Informatics View on the Challenges of Identifying Missing Proteins from Shotgun Proteomics

Comparison of different variant sequence types coupled with decoy generation methods used in concatenated target-decoy database searches for proteogenomic research

Concatenated target-decoy database searches are commonly used in proteogenomic research for variant peptide identification. Currently, protein-based and peptide-based sequence databases are applied to store variant sequences for database searches. The protein-based database records a full-length wild-type protein sequence but using the given variant events to replace the original amino acids, whereas the peptide-based database retains only the in silico digested peptides containing the variants. However, the performance of applying various decoy generation methods on the peptide-based variant sequence database is still unclear, compared to the protein-based database. In this paper, we conduct a thorough comparison on target-decoy databases constructed by the above two types of databases coupled with various decoy generation methods for proteogenomic analyses. The results show that for the protein-based variant sequence database, using the reverse or the pseudo reverse method achieves similar performance for variant peptide identification. Furthermore, for the peptide-based database, the pseudo reverse method is more suitable than the widely used reverse method, as shown by identifying 6% more variant PSMs in a HEK293 cell line data set. SIGNIFICANCE: In our survey of publications on proteogenomic studies, 57% of the studies adopt the peptide-based variant sequence database coupled with the reverse method for decoy generation to construct a target-decoy database for searches. However, our results show that when using the peptide-based variant sequence database, it is better to adopt the pseudo reverse method for generating decoy sequences, to avoid leading to fewer variant peptides being identified.

MinProtMaxVP: Generating a minimized number of protein variant sequences containing all possible variant peptides for proteogenomic analysis

Identifying single-amino-acid variants (SAVs) from mass spectrometry-based experiments is critical for validating single-nucleotide variants (SNVs) at the protein level to facilitate biomedical research. Currently, two approaches are usually applied to convert SNV annotations into SAV-harboring protein sequences. One approach generates one sequence containing exactly one SAV, and the other all SAVs. However, they may neglect the possibility of SAV combinations, e.g., haplotypes, existing in bio-samples. Therefore, it is necessary to consider all SAV combinations of a protein when generating SAV-harboring protein sequences. In this paper, we propose MinProtMaxVP, a novel approach which selects a minimized number of SAV-harboring protein sequences generated from the exhaustive approach, while still accommodating all possible variant peptides, by solving a classic set covering problem. Our study on known haplotype variations of TAS2R38 justifies the necessity for MinProtMaxVP to consider all combinations of SAVs. The performance of MinProtMaxVP is demonstrated by an in silico study on OR2T27 with five SAVs and real experimental data of the HEK293 cell line. Furthermore, assuming simulated somatic and germline variants of OR2T27 in tumor and normal tissues demonstrates that when adopting the appropriate somatic and germline SAV integration strategy, MinProtMaxVP is adaptable to labeling and label-free mass spectrometry-based experiments. SIGNIFICANCE: We present MinProtMaxVP, a novel approach to generate SAV-harboring protein sequences for constructing a customized protein sequence database, which is used in database searching for variant peptide identification. This approach outperforms the existing approaches in generating all possible variant peptides to be included in protein sequences and possibly leading to identification of more variant peptides in proteogenomic analysis.

Is It Possible to Find Needles in a Haystack? Meta-Analysis of 1000+ MS/MS Files Provided by the Russian Proteomic Consortium for Mining Missing Proteins

Despite direct or indirect efforts of the proteomic community, the fraction of blind spots on the protein map is still significant. Almost 11% of human genes encode missing proteins; the existence of which proteins is still in doubt. Apparently, proteomics has reached a stage when more attention and curiosity need to be exerted in the identification of every novel protein in order to expand the unusual types of biomaterials and/or conditions. It seems that we have exhausted the current conventional approaches to the discovery of missing proteins and may need to investigate alternatives. Here, we present an approach to deciphering missing proteins based on the use of non-standard methodological solutions and encompassing diverse MS/MS data, obtained for rare types of biological samples by members of the Russian Proteomic community in the last five years. These data were re-analyzed in a uniform manner by three search engines, which are part of the SearchGUI package. The study resulted in the identification of two missing and five uncertain proteins detected with two peptides. Moreover, 149 proteins were detected with a single proteotypic peptide. Finally, we analyzed the gene expression levels to suggest feasible targets for further validation of missing and uncertain protein observations, which will fully meet the requirements of the international consortium. The MS data are available on the ProteomeXchange platform (PXD014300).

iHPDM: In Silico Human Proteome Digestion Map with Proteolytic Peptide Analysis and Graphical Visualizations

When conducting proteomics experiments to detect missing proteins and protein isoforms in the human proteome, it is desirable to use a protease that can yield more unique peptides with properties amenable for mass spectrometry analysis. Though trypsin is currently the most widely used protease, some proteins can yield only a limited number of unique peptides by trypsin digestion. Other proteases and multiple proteases have been applied in reported studies to increase the number of identified proteins and protein sequence coverage. To facilitate the selection of proteases, we developed a web-based resource, called in silico Human Proteome Digestion Map (iHPDM), which contains a comprehensive proteolytic peptide database constructed from human proteins, including isoforms, in neXtProt digested by 15 protease combinations of one or two proteases. iHPDM provides convenient functions and graphical visualizations for users to examine and compare the digestion results of different proteases. Notably, it also supports users to input filtering criteria on digested peptides, e.g., peptide length and uniqueness, to select suitable proteases. iHPDM can facilitate protease selection for shotgun proteomics experiments to identify missing proteins, protein isoforms, and single amino acid variant peptides.

ASV-ID, a Proteogenomic Workflow To Predict Candidate Protein Isoforms on the Basis of Transcript Evidence

One of the goals of the Chromosome-Centric Human Proteome Project (C-HPP) is to map and characterize the functions of protein isoforms produced by alternative splicing of genes. However, identifying alternative splice variants (ASVs) via mass spectrometry remains a major challenge, because ASVs usually contain highly homologous peptide sequences. A routine protein sequence analysis suggests that more than half of the investigated proteins do not generate two or more uniquely mapping peptides that would enable their isoforms to be distinguished. Here, we develop a new proteogenomics method, named "ASV-ID" (alternative splicing variants identification), which enables identification of ASVs by using a cell type-specific protein sequence database that is supported by RNA-Seq data. Using this workflow, we identify 1935 distinct proteins under highly stringent conditions. In fact, transcript evidence on these 841 proteins helps us distinguish them from other isoforms, despite the fact that these proteins are not predicted to make 2 or more uniquely mapping peptides. We also demonstrate that ASV-ID enables detection of 19 differently expressed isoforms present in several cell lines. Thus, a new workflow using ASV-ID has the potential to map yet-to-be-identified difficult protein isoforms in a simple and robust way.

Evaluating the Possibility of Detecting Variants in Shotgun Proteomics via LeTE-Fusion Analysis Pipeline

In proteogenomic studies, many genome-annotated events, for example, single amino acid variation (SAAV) and short INDEL, are often unobserved in shotgun proteomics. Therefore, we propose an analysis pipeline called LeTE-fusion (Le, peptide length; T, theoretical values; E, experimental data) to first investigate whether peptides with certain lengths are observed more often in mass spectrometry (MS)-based proteomics, which may hinder peptide identification causing difficulty in detecting genome-annotated events. By applying LeTE-fusion on different MS-based proteome data sets, we found peptides within 7-20 amino acids are more frequently identified, possibly attributed to MS-related factors instead of proteases. We then further extended the usage of LeTE-fusion on four variant-containing-sequence data sets (SAAV-only) with various sample complexity up to the whole human proteome scale, which yields theoretically ∼70% variants observable in an ideal shotgun proteomics. However, only ∼40% of variants might be detectable in real shotgun proteomic experiments when LeTE-fusion utilizes the experimentally observed variant-site-containing wild-type peptides in PeptideAtlas to estimate the expected observable coverage of variants. Finally, we conducted a case study on HEK293 cell line with variants reported at genomic level that were also identified in shotgun proteomics to demonstrate the efficacy of LeTE-fusion on estimating expected observable coverage of variants. To the best of our knowledge, this is the first study to systematically investigate the detection limits of genome-annotated events via shotgun proteomics using such analysis pipeline.

Progressive modulation of the human olfactory bulb transcriptome during Alzheimer´s disease evolution: novel insights into the olfactory signaling across proteinopathies

Alzheimer´s disease (AD) is characterized by progressive dementia, initially presenting olfactory dysfunction. Despite the olfactory bulb (OB) is the first central structure of the olfactory pathway, we lack a complete molecular characterization of the transcriptional events that occurs in this olfactory area during AD progression. To address this gap in knowledge, we have assessed the genome-wide expression in postmortem OBs from subjects with varying degree of AD pathology. A stage-dependent deregulation of specific pathways was observed, revealing transmembrane transport, and neuroinflammation as part of the functional modules that are disrupted across AD grading. Potential drivers of neurodegeneration predicted by network-driven transcriptomics were monitored across different types of dementia, including progressive supranuclear palsy (PSP), mixed dementia, and frontotemporal lobar degeneration (FTLD). Epidermal growth factor receptor (EGFR) expression was significantly increased in the OB of AD and mixed dementia subjects. Moreover, a significant increment in the activation of signal transducer and activator of transcription 3 (STAT3) was exclusively detected in advanced AD stages, whereas total STAT3 levels were specifically overexpressed in mixed dementia. Furthermore, transcription factors deregulated in the OB of mixed dementia subjects such as cAMP Responsive Element Binding Protein 1 (CREB1) and AP-1 Transcription Factor Subunit (c-Jun) were not differentially modulated at olfactory level across AD grading. On the other hand, olfactory expression of this signal transducer panel was unchanged in PSP and FTLD subjects. Taken together, this study unveils cross-disease similarities and differences for specific signal transducers, providing mechanistic clues to the intriguing divergence of AD pathology across proteinopathies.

Accelerating the search for the missing proteins in the human proteome

The Human Proteome Project (HPP) aims to discover high-stringency data for all proteins encoded by the human genome. Currently, ∼18% of the proteins in the human proteome (the missing proteins) do not have high-stringency evidence (for example, mass spectrometry) confirming their existence, while much additional information is available about many of these missing proteins. Here, we present MissingProteinPedia as a community resource to accelerate the discovery and understanding of these missing proteins.

The Human Proteome Project aims to catalogue the ∼20,000 proteins encoded by the human genome. In this review, Baker et al . focus on the missing proteins, proteins that lack high stringency proteomic evidence, and launch MissingProteinPedia, a database aimed at accelerating the search for missing proteins.

Progress and pitfalls in finding the 'missing proteins' from the human proteome map

INTRODUCTION

The Human Proteome Project was launched with two main goals: the comprehensive and systematic definition of the human proteome map and the development of ready to use analytical tools to measure relevant proteins in their biological context in health and disease. Despite the great progress in this endeavour, there is still a group of reluctant proteins with no, or scarce, experimental evidence supporting their existence. These are called the 'missing proteins' and represent one of the biggest challenges to complete the human proteome map. Areas covered: This review focuses on the description of the missing proteome based on the HUPO standards, the analysis of the reasons explaining the difficulty of detecting missing proteins and the strategies currently used in the search for missing proteins. The present and future of the quest for the missing proteins is critically revised hereafter. Expert commentary: An overarching multidisciplinary effort is currently being done under the HUPO umbrella to allow completion of the human proteome map. It is expected that the detection of missing proteins will grow in the coming years since the methods and the best tissue/cell type sample for their search are already on the table.

Deconstructing the molecular architecture of olfactory areas using proteomics

The anatomy of the olfactory system is highly complex, comprising a system of olfactory receptors, pathways for the transmission of olfactory information, and structures for the recognition, discrimination, and memorization of odors. During the last years, proteomics has emerged as a large-scale comprehensive approach to characterize and quantify specific olfactory-related proteomes in different biological conditions such as olfactory learning, neurodegeneration, and ageing between others. The current work reviews recent applications of proteomics to olfaction with particular focus on quantitative proteome profiling studies performed on olfactory areas from laboratory animal models as well as proteomic characterizations performed on specific brain structures and fluids involved in human smell. Finally, we will also discuss the potential application of proteomics to study global proteome dynamics and posttranslationally modified proteomes in order to unravel cell-signaling networks that occur from peripheral structures to olfactory cortical areas during odor processing.