High-quality peptide evidence for annotating non-canonical open reading frames as human proteins

Abstract

A major scientific drive is to characterize the protein-coding genome as it provides the primary basis for the study of human health. But the fundamental question remains: what has been missed in prior genomic analyses? Over the past decade, the translation of non-canonical open reading frames (ncORFs) has been observed across human cell types and disease states, with major implications for proteomics, genomics, and clinical science. However, the impact of ncORFs has been limited by the absence of a large-scale understanding of their contribution to the human proteome. Here, we report the collaborative efforts of stakeholders in proteomics, immunopeptidomics, Ribo-seq ORF discovery, and gene annotation, to produce a consensus landscape of protein-level evidence for ncORFs. We show that at least 25% of a set of 7,264 ncORFs give rise to translated gene products, yielding over 3,000 peptides in a pan-proteome analysis encompassing 3.8 billion mass spectra from 95,520 experiments. With these data, we developed an annotation framework for ncORFs and created public tools for researchers through GENCODE and PeptideAtlas. This work will provide a platform to advance ncORF-derived proteins in biomedical discovery and, beyond humans, diverse animals and plants where ncORFs are similarly observed.

Keywords: GENCODE; Human Proteome Project; Ribo-seq; immunopeptidomics; mass spectrometry; microproteins; non-canonical ORFs; proteomics; translation.

Figure 1.

Overviews of the centers participating in the annotation effort and the PeptideAtlas framework for protease-digested (mostly trypsin) sample MS and immunopeptidomics builds. (a) Map showing the participating institutions included in the annotation effort. Coordinating centers are highlighted. (b) Schematic overview of the datasets included in the non-HLA and HLA builds. The biotypes of the 7,264 ncORFs are shown in the middle.

Figure 2.

Overview of the 2023–06 non-HLA PeptideAtlas analysis. (a) Number of detected peptides in the non-HLA data categorized per ncORF biotype. (b) The left graph displays the number of detected ncORFs categorized per ncORF biotype. Bars are shaded by whether an ncORF was detected by a single or multiple peptides. The right bar shows the total number of ncORFs, shaded similar to the bars on the left. (c) Pie chart displaying the number of ncORFs that pass after manual inspection of the peptides. The upper pie chart shows the inspection results of the 42 ncORFs detected by multiple peptides. The bottom pie chart shows the inspection results of the 141 ORFs detected by a single peptide. (d) Bar plot showing the number of ncORFs passing inspection, categorized by the number of peptides by which they were detected.

Figure 3.

Overview of the 2023–11 HLA PeptideAtlas detected ncORFs. (a) The number of distinct peptides and ncORFs detected in the HLA data grouped by ncORF biotype. (b) The number of distinct peptides by which an ORF was detected. (c) The percentage of the total ncORF sequence covered by HLA peptides plotted against ncORF length. Colors indicate whether a ncORF was detected by one or multiple peptides. Lines were fitted through both groups using Local Polynomial Regression Fitting. Confidence intervals of those lines are shown in gray. (d) The number of ncORFs for which the Ribo-seq data quality after manual inspection was judged to be sufficient or insufficient. Only 691 ncORFs detected with two HLA peptides are included. ncORFs are grouped by whether they were detected in a single or multiple studies. (e) Dot plots showing the outcomes of the binding affinity predictions. The plots visualize the correlation between mean peptide length and the percentage of predicted binders amongst peptides with a length between 8 and 12 amino acids (NetMHCpan rank ≤ 2) per sample. The left side encompasses all MS-runs, while the right side focuses on samples with at least one ncORF-derived peptide (“ncORF peptide”). Dot size on the left corresponds to the total number of peptides per MS-run, while on the right it corresponds to the count of ncORF-derived peptides. Dot color corresponds with the percentage of ncORF-derived peptides per MS-run. One outlier MS-run (average length 22.75 aa) is not shown. (f) Dot plot contrasting the percentage of predicted binders (NetMHCpan rank ≤ 2) per dataset for canonical and ncORF-derived peptides. Dot color corresponds with the percentage of ncORF-derived peptides per dataset. Datasets PXD000171 and PXD022194 are not shown because they have no ncORFs with binding predictions. (g) Heatmap indicating whether ncORF peptide detections were verified by NetMHCpan portioned by sample type. HLA typing groups samples based on their associated set of one to six HLA alleles. The upper bar plots display the total number of non-canonical peptides predicted to bind to HLA alleles within a typing and the total distinct peptides associated with it. The right bar plots indicate for each peptide the total count of positive and negative predictions for the HLA typings. Differences in peptide detectability exist across various HLA typings. Overall, peptide detectability concurs with binding predictions.

Figure 4.

Determinants of ncORF peptide detection. (a) Comparison of different sequence properties between detected and undetected ncORFs and canonical proteins (the number of canonical proteins is larger than in (Supplementary Figure S1d) because these were selected using less stringent criteria than the PeptideAtlas workflow). The comparisons are based on sequence length, hydrophobicity by the Kyle-Doolittle scale, and the isoelectric point. Statistical tests were performed with the two-sided Wilcoxon test, reported p-values were adjusted for multiple testing with Bonferroni correction. (b) Comparison of the hydrophobicity per ncORF biotype. Each dot represents the average hydrophobicity of the amino acids at that position and the 14 amino acids before that position per ncORF biotype or CDS. The lines were fitted using Local Polynomial Regression Fitting. Vertical bars represent 95% confidence intervals. doORFs and processed transcript ORFs are not shown because of their relatively low abundance. Note that because ncORFs are mostly smaller than 100 aa, confidence intervals get larger with increasing C-terminus offset. (c) Comparison of the expression levels of detected and undetected ncORFs. On the y-axis, the mean FPKM in GTEX of genes expressing an ncORF is shown on a pseudo-log scale. 326 ncORFs for which the gene id was not present in GTEX are not shown. Significance was determined using the two-sided Wilcoxon test. (d) Overview of the location of detected peptides within the full protein (top) and ncORF (bottom) sequence. The left histograms show the distance between the start codon and the start of the detected peptides. The right histograms show the distance between the end of the detected peptides and the last amino acid of the sequence. (e) Overview of HLA ligand atlas data grouped by tissue. The top two plots show the number of ncORF peptides and canonical peptides per tissue. The bottom bar graph shows the percentage of ncORF peptides per tissue relative to the total number of ncORF and canonical peptides. Significant differences as determined by Fisher exact tests and Bonferroni correction are colored red. The dashed line shows the mean percentage of ncORFs.

Figure 5.

Overview of the Tier system. (a) Schematic showing how provisional and final tiers can be assigned to ncORFs. First Ribo-seq, proteomics and immunopeptidomics data can be (computationally) integrated to assign provisional tiers based on the quality of each data entity. Manual inspection of each data entity is then necessary to assign a final tier to each ncORF. In this figure, ‘+’ denotes detection, ‘++’ denotes abundant detection, ‘+/−’ denotes either presence or absence of detection, and ‘−’ denotes absence of detection. (b) Results of the provisional and final tier assignment for the 7,264 ncORFs analyzed for this study. (c) Overview of the curation process for the provisional Tier 1A ncORFs.

Figure 6.

Examples of two ncORFs detected by either non-HLA or HLA data. (a) Ribo-seq, mass spectrometry, and evolutionary information for c11riboseqorf4, one of the best detected ncORFs in tryptic digests. This ncORF has 11 distinct peptides across 94 different experiments, 8 of which we classified as excellent evidence (green). The spectra for peptides SGLQGPSVGDGCNGGGAR and GLPAAAAPVCPAASAAAAGGILASEHSR are depicted with nearly complete y ion coverage and substantial b ion coverage, providing highly compelling evidence. We also note that SGLQGPSVGDGCNGGGAR begins as position 2 of the ORF and has peptide N-terminal acetylation, indicating ORF N-terminal acetylation after removal of the initiator methionine. (b) Overview of data available for c17norep146, an uoORF in the PSMC5 gene. Ribo-seq data shows the initiation of translation at the methionine translation initiation codon (green). A-sites are colored by the reading frame (orange for the uoORF, blue for PSMC5. Two peptide spectral matches for HLA-I peptides RLTDQSRWSW and DSANIICPR are shown (USIs are mzspec:PXD004894:20141214_QEp7_MiBa_SA_HLA-Ip_MMf_4_2:scan:31976:RLTDQSRWSW/2, mzspec:PXD029567:UPN20_class_I_Rep3:scan:6685:DSANIIC[Cysteinyl]PR/2, respectively). The lowest panel shows the position of all 8 peptides that were observed in the immunopeptidomics data. The color shading indicates the number of MS runs in which each peptide was observed. The middle panel shows all peptides that are predicted with NetMHCpan to be observable in the MS runs (i.e. they are predicted to bind with NetMHCpan score <2 to at least one allele in one of the samples in which peptides were observed). The top part shows the number of predicted binding peptides in which each amino acid was located. Green shadings indicate which part of the ORF sequence was observed. Detected peptides occurred in the regions with the highest numbers of predicted binders.