Protein Inference Using Peptide Quantification Patterns

Determining the list of proteins present in a sample, based on the list of identified peptides, is a crucial step in the untargeted proteomics LC–MS/MS data-processing pipeline. This step, commonly referred to as protein inference, turns out to be a very challenging problem because many peptide sequences are found across multiple proteins. Current protein inference engines typically use peptide to spectrum match (PSM) quality measures and spectral count information to score protein identifications in LC–MS/MS data sets. This is, however, not enough to confidently validate or otherwise rule out many of the proteins. Here we introduce the basis for a new way of performing protein inference based on accurate quantification patterns of identified peptides using the correlation of these patterns to validate peptide to protein matches. For the first implementation of this new approach, we focused on (1) distinguishing between unambiguously and ambiguously identified proteins and (2) generating hypotheses for the discrimination of subsets of the ambiguously identified proteins. Our preprocessing pipelines support both labeled LC–MS/MS or label-free LC–MS followed by LC–MS/MS providing the peptide quantification. We apply our procedure to two published data sets and show that it is able to detect and infer proteins that would otherwise not be confidently inferred

Protein Inference Using Peptide Quantification Patterns

Determining the list of proteins present in a sample, based on the list of identified peptides, is a crucial step in the untargeted proteomics LC–MS/MS data-processing pipeline. This step, commonly referred to as protein inference, turns out to be a very challenging problem because many peptide sequences are found across multiple proteins. Current protein inference engines typically use peptide to spectrum match (PSM) quality measures and spectral count information to score protein identifications in LC–MS/MS data sets. This is, however, not enough to confidently validate or otherwise rule out many of the proteins. Here we introduce the basis for a new way of performing protein inference based on accurate quantification patterns of identified peptides using the correlation of these patterns to validate peptide to protein matches. For the first implementation of this new approach, we focused on (1) distinguishing between unambiguously and ambiguously identified proteins and (2) generating hypotheses for the discrimination of subsets of the ambiguously identified proteins. Our preprocessing pipelines support both labeled LC–MS/MS or label-free LC–MS followed by LC–MS/MS providing the peptide quantification. We apply our procedure to two published data sets and show that it is able to detect and infer proteins that would otherwise not be confidently inferred