivity in high-quality samples.

Identification performance varies across different amino acid substitutions.

Closed searches are limited by PTM mislocalization for translational error IDs.

Amino acid substitutions caused by translational errors have been explored through the secondary analysis of large-scale mass spectrometry proteomics data. However, the effectiveness of current proteomics data analysis methods in identifying these substitutions has not been thoroughly assessed in this context. Here, we demonstrate that cross-species single amino acid variants in patient-derived xenograft proteomics data provide a robust framework for this assessment. We benchmarked open and closed search strategies at the peptide-spectrum match level, offering insights for biological discovery and tool optimization in translational error-single amino acid variant identification.

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