Enzyme-substrate Relationships Research Articles

A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships.

The activities of most enzymes and drugs depend on interactions between proteins and small molecules. Accurate prediction of these interactions could greatly accelerate pharmaceutical and biotechnological research. Current machine learning models designed for this task have a limited ability to generalize beyond the proteins used for training. This limitation is likely due to a lack of information exchange between the protein and the small molecule during the generation of the required numerical representations. Here, we introduce ProSmith, a machine learning framework that employs a multimodal Transformer Network to simultaneously process protein amino acid sequences and small molecule strings in the same input. This approach facilitates the exchange of all relevant information between the two molecule types during the computation of their numerical representations, allowing the model to account for their structural and functional interactions. Our final model combines gradient boosting predictions based on the resulting multimodal Transformer Network with independent predictions based on separate deep learning representations of the proteins and small molecules. The resulting predictions outperform recently published state-of-the-art models for predicting protein-small molecule interactions across three diverse tasks: predicting kinase inhibitions; inferring potential substrates for enzymes; and predicting Michaelis constants KM. The Python code provided can be used to easily implement and improve machine learning predictions involving arbitrary protein-small molecule interactions.

The protein methylation network in yeast: A landmark in completeness for a eukaryotic post-translational modification

Defining all sites for a post-translational modification in the cell, and identifying their upstream modifying enzymes, is essential for a complete understanding of a modification’s function. However, the complete mapping of a modification in the proteome and definition of its associated enzyme–substrate network is rarely achieved. Here, we present the protein methylation network for Saccharomyces cerevisiae. Through a formal process of defining and quantifying all potential sources of incompleteness, for both the methylation sites in the proteome and also protein methyltransferases, we prove that this protein methylation network is now near-complete. It contains 33 methylated proteins and 28 methyltransferases, comprising 44 enzyme-substrate relationships, and a predicted further three enzymes. While the precise molecular function of most methylation sites is unknown, and it remains possible that other sites and enzymes remain undiscovered, the completeness of this protein modification network is unprecedented and allows us to holistically explore the role and evolution of protein methylation in the eukaryotic cell. We show that while no single protein methylation event is essential in yeast, the vast majority of methylated proteins are themselves essential, being primarily involved in the core cellular processes of transcription, RNA processing, and translation. This suggests that protein methylation in lower eukaryotes exists to fine-tune proteins whose sequences are evolutionarily constrained, providing an improvement in the efficiency of their cognate processes. The approach described here, for the construction and evaluation of post-translational modification networks and their constituent enzymes and substrates, defines a formal process of utility for other post-translational modifications.

Mapping the signaling network of BIN2 kinase using TurboID-mediated biotin labeling and phosphoproteomics.

Elucidating enzyme-substrate relationships in posttranslational modification (PTM) networks is crucial for understanding signal transduction pathways but is technically difficult because enzyme-substrate interactions tend to be transient. Here, we demonstrate that TurboID-based proximity labeling (TbPL) effectively and specifically captures the substrates of kinases and phosphatases. TbPL-mass spectrometry (TbPL-MS) identified over 400 proximal proteins of Arabidopsis thaliana BRASSINOSTEROID-INSENSITIVE2 (BIN2), a member of the GLYCOGEN SYNTHASE KINASE 3 (GSK3) family that integrates signaling pathways controlling diverse developmental and acclimation processes. A large portion of the BIN2-proximal proteins showed BIN2-dependent phosphorylation in vivo or in vitro, suggesting that these are BIN2 substrates. Protein-protein interaction network analysis showed that the BIN2-proximal proteins include interactors of BIN2 substrates, revealing a high level of interactions among the BIN2-proximal proteins. Our proteomic analysis establishes the BIN2 signaling network and uncovers BIN2 functions in regulating key cellular processes such as transcription, RNA processing, translation initiation, vesicle trafficking, and cytoskeleton organization. We further discovered significant overlap between the GSK3 phosphorylome and the O-GlcNAcylome, suggesting an evolutionarily ancient relationship between GSK3 and the nutrient-sensing O-glycosylation pathway. Our work presents a powerful method for mapping PTM networks, a large dataset of GSK3 kinase substrates, and important insights into the signaling network that controls key cellular functions underlying plant growth and acclimation.

Validation of a metabolite-GWAS network for Populus trichocarpa family 1 UDP-glycosyltransferases.

Metabolite genome-wide association studies (mGWASs) are increasingly used to discover the genetic basis of target phenotypes in plants such as Populus trichocarpa, a biofuel feedstock and model woody plant species. Despite their growing importance in plant genetics and metabolomics, few mGWASs are experimentally validated. Here, we present a functional genomics workflow for validating mGWAS-predicted enzyme-substrate relationships. We focus on uridine diphosphate-glycosyltransferases (UGTs), a large family of enzymes that catalyze sugar transfer to a variety of plant secondary metabolites involved in defense, signaling, and lignification. Glycosylation influences physiological roles, localization within cells and tissues, and metabolic fates of these metabolites. UGTs have substantially expanded in P. trichocarpa, presenting a challenge for large-scale characterization. Using a high-throughput assay, we produced substrate acceptance profiles for 40 previously uncharacterized candidate enzymes. Assays confirmed 10 of 13 leaf mGWAS associations, and a focused metabolite screen demonstrated varying levels of substrate specificity among UGTs. A substrate binding model case study of UGT-23 rationalized observed enzyme activities and mGWAS associations, including glycosylation of trichocarpinene to produce trichocarpin, a major higher-order salicylate in P. trichocarpa. We identified UGTs putatively involved in lignan, flavonoid, salicylate, and phytohormone metabolism, with potential implications for cell wall biosynthesis, nitrogen uptake, and biotic and abiotic stress response that determine sustainable biomass crop production. Our results provide new support for in silico analyses and evidence-based guidance for in vivo functional characterization.

Oxaloacetate: signaling molecule, molecular mechanisms of interaction, prospects for clinical application

Small molecules make up a majority of cellular molecules, their intracellular concentrations vary over a wide range, and they are involved in a multifarious molecular transformations. However, there is not enough information about how small molecules regulate protein functions through intermolecular binding, which creates an urgent need for fundamental research and the study of the metabolites role. Our attention was focused on small molecules located at the intersection point of metabolic pathways of proteins, fats, carbohydrates, the structural and functional potential of which provides numerous biomolecular processes. The intermediates availability can regulate energy and intermediate metabolism, cellular redox potential, and production of adenosine triphosphate, determining the direction of metabolism that is priority for the cell at this point in time. Of particular interest in the study of metabolite-protein interactions are those studies that can reveal new enzyme-substrate relationships and cases of metabolite-induced remodeling of protein complexes. The review is devoted to the study of the role of the small molecule oxaloacetate and malate, as well as malate dehydrogenase involved in their transformations, the activity of which can be used as a diagnostic marker in oncological and neurodegenerative diseases. Oxaloacetate has protective and promitochondrial effects, it serves as a neuroprotector, prevents inflammation and neurodegeneration. The penetration of oxaloacetate through the blood-brain barrier into the central nervous system was established, which became a prerequisite for conducting preclinical trials of drugs containing it in models of Alzheimer's disease and ischemic stroke.

PiNET: a versatile web platform for downstream analysis and visualization of proteomics data.

Rapid progress in proteomics and large-scale profiling of biological systems at the protein level necessitates the continued development of efficient computational tools for the analysis and interpretation of proteomics data. Here, we present the piNET server that facilitates integrated annotation, analysis and visualization of quantitative proteomics data, with emphasis on PTM networks and integration with the LINCS library of chemical and genetic perturbation signatures in order to provide further mechanistic and functional insights. The primary input for the server consists of a set of peptides or proteins, optionally with PTM sites, and their corresponding abundance values. Several interconnected workflows can be used to generate: (i) interactive graphs and tables providing comprehensive annotation and mapping between peptides and proteins with PTM sites; (ii) high resolution and interactive visualization for enzyme-substrate networks, including kinases and their phospho-peptide targets; (iii) mapping and visualization of LINCS signature connectivity for chemical inhibitors or genetic knockdown of enzymes upstream of their target PTM sites. piNET has been built using a modular Spring-Boot JAVA platform as a fast, versatile and easy to use tool. The Apache Lucene indexing is used for fast mapping of peptides into UniProt entries for the human, mouse and other commonly used model organism proteomes. PTM-centric network analyses combine PhosphoSitePlus, iPTMnet and SIGNOR databases of validated enzyme-substrate relationships, for kinase networks augmented by DeepPhos predictions and sequence-based mapping of PhosphoSitePlus consensus motifs. Concordant LINCS signatures are mapped using iLINCS. For each workflow, a RESTful API counterpart can be used to generate the results programmatically in the json format. The server is available at http://pinet-server.org, and it is free and open to all users without login requirement.

The Use of Biometrics and Rubric Analysis to Assess How Students View, Cognitively Process, and Respond to 3D Physical Molecular Models in Undergraduate Biochemistry

Among the STEM disciplines biochemistry curricula present a particular challenge to students with abstract concepts that can lead to rampant and resistant misconceptions impeding learning and therefore impact retention. Moreover, research has shown two‐dimensional representations not only fail to effectively convey biochemical concepts, but also propagate misconceptions. In particular, research has shown students have significant difficulty understanding enzyme‐substrate relationships. One way instructors have suggested to support students while learning this content is through the use of 3D physical models of the enzyme and substrate. However, little research has been conducted on how the models impact the learning process and how to best design these models and corresponding activities. This presentation will describe an iterative design process based on biometric, observational and rubric data analysis. The biometric data is collected using electroencephalographic (EEG) and eye tracking tools and voice recordings from simulated learning environments where biochemistry students completed a serine protease activity with corresponding physical models. These results offer insights into how students view and cognitively process activities using physical models with respect to how the instructor intends students to learn with and use these tools. In addition, observational and rubric analyses of student responses to activities from the simulated learning environment and real classroom sessions provide further data for the iterative activity design process.Support or Funding InformationThis project is supported by the National Science Foundation under award number IUSE 1711402/1711425 to University of Minnesota, Rochester and Kennesaw State University.

Depalmitoylation by Palmitoyl-Protein Thioesterase 1 in Neuronal Health and Degeneration.

Protein palmitoylation is the post-translational, reversible addition of a 16-carbon fatty acid, palmitate, to proteins. Protein palmitoylation has recently garnered much attention, as it robustly modifies the localization and function of canonical signaling molecules and receptors. Protein depalmitoylation, on the other hand, is the process by which palmitic acid is removed from modified proteins and contributes, therefore, comparably to palmitoylated-protein dynamics. Palmitoylated proteins also require depalmitoylation prior to lysosomal degradation, demonstrating the significance of this process in protein sorting and turnover. Palmitoylation and depalmitoylation serve as particularly crucial regulators of protein function in neurons, where a specialized molecular architecture and cholesterol-rich membrane microdomains contribute to synaptic transmission. Three classes of depalmitoylating enzymes are currently recognized, the acyl protein thioesterases, α/β hydrolase domain-containing 17 proteins (ABHD17s), and the palmitoyl-protein thioesterases (PPTs). However, a clear picture of depalmitoylation has not yet emerged, in part because the enzyme-substrate relationships and specific functions of depalmitoylation are only beginning to be uncovered. Further, despite the finding that loss-of-function mutations affecting palmitoyl-protein thioesterase 1 (PPT1) function cause a severe pediatric neurodegenerative disease, the role of PPT1 as a depalmitoylase has attracted relatively little attention. Understanding the role of depalmitoylation by PPT1 in neuronal function is a fertile area for ongoing basic science and translational research that may have broader therapeutic implications for neurodegeneration. Here, we will briefly introduce the rapidly growing field surrounding protein palmitoylation and depalmitoylation, then will focus on the role of PPT1 in development, health, and neurological disease.

Proteome-wide Tyrosine Phosphorylation Analysis Reveals Dysregulated Signaling Pathways in Ovarian Tumors

The recent accomplishment of comprehensive proteogenomic analysis of high-grade serous ovarian carcinoma (HGSOC) tissues reveals cancer associated molecular alterations were not limited to variations among DNA, and mRNA/protein expression, but are a result of complex reprogramming of signaling pathways/networks mediated by the protein and post-translational modification (PTM) interactomes. A systematic, multiplexed approach interrogating enzyme-substrate relationships in the context of PTMs is fundamental in understanding the dynamics of these pathways, regulation of cellular processes, and their roles in disease processes. Here, as part of Clinical Proteomic Tumor Analysis Consortium (CPTAC) project, we established a multiplexed PTM assay (tyrosine phosphorylation, and lysine acetylation, ubiquitylation and SUMOylation) method to identify protein probes' PTMs on the human proteome array. Further, we focused on the tyrosine phosphorylation and identified 19 kinases are potentially responsible for the dysregulated signaling pathways observed in HGSOC. Additionally, elevated kinase activity was observed when 14 ovarian cancer cell lines or tumor tissues were subjected to test the autophosphorylation status of PTK2 (pY397) and PTK2B (pY402) as a proxy for kinase activity. Taken together, this report demonstrates that PTM signatures based on lysate reactions on human proteome array is a powerful, unbiased approach to identify dysregulated PTM pathways in tumors.

A Map of Protein-Metabolite Interactions Reveals Principles of Chemical Communication

Metabolite-protein interactions control a variety of cellular processes, thereby playing a major role in maintaining cellular homeostasis. Metabolites comprise the largest fraction of molecules in cells, but our knowledge of the metabolite-protein interactome lags behind our understanding of protein-protein or protein-DNA interactomes. Here, we present a chemoproteomic workflow for the systematic identification of metabolite-protein interactions directly in theirnative environment. The approach identified a network of known and novel interactions and binding sites in Escherichia coli, and we demonstrated the functional relevance of a number of newly identified interactions. Our data enabled identification of new enzyme-substrate relationships and cases of metabolite-induced remodeling of protein complexes. Our metabolite-protein interactome consists of 1,678 interactions and 7,345 putative binding sites. Our data reveal functional and structural principles of chemical communication, shed light on the prevalence and mechanisms of enzyme promiscuity, and enable extraction of quantitative parameters of metabolite binding on a proteome-wide scale.

Enzyme-substrate relationships in the ubiquitin system: approaches for identifying substrates of ubiquitin ligases.

Protein ubiquitylation is an importantpost-translational modification, regulating aspects of virtually every biochemical pathway in eukaryotic cells. Hundreds of enzymes participate in the conjugation and deconjugation of ubiquitin, as well as the recognition, signaling functions, and degradation of ubiquitylated proteins. Regulation of ubiquitylation is most commonly at the level of recognition of substrates by E3 ubiquitin ligases. Characterization of the network of E3-substrate relationships is a major goal and challenge in the field, as this expected to yield fundamental biological insights and opportunities for drug development. There has been remarkable success in identifying substrates for some E3 ligases, in many instances using the standard protein-protein interaction techniques (e.g., two-hybrid screens and co-immunoprecipitations paired with mass spectrometry). However, some E3s have remained refractory to characterization, while others have simply not yet been studied due to the sheer number and diversity of E3s. This review will discuss the range of tools and techniques that can be used for substrate profiling of E3 ligases.

Validation of Biomarker Proteins Using Reverse Capture Protein Microarrays.

Genomics has revolutionized large-scale and high-throughput sequencing and has led to the discovery of thousands of new proteins. Protein chip technology is emerging as a miniaturized and highly parallel platform that is suited to rapid, simultaneous screening of large numbers of proteins and the analysis of various protein-binding activities, enzyme substrate relationships, and posttranslational modifications. Specifically, reverse capture protein microarrays provide the most appropriate platform for identifying low-abundance, disease-specific biomarker proteins in a sea of high-abundance proteins from biological fluids such as blood, serum, plasma, saliva, urine, and cerebrospinal fluid as well as tissues and cells obtained by biopsy. Samples from hundreds of patients can be spotted in serial dilutions on many replicate glass slides. Each slide can then be probed with one specific antibody to the biomarker of interest. That antibody's titer can then be determined quantitatively for each patient, allowing for the statistical assessment and validation of the diagnostic or prognostic utility of that particular antigen. As the technology matures and the availability of validated, platform-compatible antibodies increases, the platform will move further into the desirable realm of discovery science for detecting and quantitating low-abundance signaling proteins. In this chapter, we describe methods for the successful application of the reverse capture protein microarray platform for which we have made substantial contributions to the development and application of this method, particularly in the use of body fluids other than serum/plasma.

A visual review of the interactome of LRRK2: Using deep-curated molecular interaction data to represent biology.

Molecular interaction databases are essential resources that enable access to a wealth of information on associations between proteins and other biomolecules. Network graphs generated from these data provide an understanding of the relationships between different proteins in the cell, and network analysis has become a widespread tool supporting –omics analysis. Meaningfully representing this information remains far from trivial and different databases strive to provide users with detailed records capturing the experimental details behind each piece of interaction evidence. A targeted curation approach is necessary to transfer published data generated by primarily low-throughput techniques into interaction databases. In this review we present an example highlighting the value of both targeted curation and the subsequent effective visualization of detailed features of manually curated interaction information. We have curated interactions involving LRRK2, a protein of largely unknown function linked to familial forms of Parkinson's disease, and hosted the data in the IntAct database. This LRRK2-specific dataset was then used to produce different visualization examples highlighting different aspects of the data: the level of confidence in the interaction based on orthogonal evidence, those interactions found under close-to-native conditions, and the enzyme–substrate relationships in different in vitro enzymatic assays. Finally, pathway annotation taken from the Reactome database was overlaid on top of interaction networks to bring biological functional context to interaction maps.

Global and Specific Responses of the Histone Acetylome to Systematic Perturbation

Regulation of histone acetylation is fundamental to the utilization of eukaryotic genomes in chromatin. Aberrant acetylation contributes to disease and can be clinically combated by inhibiting the responsible enzymes. Our knowledge of the histone acetylation system is patchy because we so far lacked the methodology to describe acetylation patterns and their genesis by integrated enzyme activities. We devised a generally applicable, mass spectrometry-based strategy to precisely and accurately quantify combinatorial modification motifs. This was applied to generate a comprehensive inventory of acetylation motifs on histones H3 and H4 in Drosophila cells. Systematic depletion of known or suspected acetyltransferases and deacetylases revealed specific alterations of histone acetylation signatures, established enzyme-substrate relationships, and unveiled an extensive crosstalk between neighboring modifications. Unexpectedly, overall histone acetylation levels remained remarkably constant upon depletion of individual acetyltransferases. Conceivably, the acetylation level is adjusted to maintain the global charge neutralization of chromatin and the stability of nuclei.

Substrate Specificity of R3 Receptor-like Protein-tyrosine Phosphatase Subfamily toward Receptor Protein-tyrosine Kinases

Receptor-like protein-tyrosine phosphatases (RPTPs) are involved in various aspects of cellular functions, such as proliferation, differentiation, survival, migration, and metabolism. A small number of RPTPs have been reported to regulate activities of some cellular proteins including receptor protein-tyrosine kinases (RPTKs). However, our understanding about the roles of individual RPTPs in the regulation of RPTKs is still limited. The R3 RPTP subfamily reportedly plays pivotal roles in the development of several tissues including the vascular and nervous systems. Here, we examined enzyme-substrate relationships between the four R3 RPTP subfamily members and 21 RPTK members selected from 14 RPTK subfamilies by using a mammalian two-hybrid system with substrate-trapping RPTP mutants. Among the 84 RPTP-RPTK combinations conceivable, we detected 30 positive interactions: 25 of the enzyme-substrate relationships were novel. We randomly chose several RPTKs assumed to be substrates for R3 RPTPs, and validated the results of this screen by in vitro dephosphorylation assays, and by cell-based assays involving overexpression and knock-down experiments. Because their functional relationships were verified without exception, it is probable that the RPTKs identified as potential substrates are actually physiological substrates for the R3 RPTPs. Interestingly, some RPTKs were recognized as substrates by all R3 members, but others were recognized by only one or a few members. The enzyme-substrate relationships identified in the present study will shed light on physiological roles of the R3 RPTP subfamily.

Access guide to human proteinpedia.

Human Proteinpedia (http://www.humanproteinpedia.org) is a publicly available proteome repository for sharing human protein data derived from multiple experimental platforms. It incorporates diverse features of the human proteome including protein-protein interactions, enzyme-substrate relationships, PTMs, subcellular localization, and expression of proteins in various human tissues and cell lines in diverse biological conditions including diseases. Through a publicly distributed annotation system developed especially for proteomic data, investigators across the globe can upload, view, and edit proteomic data even before they are published. Inclusion of information on investigators and laboratories that generated the data, as well as visualization of tandem mass spectra, stained tissue sections, protein/peptide microarrays, fluorescent micrographs, and western blots, ensures quality of proteomic data assimilated in Human Proteinpedia. Many of the protein annotations submitted to Human Proteinpedia have also been made available to the scientific community through Human Protein Reference Database (http://www.hprd.org), another resource developed by our group. In this protocol, we describe how to submit, edit, and retrieve proteomic data in Human Proteinpedia.

Websites of note

Websites of note

Exploring the Yeast Acetylome Using Functional Genomics

Lysine acetylation is a dynamic posttranslational modification with a well-defined role in regulating histones. The impact of acetylation on other cellular functions remains relatively uncharacterized. We explored the budding yeast acetylome with a functional genomics approach, assessing the effects of gene overexpression in the absence of lysine deacetylases (KDACs). We generated a network of 463 synthetic dosage lethal (SDL) interactions involving class I and II KDACs, revealing many cellular pathways regulated by different KDACs. A biochemical survey of genes interacting with the KDAC RPD3 identified 72 proteins acetylated in vivo. In-depth analysis of one of these proteins, Swi4, revealed a role for acetylation in G1-specific gene expression. Acetylation of Swi4 regulates interaction with its partner Swi6, both components of the SBF transcription factor. This study expands our view of the yeast acetylome, demonstrates the utility of functional genomic screens for exploring enzymatic pathways, and provides functional information that can be mined for future studies.

Functional dissection of lysine deacetylases reveals that HDAC1 and p300 regulate AMPK

First identified as histone-modifying proteins, lysine acetyltranferases (KATs) and deacetylases (KDACs) antagonize each other through modification of the side chains of lysine residues in histone proteins1. (De)acetylation of many non-histone proteins involved in chromatin, metabolism or cytoskeleton regulation were further identified in eukaryotic organisms2–6, but the corresponding modifying enzymes and substrate-specific functions of the modification are unclear. Moreover, mechanisms underlying functional specificity of individual KDACs7 remain enigmatic, and the substrate spectra of each KDAC lack comprehensive definition. Here we dissect the functional specificity of twelve critical human KDACs using a genome-wide synthetic lethality screen8–13 in cultured human cells. The genetic interaction profiles revealed enzyme-substrate relationships between individual KDACs and many important substrates governing a wide array of biological processes including metabolism, development and cell cycle progression. We further confirmed that (de)acetylation of the catalytic subunit of the adenosine monophosphate-activated protein kinase (AMPK), a critical cellular energy-sensing protein kinase complex, is controlled by the opposing catalytic activities of HDAC1 and p300. Its deacetylation enhances physical interaction with the upstream kinase LKB1, in turn leading to AMPK phosphorylation and activation, resulting in lipid breakdown in human liver cells. These findings provide new insights into previously underappreciated metabolism-regulatory roles of HDAC1 in coordinating nutrient availability and cellular responses upstream of AMPK, and demonstrate the importance of high-throughput genetic interaction profiling to elucidate functional specificity and critical substrates of individual human KDACs potentially valuable for therapeutic applications.

Mapping the human phosphatome on growth pathways

Large-scale siRNA screenings allow linking the function of poorly characterized genes to phenotypic readouts. According to this strategy, genes are associated with a function of interest if the alteration of their expression perturbs the phenotypic readouts. However, given the intricacy of the cell regulatory network, the mapping procedure is low resolution and the resulting models provide little mechanistic insights. We have developed a new strategy that combines multiparametric analysis of cell perturbation with logic modeling to achieve a more detailed functional mapping of human genes onto complex pathways. A literature-derived optimized model is used to infer the cell activation state following upregulation or downregulation of the model entities. By matching this signature with the experimental profile obtained in the high-throughput siRNA screening it is possible to infer the target of each protein, thus defining its 'entry point' in the network. By this novel approach, 41 phosphatases that affect key growth pathways were identified and mapped onto a human epithelial cell-specific growth model, thus providing insights into the mechanisms underlying their function.