The Size of the Human Proteome: The Width and Depth

Single-Molecule SERS Discrimination of Proline from Hydroxyproline Assisted by a Deep Learning Model

Discriminating low-abundance hydroxylation is a crucial and unmet need for early disease diagnostics and therapeutic development due to the small hydroxyl group with 17.01 Da. While single-molecule surface-enhanced Raman spectroscopy (SERS) sensors can detect hydroxylation, subsequent data analysis suffers from signal fluctuations and strong interference from citrates. Here, we used our plasmonic particle-in-pore sensor, occurrence frequency histogram of the single-molecule SERS spectra, and a one-dimensional convolutional neural network (1D-CNN) model to achieve single-molecule discrimination of hydroxylation. The histogram extracted spectral features of the whole data set to overcome the signal fluctuations and helped the citrate-replaced particle-in-pore sensor to generate clean signals of the hydroxylation for model training. As a result, the discrimination of single-molecule SERS signals of proline and hydroxyproline was successful by the 1D-CNN model with 96.6% accuracy for the first time. The histogram further validated that the features extracted by the 1D-CNN model corresponded to hydroxylation-induced spectral changes.

Time-evolved metrics for safety pharmacological assessments of small molecules and biologics

Safety of a small-molecule drug is oftentimes a more important criterion than efficacy in determining drug approval. Aspects of safety pharmacological and toxicological liabilities, often resulting from dose-dependent undesirable interaction with either the primary target of interest or secondary targets, have a huge role to play in determining 'first-in-human' dosage and phase I clinical trials. Given the open thermodynamic nature of the human subjects, it is mandatory that kinetics of drug-target and drug-off-target interactions govern the way selectivity margins are assessed, and dose is decided. However, lack of sufficient thrust on kinetics in guiding early drug discovery decisions has resulted in an overreliance on IC50 measure (a proxy for thermodynamic Ki) as a means of computing safety across the target of interest and potential off-targets. Moreover, based on established practises and known weight of evidence of targets with safety adverse events, the primary panel of secondary pharmacology targets are biased with greater preference for G-protein coupled receptors, transporters and ion-channels with a paucity of enzymes. This can pose unique challenges in assessing safety, especially for advancing and emergent modalities. In this perspective, the critical role kinetic margins should play in assessing safety is emphasised given the myriad assay conditions that can modulate the equilibrium thermodynamic measure as embodied in the proxy report of IC50. Further, it advocates selective and judicious expansion of primary safety panels with greater representation of enzymes and reduced redundancy in eventual read-outs based on potential for correlative output among the off-target classes assessed.

UANanoDock: A Web-Based UnitedAtom Multiscale Nanodocking Tool for Predicting Protein Adsorption onto Nanoparticles

UANanoDock is a web-based application with a graphical user interface designed for modeling protein-nanomaterial interactions, accessible via the Enalos Cloud Platform (https://www.enaloscloud.novamechanics.com/compsafenano/uananodock/). The application's foundation lies in the UnitedAtom multiscale model, previously reported for predicting the adsorption energies of biopolymers and small molecules onto nanoparticles (NPs). UANanoDock offers insights into optimal protein orientations when bound to spherical NP surfaces, considering factors such as material type, NP radius, surface potential, and amino acid (AA) ionization states at specific pH levels. The tool's computational time is determined solely by the protein's AA count, regardless of NP size. With its efficiency (e.g., approximately 60 s processing time for a 1331 AA protein) and versatility (accommodating any protein with a standard AA sequence in PDB format), UANanoDock serves as a prescreening tool for identifying proteins likely to adsorb onto NP surfaces. An illustration of UANanoDock's utility is provided, demonstrating its application in the rational design of immunoassays by determining the preferred orientation of the immunoglobulin G (IgG) antibody adsorbed on Ag NPs.

A Career Doing STUFF

In this Guest Editorial for the MedChem Musings series, Mike, Former Senior Research Director at GSK, reflects on a career doing "STUFF": Science, Technology, Useful Functions and Fun. Mike summarises some of the many things he has been involved in throughout his 45 year-career in drug discovery, from which future generations of researchers can hopefully find practical advice and inspiration.

The Role of the Sirtuin Family Histone Deacetylases in Acute Myeloid Leukemia-A Promising Road Ahead

Acute myeloid leukemia (AML) corresponds to a heterogeneous group of clonal hematopoietic diseases, which are characterized by uncontrolled proliferation of malignant transformed myeloid precursors and their inability to differentiate into mature blood cells. The prognosis of AML depends on many variables, including the genetic features of the disease. Treatment outcomes, despite the introduction of new targeted therapies, are still unsatisfactory. Recently, there have been an increasing number of reports on enzymatic proteins of the sirtuin family and their potential importance in cancer in general. Sirtuins are a group of 7 (SIRT1-7) NAD+-dependent histone deacetylases with pleiotropic effects on metabolism, aging processes, and cell survival. They are not only responsible for post-translational modification of histones but also play various biochemical functions and interact with other proteins regulating cell survival, such as p53. Thus, their role in key mechanisms of tumorigenesis makes them a worthwhile topic in AML. Different sirtuins have been shown to act oppositely depending on the biological context, the mechanism of which requires further exploration. This review provides a comprehensive description of the significance and role of sirtuins in AML in light of the current state of knowledge. It focuses in particular on molecular mechanisms regulated by sirtuins and signaling pathways involved in leukemogenesis, as well as clinical aspects and potential therapeutic targets in AML.

Towards Next Generation Protein Sequencing

Understanding the structure and function of proteins is a critical objective in the life sciences. Protein sequencing, a central aspect of this endeavor, was first accomplished through Edman degradation in the 1950s. Since the late 20th century, mass spectrometry has emerged as a prominent method for protein sequencing. In recent years, single-molecule technologies have increasingly been applied to this field, yielding numerous innovative results. Among these, nanopore sensing has proven to be a reliable single-molecule technology, enabling advancements in amino acid recognition, short peptide differentiation, and peptide sequence reading. These developments are set to elevate protein sequencing technology to new heights. The next generation of protein sequencing technologies is anticipated to revolutionize our understanding of molecular mechanisms in biological processes and significantly enhance clinical diagnostics and treatments.

Data-Driven Molecular Typing: A New Frontier in Esophageal Cancer Management

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is a predominant and highly lethal form of esophageal cancer, with a five-year survival rate below 20%. Despite advancements, most patients are diagnosed at advanced stages, limiting effective treatment options. Multi-omics integration, encompassing somatic genomic alterations, inherited genetic mutations, transcriptomics, proteomics, metabolomics, and single-cell sequencing, has enabled the identification of distinct molecular subtypes of ESCC.

METHOD

This article systematically reviewed the current status of molecular subtyping of ESCC based on big data, summarized unique subtypes with differing treatment responses and prognostic outcomes.

RESULT

Key findings included subtype-specific genetic mutations, signaling pathway alterations, and metabolomic profiles, which offer novel biomarkers and therapeutic targets. Furthermore, this review discusses the link between molecular subtypes and immunotherapy efficacy, chemotherapy response, and drug development.

CONCLUSION

These insights highlight the potential of omics-based molecular typing to transform ESCC management and facilitate personalized treatment strategies.

Small Heat Shock Proteins: Protein Aggregation Amelioration and Neuro- and Age-Protective Roles

Protein misfolding, aggregation, and aberrant aggregate accumulation play a central role in neurodegenerative disease progression. The proteotoxic factors also govern the aging process to a large extent. Molecular chaperones modulate proteostasis and thereby impact aberrant-protein-induced proteotoxicity. These chaperones have a diverse functional spectrum, including nascent protein folding, misfolded protein sequestration, refolding, or degradation. Small heat shock proteins (sHsps) possess an ATP-independent chaperone-like activity that prevents protein aggregation by keeping target proteins in a folding-competent state to be refolded by ATP-dependent chaperones. Due to their near-universal upregulation and presence in sites of proteotoxic stress like diseased brains, sHsps were considered pathological. However, gene knockdown and overexpression studies have established their protective functions. This review provides an updated overview of the sHsp role in protein aggregation amelioration and highlights evidence for sHsp modulation of neurodegenerative disease-related protein aggregation and aging.

Emerging opportunities for intact and native protein analysis using chemical proteomics

Chemical proteomics has advanced small molecule ligand discovery by providing insights into protein-ligand binding mechanism and enabling medicinal chemistry optimization of protein selectivity on a global scale. Mass spectrometry is the predominant analytical method for chemoproteomics, and various approaches have been deployed to investigate and target a rapidly growing number of protein classes and biological systems. Two methods, intact mass analysis (IMA) and top-down proteomics (TDMS), have gained interest in recent years due to advancements in high resolution mass spectrometry instrumentation. Both methods apply mass spectrometry analysis at the proteoform level, as opposed to the peptide level of bottom-up proteomics (BUMS), thus addressing some of the challenges of protein inference and incomplete information on modification stoichiometry. This Review covers recent research progress utilizing MS-based proteomics methods, discussing in detail the capabilities and opportunities for improvement of each method. Further, heightened attention is given to IMA and TDMS, highlighting these methods' strengths and considerations when utilized in chemoproteomic studies. Finally, we discuss the capabilities of native mass spectrometry (nMS) and ion mobility mass spectrometry (IM-MS) and how these methods can be used in chemoproteomics research to complement existing approaches to further advance the field of functional proteomics.

Stoichiometric constraints for detection of EV-borne biomarkers in blood

Stochiometric issues, encompassing both the quantity and heterogeneity of extracellular vesicles (EVs) derived from tumour or other tissues in blood, pose important challenges across various stages of biomarker discovery and detection, affecting the integrity of data, introducing losses and artifacts during blood processing, EV purification and analysis. These challenges shape the diagnostic utility of EVs especially within the framework of established and emerging methodologies. By addressing these challenges, we aim to delineate crucial parameters and requirements for tumour-specific EV detection, or more precisely, for tumour identification via EV based assays. Our endeavour involves a comprehensive examination of the layers that mask or confound the traceability of EV markers such as nucleic acids and proteins, and focus on 'low prevalence-low concentration' scenario. Finally, we evaluate the advantages versus limitations of single-particle analysers over more conventional bulk assays, suggesting that the combined use of both to capture and interpret the EV signals, in particular the EV surface displayed proteins, may ultimately provide quantitative information on their absolute abundance and distribution.

Secretome and Proteome of Extracellular Vesicles Provide Protein Markers of Lung and Colorectal Cancer

Colorectal cancer (CRC) and lung cancer (LC) are leading causes of cancer-related mortality, highlighting the need for minimally invasive diagnostic, prognostic, and predictive markers for these cancers. Proteins secreted by a tumor into the extracellular space directly, known as the tumor secretome, as well as proteins in the extra-cellular vesicles (EVs), represent an attractive source of biomarkers for CRC and LC. We performed proteomic analyses on secretome and EV samples from LC (A549, NCI-H23, NCI-H460) and CRC (Caco2, HCT116, HT-29) cell lines and targeted mass spectrometry on EVs from plasma samples of 20 patients with CRC and 19 healthy controls. A total of 782 proteins were identified across the CRC and LC secretome and EV samples. Of these, 22 and 44 protein markers were significantly elevated in the CRC and LC samples, respectively. Functional annotation revealed enrichment in proteins linked to metastasis and tumor progression for both cancer types. In EVs isolated from the plasma of patients with CRC, ITGB3, HSPA8, TUBA4A, and TLN1 were reduced, whereas FN1, SERPINA1, and CST3 were elevated, compared to healthy controls. These findings support the development of minimally invasive liquid biopsy methods for the detection, prognosis, and treatment monitoring of LC and CRC.

Optimization of protein identifications through the use of different chromatographic approaches and bioinformatic pipelines

RATIONALE

Selection of proteomic workflows for a given project can be a daunting task. This research provides a guide outlining the impact on protein identification of different steps such as chromatographic separation, data acquisition strategies, and bioinformatic pipelines. The data presented here will help experts and nonexpert proteomic users to increase proteome coverage and peptide identification.

METHODS

HeLa protein digests were analyzed through different C18 chromatographic columns (15 and 50 cm in length), using top 12 data-dependent acquisition (DDA), top 20 DDA, and data-independent acquisition (DIA) with a nanospray source in positive mode in a Thermo Q Exactive instrument. The raw data were analyzed using different search engines, rescoring approaches, and multi-engine searches. The results were analyzed in the context of peptide and protein identifications, precursor properties, and computation requirements to understand the differences between methods.

RESULTS

Our results showed that higher column lengths and top N DDA approaches were able to significantly increase protein identifications. The use of multiple search engines yielded limited gains, whereas the use of rescoring methods clearly outperformed other strategies. Finally, DIA approaches, although successful at generating new identifications, had a limited performance influenced by the previous collection of DDA data, which could prohibitively increase instrument time. Nonetheless, the use of library-free methods showed promising results.

CONCLUSIONS

Our results highlight the impact of different experimental approaches on proteome coverage. Changes in chromatographic columns, data acquisition, or bioinformatic analysis can significantly increase the number of protein identifications (>400%). Thus, this research provides a reference upon which to build a successful proteomic workflow with different considerations at every step.

Prediction and Evaluation of Protein Aggregation with Computational Methods

Protein and peptide aggregation has recently become one of the most studied biomedical problems due to its central role in several neurodegenerative disorders and of biotechnological importance. Multiple in silico methods, databases, tools, and algorithms have been developed to predict aggregation of proteins and peptides to better understand fundamental mechanisms of various aggregation diseases. Here, we attempt to provide a brief overview of bioinformatic methods and tools to better understand molecular mechanisms of aggregation disorders. Furthermore, through a better understanding of protein aggregation mechanisms, it might be possible to design novel therapeutic agents to treat and hopefully prevent protein aggregation diseases.

Model Organisms in Aging Research: Evolution of Database Annotation and Ortholog Discovery

BACKGROUND

This study aims to analyze the exploration degree of popular model organisms by utilizing annotations from the UniProtKB (Swiss-Prot) knowledge base. The research focuses on understanding the genomic and post-genomic data of various organisms, particularly in relation to aging as an integral model for studying the molecular mechanisms underlying pathological processes and physiological states.

METHODS

Having characterized the organisms by selected parameters (numbers of gene splice variants, post-translational modifications, etc.) using previously developed information models, we calculated proteome sizes: the number of possible proteoforms for each species. Our analysis also involved searching for orthologs of human aging genes within these model species.

RESULTS

Our findings indicate that genomic and post-genomic data for more primitive species, such as bacteria and fungi, are more comprehensively characterized compared to other organisms. This is attributed to their experimental accessibility and simplicity. Additionally, we discovered that the genomes of the most studied model organisms allow for a detailed analysis of the aging process, revealing a greater number of orthologous genes related to aging.

CONCLUSIONS

The results highlight the importance of annotating the genomes of less-studied species to identify orthologs of marker genes associated with complex physiological processes, including aging. Species that potentially possess unique traits associated with longevity and resilience to age-related changes require comprehensive genomic studies.

Spotting targets with 2D-DIGE proteomics

Two-dimensional difference gel electrophoresis (2D-DIGE) has been a staple of protein studies for almost three decades since first described in 1997. Although the advent of omic technologies has greatly expanded protein research and discovery, 2D-DIGE has consistently been the mainstay in biomedical applications. Differential protein expression is a hallmark of many disease states and identification of these biomarkers can improve diagnosis, prognosis and treatment. In this review, we examine the use of 2D-DIGE in exploring the cellular environment in physiologic and pathophysiologic states. We highlight this technology in protein identification and quantification, functional modification and biochemical pathways of interest. 2D-DIGE remains a useful tool due low cost and high resolving power for comparative and quantitative purposes in assessing disease states and facilitating identification of unique and novel biomarkers.

Advances in designing ternary complexes: Integrating in-silico and biochemical methods for PROTAC optimisation in target protein degradation

Target protein degradation (TPD) is an emerging approach to mitigate disease-causing proteins. TPD contains several strategies, and one of the strategies that gained immersive importance in recent times is Proteolysis Targeting Chimeras (PROTACs); the PROTACs recruit small molecules to induce the poly-ubiquitination of disease-causing protein by hijacking the ubiquitin-proteasome system (UPS) by bringing the E3 ligase and protein of interest (POI) into appropriate proximity. The steps involved in designing and evaluating the PROTACs remain critical in optimising the PROTACs to degrade the POI. It is observed that using in-silico and biochemical methods to study the ternary complexes (TCs) of the POI-PROTAC-E3 ligase is essential to understanding the structural activity, cooperativity, and stability of formed TCs. A better understanding of the above-mentioned leads to an appropriate rationale for designing the PROTACs targeting the disease-causing proteins. In this review, we tried to summarise the approaches used to design the ternary complexes, i.e., in-silico and in-vitro methods, to understand the behaviour of the PROTAC-induced ternary complexes.

Extended range proteomic analysis of blood plasma from schizophrenia patients

Introduction

The high prevalence of schizophrenia worldwide makes it necessary to proceed from subjective assessment of patient's clinical symptoms in diagnosis making to searching for circulating blood biomarkers. On the one hand, searching for molecular markers and targets for therapeutics will make it possible to refine and detail the molecular mechanisms of pathology development, while on the other hand, it will offer new opportunities for elaborating novel approaches to disease diagnosis and enhance efficacy and timeliness of drug therapy.

Methods

In this study, we performed an extended-range proteomic analysis of plasma samples collected from 48 study subjects with confirmed diagnosis of schizophrenia and 50 healthy volunteers. The high-resolution tandem mass spectra recorded in the data-dependent acquisition mode were analyzed using the MaxQuant algorithm for the library of known protein sequences and the PowerNovo algorithm for de novo protein sequencing.

Results

It was demonstrated that both strategies show similar results for high-abundance proteins (≥1 μg/mL). For mid-abundance (10 ng/mL - 1 μg/mL) and low-abundance (<10 ng/mL) proteins, the results obtained by the two search strategies complement each other.

Discussion

Group-specific proteins for the samples of schizophrenia patients were identified, presumably being involved in synaptic plasticity, angiogenesis, transcriptional regulation, protein stabilization and degradation.

On the Roles of Protein Intrinsic Disorder in the Origin of Life and Evolution

Obviously, the discussion of different factors that could have contributed to the origin of life and evolution is clear speculation, since there is no way of checking the validity of most of the related hypotheses in practice, as the corresponding events not only already happened, but took place in a very distant past. However, there are a few undisputable facts that are present at the moment, such as the existence of a wide variety of living forms and the abundant presence of intrinsically disordered proteins (IDPs) or hybrid proteins containing ordered domains and intrinsically disordered regions (IDRs) in all living forms. Since it seems that the currently existing living forms originated from a common ancestor, their variety is a result of evolution. Therefore, one could ask a logical question of what role(s) the structureless and highly dynamic but vastly abundant and multifunctional IDPs/IDRs might have in evolution. This study represents an attempt to consider various ideas pertaining to the potential roles of protein intrinsic disorder in the origin of life and evolution.

Polymers for Disrupting Protein-Protein Interactions: Where Are We and Where Should We Be?

Protein-protein interactions (PPIs) are central to the cellular signaling and regulatory networks that underlie many physiological and pathophysiological processes. It is challenging to target PPIs using traditional small molecule or peptide-based approaches due to the frequent lack of well-defined binding pockets at the large and flat PPI interfaces. Synthetic polymers offer an opportunity to circumvent these challenges by providing unparalleled flexibility in tuning their physiochemical properties to achieve the desired binding properties. In this review, we summarize the current state of the field pertaining to polymer-protein interactions in solution, highlighting various polyelectrolyte systems, their tunable parameters, and their characterization. We provide an outlook on how these architectures can be improved by incorporating sequence control, foldability, and machine learning to mimic proteins at every structural level. Advances in these directions will enable the design of more specific protein-binding polymers and provide an effective strategy for targeting dynamic proteins, such as intrinsically disordered proteins.

The application of omics tools in food mycology

This chapter explores the application of omics technologies in food mycology, emphasizing the significant impact of filamentous fungi on agriculture, medicine, biotechnology and the food industry. The chapter delves into the importance of understanding fungal secondary metabolism due to its implications for human health and industrial use. Several omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, are reviewed for their role in studying the genetic potential and metabolic capabilities of food-related fungi. The potential of CRISPR/Cas9 in fungal research is highlighted, showing its ability to unlock the full genetic potential of these organisms. The chapter also addresses the challenges posed by Big Data research in Omics and the need for advanced data processing methods. Through these discussions, the chapter highlights the future benefits and challenges of omics-based research in food mycology and its potential to revolutionize our understanding and utilization of fungi in various domains.

From Proteomics to the Analysis of Single Protein Molecules

Limit of detection (LoD) is a term that is used to characterize the sensitivity of an analytical method. The existing limitation of the sensitivity of analysis using modern mass spectrometry methods has been experimentally shown to be a limiting factor in the application of proteomic technologies in medicine. This article proposes a concept of a new technology that will set a new vector of development in the development of systems for solving problems of medical diagnostics and deals with theoretical and practical aspects of creating a new technology for the detection of single biomacromolecules (in particular, proteins) in biological samples. Such technology should be based on the principle of signal registration similar to that used in a Geiger counter (also known as a Geiger-Müller counter or G-M counter), a device that automatically counts the number of ionizing particles that hit it. This counter is free from probabilistic components; it registers a signal if there is at least one target molecule in the analysis chamber. Predictive medical diagnostics require technology based on methods where sensitivity allows for the detection of single marker molecules in a biological sample volume of 1-10 µL, the smallest volume of biomaterial used in laboratory diagnostics. Creation of a detector with a sensitivity of 10-18 M would allow for the detection of one molecule in 1 µL of the sample, which fundamentally makes this approach analogous to a G-M counter for solutions. To date, bioanalytical methods are limited to a sensitivity of 10-12 M (which is approximately 1 million molecules per 1 μL), which is insufficient to capture the early stages of pathological processes.

The human proteome size as a technological development function

Changes in information on the number of human proteoforms, post-translational modification (PTM) events, alternative splicing (AS), single-amino acid polymorphisms (SAP) associated with protein-coding genes in the neXtProt database have been retrospectively analyzed. In 2016, our group proposed three mathematical models for predicting the number of different proteins (proteoforms) in the human proteome. Eight years later, we compared the original data of the information resources and their contribution to the prediction results, correlating the differences with new approaches to experimental and bioinformatic analysis of protein modifications. The aim of this work is to update information on the status of records in the databases of identified proteoforms since 2016, as well as to identify trends in changes in the quantities of these records. According to various information models, modern experimental methods may identify from 5 to 125 million different proteoforms: the proteins formed due to alternative splicing, the implementation of single nucleotide polymorphisms at the proteomic level, and post-translational modifications in various combinations. This result reflects an increase in the size of the human proteome by 20 or more times over the past 8 years.

Detection of low-copy proteins in proteomic studies: issues and solutions

Detection of low-copy proteins in complex biological samples is one of the most important issues of modern proteomics. The main reason for inefficient detection of low protein concentrations is the insufficient sensitivity of mass spectrometric detectors and the high dynamic range of protein concentrations. In this study we have investigated the possibilities and limitations of a targeted mass spectrometric analysis using the reconstructed system of standard proteins UPS1 (Universal Proteomic Standard 1) as an example. The study has shown that the sensitivity of the method is affected by the concentration of target proteins of the UPS1 system, as well as by a high level of biological noise modelled by proteins of whole E. coli cell lysate. The limitations of the method have been overcome by concentrating and pre-fractionating the sample peptides in a reversed phase chromatographic system under alkaline elution conditions. Proteomic analysis of the biological sample (proteins of the human hepatocellular carcinoma cell line HepG2 encoded by genes of human chromosome 18) showed an increase in the sensitivity of the method as compared to the standard targeted mass spectrometric analysis. This culminated in registration of 94 proteins encoded by genes located on human chromosome18.

In silico and in cellulo approaches for functional annotation of human protein splice variants

The elegance of pre-mRNA splicing mechanisms continues to interest scientists even after over a half century, since the discovery of the fact that coding regions in genes are interrupted by non-coding sequences. The vast majority of human genes have several mRNA variants, coding structurally and functionally different protein isoforms in a tissue-specific manner and with a linkage to specific developmental stages of the organism. Alteration of splicing patterns shifts the balance of functionally distinct proteins in living systems, distorts normal molecular pathways, and may trigger the onset and progression of various pathologies. Over the past two decades, numerous studies have been conducted in various life sciences disciplines to deepen our understanding of splicing mechanisms and the extent of their impact on the functioning of living systems. This review aims to summarize experimental and computational approaches used to elucidate the functions of splice variants of a single gene based on our experience accumulated in the laboratory of interactomics of proteoforms at the Institute of Biomedical Chemistry (IBMC) and best global practices.

Proteome of plasma extracellular vesicles as a source of colorectal cancer biomarkers

The search for minimally invasive methods for diagnostics of colorectal cancer (CRC) is the most important task for early diagnostics of the disease and subsequent successful treatment. Human plasma represents the main type of biological material used in the clinical practice; however, the complex dynamic range of substances circulating in it complicates determination of CRC protein markers by the mass spectrometric (MS) method. Studying the proteome of extracellular vesicles (EVs) isolated from human plasma represents an attractive approach for the discovery of tissue-secreted CRC markers. We performed shotgun mass spectrometry analysis of EV samples obtained from plasma of CRC patients and healthy volunteers. This MS analysis resulted in identification of 370 proteins (which were registered by at least two peptides). Stable isotope-free relative quantitation identified 55 proteins with altered abundance in EV samples obtained from plasma samples of CRC patients as compared to healthy controls. Among the EV proteins isolated from blood plasma we found components involved in cell adhesion and the VEGFA-VEGFR2 signaling pathway (TLN1, HSPA8, VCL, MYH9, and others), as well as proteins expressed predominantly by gastrointestinal tissues (polymeric immunoglobulin receptor, PIGR). The data obtained using the shotgun proteomic profiling may be added to the panel for targeted MS analysis of EV-associated protein markers, previously developed using CRC cell models.

Biomarkers Identification in the Microenvironment of Oral Squamous Cell Carcinoma: A Systematic Review of Proteomic Studies

An important determinant for oral squamous cell carcinoma (OSCC) onset and outcome is the composition of the tumor microenvironment (TME). Thus, the study of the interactions occurring among cancer cells, immune cells, and cancer-associated fibroblasts within the TME could facilitate the understanding of the mechanisms underlying OSCC development and progression, as well as of its sensitivity or resistance to the therapy. In this context, it must be highlighted that the characterization of TME proteins is enabled by proteomic methodologies, particularly mass spectrometry (MS). Aiming to identify TME protein markers employable for diagnosing and prognosticating OSCC, we have retrieved a total of 119 articles spanning 2001 to 2023, of which 17 have passed the selection process, satisfying all its criteria. We have found a total of 570 proteins detected by MS-based proteomics in the TME of OSCC; among them, 542 are identified by a single study, while 28 are cited by two or more studies. These 28 proteins participate in extracellular matrix remodeling and/or energy metabolism. Here, we propose them as markers that could be used to characterize the TME of OSCC for diagnostic/prognostic purposes. Noteworthy, most of the 28 individuated proteins share one feature: being modulated by the hypoxia that is present in the proliferating OSCC mass.

Landscape analysis of alternative splicing in kidney renal clear cell carcinoma and their clinical significance

A growing number of studies reveal that alternative splicing (AS) is associated with tumorigenesis, progression, and metastasis. Systematic analysis of alternative splicing signatures in renal cancer is lacking. In our study, we investigated the AS landscape of kidney renal clear cell carcinoma (KIRC) and identified AS predictive model to improve the prognostic prediction of KIRC. We obtained clinical data and gene expression profiles of KIRC patients from the TCGA database to evaluate AS events. The calculation results for seven types of AS events indicated that 46276 AS events from 10577 genes were identified. Next, we applied Cox regression analysis to identify 5864 prognostic-associated AS events. We used the Metascape database to verify the potential pathways of prognostic-associated AS. Moreover, we constructed KIRC prediction systems with prognostic-associated AS events by the LASSO Cox regression model. AUCs demonstrated that these prediction systems had excellent prognostic accuracy simultaneously. We identified 34 prognostic associated splicing factors (SFs) and constructed homologous regulatory networks. Furthermore, in vitro experiments were performed to validate the favorable effect of SFs FMR1 in KIRC. In conclusion, we overviewed AS events in KIRC and identified AS-based prognostic models to assist the survival prediction of KIRC patients. Our study may provide a novel predictive signature to improve the prognostic prediction of KIRC, which might facilitate KIRC patient counseling and individualized management.

Integrated plasma proteomics identifies tuberculosis-specific diagnostic biomarkers

BACKGROUNDNovel biomarkers to identify infectious patients transmitting Mycobacterium tuberculosis are urgently needed to control the global tuberculosis (TB) pandemic. We hypothesized that proteins released into the plasma in active pulmonary TB are clinically useful biomarkers to distinguish TB cases from healthy individuals and patients with other respiratory infections.METHODSWe applied a highly sensitive non-depletion tandem mass spectrometry discovery approach to investigate plasma protein expression in pulmonary TB cases compared to healthy controls in South African and Peruvian cohorts. Bioinformatic analysis using linear modeling and network correlation analyses identified 118 differentially expressed proteins, significant through 3 complementary analytical pipelines. Candidate biomarkers were subsequently analyzed in 2 validation cohorts of differing ethnicity using antibody-based proximity extension assays.RESULTSTB-specific host biomarkers were confirmed. A 6-protein diagnostic panel, comprising FETUB, FCGR3B, LRG1, SELL, CD14, and ADA2, differentiated patients with pulmonary TB from healthy controls and patients with other respiratory infections with high sensitivity and specificity in both cohorts.CONCLUSIONThis biomarker panel exceeds the World Health Organization Target Product Profile specificity criteria for a triage test for TB. The new biomarkers have potential for further development as near-patient TB screening assays, thereby helping to close the case-detection gap that fuels the global pandemic.FUNDINGMedical Research Council (MRC) (MR/R001065/1, MR/S024220/1, MR/P023754/1, and MR/W025728/1); the MRC and the UK Foreign Commonwealth and Development Office; the UK National Institute for Health Research (NIHR); the Wellcome Trust (094000, 203135, and CC2112); Starter Grant for Clinical Lecturers (Academy of Medical Sciences UK); the British Infection Association; the Program for Advanced Research Capacities for AIDS in Peru at Universidad Peruana Cayetano Heredia (D43TW00976301) from the Fogarty International Center at the US NIH; the UK Technology Strategy Board/Innovate UK (101556); the Francis Crick Institute, which receives funding from UKRI-MRC (CC2112); Cancer Research UK (CC2112); and the NIHR Biomedical Research Centre of Imperial College NHS.

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.

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.

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.

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.