Short communicationComputation-assisted targeted proteomics of alternative splicing protein isoforms in the human heart
Graphical abstract
Introduction
Many cardiac proteins have alternatively spliced variants, e.g., the tropomyosin 1 smooth vs. striated muscle and titin fetal N2BA vs. adult N2B isoforms, and several have been implicated in disease development [1]. Although some isoforms can be distinguished by their electrophoretic migration patterns, gel bands do not directly inform on sequence identity and isoforms frequently share similar molecular weights. Relatively few antibody probes currently exist that bind to specific isoforms without cross-reactivity with their canonical proteins, and their availability is hampered by the lengthy vetting and validation steps needed to establish probe specificity and reproducibility [2]. There is therefore a need for additional strategies that can detect and verify the existence of alternative protein isoforms in the heart.
Targeted mass spectrometry methods such as selected reaction monitoring (SRM) and parallel reaction monitoring (PRM) [3] offer high specificity and reproducibility for distinguishing isoform sequences, even if they differ from the canonical protein by one or few amino acid residues. These assays can also be easily disseminated across laboratories without proprietary reagents [4,5]. A challenge however is that building and verifying SRM/PRM methods for specific proteins can be laborious, as it requires knowledge of the MS2 fragmentation pattern and chromatographic retention time of the target peptide under a particular instrument setup. Target specificity in PRM is commonly achieved by co-injecting samples with isotope-labeled synthetic peptide standards. The synthetic peptides are identical in sequence to their endogenous counterparts but are tagged with 13C or 15N, hence they can be differentiated by their mass differences but contain identical MS2 fragmentation patterns and retention times to endogenous peptides of interest. The fragmentation patterns and retention time information can then be compared between the standards and endogenous peptides to verify the identity of non-canonical isoforms. However, the synthesis of labeled peptides can be expensive and time-consuming, especially if a large number of non-canonical peptides need to be detected.
Advances in machine learning have recently enabled more accurate computational prediction of peptide chromatographic retention time and fragmentation profiles [[6], [7], [8]], thus creating opportunities to build virtual spectral libraries from predicted spectra to be used in targeted proteomics assays [9,10]. Here we describe an application of MS2 spectrum prediction to assist the generation of PRM assays for detecting alternative protein isoforms in the heart. We first nominated a list of likely-translated non-canonical protein isoforms using a proteogenomics approach we recently described [11,12]. For each alternative peptide, we then generated virtual spectral libraries using an available deep learning tool to predict peptide fragmentation patterns [6] to build detection assays for their detection in the endogenous human heart proteome. Our results suggest that predictions-aided PRM mass spectrometry can present a viable strategy to detect and verify alternative isoform proteins in the heart.
Section snippets
Method summary
We constructed computation-assisted protein assays using computationally predicted peptide fragmentation spectra. The spectra were predicted using the neural network implemented in Prosit [6]. Collision energy and retention time predictions were calibrated with HeLa cell digest experiment data using data-dependent acquisition (DDA) mass spectrometry. Human adult heart lysate (adult female donor, aged 76 years, no known heart diseases) was purchased from Novus Biologicals. Three technical
Selecting candidate cardiac alternative isoform peptides
Supplementary Table S1
Prediction of MS2 spectra using a neural network
Supplementary Table S1
Targeted proteomics detection of human heart protein isoforms
Supplementary Table S2Supplementary Table S3, Supplementary Table S4
Supplementary Data S44A7L
Conclusion
Reliable detection methods are essential for understanding protein biological function [15]. There is currently a dearth of targeted quantification assays for protein isoforms arising from alternative splicing. Here, we used machine learning predicted mass spectra as an alternative to labeled synthetic peptides to support the detection of non-canonical protein isoform sequences in targeted mass spectrometry. We found that the predicted peptide fragmentation patterns are compared to endogenous
Disclosures
None.
Data availability
Raw mass spectrometry data files are available on ProteomeXchange (human heart tissue: PXD020074, AC16: PXD023563).
The following are the supplementary data related to this article.
Acknowledgments
This study was supported in part by the NIH NHLBI awards R00-HL127302, R01-HL141278, R21-HL150456 to M.L. and R00-HL144829 to E.L.; the NIH NRSA Postdoctoral Fellowship F32-HL149191 to Y.H.; the University of Colorado Postdoctoral Fellowship in Cardiovascular Research T32-HL007822 to V.D. and Y.H.; the University of Colorado Consortium for Fibrosis Research and Translation Pilot Grant to M.L.; and the Univeristy of Colorado Undergraduate Research Opportunity Program award to J.M.W.
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