Improvement of mutated peptide identification through MS/MS signals searching against the protein libraries generated from transcriptome and translatome

Single-nucleotide variants (SNVs) in the coding region of human genome, termed as nonsynonymous SNVs (nsSNVs), are regarded a causal factor of cancer. The cancer related SNVs have not been widely confirmed at protein level. Translatome is referred to the active mRNA population associated with ri-bosomes and is measurable through ribosome-nascent chain complex sequencing (RNC-seq). Herein, an approach was proposed, in which the protein library derived from transcriptome or translatome was generated and served for MS/MS signals searching. A cell line of human colorectal cancer, HCT116, was cultured and its mRNAs, ribosome-bound mRNAs and proteins were extracted and measured in parallel. The nsSNVs calling from transcriptome or translatome were taken to construct the protein libraries with single amino acid variants (SAVs). Meanwhile, another protein library with SAVs was made by COSMIC dataset. The MS/MS data were acquired from HCT116 proteins and were searched for the corresponding peptides against the protein libraries derived from transcriptome, translatome and COSMIC, resulting in 258,146 and 62 SAV peptides identified, respectively. Of these peptides, 96% of peptides come from the translatome library were overlapped with that from transcriptome, whereas only three peptides from the COSMIC library were co-identified by the other two libraries. The corresponding MS/MS spectra of these identified SAV peptides were further assessed by posterior error probability (PEP). The SAV peptides from the transcriptome or translatome library possessed significantly lower PEP values as compared with that from the COSMIC library. Importantly, further evaluation with different PEP thresholds appeared the percentiles of the remaining SAV peptides from translatome consistently higher than that from tran-scriptome. MS/MS signal searching against the translatome library, therefore, has been proven as an efficient approach to identify SAV peptides. (c) 2022 Published by Elsevier B.V.