Proteome-wide Prediction of Lysine Methylation Leads to Identification of H2BK43 Methylation and Outlines the Potential Methyllysine Proteome

Liquid Liver Biopsy for Disease Diagnosis and Prognosis

Liver diseases are a major burden worldwide, the scope of which is expected to further grow in the upcoming years. Clinically relevant liver dysfunction-related blood markers such as alanine aminotransferase and aspartate aminotransferase have limited accuracy. Nowadays, liver biopsy remains the gold standard for several liver-related pathologies, posing a risk of complication due to its invasive nature. Liquid biopsy is a minimally invasive approach, which has shown substantial potential in the diagnosis, prognosis, and monitoring of liver diseases by detecting disease-associated particles such as proteins and RNA molecules in biological fluids. Histones are the core components of the nucleosomes, regulating essential cellular processes, including gene expression and DNA repair. Following cell death or activation of immune cells, histones are released in the extracellular space and can be detected in circulation. Histones are stable in circulation, have a long half-life, and retain their post-translational modifications. Here, we provide an overview of the current research on histone-mediated liquid biopsy methods for liver diseases, with a focus on the most common detection methods.

Unraveling the battle for lysine: A review of the competition among post-translational modifications

Proteins play a critical role as key regulators in various biological systems, influencing crucial processes such as gene expression, cell cycle progression, and cellular proliferation. However, the functions of proteins can be further modified through post-translational modifications (PTMs), which expand their roles and contribute to disease progression when dysregulated. In this review, we delve into the methodologies employed for the characterization of PTMs, shedding light on the techniques and tools utilized to help unravel their complexity. Furthermore, we explore the prevalence of crosstalk and competition that occurs between different types of PTMs, specifically focusing on both histone and non-histone proteins. The intricate interplay between different modifications adds an additional layer of regulation to protein function and cellular processes. To gain insights into the competition for lysine residues among various modifications, computational systems such as MethylSight have been developed, allowing for a comprehensive analysis of the modification landscape. Additionally, we provide an overview of the exciting developments in the field of inhibitors or drugs targeting PTMs, highlighting their potential in combatting prevalent diseases. The discovery and development of drugs that modulate PTMs present promising avenues for therapeutic interventions, offering new strategies to address complex diseases. As research progresses in this rapidly evolving field, we anticipate remarkable advancements in our understanding of PTMs and their roles in health and disease, ultimately paving the way for innovative treatment approaches.

Identification of non-histone substrates of the lysine methyltransferase PRDM9

Lysine methylation is a dynamic, post-translational mark that regulates the function of histone and non-histone proteins. Many of the enzymes that mediate lysine methylation, known as lysine methyltransferases (KMTs), were originally identified to modify histone proteins but have also been discovered to methylate non-histone proteins. In this work, we investigate the substrate selectivity of the lysine methyltransferase PRDM9 to identify both potential histone and non-histone substrates. Though normally expressed in germ cells, PRDM9 is significantly upregulated across many cancer types. The methyltransferase activity of PRDM9 is essential for double-strand break formation during meiotic recombination. PRDM9 has been reported to methylate histone H3 at lysine residues 4 and 36; however, PRDM9 KMT activity had not previously been evaluated on non-histone proteins. Using lysine-oriented peptide (K-OPL) libraries to screen potential substrates of PRDM9, we determined that PRDM9 preferentially methylates peptide sequences not found in any histone protein. We confirmed PRDM9 selectivity through in vitro KMT reactions using peptides with substitutions at critical positions. A multisite λ-dynamics computational analysis provided a structural rationale for the observed PRDM9 selectivity. The substrate selectivity profile was then used to identify putative non-histone substrates, which were tested by peptide spot array, and a subset were further validated at the protein level by in vitro KMT assays on recombinant proteins. Finally, one of the non-histone substrates, CTNNBL1, was found to be methylated by PRDM9 in cells.

Global lysine methylome profiling using systematically characterized affinity reagents

Lysine methylation modulates the function of histone and non-histone proteins, and the enzymes that add or remove lysine methylation-lysine methyltransferases (KMTs) and lysine demethylases (KDMs), respectively-are frequently mutated and dysregulated in human diseases. Identification of lysine methylation sites proteome-wide has been a critical barrier to identifying the non-histone substrates of KMTs and KDMs and for studying functions of non-histone lysine methylation. Detection of lysine methylation by mass spectrometry (MS) typically relies on the enrichment of methylated peptides by pan-methyllysine antibodies. In this study, we use peptide microarrays to show that pan-methyllysine antibodies have sequence bias, and we evaluate how the differential selectivity of these reagents impacts the detection of methylated peptides in MS-based workflows. We discovered that most commercially available pan-Kme antibodies have an in vitro sequence bias, and multiple enrichment approaches provide the most comprehensive coverage of the lysine methylome. Overall, global lysine methylation proteomics with multiple characterized pan-methyllysine antibodies resulted in the detection of 5089 lysine methylation sites on 2751 proteins from two human cell lines, nearly doubling the number of reported lysine methylation sites in the human proteome.

Characterization of KDM5 lysine demethylase family substrate preference and identification of novel substrates

The KDM5/JARID1 sub-family are 2-oxoglutarate and Fe(II)-dependent lysine-specific histone demethylases that are characterized by their Jumonji catalytic domains. The KDM5 family is known to remove tri-/di-methyl modifications from lysine-4 of histone H3 (i.e. H3-K4me2/3), a mark associated with active gene expression. As a result, studies to date have revolved around the influence of KDM5 on disease through their ability to regulate H3-K4me2/3. Recent evidence demonstrates that KDM5 may influence disease beyond H3-K4 demethylation, making it critical to further investigate KDM5-mediated demethylation of non-histone proteins. To help identify potential non-histone substrates for the KDM5 family, we developed a library of 180 permutated peptide substrates, with sequences that are systematically altered from the wild-type H3-K4me3 substrate. From this library, we characterized recombinant KDM5A/B/C/D substrate preference and developed recognition motifs for each KDM5 demethylase. The recognition motifs developed were used to predict potential substrates for KDM5A/B/C/D and profiled to generate a list of high-ranking and medium/low-ranking substrates for further in vitro validation. Through this approach, we identified 66 high-ranking substrates in which KDM5 demethylases displayed significant in vitro activity towards.

ABP-Finder: A Tool to Identify Antibacterial Peptides and the Gram-Staining Type of Targeted Bacteria

Multi-drug resistance in bacteria is a major health problem worldwide. To overcome this issue, new approaches allowing for the identification and development of antibacterial agents are urgently needed. Peptides, due to their binding specificity and low expected side effects, are promising candidates for a new generation of antibiotics. For over two decades, a large diversity of antimicrobial peptides (AMPs) has been discovered and annotated in public databases. The AMP family encompasses nearly 20 biological functions, thus representing a potentially valuable resource for data mining analyses. Nonetheless, despite the availability of machine learning-based approaches focused on AMPs, these tools lack evidence of successful application for AMPs' discovery, and many are not designed to predict a specific function for putative AMPs, such as antibacterial activity. Consequently, among the apparent variety of data mining methods to screen peptide sequences for antibacterial activity, only few tools can deal with such task consistently, although with limited precision and generally no information about the possible targets. Here, we addressed this gap by introducing a tool specifically designed to identify antibacterial peptides (ABPs) with an estimation of which type of bacteria is susceptible to the action of these peptides, according to their response to the Gram-staining assay. Our tool is freely available via a web server named ABP-Finder. This new method ranks within the top state-of-the-art ABP predictors, particularly in terms of precision. Importantly, we showed the successful application of ABP-Finder for the screening of a large peptide library from the human urine peptidome and the identification of an antibacterial peptide.

PPI-Affinity: A Web Tool for the Prediction and Optimization of Protein-Peptide and Protein-Protein Binding Affinity

Virtual screening of protein–protein and protein–peptide interactions is a challenging task that directly impacts the processes of hit identification and hit-to-lead optimization in drug design projects involving peptide-based pharmaceuticals. Although several screening tools designed to predict the binding affinity of protein–protein complexes have been proposed, methods specifically developed to predict protein–peptide binding affinity are comparatively scarce. Frequently, predictors trained to score the affinity of small molecules are used for peptides indistinctively, despite the larger complexity and heterogeneity of interactions rendered by peptide binders. To address this issue, we introduce PPI-Affinity, a tool that leverages support vector machine (SVM) predictors of binding affinity to screen datasets of protein–protein and protein–peptide complexes, as well as to generate and rank mutants of a given structure. The performance of the SVM models was assessed on four benchmark datasets, which include protein–protein and protein–peptide binding affinity data. In addition, we evaluated our model on a set of mutants of EPI-X4, an endogenous peptide inhibitor of the chemokine receptor CXCR4, and on complexes of the serine proteases HTRA1 and HTRA3 with peptides. PPI-Affinity is freely accessible at https://protdcal.zmb.uni-due.de/PPIAffinity.

Insights into a Cancer-Target Demethylase: Substrate Prediction through Systematic Specificity Analysis for KDM3A

Jumonji C (JmjC) lysine demethylases (KDMs) catalyze the removal of methyl (-CH₃) groups from modified lysyl residues. Several JmjC KDMs promote cancerous properties and these findings have primarily been in relation to histone demethylation. However, the biological roles of these enzymes are increasingly being shown to also be attributed to non-histone demethylation. Notably, KDM3A has become relevant to tumour progression due to recent findings of this enzyme's role in promoting cancerous phenotypes, such as enhanced glucose consumption and upregulated mechanisms of chemoresistance. To aid in uncovering the mechanism(s) by which KDM3A imparts its oncogenic function(s), this study aimed to unravel KDM3A substrate specificity to predict high-confidence substrates. Firstly, substrate specificity was assessed by monitoring activity towards a peptide permutation library of histone H3 di-methylated at lysine-9 (i.e., H3K9me2). From this, the KDM3A recognition motif was established and used to define a set of high-confidence predictions of demethylation sites from within the KDM3A interactome. Notably, this led to the identification of three in vitro substrates (MLL1, p300, and KDM6B), which are relevant to the field of cancer progression. This preliminary data may be exploited in further tissue culture experiments to decipher the avenues by which KDM3A imparts cancerous phenotypes.

Metaboloepigenetics in cancer, immunity and cardiovascular disease

The influence of cellular metabolism on epigenetic pathways are well documented but misunderstood. Scientists have long known of the metabolic impact on epigenetic determinants. More often than not, that title role for DNA methylation was portrayed by the metabolite SAM or S-adenosylmethionine. Technically speaking there are many other metabolites that drive epigenetic processes that instruct seemingly distant - yet highly connect pathways - and none more so than our understanding of the cancer epigenome. Recent studies have shown that available energy link the extracellular environment to influence cellular responses. This focused review examines the recent interest in epigenomics and casts cancer, metabolism and immunity in unfamiliar roles - cooperating. There are not only language lessons from cancer research, we have come round to appreciate that reaching into areas previously thought of as too distinct are also object lessons in understanding health and disease. The Warburg effect is one such signature of how glycolysis influences metabolic shift during oncogenesis. That shift in metabolism - now recognised as central to proliferation in cancer biology - influence core enzymes that not only control gene expression but are also central to replication, condensation and the repair of nucleic acid. These nuclear processes rely on metabolism and with glucose at center stage the role of respiration and oxidative metabolism are now synonymous with the mitochondria as the powerhouses of metaboloepigenetics. The emerging evidence for metaboloepigenetics in trained innate immunity has revealed recognisable signalling pathways with antecedent extracellular stimulation. With due consideration to immunometabolism we discuss the striking signalling similarities influencing these core pathways. The immunometabolic-epigenetic axis in cardiovascular disease has deeply etched connections with inflammation and we examine the chromatin template as a carrier of epigenetic indices that determine the expression of genes influencing atherosclerosis and vascular complications of diabetes.

Identification of in vitro JMJD lysine demethylase candidate substrates via systematic determination of substrate preference

A major regulatory influence over gene expression is the dynamic post translational methylation of histone proteins, with major implications from both lysine methylation and demethylation. The KDM5/JARID1 sub-family of Fe(II)/2-oxoglutarate dependent lysine-specific demethylases is, in part, responsible for the removal of tri/dimethyl modifications from lysine 4 of histone H3 (i.e., H3K4me3/2), a mark associated with active gene expression. Although the relevance of KDM5 activity to disease progression has been primarily established through its ability to regulate gene expression via histone methylation, there is evidence that these enzymes may also target non-histone proteins. To aid in the identification of new non-histone substrates, we examined KDM5A in vitro activity towards a library of 180 permutated peptide substrates derived from the H3K4me3 sequence. From this data, a recognition motif was identified and used to predict candidate KDM5A substrates from the methyllysine proteome. High-ranking candidate substrates were then validated for in vitro KDM5A activity using representative trimethylated peptides. Our approach correctly identified activity towards 90% of high-ranked substrates. Here, we have demonstrated the usefulness of our method in identifying candidate substrates that is applicable to any Fe(II)- and 2-oxoglutarate dependent demethylase.

Deconvoluting complex protein interaction networks through reductionist strategies in peptide biochemistry: Modern approaches and research questions

Following the decoding of the first human genome, researchers have vastly improved their understanding of cell biology and its regulation. As a result, it has become clear that it is not merely genetic information, but the aberrant changes in the functionality and connectivity of its encoded proteins that drive cell response to periods of stress and external cues. Therefore, proper utilization of refined methods that help to describe protein signalling or regulatory networks (i.e., functional connectivity), can help us understand how change in the signalling landscape effects the cell. However, given the vast complexity in 'how and when' proteins communicate or interact with each other, it is extremely difficult to define, characterize, and understand these interaction networks in a tangible manner. Herein lies the challenge of tackling the functional proteome; its regulation is encoded in multiple layers of interaction, chemical modification and cell compartmentalization. To address and refine simple research questions, modern reductionist strategies in protein biochemistry have successfully used peptide-based experiments; their summation helping to simplify the overall complexity of these protein interaction networks. In this way, peptides are powerful tools used in fundamental research that can be readily applied to comparative biochemical research. Understanding and defining how proteins interact is one of the key aspects towards understanding how the proteome functions. To date, reductionist peptide-based research has helped to address a wide range of proteome-related research questions, including the prediction of enzymes substrates, identification of posttranslational modifications, and the annotation of protein interaction partners. Peptide arrays have been used to identify the binding specificity of reader domains, which are able to recognise the posttranslational modifications; forming dynamic protein interactions that are dependent on modification state. Finally, representing one of the fastest growing classes of inhibitor molecules, peptides are now begin explored as "disruptors" of protein-protein interactions or enzyme activity. Collectively, this review will discuss the use of peptides, peptide arrays, peptide-oriented computational biochemistry as modern reductionist strategies in deconvoluting the functional proteome.

Non-Histone Protein Methylation: Biological Significance and Bioengineering Potential

Protein methylation is a key post-translational modification whose effects on gene expression have been intensively studied over the last two decades. Recently, renewed interest in non-histone protein methylation has gained momentum for its role in regulating important cellular processes and the activity of many proteins, including transcription factors, enzymes, and structural complexes. The extensive and dynamic role that protein methylation plays within the cell also highlights its potential for bioengineering applications. Indeed, while synthetic histone protein methylation has been extensively used to engineer gene expression, engineering of non-histone protein methylation has not been fully explored yet. Here, we report the latest findings, highlighting how non-histone protein methylation is fundamental for certain cellular functions and is implicated in disease, and review recent efforts in the engineering of protein methylation.

Chemical Decorations of "MARs" Residents in Orchestrating Eukaryotic Gene Regulation

Genome organization plays a crucial role in gene regulation, orchestrating multiple cellular functions. A meshwork of proteins constituting a three-dimensional (3D) matrix helps in maintaining the genomic architecture. Sequences of DNA that are involved in tethering the chromatin to the matrix are called scaffold/matrix attachment regions (S/MARs), and the proteins that bind to these sequences and mediate tethering are termed S/MAR-binding proteins (S/MARBPs). The regulation of S/MARBPs is important for cellular functions and is altered under different conditions. Limited information is available presently to understand the structure–function relationship conclusively. Although all S/MARBPs bind to DNA, their context- and tissue-specific regulatory roles cannot be justified solely based on the available information on their structures. Conformational changes in a protein lead to changes in protein–protein interactions (PPIs) that essentially would regulate functional outcomes. A well-studied form of protein regulation is post-translational modification (PTM). It involves disulfide bond formation, cleavage of precursor proteins, and addition or removal of low-molecular-weight groups, leading to modifications like phosphorylation, methylation, SUMOylation, acetylation, PARylation, and ubiquitination. These chemical modifications lead to varied functional outcomes by mechanisms like modifying DNA–protein interactions and PPIs, altering protein function, stability, and crosstalk with other PTMs regulating subcellular localizations. S/MARBPs are reported to be regulated by PTMs, thereby contributing to gene regulation. In this review, we discuss the current understanding, scope, disease implications, and future perspectives of the diverse PTMs regulating functions of S/MARBPs.

Using Machine Learning and Targeted Mass Spectrometry to Explore the Methyl-Lys Proteome

Protein lysine methylation mediates a variety of biological processes, and their dysregulation has been established to play pivotal roles in human disease. A number of these sites constitute attractive drug targets. However, systematic identification of methylation sites is challenging and resource intensive. Here, we present a protocol combining MethylSight, a machine learning model trained to identify promising lysine methylation sites, and mass spectrometry for subsequent validation. Our approach can reduce the time and investment required to identify novel methylation sites.

For complete information on the use and execution of this protocol, please refer to Biggar et al. (2020).

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Identify high-confidence lysine methylation sites for experimental validation

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Webserver interface allows for user-defined prediction threshold

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Protocol has been used for the validation of 45 new histone methylation sites

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Validation of candidate methylation sites by targeted mass spectrometry