Computation-assisted targeted proteomics of alternative splicing protein isoforms in the human heart

Highlights

• PRM-MS identification of translated protein isoforms from alternative splicing.

• Machine learning prediction aids the development of specific assays for isoform peptides.

• The workflow is applied to human heart lysate and cardiomyocyte cell line.

Abstract

Alternative splicing is prevalent in the heart and implicated in many cardiovascular diseases, but not every alternative transcript is translated and detecting non-canonical isoforms at the protein level remains challenging. Here we show the use of a computation-assisted targeted proteomics workflow to detect protein alternative isoforms in the human heart. We build on a recent strategy to integrate deep RNA-seq and large-scale mass spectrometry data to identify candidate translated isoform peptides. A machine learning approach is then applied to predict their fragmentation patterns and design protein isoform-specific parallel reaction monitoring detection (PRM) assays. As proof-of-principle, we built PRM assays for 29 non-canonical isoform peptides and detected 22 peptides in a human heart lysate. The predictions-aided PRM assays closely mirrored synthetic peptide standards for non-canonical sequences. This approach may be useful for validating non-canonical protein identification and discovering functionally relevant isoforms in the heart.

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.