Mass spectrometry-based protein identification by integrating de novo sequencing with database searching

Mass Spectrometry-Based Immunopeptidomics of Peptides Presented on Human Leukocyte Antigen Proteins

Human leukocyte antigen (HLA) proteins are a group of glycoproteins that are expressed at the cell surface, where they present peptides to T cells through physical interactions with T-cell receptors (TCRs). Hence, characterizing the set of peptides presented by HLA proteins, referred to hereafter as the immunopeptidome, is fundamental for neoantigen identification, immunotherapy, and vaccine development. As a result, different methods have been used over the years to identify peptides presented by HLA proteins, including competition assays, peptide microarrays, and yeast display systems. Nonetheless, over the last decade, mass spectrometry-based immunopeptidomics (MS-immunopeptidomics) has emerged as the gold-standard method for identifying peptides presented by HLA proteins. MS-immunopeptidomics enables the direct identification of the immunopeptidome in different tissues and cell types in different physiological and pathological states, for example, solid tumors or virally infected cells. Despite its advantages, it is still an experimentally and computationally challenging technique with different aspects that need to be considered before planning an MS-immunopeptidomics experiment, while conducting the experiment and with analyzing and interpreting the results. Hence, we aim in this chapter to provide an overview of this method and discuss different practical considerations at different stages starting from sample collection until data analysis. These points should aid different groups aiming at utilizing MS-immunopeptidomics, as well as, identifying future research directions to improve the method.

Human cytokeratin 1 plays a role in the interaction of Pteropine orthoreovirus with Hek293 cells but not HeLa cells

Pteropine orthoreovirus (PRV) causes respiratory tract infections in humans. Despite its emergence as a zoonotic and respiratory virus, little is known about its cell tropism, which hampers progress in fully understanding its pathogenesis in humans. Hek293 cells are most susceptible to PRV infection, while HeLa cells are the least. Human cytokeratin 1 (CK1) was identified as the protein that interacts with PRV. The immunofluorescence assay and qPCR results revealed prior treatment with anti-CK1 may provide Hek293 cells protection against PRV. The KRT1-knockout Hek293 cells were less susceptible to PRV infection. Further study into the pathogenesis of PRV in humans is needed.

Enhanced expression and solubility of main protease (Mpro) of SARS-CoV-2 from E. coli

The main protease (Mpro) of SARS-CoV-2 is a essential enzyme that facilitates viral transcription and replication. Furthermore, the conservation of Mpro across different variants and its non-overlapping nature with human proteases make it an appealing target for therapeutic interventions against SARS-CoV-2. Multiple inhibitors specifically target Mpro to mitigate the infection caused by SARS-CoV-2. In the current study, successful cloning and expression of SARS-CoV-2 Mpro were achieved using two E. coli hosts, namely BL21-DE3 and BL21-DE3-RIL. By optimizing the conditions for induction, the expression of Mpro in the soluble fraction of E. coli was improved. Subsequently, Mpro was purified using affinity chromatography, yielding significantly higher quantities from the BL21-DE3-RIL strain compared to the BL21-DE3 strain, with the former producing nearly twice as much as the latter. The purified Mpro was further characterized by mass spectrometry, fluorescence spectroscopy and circular dichroism (CD). Through fluorescence quenching studies, it was discovered that both GC376 and chitosan, which are inhibitors of Mpro, induced structural changes in the purified Mpro protein. This indicates that the protein retained its functional activity even after being expressed in a bacterial host. Further, FRET-based assay highlighted that the enzymatic activity of Mpro was significantly reduced in presence of both GC376 and chitosan. Consequently, the utilization of optimal conditions and the BL21-DE3-RIL bacterial host facilitates the cost-effective production of Mpro on a large scale, enabling high yields. This production approach can be applied for the screening of potent therapeutic drugs, making it a valuable resource for drug development endeavors.

Homodimeric and Heterodimeric Interactions among Vertebrate Basic Helix-Loop-Helix Transcription Factors

The basic helix–loop–helix transcription factor (bHLH TF) family is involved in tissue development, cell differentiation, and disease. These factors have transcriptionally positive, negative, and inactive functions by combining dimeric interactions among family members. The best known bHLH TFs are the E-protein homodimers and heterodimers with the tissue-specific TFs or ID proteins. These cooperative and dynamic interactions result in a complex transcriptional network that helps define the cell’s fate. Here, the reported dimeric interactions of 67 vertebrate bHLH TFs with other family members are summarized in tables, including specifications of the experimental techniques that defined the dimers. The compilation of these extensive data underscores homodimers of tissue-specific bHLH TFs as a central part of the bHLH regulatory network, with relevant positive and negative transcriptional regulatory roles. Furthermore, some sequence-specific TFs can also form transcriptionally inactive heterodimers with each other. The function, classification, and developmental role for all vertebrate bHLH TFs in four major classes are detailed.

Peptidomics and proteogenomics: background, challenges and future needs

INTRODUCTION

With available genomic data and related information, it is becoming possible to better highlight mutations or genomic alterations associated with a particular disease or disorder. The advent of high-throughput sequencing technologies has greatly advanced diagnostics, prognostics, and drug development.

AREAS COVERED

Peptidomics and proteogenomics are the two post-genomic technologies that enable the simultaneous study of peptides and proteins/transcripts/genes. Both technologies add a remarkably large amount of data to the pool of information on various peptides associated with gene mutations or genome remodeling. Literature search was performed in the PubMed database and is up to date.

EXPERT OPINION

This article lists various techniques used for peptidomic and proteogenomic analyses. It also explains various bioinformatics workflows developed to understand differentially expressed peptides/proteins and their role in disease pathogenesis. Their role in deciphering disease pathways, cancer research, and biomarker discovery using biofluids is highlighted. Finally, the challenges and future requirements to overcome the current limitations for their effective clinical use are also discussed.

Prognostic prospect of soluble programmed cell death ligand-1 in cancer management

Aggressive tissue biopsy is commonly unavoidable in the management of most suspected tumor cases to conclusively verify the presence of cancerous cells through histological assessment. The extracted tissue is also immunostained for detection of antigens (tissue tumor markers) of potential prognostic or therapeutic importance to assist in treatment decision. Although liquid biopsies can be a powerful tool for monitoring treatment response, they are still excluded from standard cancer diagnostics, and their utility is still being debated in the scientific community. With a myriad of soluble tissue tumor markers now being discovered, liquid biopsies could completely change the current paradigms of cancer management. Recently, soluble programmed cell death ligand-1 (sPD-L1), which is found in the peripheral blood, i.e. serum and plasma, has shown potential as a pre-therapeutic predictive marker as well as a prognostic biomarker to monitor treatment efficacy. Thus, this review focuses on the emergence of sPD-L1 and promising technologies for its detection in order to support liquid biopsies for future cancer management.

Tracking the functional meaning of the human oral-microbiome protein-protein interactions

The interactome - the network of protein-protein interactions (PPIs) within a cell or organism - is technically difficult to assess. Bioinformatic tools can, not only, identify potential PPIs that can be later experimentally validated, but also be used to assign functional meaning to PPIs. Saliva's potential as a non-invasive diagnostic fluid is currently being explored by several research groups. But, in order to fully attain its potential, it is necessary to achieve the full characterization of the mechanisms that take place within this ecosystem. The onset of omics technologies, and specifically of proteomics, delivered a huge set of data that is largely underexplored. Quantitative information relative to proteins within a given context (for example a given disease) can be used by computational algorithms to generate information regarding PPIs. These PPIs can be further analyzed concerning their functional meaning and used to identify potential biomarkers, therapeutic targets, defense and pathogenicity mechanisms. We describe a computational pipeline that can be used to identify and analyze PPIs between human and microbial proteins. The pipeline was tested within the scenario of human PPIs of systemic (Zika Virus infection) and of oral conditions (Periodontal disease) and also in the context of microbial interactions (Candida-Streptococcus) and showed to successfully predict functionally relevant PPIs. The pipeline can be applied to different scientific areas, such as pharmacological research, since a functional meaningful PPI network can provide insights on potential drug targets, and even new uses for existing drugs on the market.

Emerging Diagnostic and Therapeutic Potentials of Human Hair Proteomics

The use of noninvasive human substrates to interrogate pathophysiological conditions has become essential in the post- Human Genome Project era. Due to its high turnover rate, and its long term capability to incorporate exogenous and endogenous substances from the circulation, hair testing is emerging as a key player in monitoring long term drug compliance, chronic alcohol abuse, forensic toxicology, and biomarker discovery, among other things. Novel high-throughput 'omics based approaches like proteomics have been underutilized globally in comprehending human hair morphology and its evolving use as a diagnostic testing substrate in the era of precision medicine. There is paucity of scientific evidence that evaluates the difference in drug incorporation into hair based on lipid content, and very few studies have addressed hair growth rates, hair forms, and the biological consequences of hair grooming or bleaching. It is apparent that protein-based identification using the human hair proteome would play a major role in understanding these parameters akin to DNA single nucleotide polymorphism profiling, up to single amino acid polymorphism resolution. Hence, this work seeks to identify and discuss the progress made thus far in the field of molecular hair testing using proteomic approaches, and identify ways in which proteomics would improve the field of hair research, considering that the human hair is mostly composed of proteins. Gaps in hair proteomics research are identified and the potential of hair proteomics in establishing a historic medical repository of normal and disease-specific proteome is also discussed.

Peptide de novo sequencing of mixture tandem mass spectra

The impact of mixture spectra deconvolution on the performance of four popular de novo sequencing programs was tested using artificially constructed mixture spectra as well as experimental proteomics data. Mixture fragmentation spectra are recognized as a limitation in proteomics because they decrease the identification performance using database search engines. De novo sequencing approaches are expected to be even more sensitive to the reduction in mass spectrum quality resulting from peptide precursor co‐isolation and thus prone to false identifications. The deconvolution approach matched complementary b‐, y‐ions to each precursor peptide mass, which allowed the creation of virtual spectra containing sequence specific fragment ions of each co‐isolated peptide. Deconvolution processing resulted in equally efficient identification rates but increased the absolute number of correctly sequenced peptides. The improvement was in the range of 20–35% additional peptide identifications for a HeLa lysate sample. Some correct sequences were identified only using unprocessed spectra; however, the number of these was lower than those where improvement was obtained by mass spectral deconvolution. Tight candidate peptide score distribution and high sensitivity to small changes in the mass spectrum introduced by the employed deconvolution method could explain some of the missing peptide identifications.