Structural Analysis of PTM Hotspots (SAPH-ire)--A Quantitative Informatics Method Enabling the Discovery of Novel Regulatory Elements in Protein Families

FuncPhos-STR: An integrated deep neural network for functional phosphosite prediction based on AlphaFold protein structure and dynamics

Phosphorylation modifications play important regulatory roles in most biological processes. However, the functional assignment for the vast majority of the identified phosphosites remains a major challenge. Here, we provide a deep learning framework named FuncPhos-STR as an online resource, for functional prediction and structural visualization of human proteome-level phosphosites. Based on our reported FuncPhos-SEQ framework, which was built by integrating phosphosite sequence evolution and protein-protein interaction (PPI) information, FuncPhos-STR was developed by further integrating the structural and dynamics information on AlphaFold protein structures. The characterized structural topology and dynamics features underlying functional phosphosites emphasized their molecular mechanism for regulating protein functions. By integrating the structural and dynamics, sequence evolutionary, and PPI network features from protein different dimensions, FuncPhos-STR has advantage over other reported models, with the best AUC value of 0.855. Using FuncPhos-STR, the phosphosites inside the pocket regions are accessible to higher functional scores, theoretically supporting their potential regulatory mechanism. Overall, FuncPhos-STR would accelerate the functional identification of huge unexplored phosphosites, and facilitate the elucidation of their allosteric regulation mechanisms. The web server of FuncPhos-STR is freely available at http://funcptm.jysw.suda.edu.cn/str.

Cross-family small GTPase ubiquitination by the intracellular pathogen Legionella pneumophila

The intracellular bacterial pathogen Legionella pneumophila (L.p.) manipulates eukaryotic host ubiquitination machinery to form its replicative vacuole. While nearly 10% of L.p.'s ∼330 secreted effector proteins are ubiquitin ligases or deubiquitinases, a comprehensive measure of temporally resolved changes in the endogenous host ubiquitinome during infection has not been undertaken. To elucidate how L.p. hijacks host cell ubiquitin signaling, we generated a proteome-wide analysis of changes in protein ubiquitination during infection. We discover that L.p. infection increases ubiquitination of host regulators of subcellular trafficking and membrane dynamics, most notably ∼40% of mammalian Ras superfamily small GTPases. We determine that these small GTPases undergo nondegradative ubiquitination at the Legionella-containing vacuole (LCV) membrane. Finally, we find that the bacterial effectors SidC/SdcA play a central role in cross-family small GTPase ubiquitination, and that these effectors function upstream of SidE family ligases in the polyubiquitination and retention of GTPases in the LCV membrane. This work highlights the extensive reconfiguration of host ubiquitin signaling by bacterial effectors during infection and establishes simultaneous ubiquitination of small GTPases across the Ras superfamily as a novel consequence of L.p. infection. Our findings position L.p. as a tool to better understand how small GTPases can be regulated by ubiquitination in uninfected contexts.

Integrating Clinical Cancer and PTM Proteomics Data Identifies a Mechanism of ACK1 Kinase Activation

Beyond the most common oncogenes activated by mutation (mut-drivers), there likely exists a variety of low-frequency mut-drivers, each of which is a possible frontier for targeted therapy. To identify new and understudied mut-drivers, we developed a machine learning (ML) model that integrates curated clinical cancer data and posttranslational modification (PTM) proteomics databases. We applied the approach to 62,746 patient cancers spanning 84 cancer types and predicted 3,964 oncogenic mutations across 1,148 genes, many of which disrupt PTMs of known and unknown function. The list of putative mut-drivers includes established drivers and others with poorly understood roles in cancer. This ML model is available as a web application. As a case study, we focused the approach on nonreceptor tyrosine kinases (NRTK) and found a recurrent mutation in activated CDC42 kinase-1 (ACK1) that disrupts the Mig6 homology region (MHR) and ubiquitin-association (UBA) domains on the ACK1 C-terminus. By studying these domains in cultured cells, we found that disruption of the MHR domain helps activate the kinase while disruption of the UBA increases kinase stability by blocking its lysosomal degradation. This ACK1 mutation is analogous to lymphoma-associated mutations in its sister kinase, TNK1, which also disrupt a C-terminal inhibitory motif and UBA domain. This study establishes a mut-driver discovery tool for the research community and identifies a mechanism of ACK1 hyperactivation shared among ACK family kinases.

IMPLICATIONS

This research identifies a potentially targetable activating mutation in ACK1 and other possible oncogenic mutations, including PTM-disrupting mutations, for further study.

Deciphering the functional landscape of phosphosites with deep neural network

Current biochemical approaches have only identified the most well-characterized kinases for a tiny fraction of the phosphoproteome, and the functional assignments of phosphosites are almost negligible. Herein, we analyze the substrate preference catalyzed by a specific kinase and present a novel integrated deep neural network model named FuncPhos-SEQ for functional assignment of human proteome-level phosphosites. FuncPhos-SEQ incorporates phosphosite motif information from a protein sequence using multiple convolutional neural network (CNN) channels and network features from protein-protein interactions (PPIs) using network embedding and deep neural network (DNN) channels. These concatenated features are jointly fed into a heterogeneous feature network to prioritize functional phosphosites. Combined with a series of in vitro and cellular biochemical assays, we confirm that NADK-S48/50 phosphorylation could activate its enzymatic activity. In addition, ERK1/2 are discovered as the primary kinases responsible for NADK-S48/50 phosphorylation. Moreover, FuncPhos-SEQ is developed as an online server.

Cross-family small GTPase ubiquitination by the intracellular pathogen Legionella pneumophila

The intracellular bacterial pathogen Legionella pneumophila (L.p.) manipulates eukaryotic host ubiquitination machinery to form its replicative vacuole. While nearly 10% of L.p.'s arsenal of ~330 secreted effector proteins have been biochemically characterized as ubiquitin ligases or deubiquitinases, a comprehensive measure of temporally resolved changes in the endogenous host ubiquitinome during infection has not been undertaken. To elucidate how L.p hijacks ubiquitin signaling within the host cell, we undertook a proteome-wide analysis of changes in protein ubiquitination during infection. We discover that L.p. infection results in increased ubiquitination of host proteins regulating subcellular trafficking and membrane dynamics, most notably 63 of ~160 mammalian Ras superfamily small GTPases. We determine that these small GTPases predominantly undergo non-degradative monoubiquitination, and link ubiquitination to recruitment to the Legionella-containing vacuole membrane. Finally, we find that the bacterial effectors SidC/SdcA play a central, but likely indirect, role in cross-family small GTPase ubiquitination. This work highlights the extensive reconfiguration of host ubiquitin signaling by bacterial effectors during infection and establishes simultaneous ubiquitination of small GTPases across the Ras superfamily as a novel consequence of L.p. infection. This work positions L.p. as a tool to better understand how small GTPases can be regulated by ubiquitination in uninfected contexts.

N-terminal intrinsic disorder is an ancestral feature of Gγ subunits that influences the balance between different Gβγ signaling axes in yeast

Activated G protein-coupled receptors promote the dissociation of heterotrimeric G proteins into Gα and Gβγ subunits that bind to effector proteins to drive intracellular signaling responses. In yeast, Gβγ subunits coordinate the simultaneous activation of multiple signaling axes in response to mating pheromones, including MAP kinase (MAPK)-dependent transcription, cell polarization, and cell cycle arrest responses. The Gγ subunit in this complex contains an N-terminal intrinsically disordered region that governs Gβγ-dependent signal transduction in yeast and mammals. Here, we demonstrate that N-terminal intrinsic disorder is likely an ancestral feature that has been conserved across different Gγ subtypes and organisms. To understand the functional contribution of structural disorder in this region, we introduced precise point mutations that produce a stepwise disorder-to-order transition in the N-terminal tail of the canonical yeast Gγ subunit, Ste18. Mutant tail structures were confirmed using circular dichroism and molecular dynamics and then substituted for the wildtype gene in yeast. We find that increasing the number of helix-stabilizing mutations, but not isometric mutation controls, has a negative and proteasome-independent effect on Ste18 protein levels as well as a differential effect on pheromone-induced levels of active MAPK/Fus3, but not MAPK/Kss1. When expressed at wildtype levels, we further show that mutants with an alpha-helical N terminus exhibit a counterintuitive shift in Gβγ signaling that reduces active MAPK/Fus3 levels whilst increasing cell polarization and cell cycle arrest. These data reveal a role for Gγ subunit intrinsically disordered regions in governing the balance between multiple Gβγ signaling axes.

Computational systems approach towards phosphoproteomics and their downstream regulation

Protein phosphorylation plays an essential role in modulating cell signalling and its downstream transcriptional and translational regulations. Until recently, protein phosphorylation has been studied mostly using low-throughput biochemical assays. The advancement of mass spectrometry (MS)-based phosphoproteomics transformed the field by enabling measurement of proteome-wide phosphorylation events, where tens of thousands of phosphosites are routinely identified and quantified in an experiment. This has brought a significant challenge in analysing large-scale phosphoproteomic data, making computational methods and systems approaches integral parts of phosphoproteomics. Previous works have primarily focused on reviewing the experimental techniques in MS-based phosphoproteomics, yet a systematic survey of the computational landscape in this field is still missing. Here, we review computational methods and tools, and systems approaches that have been developed for phosphoproteomics data analysis. We categorise them into four aspects including data processing, functional analysis, phosphoproteome annotation and their integration with other omics, and in each aspect, we discuss the key methods and example studies. Lastly, we highlight some of the potential research directions on which future work would make a significant contribution to this fast-growing field. We hope this review provides a useful snapshot of the field of computational systems phosphoproteomics and stimulates new research that drives future development.

The Integration of Proteome-Wide PTM Data with Protein Structural and Sequence Features Identifies Phosphorylations that Mediate 14-3-3 Interactions

14-3-3s are abundant proteins that regulate essentially all aspects of cell biology, including cell cycle, motility, metabolism, and cell death. 14-3-3s work by docking to phosphorylated Ser/Thr residues on a large network of client proteins and modulating client protein function in a variety of ways. In recent years, aided by improvements in proteomics, the discovery of 14-3-3 client proteins has far outpaced our ability to understand the biological impact of individual 14-3-3 interactions. The rate-limiting step in this process is often the identification of the individual phospho-serines/threonines that mediate 14-3-3 binding, which are difficult to distinguish from other phospho-sites by sequence alone. Furthermore, trial-and-error molecular approaches to identify these phosphorylations are costly and can take months or years to identify even a single 14-3-3 docking site phosphorylation. To help overcome this challenge, we used machine learning to analyze predictive features of 14-3-3 binding sites. We found that accounting for intrinsic protein disorder and the unbiased mass spectrometry identification rate of a given phosphorylation significantly improves the identification of 14-3-3 docking site phosphorylations across the proteome. We incorporated these features, coupled with consensus sequence prediction, into a publicly available web app, called "14-3-3 site-finder". We demonstrate the strength of this approach through its ability to identify 14-3-3 binding sites that do not conform to the loose consensus sequence of 14-3-3 docking phosphorylations, which we validate with 14-3-3 client proteins, including TNK1, CHEK1, MAPK7, and others. In addition, by using this approach, we identify a phosphorylation on A-kinase anchor protein-13 (AKAP13) at Ser2467 that dominantly controls its interaction with 14-3-3.

Leveraging Protein Dynamics to Identify Functional Phosphorylation Sites using Deep Learning Models

Accurate prediction of post-translational modifications (PTMs) is of great significance in understanding cellular processes, by modulating protein structure and dynamics. Nowadays, with the rapid growth of protein data at different "omics" levels, machine learning models largely enriched the prediction of PTMs. However, most machine learning models only rely on protein sequence and little structural information. The lack of the systematic dynamics analysis underlying PTMs largely limits the PTM functional predictions. In this research, we present two dynamics-centric deep learning models, namely, cDL-PAU and cDL-FuncPhos, by incorporating sequence, structure, and dynamics-based features to elucidate the molecular basis and underlying functional landscape of PTMs. cDL-PAU achieved satisfactory area under the curve (AUC) scores of 0.804-0.888 for predicting phosphorylation, acetylation, and ubiquitination (PAU) sites, while cDL-FuncPhos achieved an AUC value of 0.771 for predicting functional phosphorylation (FuncPhos) sites, displaying reliable improvements. Through a feature selection, the dynamics-based coupling and commute ability show large contributions in discovering PAU sites and FuncPhos sites, suggesting the allosteric propensity for important PTMs. The application of cDL-FuncPhos in three oncoproteins not only corroborates its strong performance in FuncPhos prioritization but also gains insight into the physical basis for the functions. The source code and data set of cDL-PAU and cDL-FuncPhos are available at https://github.com/ComputeSuda/PTM_ML.

High-throughput functional characterization of protein phosphorylation sites in yeast

Phosphorylation is a critical post-translational modification involved in the regulation of almost all cellular processes. However, fewer than 5% of thousands of recently discovered phosphosites have been functionally annotated. In this study, we devised a chemical genetic approach to study the functional relevance of phosphosites in Saccharomyces cerevisiae. We generated 474 yeast strains with mutations in specific phosphosites that were screened for fitness in 102 conditions, along with a gene deletion library. Of these phosphosites, 42% exhibited growth phenotypes, suggesting that these are more likely functional. We inferred their function based on the similarity of their growth profiles with that of gene deletions and validated a subset by thermal proteome profiling and lipidomics. A high fraction exhibited phenotypes not seen in the corresponding gene deletion, suggestive of a gain-of-function effect. For phosphosites conserved in humans, the severity of the yeast phenotypes is indicative of their human functional relevance. This high-throughput approach allows for functionally characterizing individual phosphosites at scale.

Glycomic and Glycoproteomic Techniques in Neurodegenerative Disorders and Neurotrauma: Towards Personalized Markers

The proteome represents all the proteins expressed by a genome, a cell, a tissue, or an organism at any given time under defined physiological or pathological circumstances. Proteomic analysis has provided unparalleled opportunities for the discovery of expression patterns of proteins in a biological system, yielding precise and inclusive data about the system. Advances in the proteomics field opened the door to wider knowledge of the mechanisms underlying various post-translational modifications (PTMs) of proteins, including glycosylation. As of yet, the role of most of these PTMs remains unidentified. In this state-of-the-art review, we present a synopsis of glycosylation processes and the pathophysiological conditions that might ensue secondary to glycosylation shortcomings. The dynamics of protein glycosylation, a crucial mechanism that allows gene and pathway regulation, is described. We also explain how-at a biomolecular level-mutations in glycosylation-related genes may lead to neuropsychiatric manifestations and neurodegenerative disorders. We then analyze the shortcomings of glycoproteomic studies, putting into perspective their downfalls and the different advanced enrichment techniques that emanated to overcome some of these challenges. Furthermore, we summarize studies tackling the association between glycosylation and neuropsychiatric disorders and explore glycoproteomic changes in neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Huntington disease, multiple sclerosis, and amyotrophic lateral sclerosis. We finally conclude with the role of glycomics in the area of traumatic brain injury (TBI) and provide perspectives on the clinical application of glycoproteomics as potential diagnostic tools and their application in personalized medicine.

Enhancing the Discovery of Functional Post-Translational Modification Sites with Machine Learning Models - Development, Validation, and Interpretation

Protein posttranslational modifications (PTMs) are a rapidly expanding feature class of significant importance in cell biology. Due to a high burden of experimental proof, the number of functionals PTMs in the eukaryotic proteome is currently underestimated. Furthermore, not all PTMs are functionally equivalent. Computational approaches that can confidently recommend PTMs of probable function can improve the heuristics of PTM investigation and alleviate these problems. To address this need, we developed SAPH-ire: a multifeature heuristic neural network model that takes community wisdom into account by recommending experimental PTMs similar to those which have previously been established as having regulatory impact. Here, we describe the principle behind the SAPH-ire model, how it is developed, how we evaluate its performance, and important caveats to consider when building and interpreting such models. Finally, we discus current limitations of functional PTM prediction models and highlight potential mechanisms for their improvement.

Combinatorial phosphorylation modulates the structure and function of the G protein γ subunit in yeast

Intrinsically disordered regions (IDRs) in proteins are often targets of combinatorial posttranslational modifications, which serve to regulate protein structure and function. Emerging evidence suggests that the N-terminal tails of G protein γ subunits, which are essential components of heterotrimeric G proteins, are intrinsically disordered, phosphorylation-dependent determinants of G protein signaling. Here, we found that the yeast Gγ subunit Ste18 underwent combinatorial, multisite phosphorylation events within its N-terminal IDR. G protein-coupled receptor (GPCR) activation and osmotic stress induced phosphorylation at Ser⁷, whereas glucose and acid stress induced phosphorylation at Ser³, which was a quantitative indicator of intracellular pH. Each site was phosphorylated by a distinct set of kinases, and phosphorylation of one site affected phosphorylation of the other, as determined through exposure to serial stimuli and through phosphosite mutagenesis. Last, we showed that phosphorylation resulted in changes in IDR structure and that different combinations of phosphorylation events modulated the activation rate and amplitude of the downstream mitogen-activated protein kinase Fus3. These data place Gγ subunits among intrinsically disordered proteins that undergo combinatorial posttranslational modifications that govern signaling pathway output.

Amyloid Oligomers: A Joint Experimental/Computational Perspective on Alzheimer's Disease, Parkinson's Disease, Type II Diabetes, and Amyotrophic Lateral Sclerosis

Protein misfolding and aggregation is observed in many amyloidogenic diseases affecting either the central nervous system or a variety of peripheral tissues. Structural and dynamic characterization of all species along the pathways from monomers to fibrils is challenging by experimental and computational means because they involve intrinsically disordered proteins in most diseases. Yet understanding how amyloid species become toxic is the challenge in developing a treatment for these diseases. Here we review what computer, in vitro, in vivo, and pharmacological experiments tell us about the accumulation and deposition of the oligomers of the (Aβ, tau), α-synuclein, IAPP, and superoxide dismutase 1 proteins, which have been the mainstream concept underlying Alzheimer's disease (AD), Parkinson's disease (PD), type II diabetes (T2D), and amyotrophic lateral sclerosis (ALS) research, respectively, for many years.

Aggregation and Prion-Inducing Properties of the G-Protein Gamma Subunit Ste18 are Regulated by Membrane Association

Yeast prions and mnemons are respectively transmissible and non-transmissible self-perpetuating protein assemblies, frequently based on cross-β ordered detergent-resistant aggregates (amyloids). Prions cause devastating diseases in mammals and control heritable traits in yeast. It was shown that the de novo formation of the prion form [PSI⁺] of yeast release factor Sup35 is facilitated by aggregates of other proteins. Here we explore the mechanism of the promotion of [PSI⁺] formation by Ste18, an evolutionarily conserved gamma subunit of a G-protein coupled receptor, a key player in responses to extracellular stimuli. Ste18 forms detergent-resistant aggregates, some of which are colocalized with de novo generated Sup35 aggregates. Membrane association of Ste18 is required for both Ste18 aggregation and [PSI⁺] induction, while functional interactions involved in signal transduction are not essential for these processes. This emphasizes the significance of a specific location for the nucleation of protein aggregation. In contrast to typical prions, Ste18 aggregates do not show a pattern of heritability. Our finding that Ste18 levels are regulated by the ubiquitin-proteasome system, in conjunction with the previously reported increase in Ste18 levels upon the exposure to mating pheromone, suggests that the concentration-dependent Ste18 aggregation may mediate a mnemon-like response to physiological stimuli.

Negative feedback-loop mechanisms regulating HOG- and pheromone-MAPK signaling in yeast

The pheromone response and the high osmolarity glycerol (HOG) pathways are considered the prototypical MAPK signaling systems. They are the best-understood pathways in eukaryotic cells, yet they continue to provide insights in how cells relate with the environment. These systems are subjected to tight regulatory circuits to prevent hyperactivation in length and intensity. Failure to do this may be a matter of life or death specially for unicellular organisms such as Saccharomyces cerevisiae. The signaling pathways are fine-tuned by positive and negative feedback loops exerted by pivotal control elements that allow precise responses to specific stimuli, despite the fact that some elements of the systems are common to different signaling pathways. Here we describe the experimentally proven negative feedback loops that modulate the pheromone response and the HOG pathways. As described in this review, MAP kinases are central mechanistic components of these feedback loops. They have the capacity to modulate basal signaling activity, a fast extranuclear response, and a longer-lasting transcriptional process.

Regulation of large and small G proteins by ubiquitination

Many sensory and chemical signal inputs are transmitted by intracellular GTP-binding (G) proteins. G proteins make up two major subfamilies: "large" G proteins comprising three subunits and "small" G proteins, such as the proto-oncogene product RAS, which contains a single subunit. Members of both subfamilies are regulated by post-translational modifications, including lipidation, proteolysis, and carboxyl methylation. Emerging studies have shown that these proteins are also modified by ubiquitination. Much of our current understanding of this post-translational modification comes from investigations of the large G-protein α subunit from yeast (Gpa1) and the three RAS isotypes in humans, NRAS, KRAS, and HRAS. Gα undergoes both mono- and polyubiquitination, and these modifications have distinct consequences for determining the sites and mechanisms of its degradation. Genetic and biochemical reconstitution studies have revealed the enzymes and binding partners required for addition and removal of ubiquitin, as well as the delivery and destruction of both the mono- and polyubiquitinated forms of the G protein. Complementary studies of RAS have identified multiple ubiquitination sites, each having distinct consequences for binding to regulatory proteins, shuttling to and from the plasma membrane, and degradation. Here, we review what is currently known about these two well-studied examples, Gpa1 and the human RAS proteins, that have revealed additional mechanisms of signal regulation and dysregulation relevant to human physiology. We also compare and contrast the effects of G-protein ubiquitination with other post-translational modifications of these proteins.

Receptor-Like Kinase Phosphorylation of Arabidopsis Heterotrimeric G-Protein Gα -Subunit AtGPA1

As molecular on-off switches, heterotrimeric G protein complexes, comprised of a Gα subunit and an obligate Gβγ dimer, transmit extracellular signals received by G protein-coupled receptors (GPCRs) to cytoplasmic targets that respond to biotic and abiotic stimuli. Signal transduction is modulated by phosphorylation of GPCRs and G protein complexes. In Arabidopsis thaliana, the Gα subunit AtGPA1 is phosphorylated by the receptor-like kinase (RLK) BRI1-associated Kinase 1 (BAK1), but the extent that other RLKs phosphorylates AtGPA1 is unknown. Twenty-two trans-phosphorylation sites on AtGPA1 are mapped by 12 RLKs hypothesized to act in the Arabidopsis G protein signaling pathway. Cis-phosphorylation sites are also identified on these RLKs, some newly shown to be dual specific kinases. Multiple sites are present in the core AtGPA1 functional units, including pSer52 and/or pThr53 of the conserved P-loop that directly binds nucleotide/phosphate, pThr164, and pSer175 from αE helix in the intramolecular domain interface for nucleotide exchange and GTP hydrolysis, and pThr193 and/or pThr194 in Switch I (SwI) that coordinates nucleotide exchange and protein partner binding. Several AtGPA1 S/T phosphorylation sites are potentially nucleotide-dependent phosphorylation patterns, such as Ser52/Thr53 in the P-loop and Thr193 and/or Thr194 in SwI.

G-protein βγ subunits as multi-functional scaffolds and transducers in G-protein-coupled receptor signaling

G-protein βγ subunits are key participants in G-protein signaling. These subunits facilitate interactions between receptors and G proteins that are critical for the G protein activation cycle at the plasma membrane. In addition, they play roles in directly transducing signals to an ever expanding range of downstream targets, including integral membrane and cytosolic proteins. Emerging data indicate that Gβγ may play additional roles at intracellular compartments including endosomes, the Golgi apparatus, and the nucleus. Here, we discuss the molecular and structural basis for their ability to coordinate this wide range of cellular activities.

Negative Feedback Phosphorylation of Gγ Subunit Ste18 and the Ste5 Scaffold Synergistically Regulates MAPK Activation in Yeast

Heterotrimeric G proteins (Gαβγ) are essential transducers in G protein signaling systems in all eukaryotes. In yeast, G protein signaling differentially activates mitogen-activated protein kinases (MAPKs)—Fus3 and Kss1—a phenomenon controlled by plasma membrane (PM) association of the scaffold protein Ste5. Here, we show that phosphorylation of the yeast Gγ subunit (Ste18), together with Fus3 docking on Ste5, controls the rate and stability of Ste5/PM association. Disruption of either element alone by point mutation has mild but reciprocal effects on MAPK activation. Disabling both elements results in ultra-fast and stable bulk Ste5/PM localization and Fus3 activation that is 6 times faster and 4 times more amplified compared to wild-type cells. These results further resolve the mechanism by which MAPK negative feedback phosphorylation controls pathway activation and provides compelling evidence that Gγ subunits can serve as intrinsic regulators of G protein signaling.

Mechanisms of SOD1 regulation by post-translational modifications

SOD1 is commonly known for its ROS scavenging activity, but recent work has uncovered additional roles in modulating metabolism, maintaining redox balance, and regulating transcription. This new paradigm of expanded SOD1 function raises questions regarding the regulation of SOD1 and the cellular partitioning of its biological roles. Despite decades of research on SOD1, much of which focuses on its pathogenic role in amyotrophic lateral sclerosis, relatively little is known about its regulation by post-translational modifications (PTMs). However, over the last decade, advancements in mass spectrometry have led to a boom in PTM discovery across the proteome, which has also revealed new mechanisms of SOD1 regulation by PTMs and an array of SOD1 PTMs with high likelihood of biological function. In this review, we address emerging mechanisms of SOD1 regulation by post-translational modifications, many of which begin to shed light on how the various functions of SOD1 are regulated within the cell.

Protein Post-Translational Modification Crosstalk in Acute Myeloid Leukemia Calls for Action

BACKGROUND

Post-translational modification (PTM) crosstalk is a young research field. However, there is now evidence of the extraordinary characterization of the different proteoforms and their interactions in a biological environment that PTM crosstalk studies can describe. Besides gene expression and phosphorylation profiling of acute myeloid leukemia (AML) samples, the functional combination of several PTMs that might contribute to a better understanding of the complexity of the AML proteome remains to be discovered.

OBJECTIVE

By reviewing current workflows for the simultaneous enrichment of several PTMs and bioinformatics tools to analyze mass spectrometry (MS)-based data, our major objective is to introduce the PTM crosstalk field to the AML research community.

RESULTS

After an introduction to PTMs and PTM crosstalk, this review introduces several protocols for the simultaneous enrichment of PTMs. Two of them allow a simultaneous enrichment of at least three PTMs when using 0.5-2 mg of cell lysate. We have reviewed many of the bioinformatics tools used for PTM crosstalk discovery as its complex data analysis, mainly generated from MS, becomes challenging for most AML researchers. We have presented several non-AML PTM crosstalk studies throughout the review in order to show how important the characterization of PTM crosstalk becomes for the selection of disease biomarkers and therapeutic targets.

CONCLUSION

Herein, we have reviewed the advances and pitfalls of the emerging PTM crosstalk field and its potential contribution to unravel the heterogeneity of AML. The complexity of sample preparation and bioinformatics workflows demands a good interaction between experts of several areas.

Genetic Analysis of Rare Human Variants of Regulators of G Protein Signaling Proteins and Their Role in Human Physiology and Disease

Regulators of G protein signaling (RGS) proteins modulate the physiologic actions of many neurotransmitters, hormones, and other signaling molecules. Human RGS proteins comprise a family of 20 canonical proteins that bind directly to G protein-coupled receptors/G protein complexes to limit the lifetime of their signaling events, which regulate all aspects of cell and organ physiology. Genetic variations account for diverse human traits and individual predispositions to disease. RGS proteins contribute to many complex polygenic human traits and pathologies such as hypertension, atherosclerosis, schizophrenia, depression, addiction, cancers, and many others. Recent analysis indicates that most human diseases are due to extremely rare genetic variants. In this study, we summarize physiologic roles for RGS proteins and links to human diseases/traits and report rare variants found within each human RGS protein exome sequence derived from global population studies. Each RGS sequence is analyzed using recently described bioinformatics and proteomic tools for measures of missense tolerance ratio paired with combined annotation-dependent depletion scores, and protein post-translational modification (PTM) alignment cluster analysis. We highlight selected variants within the well-studied RGS domain that likely disrupt RGS protein functions and provide comprehensive variant and PTM data for each RGS protein for future study. We propose that rare variants in functionally sensitive regions of RGS proteins confer profound change-of-function phenotypes that may contribute, in newly appreciated ways, to complex human diseases and/or traits. This information provides investigators with a valuable database to explore variation in RGS protein function, and for targeting RGS proteins as future therapeutic targets.

G protein subunit phosphorylation as a regulatory mechanism in heterotrimeric G protein signaling in mammals, yeast, and plants

Heterotrimeric G proteins composed of Gα, Gβ, and Gγ subunits are vital eukaryotic signaling elements that convey information from ligand-regulated G protein-coupled receptors (GPCRs) to cellular effectors. Heterotrimeric G protein-based signaling pathways are fundamental to human health [Biochimica et Biophysica Acta (2007) 1768, 994-1005] and are the target of >30% of pharmaceuticals in clinical use [Biotechnology Advances (2013) 31, 1676-1694; Nature Reviews Drug Discovery (2017) 16, 829-842]. This review focuses on phosphorylation of G protein subunits as a regulatory mechanism in mammals, budding yeast, and plants. This is a re-emerging field, as evidence for phosphoregulation of mammalian G protein subunits from biochemical studies in the early 1990s can now be complemented with contemporary phosphoproteomics and genetic approaches applied to a diversity of model systems. In addition, new evidence implicates a family of plant kinases, the receptor-like kinases, which are monophyletic with the interleukin-1 receptor-associated kinase/Pelle kinases of metazoans, as possible GPCRs that signal via subunit phosphorylation. We describe early and modern observations on G protein subunit phosphorylation and its functional consequences in these three classes of organisms, and suggest future research directions.

The dynamic and stress-adaptive signaling hub of 14-3-3: emerging mechanisms of regulation and context-dependent protein-protein interactions

14-3-3 proteins are a family of structurally similar phospho-binding proteins that regulate essentially every major cellular function. Decades of research on 14-3-3s have revealed a remarkable network of interacting proteins that demonstrate how 14-3-3s integrate and control multiple signaling pathways. In particular, these interactions place 14-3-3 at the center of the signaling hub that governs critical processes in cancer, including apoptosis, cell cycle progression, autophagy, glucose metabolism, and cell motility. Historically, the majority of 14-3-3 interactions have been identified and studied under nutrient-replete cell culture conditions, which has revealed important nutrient driven interactions. However, this underestimates the reach of 14-3-3s. Indeed, the loss of nutrients, growth factors, or changes in other environmental conditions (e.g., genotoxic stress) will not only lead to the loss of homeostatic 14-3-3 interactions, but also trigger new interactions, many of which are likely stress adaptive. This dynamic nature of the 14-3-3 interactome is beginning to come into focus as advancements in mass spectrometry are helping to probe deeper and identify context-dependent 14-3-3 interactions-providing a window into adaptive phosphorylation-driven cellular mechanisms that orchestrate the tumor cell's response to a variety of environmental conditions including hypoxia and chemotherapy. In this review, we discuss emerging 14-3-3 regulatory mechanisms with a focus on post-translational regulation of 14-3-3 and dynamic protein-protein interactions that illustrate 14-3-3's role as a stress-adaptive signaling hub in cancer.

Interaction modulation through arrays of clustered methyl-arginine protein modifications

Systematic analysis of human arginine methylation identifies two distinct signaling modes; either isolated modifications akin to canonical post-translational modification regulation, or clustered arrays within disordered protein sequence. Hundreds of proteins contain these methyl-arginine arrays and are more prone to accumulate mutations and more tightly expression-regulated than dispersed methylation targets. Arginines within an array in the highly methylated RNA-binding protein synaptotagmin binding cytoplasmic RNA interacting protein (SYNCRIP) were experimentally shown to function in concert, providing a tunable protein interaction interface. Quantitative immunoprecipitation assays defined two distinct cumulative binding mechanisms operating across 18 proximal arginine-glycine (RG) motifs in SYNCRIP. Functional binding to the methyltransferase PRMT1 was promoted by continual arginine stretches, whereas interaction with the methyl-binding protein SMN1 was arginine content-dependent irrespective of linear position within the unstructured region. This study highlights how highly repetitive modifiable amino acid arrays in low structural complexity regions can provide regulatory platforms, with SYNCRIP as an extreme example how arginine methylation leverages these disordered sequences to mediate cellular interactions.

Tyrosine phosphorylation switching of a G protein

Heterotrimeric G protein complexes are molecular switches relaying extracellular signals sensed by G protein-coupled receptors (GPCRs) to downstream targets in the cytoplasm, which effect cellular responses. In the plant heterotrimeric GTPase cycle, GTP hydrolysis, rather than nucleotide exchange, is the rate-limiting reaction and is accelerated by a receptor-like regulator of G signaling (RGS) protein. We hypothesized that posttranslational modification of the Gα subunit in the G protein complex regulates the RGS-dependent GTPase cycle. Our structural analyses identified an invariant phosphorylated tyrosine residue (Tyr¹⁶⁶ in the Arabidopsis Gα subunit AtGPA1) located in the intramolecular domain interface where nucleotide binding and hydrolysis occur. We also identified a receptor-like kinase that phosphorylates AtGPA1 in a Tyr¹⁶⁶-dependent manner. Discrete molecular dynamics simulations predicted that phosphorylated Tyr¹⁶⁶ forms a salt bridge in this interface and potentially affects the RGS protein-accelerated GTPase cycle. Using a Tyr¹⁶⁶ phosphomimetic substitution, we found that the cognate RGS protein binds more tightly to the GDP-bound Gα substrate, consequently reducing its ability to accelerate GTPase activity. In conclusion, we propose that phosphorylation of Tyr¹⁶⁶ in AtGPA1 changes the binding pattern with AtRGS1 and thereby attenuates the steady-state rate of the GTPase cycle. We coin this newly identified mechanism "substrate phosphoswitching."

PTMscape: an open source tool to predict generic post-translational modifications and map modification crosstalk in protein domains and biological processes

PTMscape predicts PTM sites using descriptors of sequence and physico-chemical microenvironment, and tests enrichment of single or pairs of PTMs in protein domains.

While tandem mass spectrometry can detect post-translational modifications (PTM) at the proteome scale, reported PTM sites are often incomplete and include false positives. Computational approaches can complement these datasets by additional predictions, but most available tools use prediction models pre-trained for single PTM type by the developers and it remains a difficult task to perform large-scale batch prediction for multiple PTMs with flexible user control, including the choice of training data. We developed an R package called PTMscape which predicts PTM sites across the proteome based on a unified and comprehensive set of descriptors of the physico-chemical microenvironment of modified sites, with additional downstream analysis modules to test enrichment of individual or pairs of PTMs in protein domains. PTMscape is flexible in the ability to process any major modifications, such as phosphorylation and ubiquitination, while achieving the sensitivity and specificity comparable to single-PTM methods and outperforming other multi-PTM tools. Applying this framework, we expanded proteome-wide coverage of five major PTMs affecting different residues by prediction, especially for lysine and arginine modifications. Using a combination of experimentally acquired sites (PSP) and newly predicted sites, we discovered that the crosstalk among multiple PTMs occur more frequently than by random chance in key protein domains such as histone, protein kinase, and RNA recognition motifs, spanning various biological processes such as RNA processing, DNA damage response, signal transduction, and regulation of cell cycle. These results provide a proteome-scale analysis of crosstalk among major PTMs and can be easily extended to other types of PTM.

Acylation of Superoxide Dismutase 1 (SOD1) at K122 Governs SOD1-Mediated Inhibition of Mitochondrial Respiration

In this study, we employed proteomics to identify mechanisms of posttranslational regulation on cell survival signaling proteins. We focused on Cu-Zn superoxide dismutase (SOD1), which protects cells from oxidative stress. We found that acylation of K122 on SOD1, while not impacting SOD1 catalytic activity, suppressed the ability of SOD1 to inhibit mitochondrial metabolism at respiratory complex I. We found that deacylase depletion increased K122 acylation on SOD1, which blocked the suppression of respiration in a K122-dependent manner. In addition, we found that acyl-mimicking mutations at K122 decreased SOD1 accumulation in mitochondria, initially hinting that SOD1 may inhibit respiration directly within the intermembrane space (IMS). However, surprisingly, we found that forcing the K122 acyl mutants into the mitochondria with an IMS-targeting tag did not recover their ability to suppress respiration. Moreover, we found that suppressing or boosting respiration levels toggled SOD1 in or out of the mitochondria, respectively. These findings place SOD1-mediated inhibition of respiration upstream of its mitochondrial localization. Lastly, deletion-rescue experiments show that a respiration-defective mutant of SOD1 is also impaired in its ability to rescue cells from toxicity caused by SOD1 deletion. Together, these data suggest a previously unknown interplay between SOD1 acylation, metabolic regulation, and SOD1-mediated cell survival.

Systematic analysis of non-structural protein features for the prediction of PTM function potential by artificial neural networks

Post-translational modifications (PTMs) provide an extensible framework for regulation of protein behavior beyond the diversity represented within the genome alone. While the rate of identification of PTMs has rapidly increased in recent years, our knowledge of PTM functionality encompasses less than 5% of this data. We previously developed SAPH-ire (Structural Analysis of PTM Hotspots) for the prioritization of eukaryotic PTMs based on function potential of discrete modified alignment positions (MAPs) in a set of 8 protein families. A proteome-wide expansion of the dataset to all families of PTM-bearing, eukaryotic proteins with a representational crystal structure and the application of artificial neural network (ANN) models demonstrated the broader applicability of this approach. Although structural features of proteins have been repeatedly demonstrated to be predictive of PTM functionality, the availability of adequately resolved 3D structures in the Protein Data Bank (PDB) limits the scope of these methods. In order to bridge this gap and capture the larger set of PTM-bearing proteins without an available, homologous structure, we explored all available MAP features as ANN inputs to identify predictive models that do not rely on 3D protein structural data. This systematic, algorithmic approach explores 8 available input features in exhaustive combinations (247 models; size 2-8). To control for potential bias in random sampling for holdback in training sets, we iterated each model across 100 randomized, sample training and testing sets-yielding 24,700 individual ANNs. The size of the analyzed dataset and iterative generation of ANNs represents the largest and most thorough investigation of predictive models for PTM functionality to date. Comparison of input layer combinations allows us to quantify ANN performance with a high degree of confidence and subsequently select a top-ranked, robust fit model which highlights 3,687 MAPs, including 10,933 PTMs with a high probability of biological impact but without a currently known functional role.

Bioinformatics Analysis of PTM-Modified Protein Interaction Networks and Complexes

Normal cellular functioning is maintained by macromolecular machines that control both core and specialized molecular tasks. These machines are in large part multi-subunit protein complexes that undergo regulation at multiple levels, from expression of requisite components to a vast array of post-translational modifications (PTMs). PTMs such as phosphorylation, ubiquitination, and acetylation currently number more than 200,000 in the human proteome and function within all molecular pathways. Here we provide a framework for systematically studying these PTMs in the context of global protein-protein interaction networks. This analytical framework allows insight into which functions specific PTMs tend to cluster in, and furthermore which complexes either single or multiple PTM signaling pathways converge on.

Predicted Functional Implications of Phosphorylation of Regulator of G Protein Signaling Protein in Plants

Heterotrimeric G proteins function in development, biotic, and abiotic stress responses, hormone signaling as well as sugar sensing. We previously proposed that discrimination of these various external signals in the G protein pathway is accomplished in plants by membrane-localized receptor-like kinases (RLKs) rather than G-protein-coupled receptors. Arabidopsis thaliana Regulator of G Signaling protein 1 (AtRGS1) modulates G protein activation and is phosphorylated by several RLKs and by WITH-NO-LYSINE kinases (WNKs). Here, a combination of in vitro kinase assays, mass spectrometry, and computational bioinformatics identified and functionally prioritized phosphorylation sites in AtRGS1. Phosphosites for two more RLKs (BRL3 and PEPR1) were identified and added to the AtRGS1 phosphorylation profile. Bioinformatics analyses revealed that RLKs and WNK kinases phosphorylate plant RGS proteins within regions that are conserved across eukaryotes and at a high frequency. Four phospho-sites among 14 identified are proximal to equivalent mammalian phosphosites that impact RGS function, including: pS437 and pT267 in GmRGS2, and pS339 and pS436 in AtRGS1. Based on these analyses, we propose that pS437 and pS436 regulate GmRGS2 and AtRGS1 protein interactions and/or localization, whereas pT267 is important for modulation of GmRGS2 GAP activity and localization. Moreover, pS339 most likely affects AtRGS1 activation.

Proteome-wide Structural Analysis of PTM Hotspots Reveals Regulatory Elements Predicted to Impact Biological Function and Disease

Post-translational modifications (PTMs) regulate protein behavior through modulation of protein-protein interactions, enzymatic activity, and protein stability essential in the translation of genotype to phenotype in eukaryotes. Currently, less than 4% of all eukaryotic PTMs are reported to have biological function - a statistic that continues to decrease with an increasing rate of PTM detection. Previously, we developed SAPH-ire (Structural Analysis of PTM Hotspots) - a method for the prioritization of PTM function potential that has been used effectively to reveal novel PTM regulatory elements in discrete protein families (Dewhurst et al., 2015). Here, we apply SAPH-ire to the set of eukaryotic protein families containing experimental PTM and 3D structure data - capturing 1,325 protein families with 50,839 unique PTM sites organized into 31,747 modified alignment positions (MAPs), of which 2010 (∼6%) possess known biological function. Here, we show that using an artificial neural network model (SAPH-ire NN) trained to identify MAP hotspots with biological function results in prediction outcomes that far surpass the use of single hotspot features, including nearest neighbor PTM clustering methods. We find the greatest enhancement in prediction for positions with PTM counts of five or less, which represent 98% of all MAPs in the eukaryotic proteome and 90% of all MAPs found to have biological function. Analysis of the top 1092 MAP hotspots revealed 267 of truly unknown function (containing 5443 distinct PTMs). Of these, 165 hotspots could be mapped to human KEGG pathways for normal and/or disease physiology. Many high-ranking hotspots were also found to be disease-associated pathogenic sites of amino acid substitution despite the lack of observable PTM in the human protein family member. Taken together, these experiments demonstrate that the functional relevance of a PTM can be predicted very effectively by neural network models, revealing a large but testable body of potential regulatory elements that impact hundreds of different biological processes important in eukaryotic biology and human health.

Glycosylation and other PTMs alterations in neurodegenerative diseases: Current status and future role in neurotrauma

Traumatic brain injuries (TBIs) present a chief public health threat affecting nations worldwide. As numbers of patients afflicted by TBI are expected to rise, the necessity to increase our understanding of the pathophysiological mechanism(s) as a result of TBI mounts. TBI is known to augment the risk of developing a number of neurodegenerative diseases (NDs) such as Alzheimer's disease (AD) and Parkinson's disease (PD). Hence, it is rational to assume that a common mechanistic ground links the pathophysiology of NDs to that of TBIs. Through this review, we aim to identify the protein-protein interactions, differential proteins expression, and PTMs, mainly glycosylation, that are involved in the pathogenesis of both ND and TBI. OVID and PubMed have been rigorously searched to identify studies that utilized advanced proteomic platforms (MS based) and systems biology tools to unfold the mechanism(s) behind ND in an attempt to unveil the mysterious biological processes that occur postinjury. Various PTMs have been found to be common between TBI and AD, whereas no similarities have been found between TBI and PD. Phosphorylated tau protein, glycosylated amyloid precursor protein, and many other modifications appear to be common in both TBI and AD. PTMs, differential protein profiles, and altered biological pathways appear to have critical roles in ND processes by interfering with their pathological condition in a manner similar to TBI. Advancement in glycoproteomic studies pertaining to ND and TBI is urgently needed in order to develop better diagnostic tools, therapies, and more favorable prognoses.

Chapter Two - Heterotrimeric G Protein Ubiquitination as a Regulator of G Protein Signaling

Ubiquitin-mediated regulation of G proteins has been known for over 20 years as a result of discoveries made independently in yeast and vertebrate model systems for pheromone and photoreception, respectively. Since that time, several details underlying the cause and effect of G protein ubiquitination have been determined-including the initiating signals, responsible enzymes, trafficking pathways, and their effects on protein structure, function, interactions, and cell signaling. The collective body of evidence suggests that Gα subunits are the primary targets of ubiquitination. However, longstanding and recent results suggest that Gβ and Gγ subunits are also ubiquitinated, in some cases impacting cell polarization-a process essential for chemotaxis and polarized cell growth. More recently, evidence from mass spectrometry (MS)-based proteomics coupled with advances in PTM bioinformatics have revealed that protein families representing G protein subunits contain several structural hotspots for ubiquitination-most of which have not been investigated for a functional role in signal transduction. Taken together, our knowledge and understanding of heterotrimeric G protein ubiquitination as a regulator of G protein signaling-despite 20 years of research-is still emerging.