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In a recent Journal of Proteome Researchpaper, I described some general properties and constraints of a hypothetical next generation of proteomics technology based on single-molecule peptide sequencing. This work prompted many interesting questions, both from the reviewers of the initial manuscript and later from readers and colleagues. This follow-up paper addresses some of questions by clarifying the original results, considering alternative metrics, and a number of new simulations. Specifically, the discriminative power of individual amino acids is revisited, simulating additional proteolytic agents. These simulations show allowing missed cleavages generally increases the discriminative power of the amino acids in the proteolytic motif. Additional simulations show the effect of non-ideal conditions modelled on the number of proteins lacking proteotypic reads is very small, and that the average number of proteotypic reads per protein follow the same rule on the performance of the optimal choice of labeled amino acids as the number of distinguishable proteins in NeXtProt. The goal of this paper is to expand prior results and continue the scientific discussion on the possibilities of future proteomics technologies.

Missing (MP) and functionally uncharacterized proteins (uPE1) comprise less than 5% of the total number of human Chr18 genes. Within half a year, since the January 2020 version of NextProt, the number of entries in the MP+uPE1 datasets has changed, mainly due to the achievements of antibody-based proteomics. Assuming that the proteome is closely related to the transcriptome scaffold, quantitative PCR, Illumina HiSeq, and Oxford Nanopore Technology were applied to characterize the liver samples of three male donors compared with the HepG2 cell line. The data mining of Expression Atlas (EMBL-EBI) and the profiling of our biospecimens using orthogonal methods of transcriptome analysis have shown that in HepG2 cells and the liver, the genes encoding functionally uncharacterized proteins (uPE1) are expressed as low as for the missing proteins (less than 1 copy per cell), except for selected cases of HSBP1L1, TMEM241, C18orf21, and KLHL14. The initial expectation that uPE1 genes might be expressed at higher levels than MP genes, was compromised by severe discrepancies in our semi-quantitative gene expression data and in public databanks. Such discrepancy forced us to revisit the transcriptome of Chr18, the target of Russian C-HPP Consortia. Tanglegram of highly expressed genes and further correlation analysis have shown the severe dependencies on the mRNA extraction method and analytical platform.Targeted gene expression analysis by quantitative PCR (qPCR) and high-throughput transcriptome profiling (Illumina HiSeq and ONT MinION) for the same set of samples from normal liver tissue and HepG2 cells revealed the detectable expression of 250+ (92%) protein-coding genes of Chr18 (at least one method). The expression of slightly more than 50% protein-coding genes was detected simultaneously by all three methods. Correlation analysis of the gene expression profiles showed that the grouping of the datasets depended almost equally on both the type of biological material and the experimental method, particularly cDNA/mRNA isolation and library preparation. The dependence on the choice of bioinformatics analysis pipeline was also noticeable but significantly less. Furthermore, the combination of Illumina HiSeq and ONT MinION sequencing to validate proteotypic peptides of missing and uPE1 proteins was performed for the heat-shock factor binding protein HSBP1L1 (missing protein, recently transferred to PE1 category) and uncharacterized protein C18orf21 (uPE1). We observed that a nonsynonymous SNP led to the loss of the site of trypsinolysis in HSBP1L1. The modified version of HSBP1L1 was included in the sequence database and searched against the MS/MS dataset from Kulak, Geyer & Mann (2017), but delivered no significant identification. Thus, HSBP1L1 is still missing for the MS-pillar of C-HPP, although its existence at the protein level has been confirmed.

Mass spectrometry-based proteomics enables the high-throughput identification and quantification of proteins, including sequence variants and post-translational modifications (PTMs), in biological samples. However, most workflows require that such variations be included in the search space used to analyze the data, and doing so remains challenging with most analysis tools. In order to facilitate the search for known sequence variants and PTMs, the Proteomics Standards Initiative (PSI) has designed and implemented the PSI Extended FASTA Format (PEFF). PEFF is based on the very popular FASTA format but adds a uniform mechanism for encoding substantially more metadata about the sequence collection as well as individual entries, including support for encoding known sequence variants, PTMs, and proteoforms. The format is very nearly backwards compatible, and as such, existing FASTA parsers will require little or no changes to be able to read PEFF files as FASTA files, although without supporting any of the extra capabilities of PEFF. PEFF is defined by a full specification document, controlled vocabulary terms, a set of example files, software libraries, and a file validator. Popular software and resources are starting to support PEFF, including the sequence search engine Comet and the knowledge bases neXtProt and UniProtKB. Widespread implementation of PEFF is expected to further enable proteogenomics and top-down proteomics applications by providing a standardized mechanism for encoding protein sequences and their known variations. All the related documentation, including the detailed file format specification and example files, are available athttp://www.psidev.info/peff.