The Size of the Human Proteome: The Width and Depth

Evolution of methods to detect paraneoplastic antibodies

An adaptive immune response in less than 1% of people who develop cancer produces antibodies against neuronal proteins. These antibodies can be associated with paraneoplastic syndromes, and their accurate detection should instigate a search for a specific cancer. Over the years, multiple systems, from indirect immunofluorescence to live cell-based assays, have been developed to identify these antibodies. As the specific antigens were identified, high throughput, multi-antigen substrates such as line blots and ELISAs were developed for clinical laboratories. However, the evolution of assays required to identify antibodies to membrane targets has shone a light on the importance of antigen conformation for antibody detection. This chapter discusses the early antibody assays used to detect antibodies to nuclear and cytosolic targets and how new approaches are required to detect antibodies to membrane targets. The chapter presents recent data that support international recommendations against the sole use of line blots for antibody detection and highlights a new antigen-specific approach that appears promising for the detection of submembrane targets.

The proteomic fingerprint in infants with single ventricle heart disease in the interstage period: evidence of chronic inflammation and widespread activation of biological networks

Introduction

Children with single ventricle heart disease (SVHD) experience significant morbidity across systems and time, with 70% of patients experiencing acute kidney injury, 33% neurodevelopmental impairment, 14% growth failure, and 5.5% of patients suffering necrotizing enterocolitis. Proteomics is a method to identify new biomarkers and mechanisms of injury in complex physiologic states.

Methods

Infants with SVHD in the interstage period were compared to similar-age healthy controls. Serum samples were collected, stored at -80°C, and run on a panel of 1,500 proteins in single batch analysis (Somalogic Inc., CO). Partial Least Squares-Discriminant Analysis (PLS-DA) was used to compare the proteomic profile of cases and controls and t-tests to detect differences in individual proteins (FDR <0.05). Protein network analysis with functional enrichment was performed in STRING and Cytoscape.

Results

PLS-DA readily discriminated between SVHD cases (n = 33) and controls (n = 24) based on their proteomic pattern alone (Accuracy = 0.96, R2 = 0.97, Q2 = 0.80). 568 proteins differed between groups (FDR <0.05). We identified 25 up-regulated functional clusters and 13 down-regulated. Active biological systems fell into six key groups: angiogenesis and cell proliferation/turnover, immune system activation and inflammation, altered metabolism, neural development, gastrointestinal system, and cardiac physiology and development.

Conclusions

We report a clear differentiation in the circulating proteome of patients with SVHD and healthy controls with >500 circulating proteins distinguishing the groups. These proteomic data identify widespread protein dysregulation across multiple biologic systems with promising biological plausibility as drivers of SVHD morbidity.

Post translational modifications at the verge of plant-geminivirus interaction

Plant-virus interaction is a complex phenomenon and involves the communication between plant and viral factors. Viruses have very limited coding ability yet, they are able to cause infection which results in huge agro-economic losses throughout the globe each year. Post-translational modifications (PTMs) are covalent modifications of proteins that have a drastic effect on their conformation, stability and function. Like the host proteins, geminiviral proteins are also subject to PTMs and these modifications greatly expand the diversity of their functions. Additionally, these viral proteins can also interact with the components of PTM pathways and modulate them. Several studies have highlighted the importance of PTMs such as phosphorylation, ubiquitination, SUMOylation, myristoylation, S-acylation, acetylation and methylation in plant-geminivirus interaction. PTMs also regulate epigenetic modifications during geminivirus infection which determines viral gene expression. In this review, we have summarized the role of PTMs in regulating geminiviral protein function, influence of PTMs on viral gene expression and how geminiviral proteins interact with the components of PTM pathways to modulate their function.

Systems Biology in Cancer Diagnosis Integrating Omics Technologies and Artificial Intelligence to Support Physician Decision Making

Cancer is the second major cause of disease-related death worldwide, and its accurate early diagnosis and therapeutic intervention are fundamental for saving the patient's life. Cancer, as a complex and heterogeneous disorder, results from the disruption and alteration of a wide variety of biological entities, including genes, proteins, mRNAs, miRNAs, and metabolites, that eventually emerge as clinical symptoms. Traditionally, diagnosis is based on clinical examination, blood tests for biomarkers, the histopathology of a biopsy, and imaging (MRI, CT, PET, and US). Additionally, omics biotechnologies help to further characterize the genome, metabolome, microbiome traits of the patient that could have an impact on the prognosis and patient's response to the therapy. The integration of all these data relies on gathering of several experts and may require considerable time, and, unfortunately, it is not without the risk of error in the interpretation and therefore in the decision. Systems biology algorithms exploit Artificial Intelligence (AI) combined with omics technologies to perform a rapid and accurate analysis and integration of patient's big data, and support the physician in making diagnosis and tailoring the most appropriate therapeutic intervention. However, AI is not free from possible diagnostic and prognostic errors in the interpretation of images or biochemical-clinical data. Here, we first describe the methods used by systems biology for combining AI with omics and then discuss the potential, challenges, limitations, and critical issues in using AI in cancer research.

Mapping the Evolution of Activity-Based Protein Profiling: A Bibliometric Review

Activity-based protein profiling (ABPP) is a chemoproteomic approach that employs small-molecule probes to directly evaluate protein functionality within complex proteomes. This technology has proven to be a potent strategy for mapping ligandable sites in organisms and has significantly impacted drug discovery processes by enabling the development of highly selective small-molecule inhibitors and the identification of new therapeutic molecular targets. Despite being nearly a quarter of a century old as a chemoproteomic tool, ABPP has yet to undergo a bibliometric analysis. In order to gauge its scholarly impact and evolution, a bibliometric analysis was performed, comparing all 1919 reported articles with the articles published in the last five years. Through a comprehensive data analysis, including a 5-step workflow, the most influential articles were identified, and their bibliometric parameters were determined. The 1919 analyzed articles span from 1999 to 2022, providing a comprehensive overview of the historical and current state of ABPP research. This analysis presents, for the first time, the characteristics of the most influential ABPP articles, offering valuable insight into the research conducted in this field and its potential future directions. The findings underscore the crucial role of ABPP in drug discovery and novel therapeutic target identification, as well as the need for continued advancements in the development of novel chemical probes and proteomic technologies to further expand the utility of ABPP.

The RNA interactome in the Hallmarks of Cancer

Ribonucleic acid (RNA) molecules are indispensable for cellular homeostasis in healthy and malignant cells. However, the functions of RNA extend well beyond that of a protein-coding template. Rather, both coding and non-coding RNA molecules function through critical interactions with a plethora of cellular molecules, including other RNAs, DNA, and proteins. Deconvoluting this RNA interactome, including the interacting partners, the nature of the interaction, and dynamic changes of these interactions in malignancies has yielded fundamental advances in knowledge and are emerging as a novel therapeutic strategy in cancer. Here, we present an RNA-centric review of recent advances in the field of RNA-RNA, RNA-protein, and RNA-DNA interactomic network analysis and their impact across the Hallmarks of Cancer. This article is categorized under: RNA in Disease and Development > RNA in Disease RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes.

Identification of Splice Variants and Isoforms in Transcriptomics and Proteomics

Alternative splicing is pivotal to the regulation of gene expression and protein diversity in eukaryotic cells. The detection of alternative splicing events requires specific omics technologies. Although short-read RNA sequencing has successfully supported a plethora of investigations on alternative splicing, the emerging technologies of long-read RNA sequencing and top-down mass spectrometry open new opportunities to identify alternative splicing and protein isoforms with less ambiguity. Here, we summarize improvements in short-read RNA sequencing for alternative splicing analysis, including percent splicing index estimation and differential analysis. We also review the computational methods used in top-down proteomics analysis regarding proteoform identification, including the construction of databases of protein isoforms and statistical analyses of search results. While many improvements in sequencing and computational methods will result from emerging technologies, there should be future endeavors to increase the effectiveness, integration, and proteome coverage of alternative splicing events.

Theoretical and Data-Driven Approaches for Biomolecular Condensates

Biomolecular condensation processes are increasingly recognized as a fundamental mechanism that living cells use to organize biomolecules in time and space. These processes can lead to the formation of membraneless organelles that enable cells to perform distinct biochemical processes in controlled local environments, thereby supplying them with an additional degree of spatial control relative to that achieved by membrane-bound organelles. This fundamental importance of biomolecular condensation has motivated a quest to discover and understand the molecular mechanisms and determinants that drive and control this process. Within this molecular viewpoint, computational methods can provide a unique angle to studying biomolecular condensation processes by contributing the resolution and scale that are challenging to reach with experimental techniques alone. In this Review, we focus on three types of dry-lab approaches: theoretical methods, physics-driven simulations and data-driven machine learning methods. We review recent progress in using these tools for probing biomolecular condensation across all three fields and outline the key advantages and limitations of each of the approaches. We further discuss some of the key outstanding challenges that we foresee the community addressing next in order to develop a more complete picture of the molecular driving forces behind biomolecular condensation processes and their biological roles in health and disease.

Creating an Innovative Artificial Intelligence-Based Technology (TCRact) for Designing and Optimizing T Cell Receptors for Use in Cancer Immunotherapies: Protocol for an Observational Trial

BACKGROUND

Cancer continues to be the leading cause of mortality in high-income countries, necessitating the development of more precise and effective treatment modalities. Immunotherapy, specifically adoptive cell transfer of T cell receptor (TCR)-engineered T cells (TCR-T therapy), has shown promise in engaging the immune system for cancer treatment. One of the biggest challenges in the development of TCR-T therapies is the proper prediction of the pairing between TCRs and peptide-human leukocyte antigen (pHLAs). Modern computational immunology, using artificial intelligence (AI)-based platforms, provides the means to optimize the speed and accuracy of TCR screening and discovery.

OBJECTIVE

This study proposes an observational clinical trial protocol to collect patient samples and generate a database of pHLA:TCR sequences to aid the development of an AI-based platform for efficient selection of specific TCRs.

METHODS

The multicenter observational study, involving 8 participating hospitals, aims to enroll patients diagnosed with stage II, III, or IV colorectal cancer adenocarcinoma.

RESULTS

Patient recruitment has recently been completed, with 100 participants enrolled. Primary tumor tissue and peripheral blood samples have been obtained, and peripheral blood mononuclear cells have been isolated and cryopreserved. Nucleic acid extraction (DNA and RNA) has been performed in 86 cases. Additionally, 57 samples underwent whole exome sequencing to determine the presence of somatic mutations and RNA sequencing for gene expression profiling.

CONCLUSIONS

The results of this study may have a significant impact on the treatment of patients with colorectal cancer. The comprehensive database of pHLA:TCR sequences generated through this observational clinical trial will facilitate the development of the AI-based platform for TCR selection. The results obtained thus far demonstrate successful patient recruitment and sample collection, laying the foundation for further analysis and the development of an innovative tool to expedite and enhance TCR selection for precision cancer treatments.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04994093; https://clinicaltrials.gov/ct2/show/NCT04994093.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/45872.

The Conscious Nematode: Exploring Hallmarks of Minimal Phenomenal Consciousness in Caenorhabditis Elegans

While subcellular components of cognition and affectivity that involve the interaction between experience, environment, and physiology -such as learning, trauma, or emotion- are being identified, the physical mechanisms of phenomenal consciousness remain more elusive. We are interested in exploring whether ancient, simpler organisms such as nematodes have minimal consciousness. Is there something that feels like to be a worm? Or are worms blind machines? 'Simpler' models allow us to simultaneously extract data from multiple levels such as slow and fast neural dynamics, structural connectivity, molecular dynamics, behavior, decision making, etc., and thus, to test predictions of the current frameworks in dispute. In the present critical review, we summarize the current models of consciousness in order to reassess in light of the new evidence whether Caenorhabditis elegans, a nematode with a nervous system composed of 302 neurons, has minimal consciousness. We also suggest empirical paths to further advance consciousness research using C. elegans.

Quantitative Aspects of the Human Cell Proteome

The number and identity of proteins and proteoforms presented in a single human cell (a cellular proteome) are fundamental biological questions. The answers can be found with sophisticated and sensitive proteomics methods, including advanced mass spectrometry (MS) coupled with separation by gel electrophoresis and chromatography. So far, bioinformatics and experimental approaches have been applied to quantitate the complexity of the human proteome. This review analyzed the quantitative information obtained from several large-scale panoramic experiments in which high-resolution mass spectrometry-based proteomics in combination with liquid chromatography or two-dimensional gel electrophoresis (2DE) were used to evaluate the cellular proteome. It is important that even though all these experiments were performed in different labs using different equipment and calculation algorithms, the main conclusion about the distribution of proteome components (proteins or proteoforms) was basically the same for all human tissues or cells. It follows Zipf's law and has a formula N = A/x, where N is the number of proteoforms, A is a coefficient, and x is the limit of proteoform detection in terms of abundance.

On the origin and nature of nongenetic information in eumetazoans

Nongenetic information implies all the forms of biological information not related to genes and DNA in general. Despite the deep scientific relevance of the concept, we currently lack reliable knowledge about its carriers and origins; hence, we still do not understand its true nature. Given that genes are the targets of nongenetic information, it appears that a parsimonious approach to find the ultimate source of that information is to trace back the sequential steps of the causal chain upstream of the target genes up to the ultimate link as the source of the nongenetic information. From this perspective, I examine seven nongenetically determined phenomena: placement of locus-specific epigenetic marks on DNA and histones, changes in snRNA expression patterns, neural induction of gene expression, site-specific alternative gene splicing, predator-induced morphological changes, and cultural inheritance. Based on the available evidence, I propose a general model of the common neural origin of all these forms of nongenetic information in eumetazoans.

MPT51 and MPT64-based antigen detection assay for the diagnosis of extrapulmonary tuberculosis from urine samples

In view of WHO's "End-TB" strategy, we developed a non-invasive, urine-based ELISA, targeting 2 Mycobacterium tuberculosis antigens namely MPT51 and MPT64 for extrapulmonary TB (EPTB) diagnosis. Suspected EPTB patients (n = 137) [Pleural TB, Abdominal TB and Tuberculous meningitis] were categorized in "Definite" EPTB (n = 10) [Xpert-MTB/RIF and/or culture-positive], "Probable" EPTB (n = 77) and "Non-EPTB" (n = 50) groups using defined composite reference standards. ROC-curves were generated using ELISA results of "Definite" EPTB and "Non-EPTB" groups for both antigens independently and cut-off values were selected to provide 86.3% (95%CI:73.3-94.2) specificity for MPT51 and 92% (95%CI:80.8-97.8) for MPT64. The sensitivity of MPT51-ELISA and MPT64-ELISA was 70% (95%CI:34.7-93.3) and 90% (95%CI:55.5-99.7) for "Definite" EPTB group and 32.5% (95%CI:22.2-44.1) and 30.8% (95%CI:20.8-42.2) for "Probable" EPTB group, respectively. Combining the results of both ELISAs showed a 100% (95%CI:69.1-100) sensitivity in "Definite" EPTB group and 41.6% (95%CI:30.4-53.4) in "Probable" EPTB group, with an 80% (95%CI:66.3-89.9) specificity. The results demonstrated the potential of urine-based ELISAs as screening tests for EPTB diagnosis.

Combining offline high performance liquid chromatography fractionation of peptides and intact proteins to enhance proteome coverage in bottom-up proteomics

Offline peptide separation (PS) using high-performance liquid chromatography (HPLC) is currently used to enhance liquid chromatography-tandem mass spectrometry (LC-MS/MS) detection of proteins. In search of more effective methods for enhancing MS proteome coverage, we developed a robust method for intact protein separation (IPS), an alternative first-dimension separation technique, and explored additional benefits that it offers. Comparing IPS to the traditional PS method, we found that both enhance detection of unique protein IDs to a similar magnitude, though in diverse ways. IPS was especially effective in serum, which has a small number of extremely high abundance proteins. PS was more effective in tissues with fewer dominating high-abundance proteins and was more effective in enhancing detection of post-translational modifications (PTMs). Combining the IPS and PS methods together (IPS+PS) was especially beneficial, enhancing proteome detection more than either method could independently. The comparison of IPS+PS versus six PS fractionation pools increased total number of proteins IDs by nearly double, while also significantly increasing number of unique peptides detected per protein, percent peptide sequence coverage of each protein, and detection of PTMs. This IPS+PS combined method requires fewer LC-MS/MS runs than current PS methods would need to obtain similar improvements in proteome detection, and it is robust, time- and cost-effective, and generally applicable to various tissue and sample types.

Chemical conjugation to differentiate monosaccharides by Raman and surface enhanced Raman spectroscopy

Sugars play important roles in numerous biological processes, from providing energy to modifying proteins to alter their function. Glycosylation, the attachment of a sugar residue to a protein, is the most common post translational modification. Identifying the glycans on a protein is a useful tool both for pharmaceutical development as well as probing the proteome and glycome further. Sugars, however, are difficult analytes to probe due to their isomeric nature. In this work, Raman spectroscopy and surface enhanced Raman spectroscopy (SERS) are used to identify different monosaccharide species based on the vibrational modes of these isomeric analytes. The weak scattering of the sugars was overcome through conjugation with phenylboronic acid to provide a larger Raman scattering cross section and induce slight changes in the observed spectra associated with the structure of the monosaccharides. Spontaneous Raman, SERS in flow, and static SERS detection were performed in order to discriminate between arabinose, fructose, galactose, glucose, mannose, and ribose, as well as provide a method for identification and quantification for these sugar conjugates.

Comprehensive and deep profiling of the plasma proteome with protein corona on zeolite NaY

Proteomic characterization of plasma is critical for the development of novel pharmacodynamic biomarkers. However, the vast dynamic range renders the profiling of proteomes extremely challenging. Here, we synthesized zeolite NaY and developed a simple and rapid method to achieve comprehensive and deep profiling of the plasma proteome using the plasma protein corona formed on zeolite NaY. Specifically, zeolite NaY and plasma were co-incubated to form plasma protein corona on zeolite NaY (NaY-PPC), followed by conventional protein identification using liquid chromatography-tandem mass spectrometry. NaY was able to significantly enhance the detection of low-abundance plasma proteins, minimizing the "masking" effect caused by high-abundance proteins. The relative abundance of middle- and low-abundance proteins increased substantially from 2.54% to 54.41%, and the top 20 high-abundance proteins decreased from 83.63% to 25.77%. Notably, our method can quantify approximately 4000 plasma proteins with sensitivity up to pg/mL, compared to only about 600 proteins identified from untreated plasma samples. A pilot study based on plasma samples from 30 lung adenocarcinoma patients and 15 healthy subjects demonstrated that our method could successfully distinguish between healthy and disease states. In summary, this work provides an advantageous tool for the exploration of plasma proteomics and its translational applications.

A Bayesian hierarchical model for signal extraction from protein microarrays

Protein microarrays are a promising technology that measure protein levels in serum or plasma samples. Due to their high technical variability and high variation in protein levels across serum samples in any population, directly answering biological questions of interest using protein microarray measurements is challenging. Analyzing preprocessed data and within-sample ranks of protein levels can mitigate the impact of between-sample variation. As for any analysis, ranks are sensitive to preprocessing, but loss function based ranks that accommodate major structural relations and components of uncertainty are very effective. Bayesian modeling with full posterior distributions for quantities of interest produce the most effective ranks. Such Bayesian models have been developed for other assays, for example, DNA microarrays, but modeling assumptions for these assays are not appropriate for protein microarrays. Consequently, we develop and evaluate a Bayesian model to extract the full posterior distribution of normalized protein levels and associated ranks for protein microarrays, and show that it fits well to data from two studies that use protein microarrays produced by different manufacturing processes. We validate the model via simulation and demonstrate the downstream impact of using estimates from this model to obtain optimal ranks.

Identification of Potential Therapeutic Targets on the Level of DNA/mRNAs, Proteins and Metabolites: A Systematic Mapping Review of Scientific Texts' Fragments from Open Targets

Database records contain useful information, which is readily available, but, unfortunately, limited compared to the source (publications). Our study reviewed the text fragments supporting the association between the biological macromolecules and diseases from Open Targets to map them on the biological level of study (DNA/RNA, proteins, metabolites). We screened records using a dictionary containing terms related to the selected levels of study, reviewed 600 hits manually and used machine learning to classify 31,260 text fragments. Our results indicate that association studies between diseases and macromolecules conducted on the level of DNA and RNA prevail, followed by the studies on the level of proteins and metabolites. We conclude that there is a clear need to translate the knowledge from the DNA/RNA level to the evidence on the level of proteins and metabolites. Since genes and their transcripts rarely act in the cell by themselves, more direct evidence may be of greater value for basic and applied research.

Multifunctional activity-based chemical probes for sirtuins

The sirtuin family of NAD⁺-dependent protein deacylases has gained significant attention during the last two decades, owing to their unique enzymatic activities as well as their critical roles in a broad array of cellular events. Innovative chemical probes are heavily pursued for the functional annotation and pharmacological perturbation of this group of "eraser" enzymes. We have developed several series of activity-based chemical probes (ABPs) to interrogate the functional state of active sirtuins in complex biological samples. They feature a simple Ala-Ala-Lys tripeptide backbone with a thioacyl "warhead", a photoaffinity group (benzophenone or diazirine), and a bioorthogonal group (terminal alkyne or azido) for conjugation to reporters. When applied in a comparative fashion, these probes reveal the changes of active sirtuin contents under different physiological conditions. Additionally, they can also be utilized in a competitive manner for inhibitor discovery. The Nobel-winning "click" conjugation to a fluorophore allows the visualization of the active enzymes, while the covalent adduct to a biotin leads to the affinity capture of the protein of interest. Furthermore, the "clickable" tag enables the easy access to proteolysis targeting chimeras (PROTACs) that effectively degrade human SIRT2 in HEK293 cells, albeit at micromolar concentrations. These small molecule probes offer unprecedented opportunities to investigate the biological functions and physiological relevance of the sirtuin family.

Food Antioxidants and Their Interaction with Human Proteins

Common to all biological systems and living organisms are molecular interactions, which may lead to specific physiological events. Most often, a cascade of events occurs, establishing an equilibrium between possibly competing and/or synergistic processes. Biochemical pathways that sustain life depend on multiple intrinsic and extrinsic factors contributing to aging and/or diseases. This article deals with food antioxidants and human proteins from the circulation, their interaction, their effect on the structure, properties, and function of antioxidant-bound proteins, and the possible impact of complex formation on antioxidants. An overview of studies examining interactions between individual antioxidant compounds and major blood proteins is presented with findings. Investigating antioxidant/protein interactions at the level of the human organism and determining antioxidant distribution between proteins and involvement in the particular physiological role is a very complex and challenging task. However, by knowing the role of a particular protein in certain pathology or aging, and the effect exerted by a particular antioxidant bound to it, it is possible to recommend specific food intake or resistance to it to improve the condition or slow down the process.

Strategies for Conditional Regulation of Proteins

Design of the next-generation of therapeutics, biosensors, and molecular tools for basic research requires that we bring protein activity under control. Each protein has unique properties, and therefore, it is critical to tailor the current techniques to develop new regulatory methods and regulate new proteins of interest (POIs). This perspective gives an overview of the widely used stimuli and synthetic and natural methods for conditional regulation of proteins.

The Knowns and Unknowns in Protein-Metabolite Interactions

Increasing attention has been focused on the study of protein-metabolite interactions (PMI), which play a key role in regulating protein functions and directing an orchestra of cellular processes. The investigation of PMIs is complicated by the fact that many such interactions are extremely short-lived, which requires very high resolution in order to detect them. As in the case of protein-protein interactions, protein-metabolite interactions are still not clearly defined. Existing assays for detecting protein-metabolite interactions have an additional limitation in the form of a limited capacity to identify interacting metabolites. Thus, although recent advances in mass spectrometry allow the routine identification and quantification of thousands of proteins and metabolites today, they still need to be improved to provide a complete inventory of biological molecules, as well as all interactions between them. Multiomic studies aimed at deciphering the implementation of genetic information often end with the analysis of changes in metabolic pathways, as they constitute one of the most informative phenotypic layers. In this approach, the quantity and quality of knowledge about PMIs become vital to establishing the full scope of crosstalk between the proteome and the metabolome in a biological object of interest. In this review, we analyze the current state of investigation into the detection and annotation of protein-metabolite interactions, describe the recent progress in developing associated research methods, and attempt to deconstruct the very term "interaction" to advance the field of interactomics further.

Alternative polyadenylation alters protein dosage by switching between intronic and 3'UTR sites

Alternative polyadenylation (APA) creates distinct transcripts from the same gene by cleaving the pre-mRNA at poly(A) sites that can lie within the 3' untranslated region (3'UTR), introns, or exons. Most studies focus on APA within the 3'UTR; however, here, we show that CPSF6 insufficiency alters protein levels and causes a developmental syndrome by deregulating APA throughout the transcript. In neonatal humans and zebrafish larvae, CPSF6 insufficiency shifts poly(A) site usage between the 3'UTR and internal sites in a pathway-specific manner. Genes associated with neuronal function undergo mostly intronic APA, reducing their expression, while genes associated with heart and skeletal function mostly undergo 3'UTR APA and are up-regulated. This suggests that, under healthy conditions, cells toggle between internal and 3'UTR APA to modulate protein expression.

Estimating Total Quantitative Protein Content in Escherichia coli, Saccharomyces cerevisiae, and HeLa Cells

The continuous improvement of proteomic techniques, most notably mass spectrometry, has generated quantified proteomes of many organisms with unprecedented depth and accuracy. However, there is still a significant discrepancy in the reported numbers of total protein molecules per specific cell type. In this article, we explore the results of proteomic studies of Escherichia coli, Saccharomyces cerevisiae, and HeLa cells in terms of total protein copy numbers per cell. We observe up to a ten-fold difference between reported values. Investigating possible reasons for this discrepancy, we conclude that neither an unmeasured fraction of the proteome nor biases in the quantification of individual proteins can explain the observed discrepancy. We normalize protein copy numbers in each study using a total protein amount per cell as reported in the literature and create integrated proteome maps of the selected model organisms. Our results indicate that cells contain from one to three million protein molecules per µm³ and that protein copy density decreases with increasing organism complexity.

Gene-centric coverage of the human liver transcriptome: QPCR, Illumina, and Oxford Nanopore RNA-Seq

It has been shown that the best coverage of the HepG2 cell line transcriptome encoded by genes of a single chromosome, chromosome 18, is achieved by a combination of two sequencing platforms, Illumina RNA-Seq and Oxford Nanopore Technologies (ONT), using cut-off levels of FPKM > 0 and TPM > 0, respectively. In this study, we investigated the extent to which the combination of these transcriptomic analysis methods makes it possible to achieve a high coverage of the transcriptome encoded by the genes of other human chromosomes. A comparative analysis of transcriptome coverage for various types of biological material was carried out, and the HepG2 cell line transcriptome was compared with the transcriptome of liver tissue cells. In addition, the contribution of variability in the coverage of expressed genes in human transcriptomes to the creation of a draft human transcriptome was evaluated. For human liver tissues, ONT makes an extremely insignificant contribution to the overall coverage of the transcriptome. Thus, to ensure maximum coverage of the liver tissue transcriptome, it is sufficient to apply only one technology: Illumina RNA-Seq (FPKM > 0).

Investigation of native and aggregated therapeutic proteins in human plasma with asymmetrical flow field-flow fractionation and mass spectrometry

Physiochemical degradation of therapeutic proteins in vivo during plasma circulation after administration can have a detrimental effect on their efficacy and safety profile. During drug product development, in vivo animal studies are necessary to explore in vivo protein behaviour. However, these studies are very demanding and expensive, and the industry is working to decrease the number of in vivo studies. Consequently, there is considerable interest in the development of methods to pre-screen the behaviour of therapeutic proteins in vivo using in vitro analysis. In this work, asymmetrical flow field-flow fractionation (AF4) and liquid chromatography-mass spectrometry (LC-MS) were combined to develop a novel analytical methodology for predicting the behaviour of therapeutic proteins in vivo. The method was tested with two proteins, a monoclonal antibody and a serum albumin binding affibody. After incubation of the proteins in plasma, the method was successfully used to investigate and quantify serum albumin binding, analyse changes in monoclonal antibody size, and identify and quantify monoclonal antibody aggregates.

Using genetic programming to predict and optimize protein function

Protein engineers conventionally use tools such as Directed Evolution to find new proteins with better functionalities and traits. More recently, computational techniques and especially machine learning approaches have been recruited to assist Directed Evolution, showing promising results. In this article, we propose POET, a computational Genetic Programming tool based on evolutionary computation methods to enhance screening and mutagenesis in Directed Evolution and help protein engineers to find proteins that have better functionality. As a proof-of-concept, we use peptides that generate MRI contrast detected by the Chemical Exchange Saturation Transfer contrast mechanism. The evolutionary methods used in POET are described, and the performance of POET in different epochs of our experiments with Chemical Exchange Saturation Transfer contrast are studied. Our results indicate that a computational modeling tool like POET can help to find peptides with 400% better functionality than used before.

The Utilization of Tunable Transducer Elements Formed by the Manipulation of Magnetic Beads with Different Sizes via Optically Induced Dielectrophoresis (ODEP) for High Signal-to-Noise Ratios (SNRs) and Multiplex Fluorescence-Based Biosensing Applications

Magnetic beads improve biosensing performance by means of their small volume and controllability by magnetic force. In this study, a new technique composed of optically induced dielectrodphoresis (ODEP) manipulation and image processing was used to enhance the signal-to-noise ratio of the fluorescence for stained magnetic beads. According to natural advantages of size-dependent particle isolation by ODEP manipulation, biomarkers in clinical samples can be easily separated by different sizes of magnetic beads with corresponding captured antibodies, and rapidly distinguished by separated location of immunofluorescence. To verify the feasibility of the concept, magnetic beads with three different diameters, including 21.8, 8.7, and 4.2 μm, were easily separated and collected into specific patterns in the defined target zone treated as three dynamic transducer elements to evaluate fluorescence results. In magnetic beads with diameter of 4.2 μm, the lowest signal-to-noise ratio between stained and nonstained magnetic beads was 3.5. With the help of ODEP accumulation and detection threshold setting of 32, the signal-to-noise ratio was increased to 77.4, which makes this method more reliable. With the further optimization of specific antibodies immobilized on different-size magnetic beads in the future, this platform can be a potential candidate for a high-efficiency sensor array in clinical applications.

Proteomic Applications in Aquatic Environment Studies

Genome determines the unique individualities of organisms; however, proteins play significant roles in the generation of the colorful life forms below water. Aquatic systems are usually complex and multifaceted and can take on unique modifications and adaptations to environmental changes by altering proteins at the cellular level. Proteomics is an essential strategy for exploring aquatic ecosystems due to the diverse involvement of proteins, proteoforms, and their complexity in basic and advanced cellular functions. Proteomics can expedite the analysis of molecular mechanisms underlying biological processes in an aquatic environment. Previous proteomic studies on aquatic environments have mainly focused on pollution assessments, ecotoxicology, their role in the food industry, and extraction and identification of natural products. Aquatic protein biomarkers have been comprehensively reported and are currently extensively applied in the pharmaceutical and medical industries. Cellular- and molecular-level responses of organisms can be used as indicators of environmental changes and stresses. Conversely, environmental changes are expedient in predicting aquatic health and productivity, which are crucial for ecosystem management and conservation. Recent advances in proteomics have contributed to the development of sustainable aquaculture, seafood safety, and high aquatic food production. Proteomic approaches have expanded to other aspects of the aquatic environment, such as protein fingerprinting for species identification. In this review, we encapsulated current proteomic applications and evaluated the potential strengths, weaknesses, opportunities, and threats of proteomics for future aquatic environmental studies. The review identifies both pros and cons of aquatic proteomics and projects potential challenges and recommendations. We postulate that proteomics is an emerging, powerful, and integrated omics approach for aquatic environmental studies.

On the Prediction of In Vitro Arginine Glycation of Short Peptides Using Artificial Neural Networks

One of the hallmarks of diabetes is an increased modification of cellular proteins. The most prominent type of modification stems from the reaction of methylglyoxal with arginine and lysine residues, leading to structural and functional impairments of target proteins. For lysine glycation, several algorithms allow a prediction of occurrence; thus, making it possible to pinpoint likely targets. However, according to our knowledge, no approaches have been published for predicting the likelihood of arginine glycation. There are indications that arginine and not lysine is the most prominent target for the toxic dialdehyde. One of the reasons why there is no arginine glycation predictor is the limited availability of quantitative data. Here, we used a recently published high-quality dataset of arginine modification probabilities to employ an artificial neural network strategy. Despite the limited data availability, our results achieve an accuracy of about 75% of correctly predicting the exact value of the glycation probability of an arginine-containing peptide without setting thresholds upon whether it is decided if a given arginine is modified or not. This contribution suggests a solution for predicting arginine glycation of short peptides.

A second chance for protein targeting/folding: Ubiquitination and deubiquitination of nascent proteins

Molecular chaperones in cells constantly monitor and bind to exposed hydrophobicity in newly synthesized proteins and assist them in folding or targeting to cellular membranes for insertion. However, proteins can be misfolded or mistargeted, which often causes hydrophobic amino acids to be exposed to the aqueous cytosol. Again, chaperones recognize exposed hydrophobicity in these proteins to prevent nonspecific interactions and aggregation, which are harmful to cells. The chaperone-bound misfolded proteins are then decorated with ubiquitin chains denoting them for proteasomal degradation. It remains enigmatic how molecular chaperones can mediate both maturation of nascent proteins and ubiquitination of misfolded proteins solely based on their exposed hydrophobic signals. In this review, we propose a dynamic ubiquitination and deubiquitination model in which ubiquitination of newly synthesized proteins serves as a "fix me" signal for either refolding of soluble proteins or retargeting of membrane proteins with the help of chaperones and deubiquitinases. Such a model would provide additional time for aberrant nascent proteins to fold or route for membrane insertion, thus avoiding excessive protein degradation and saving cellular energy spent on protein synthesis. Also see the video abstract here: https://youtu.be/gkElfmqaKG4.

Nanoparticle Biomolecular Corona-Based Enrichment of Plasma Glycoproteins for N-Glycan Profiling and Application in Biomarker Discovery

Biomolecular corona formation has emerged as a recurring and important phenomenon in nanomedicine that has been investigated for potential applications in disease diagnosis. In this study, we have combined the "personalized protein corona" with the N-glycosylation profiling that has recently gained considerable interest in human plasma biomarker discovery as a powerful early warning diagnostic and patient stratification tool. We envisioned that the protein corona formation could be exploited as an enrichment step that is critically important in both proteomic and proteoglycomic workflows. By using silica nanoparticles, plasma fibrinogen was enriched to a level in which its proteomic and glycomic "fingerprints" could be traced with confidence. Despite being a more simplified glycan profile compared to full plasma, the corona glycan profile revealed a fibrinogen-derived glycan peak that was found to potentially distinguish lung cancer patients from controls in a pilot study.

The emerging role of mass spectrometry-based proteomics in drug discovery

Proteins are the main targets of most drugs; however, system-wide methods to monitor protein activity and function are still underused in drug discovery. Novel biochemical approaches, in combination with recent developments in mass spectrometry-based proteomics instrumentation and data analysis pipelines, have now enabled the dissection of disease phenotypes and their modulation by bioactive molecules at unprecedented resolution and dimensionality. In this Review, we describe proteomics and chemoproteomics approaches for target identification and validation, as well as for identification of safety hazards. We discuss innovative strategies in early-stage drug discovery in which proteomics approaches generate unique insights, such as targeted protein degradation and the use of reactive fragments, and provide guidance for experimental strategies crucial for success.

Evolution of Protein Functional Annotation: Text Mining Study

Within the Human Proteome Project initiative framework for creating functional annotations of uPE1 proteins, the neXt-CP50 Challenge was launched in 2018. In analogy with the missing-protein challenge, each command deciphers the functional features of the proteins in the chromosome-centric mode. However, the neXt-CP50 Challenge is more complicated than the missing-protein challenge: the approaches and methods for solving the problem are clear, but neither the concept of protein function nor specific experimental and/or bioinformatics protocols have been standardized to address it. We proposed using a retrospective analysis of the key HPP repository, the neXtProt database, to identify the most frequently used experimental and bioinformatic methods for analyzing protein functions, and the dynamics of accumulation of functional annotations. It has been shown that the dynamics of the increase in the number of proteins with known functions are greater than the progress made in the experimental confirmation of the existence of questionable proteins in the framework of the missing-protein challenge. At the same time, the functional annotation is based on the guilty-by-association postulate, according to which, based on large-scale experiments on API-MS and Y2H, proteins with unknown functions are most likely mapped through “handshakes” to biochemical processes.

Cataloguing the proteome: Current developments in single-molecule protein sequencing

The cellular proteome is complex and dynamic, with proteins playing a critical role in cell-level biological processes that contribute to homeostasis, stimuli response, and disease pathology, among others. As such, protein analysis and characterization are of extreme importance in both research and clinical settings. In the last few decades, most proteomics analysis has relied on mass spectrometry, affinity reagents, or some combination thereof. However, these techniques are limited by their requirements for large sample amounts, low resolution, and insufficient dynamic range, making them largely insufficient for the characterization of proteins in low-abundance or single-cell proteomic analysis. Despite unique technical challenges, several single-molecule protein sequencing (SMPS) technologies have been proposed in recent years to address these issues. In this review, we outline several approaches to SMPS technologies and discuss their advantages, limitations, and potential contributions toward an accurate, sensitive, and high-throughput platform.

Instabilities of complex fluids with partially structured and partially random interactions

We develop a theory for thermodynamic instabilities of complex fluids composed of many interacting chemical species organised in families. This model includes partially structured and partially random interactions and can be solved exactly using tools from random matrix theory. The model exhibits three kinds of fluid instabilities: one in which the species form a condensate with a local density that depends on their family (family condensation); one in which species demix in two phases depending on their family (family demixing); and one in which species demix in a random manner irrespective of their family (random demixing). We determine the critical spinodal density of the three types of instabilities and find that the critical spinodal density is finite for both family condensation and family demixing, while for random demixing the critical spinodal density grows as the square root of the number of species. We use the developed framework to describe phase-separation instability of the cytoplasm induced by a change in pH.

Yuel: Improving the Generalizability of Structure-Free Compound-Protein Interaction Prediction

Predicting binding affinities between small molecules and the protein target is at the core of computational drug screening and drug target identification. Deep learning-based approaches have recently been adapted to predict binding affinities and they claim to achieve high prediction accuracy in their tests; we show that these approaches do not generalize, that is, they fail to predict interactions between unknown proteins and unknown small molecules. To address these shortcomings, we develop a new compound-protein interaction predictor, Yuel, which predicts compound-protein interactions with a higher generalizability than the existing methods. Upon comprehensive tests on various data sets, we find that out of all the deep-learning approaches surveyed, Yuel manifests the best ability to predict interactions between unknown compounds and unknown proteins.

Nanopore-Based Protein Identification

The implementation of a reliable, rapid, inexpensive, and simple method for whole-proteome identification would greatly benefit cell biology research and clinical medicine. Proteins are currently identified by cleaving them with proteases, detecting the polypeptide fragments with mass spectrometry, and mapping the latter to sequences in genomic/proteomic databases. Here, we demonstrate that the polypeptide fragments can instead be detected and classified at the single-molecule limit using a nanometer-scale pore formed by the protein aerolysin. Specifically, three different water-soluble proteins treated with the same protease, trypsin, produce different polypeptide fragments defined by the degree by which the latter reduce the nanopore's ionic current. The fragments identified with the aerolysin nanopore are consistent with the predicted fragments that trypsin could produce.

Proximity labeling for investigating protein-protein interactions

The study of protein complexes and protein-protein interactions is of great importance due to their fundamental roles in cellular function. Proximity labeling, often coupled with mass spectrometry, has become a powerful and versatile tool for studying protein-protein interactions by enriching and identifying proteins in the vicinity of a specified protein-of-interest. Here, we describe and compare traditional approaches to investigate protein-protein interactions to current day state-of-the-art proximity labeling methods. We focus on the wide array of proximity labeling strategies and underscore studies using diverse model systems to address numerous biological questions. In addition, we highlight current advances in mass spectrometry-based technology that exhibit promise in improving the depth and breadth of the data acquired in proximity labeling experiments. In all, we show the diversity of proximity labeling strategies and emphasize the broad range of applications and biological inquiries that can be addressed using this technology.

Anti-Coagulant and Antimicrobial Recombinant Heparin-Binding Major Ampullate Spidroin 2 (MaSp2) Silk Protein

Governed by established structure–property relationships, peptide motifs comprising major ampullate spider silk confer a balance of strength and extensibility. Other biologically inspired small peptide motifs correlated to specific functionalities can be combined within these units to create designer silk materials with new hybrid properties. In this study, a small basic peptide, (ARKKAAKA) known to both bind heparin and mimic an antimicrobial peptide, was genetically linked to a protease-resistant, mechanically robust silk-like peptide, MaSp2. Purified fusion proteins (four silk domains and four heparin-binding peptide repeats) were expressed in E. coli. Successful fusion of a MaSp2 spider silk peptide with the heparin-binding motif was shown using a variety of analytical assays. The ability of the fusion peptide to bind heparin was assessed with ELISA and was further tested for its anticoagulant property using aPTT assay. Its intrinsic property to inhibit bacterial growth was evaluated using zone of inhibition and crystal violet (CV) assays. Using this strategy, we were able to link the two types of genetic motifs to create a designer silk-like protein with improved hemocompatibility and antimicrobial properties.

Label-Free Physical Techniques and Methodologies for Proteins Detection in Microfluidic Biosensor Structures

Proteins in biological fluids (blood, urine, cerebrospinal fluid) are important biomarkers of various pathological conditions. Protein biomarkers detection and quantification have been proven to be an indispensable diagnostic tool in clinical practice. There is a growing tendency towards using portable diagnostic biosensor devices for point-of-care (POC) analysis based on microfluidic technology as an alternative to conventional laboratory protein assays. In contrast to universally accepted analytical methods involving protein labeling, label-free approaches often allow the development of biosensors with minimal requirements for sample preparation by omitting expensive labelling reagents. The aim of the present work is to review the variety of physical label-free techniques of protein detection and characterization which are suitable for application in micro-fluidic structures and analyze the technological and material aspects of label-free biosensors that implement these methods. The most widely used optical and impedance spectroscopy techniques: absorption, fluorescence, surface plasmon resonance, Raman scattering, and interferometry, as well as new trends in photonics are reviewed. The challenges of materials selection, surfaces tailoring in microfluidic structures, and enhancement of the sensitivity and miniaturization of biosensor systems are discussed. The review provides an overview for current advances and future trends in microfluidics integrated technologies for label-free protein biomarkers detection and discusses existing challenges and a way towards novel solutions.

Recent Developments in Clinical Plasma Proteomics—Applied to Cardiovascular Research

The human plasma proteome mirrors the physiological state of the cardiovascular system, a fact that has been used to analyze plasma biomarkers in routine analysis for the diagnosis and monitoring of cardiovascular diseases for decades. These biomarkers address, however, only a very limited subset of cardiovascular diseases, such as acute myocardial infarct or acute deep vein thrombosis, and clinical plasma biomarkers for the diagnosis and stratification cardiovascular diseases that are growing in incidence, such as heart failure and abdominal aortic aneurysm, do not exist and are urgently needed. The discovery of novel biomarkers in plasma has been hindered by the complexity of the human plasma proteome that again transforms into an extreme analytical complexity when it comes to the discovery of novel plasma biomarkers. This complexity is, however, addressed by recent achievements in technologies for analyzing the human plasma proteome, thereby facilitating the possibility for novel biomarker discoveries. The aims of this article is to provide an overview of the recent achievements in technologies for proteomic analysis of the human plasma proteome and their applications in cardiovascular medicine.

Phylogeny, Structure, Functions, and Role of AIRE in the Formation of T-Cell Subsets

It is well known that the most important feature of adaptive immunity is the specificity that provides highly precise recognition of the self, altered-self, and non-self. Due to the high specificity of antigen recognition, the adaptive immune system participates in the maintenance of genetic homeostasis, supports multicellularity, and protects an organism from different pathogens at a qualitatively different level than innate immunity. This seemingly simple property is based on millions of years of evolution that led to the formation of diversification mechanisms of antigen-recognizing receptors and later to the emergence of a system of presentation of the self and non-self antigens. The latter could have a crucial significance because the presentation of nearly complete diversity of auto-antigens in the thymus allows for the "calibration" of the forming repertoires of T-cells for the recognition of self, altered-self, and non-self antigens that are presented on the periphery. The central role in this process belongs to promiscuous gene expression by the thymic epithelial cells that express nearly the whole spectrum of proteins encoded in the genome, meanwhile maintaining their cellular identity. This complex mechanism requires strict control that is executed by several transcription factors. One of the most important of them is AIRE. This noncanonical transcription factor not only regulates the processes of differentiation and expression of peripheral tissue-specific antigens in the thymic medullar epithelial cells but also controls intercellular interactions in the thymus. Besides, it participates in an increase in the diversity and transfer of presented antigens and thus influences the formation of repertoires of maturing thymocytes. Due to these complex effects, AIRE is also called a transcriptional regulator. In this review, we briefly described the history of AIRE discovery, its structure, functions, and role in the formation of antigen-recognizing receptor repertoires, along with other transcription factors. We focused on the phylogenetic prerequisites for the development of modern adaptive immunity and emphasized the importance of the antigen presentation system.

Defining Blood Plasma and Serum Metabolome by GC-MS

Metabolomics uses advanced analytical chemistry methods to analyze metabolites in biological samples. The most intensively studied samples are blood and its liquid components: plasma and serum. Armed with advanced equipment and progressive software solutions, the scientific community has shown that small molecules’ roles in living systems are not limited to traditional “building blocks” or “just fuel” for cellular energy. As a result, the conclusions based on studying the metabolome are finding practical reflection in molecular medicine and a better understanding of fundamental biochemical processes in living systems. This review is not a detailed protocol of metabolomic analysis. However, it should support the reader with information about the achievements in the whole process of metabolic exploration of human plasma and serum using mass spectrometry combined with gas chromatography.

Mass Spectrometry as a Crucial Analytical Basis for Omics Sciences

This review is devoted to the consideration of mass spectrometric platforms as applied to omics sciences. The most significant attention is paid to omics related to life sciences (genomics, proteomics, meta-bolomics, lipidomics, glycomics, plantomics, etc.). Mass spectrometric approaches to solving the problems of petroleomics, polymeromics, foodomics, humeomics, and exosomics, related to inorganic sciences, are also discussed. The review comparatively presents the advantages of various principles of separation and mass spectral techniques, complementary derivatization, used to obtain large arrays of various structural and quantitative information in the mentioned omics sciences.

In-silico selection of cancer blood plasma proteins by integrating genomic and proteomic databases

Blood protein markers have been studied for the clinical management of cancer. Due to the large number of the proteins existing in blood, it is often necessary to pre-select potential protein markers before experimental studies. However, to date there is a lack of automated method for in-silico selection of cancer blood proteins that integrates the information from both genetic and proteomic studies in a cancer-specific manner. In this work, we synthesized both genomic and proteomic information from several open access databases and established a bioinformatic pipeline for in-silico selection of blood plasma proteins overexpressed in specific type of cancer. We demonstrated the workflow of this pipeline with an example of breast cancer, while the methodology was applicable for other cancer types. With this pipeline we obtained 10 candidate biomarkers for breast cancer. The proposed pipeline provides a useful and convenient tool for in-silico selection of candidate blood protein biomarkers for a variety of cancer research.