Mass spectrometry-based proteomics is constantly challenged by the presence of contaminant background signals. In particular, protein contaminants from reagents and sample handling are almost impossible to avoid. For data-dependent acquisition (DDA) proteomics, an exclusion list can be used to reduce the influence of protein contaminants. However, protein contamination has not been evaluated and is rarely addressed in data-independent acquisition (DIA). How protein contaminants influence proteomic data is also unclear. In this study, we established new protein contaminant FASTA and spectral libraries that are applicable to all proteomic workflows and evaluated the impact of protein contaminants on both DDA and DIA proteomics. We demonstrated that including our contaminant libraries can reduce false discoveries and increase protein identifications, without influencing the quantification accuracy in various proteomic software platforms. With the pressing need to standardize proteomic workflow in the research community, we highly recommend including our contaminant FASTA and spectral libraries in all bottom-up proteomic data analysis. Our contaminant libraries and a step-by-step tutorial to incorporate these libraries in various DDA and DIA data analysis platforms can be valuable resources for proteomic researchers, freely accessible at https://github.com/HaoGroup-ProtContLib.

No abstract

Mass spectrometry-based proteomics is constantly challenged by the presence of contaminant background signals. In particular, protein contaminants from reagents and sample handling are often abundant and impossible to avoid. For data-dependent acquisition (DDA) proteomics, exclusion list can be used to reduce the influence of protein contaminants. However, protein contamination has not been evaluated and is rarely addressed in data-independent acquisition (DIA). How protein contaminants influence proteomics data is also unclear. In this study, we established the protein contaminant FASTA and spectral libraries that are applicable to all proteomic workflows and evaluated the impact of protein contaminants on both DDA and DIA proteomics. We demonstrated that including our contaminant libraries can reduce false discoveries and increase protein identifications, without influencing the quantification accuracy in various proteomic software platforms. With the pressing need to standardize proteomic workflow in the research community, we highly recommend including our contaminant FASTA or spectral libraries in all bottom-up proteomics workflow. Our contaminant libraries and a step-by-step tutorial to incorporate these libraries in different DDA and DIA data analysis platforms can be a valuable resource for proteomics researchers, which are freely accessible at https://github.com/HaoGroup-ProtContLib.