A Computational Tool to Detect and Avoid Redundancy in Selected Reaction Monitoring

Abstract

Selected reaction monitoring (SRM), also called multiple reaction monitoring, has become an invaluable tool for targeted quantitative proteomic analyses, but its application can be compromised by nonoptimal selection of transitions. In particular, complex backgrounds may cause ambiguities in SRM measurement results because peptides with interfering transitions similar to those of the target peptide may be present in the sample. Here, we developed a computer program, the SRMCollider, that calculates nonredundant theoretical SRM assays, also known as unique ion signatures (UIS), for a given proteomic background. We show theoretically that UIS of three transitions suffice to conclusively identify 90% of all yeast peptides and 85% of all human peptides. Using predicted retention times, the SRMCollider also simulates time-scheduled SRM acquisition, which reduces the number of interferences to consider and leads to fewer transitions necessary to construct an assay. By integrating experimental fragment ion intensities from large scale proteome synthesis efforts (SRMAtlas) with the information content-based UIS, we combine two orthogonal approaches to create high quality SRM assays ready to be deployed. We provide a user friendly, open source implementation of an algorithm to calculate UIS of any order that can be accessed online at http://www. srmcollider.org to find interfering transitions. Finally, our tool can also simulate the specificity of novel data-independent MS acquisition methods in Q1-Q3 space. This allows us to predict parameters for these methods that deliver a specificity comparable with that of SRM. Using SRM interference information in addition to other sources of information can increase the confidence in an SRM measurement. We expect that the consideration of information content will become a standard step in SRM assay design and analysis, facilitated by the SRMCollider. Molecular & Cellular

Targeted Data Extraction of the MS/MS Spectra Generated by Data-independent Acquisition: A New Concept for Consistent and Accurate Proteome Analysis

“…For each target peptide, the number of transitions that were interfered with was recorded and later used to compute the statistics. The detailed algorithm for the computation of the product ion interferences will be the subject of a separate study (51). This algorithm essentially expands on the principle of the "unique ion signatures" described by Sherman et al (29) by taking into account peptide RT as an additional constraint for the calculation of fragment ion interferences.…”

Automated Validation of Results and Removal of Fragment Ion Interferences in Targeted Analysis of Data-independent Acquisition Mass Spectrometry (MS) using SWATHProphet

“…Such fragment ion interferences can result in excess peak area quantitation contributed by the interfering precursor, lower intensity correlation with library, peak shape, and co-elution scores as well as computed probability. The number of interferences can be reduced by use of experimental designs with narrow precursor windows (7) or by selection of library assay fragment ions predicted to be unique to their precursor ion within a range of retention time using programs such as SRMCollider (21). Interferences are nevertheless inevitable because the chromatography and peptides in the sample, including semitryptic and modified peptides, can vary from run to run.…”

Targeted proteome investigation via selected reaction monitoring mass spectrometry

“…First, false assignments are easily identified since the sequences of the peptides present in the samples are known and second, because the fragmentation of relatively high abundant peptides generally produces higher quality fragmentation spectra, which simplifies peptide identifications [106]. At this stage, tools like AuDIT [121] can filter out interfering transitions, or the SRM collider tool [122,123] can identify redundant transitions for a given peptide considering the specific proteome background under investigation. Estimation of the FDR for the use in SRM assays is critical for the objective analysis of large SRM datasets.…”