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

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.

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.

Sequence-based machine learning method for predicting the effects of phosphorylation on protein-protein interactions

Protein phosphorylation, catalyzed by kinases, is an important biochemical process, which plays an essential role in multiple cell signaling pathways. Meanwhile, protein-protein interactions (PPI) constitute the signaling pathways. Abnormal phosphorylation status on protein can regulate protein functions through PPI to evoke severe diseases, such as Cancer and Alzheimer's disease. Due to the limited experimental evidence and high costs to experimentally identify novel evidence of phosphorylation regulation on PPI, it is necessary to develop a high-accuracy and user-friendly artificial intelligence method to predict phosphorylation effect on PPI. Here, we proposed a novel sequence-based machine learning method named PhosPPI, which achieved better identification performance (Accuracy and AUC) than other competing predictive methods of Betts, HawkDock and FoldX. PhosPPI is now freely available in web server (https://phosppi.sjtu.edu.cn/). This tool can help the user to identify functional phosphorylation sites affecting PPI and explore phosphorylation-associated disease mechanism and drug development.

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.

Protein Lipidation Types: Current Strategies for Enrichment and Characterization

Post-translational modifications regulate diverse activities of a colossal number of proteins. For example, various types of lipids can be covalently linked to proteins enzymatically or non-enzymatically. Protein lipidation is perhaps not as extensively studied as protein phosphorylation, ubiquitination, or glycosylation although it is no less significant than these modifications. Evidence suggests that proteins can be attached by at least seven types of lipids, including fatty acids, lipoic acids, isoprenoids, sterols, phospholipids, glycosylphosphatidylinositol anchors, and lipid-derived electrophiles. In this review, we summarize types of protein lipidation and methods used for their detection, with an emphasis on the conjugation of proteins with polyunsaturated fatty acids (PUFAs). We discuss possible reasons for the scarcity of reports on PUFA-modified proteins, limitations in current methodology, and potential approaches in detecting PUFA modifications.

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.

Structure-based prediction of post-translational modification cross-talk within proteins using complementary residue- and residue pair-based features

Post-translational modification (PTM)-based regulation can be mediated not only by the modification of a single residue but also by the interplay of different modifications. Accurate prediction of PTM cross-talk is a highly challenging issue and is in its infant stage. Especially, less attention has been paid to the structural preferences (except intrinsic disorder and spatial proximity) of cross-talk pairs and the characteristics of individual residues involved in cross-talk, which may restrict the improvement of the prediction accuracy. Here we report a structure-based algorithm called PCTpred to improve the PTM cross-talk prediction. The comprehensive residue- and residue pair-based features were designed for paired PTM sites at the sequence and structural levels. Through feature selection, we reserved 23 newly introduced descriptors and 3 traditional descriptors to develop a sequence-based predictor PCTseq and a structure-based predictor PCTstr, both of which were integrated to construct our final prediction model. According to pair- and protein-based evaluations, PCTpred yielded area under the curve values of approximately 0.9 and 0.8, respectively. Even when removing the distance preference of samples or using the input of modeled structures, our prediction performance was maintained or moderately reduced. PCTpred displayed stable and reliable improvements over the state-of-the-art methods based on various evaluations. The source code and data set are freely available at https://github.com/Liulab-HZAU/PCTpred or http://liulab.hzau.edu.cn/PCTpred/.

Dynamic posttranslational modifications of cytoskeletal proteins unveil hot spots under nitroxidative stress

The cytoskeleton is a supramolecular structure consisting of interacting protein networks that support cell dynamics in essential processes such as migration and division, as well as in responses to stress. Fast cytoskeletal remodeling is achieved with the participation of regulatory proteins and posttranslational modifications (PTMs). Redox-related PTMs are emerging as critical players in cytoskeletal regulation. Here we used a cellular model of mild nitroxidative stress in which a peroxynitrite donor induced transient changes in the organization of three key cytoskeletal proteins, i.e., vimentin, actin and tubulin. Nitroxidative stress-induced reconfiguration of intermediate filaments, microtubules and actin structures were further correlated with their PTM profiles and dynamics of the PTM landscape. Using high-resolution mass spectrometry, 62 different PTMs were identified and relatively quantified in vimentin, actin and tubulin, including 12 enzymatic, 13 oxidative and 2 nitric oxide-derived modifications as well as 35 modifications by carbonylated lipid peroxidation products, thus evidencing the occurrence of a chain reaction with formation of numerous reactive species and activation of multiple signaling pathways. Our results unveil the presence of certain modifications under basal conditions and their modulation in response to stress in a target-, residue- and reactive species-dependent manner. Thus, some modifications accumulated during the experiment whereas others varied transiently. Moreover, we identified protein PTM "hot spots", such as the single cysteine residue of vimentin, which was detected in seven modified forms, thus, supporting its role in PTM crosstalk and redox sensing. Finally, identification of novel PTMs in these proteins paves the way for unveiling new cytoskeleton regulatory mechanisms.

Protein acetylation: a novel modus of obesity regulation

Obesity is a chronic epidemic disease worldwide which has become one of the important public health issues. It is a process that excessive accumulation of adipose tissue caused by long-term energy intake exceeding energy expenditure. So far, the prevention and treatment strategies of obesity on individuals and population have not been successful in the long term. Acetylation is one of the most common ways of protein post-translational modification (PTM). It exists on thousands of non-histone proteins in almost every cell chamber. It has many influences on protein levels and metabolome levels, which is involved in a variety of metabolic reactions, including sugar metabolism, tricarboxylic acid cycle, and fatty acid metabolism, which are closely related to biological activities. Studies have shown that protein acetylation levels are dynamically regulated by lysine acetyltransferases (KATs) and lysine deacetylases (KDACs). Protein acetylation modifies protein-protein and protein-DNA interactions and regulates the activity of enzymes or cytokines which is related to obesity in order to participate in the occurrence and treatment of obesity-related metabolic diseases. Therefore, we speculated that acetylation was likely to become effective means of controlling obesity in the future. In consequence, this review focuses on the mechanisms of protein acetylation controlled obesity, to provide theoretical basis for controlling obesity and curing obesity-related diseases, which is a significance for regulating obesity in the future. This review will focus on the role of protein acetylation in controlling obesity.

ProteoSushi: A Software Tool to Biologically Annotate and Quantify Modification-Specific, Peptide-Centric Proteomics Data Sets

Large-scale proteomic profiling of protein post-translational modifications has provided important insights into the regulation of cell signaling and disease. These modification-specific proteomics workflows nearly universally enrich modified peptides prior to mass spectrometry analysis, but protein-centric proteomic software tools have many limitations evaluating and interpreting these peptide-centric data sets. We, therefore, developed ProteoSushi, a software tool tailored to analysis of each modified site in peptide-centric proteomic data sets that is compatible with any post-translational modification or chemical label. ProteoSushi uses a unique approach to assign identified peptides to shared proteins and genes, minimizing redundancy by prioritizing shared assignments based on UniProt annotation score and optional user-supplied protein/gene lists. ProteoSushi simplifies quantitation by summing or averaging intensities for each modified site, merging overlapping peptide charge states, missed cleavages, spectral matches, and variable modifications into a single value. ProteoSushi also annotates each PTM site with the most up-to-date biological information available from UniProt, such as functional roles or known modifications, the protein domain in which the site resides, the protein's subcellular location and function, and more. ProteoSushi has a graphical user interface for ease of use. ProteoSushi's flexibility and combination of analysis features streamlines peptide-centric data processing and knowledge mining of large modification-specific proteomics data sets.

Discovering the landscape of protein modifications

Protein modifications modulate nearly every aspect of cell biology in organisms, ranging from Archaea to Eukaryotes. The earliest evidence of covalent protein modifications was found in the early 20th century by studying the amino acid composition of proteins by chemical hydrolysis. These discoveries challenged what defined a canonical amino acid. The advent and rapid adoption of mass-spectrometry-based proteomics in the latter part of the 20th century enabled a veritable explosion in the number of known protein modifications, with more than 500 discrete modifications counted today. Now, new computational tools in data science, machine learning, and artificial intelligence are poised to allow researchers to make significant progress in discovering new protein modifications and determining their function. In this review, we take an opportunity to revisit the historical discovery of key post-translational modifications, quantify the current landscape of covalent protein adducts, and assess the role that new computational tools will play in the future of this field.

The functional landscape of the human phosphoproteome

Protein phosphorylation is a key post-translational modification regulating protein function in almost all cellular processes. Although tens of thousands of phosphorylation sites have been identified in human cells, approaches to determine the functional importance of each phosphosite are lacking. Here, we manually curated 112 datasets of phospho-enriched proteins, generated from 104 different human cell types or tissues. We re-analyzed the 6,801 proteomics experiments that passed our quality control criteria, creating a reference phosphoproteome containing 119,809 human phosphosites. To prioritize functional sites, we used machine learning to identify 59 features indicative of proteomic, structural, regulatory or evolutionary relevance and integrate them into a single functional score. Our approach identifies regulatory phosphosites across different molecular mechanisms, processes and diseases, and reveals genetic susceptibilities at a genomic scale. Several regulatory phosphosites were experimentally validated, including identifying a role in neuronal differentiation for phosphosites in SMARCC2, a member of the SWI/SNF chromatin-remodeling complex.

Systematic analysis of linker histone PTM hotspots reveals phosphorylation sites that modulate homologous recombination and DSB repair

Double strand-breaks (DSBs) of genomic DNA caused by ionizing radiation or mutagenic chemicals are a common source of mutation, recombination, chromosomal aberration, and cell death. Linker histones are DNA packaging proteins with established roles in chromatin compaction, gene transcription, and in homologous recombination (HR)-mediated DNA repair. Using a machine-learning model for functional prioritization of eukaryotic post-translational modifications (PTMs) in combination with genetic and biochemical experiments with the yeast linker histone, Hho1, we discovered that site-specific phosphorylation sites regulate HR and HR-mediated DSB repair. Five total sites were investigated (T10, S65, S141, S173, and S174), ranging from high to low function potential as determined by the model. Of these, we confirmed S173/174 are phosphorylated in yeast by mass spectrometry and found no evidence of phosphorylation at the other sites. Phospho-nullifying mutations at these two sites results in a significant decrease in HR-mediated DSB repair templated either with oligonucleotides or a homologous chromosome, while phospho-mimicing mutations have no effect. S65, corresponding to a mammalian phosphosite that is conserved in yeast, exhibited similar effects. None of the mutations affected base- or nucleotide-excision repair, nor did they disrupt non-homologous end joining or RNA-mediated repair of DSBs when sequence heterology between the break and repair template strands was low. More extensive analysis of the S174 phospho-null mutant revealed that its repression of HR and DSB repair is proportional to the degree of sequence heterology between DSB ends and the HR repair template. Taken together, these data demonstrate the utility of machine learning for the discovery of functional PTM hotspots, reveal linker histone phosphorylation sites necessary for HR and HR-mediated DSB repair, and provide insight into the context-dependent control of DNA integrity by the yeast linker histone Hho1.

Genetic heterogeneity within collective invasion packs drives leader and follower cell phenotypes

Collective invasion, the coordinated movement of cohesive packs of cells, has become recognized as a major mode of metastasis for solid tumors. These packs are phenotypically heterogeneous and include specialized cells that lead the invasive pack and others that follow behind. To better understand how these unique cell types cooperate to facilitate collective invasion, we analyzed transcriptomic sequence variation between leader and follower populations isolated from the H1299 non-small cell lung cancer cell line using an image-guided selection technique. We now identify 14 expressed mutations that are selectively enriched in leader or follower cells, suggesting a novel link between genomic and phenotypic heterogeneity within a collectively invading tumor cell population. Functional characterization of two phenotype-specific candidate mutations showed that ARP3 enhances collective invasion by promoting the leader cell phenotype and that wild-type KDM5B suppresses chain-like cooperative behavior. These results demonstrate an important role for distinct genetic variants in establishing leader and follower phenotypes and highlight the necessity of maintaining a capacity for phenotypic plasticity during collective cancer invasion.

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.

Vps11 and Vps18 of Vps-C membrane traffic complexes are E3 ubiquitin ligases and fine-tune signalling

In response to extracellular signals, many signalling proteins associated with the plasma membrane are sorted into endosomes. This involves endosomal fusion, which depends on the complexes HOPS and CORVET. Whether and how their subunits themselves modulate signal transduction is unknown. We show that Vps11 and Vps18 (Vps11/18), two common subunits of the HOPS/CORVET complexes, are E3 ubiquitin ligases. Upon overexpression of Vps11/Vps18, we find perturbations of ubiquitination in signal transduction pathways. We specifically demonstrate that Vps11/18 regulate several signalling factors and pathways, including Wnt, estrogen receptor α (ERα), and NFκB. For ERα, we demonstrate that the Vps11/18-mediated ubiquitination of the scaffold protein PELP1 impairs the activation of ERα by c-Src. Thus, proteins involved in membrane traffic, in addition to performing their well-described role in endosomal fusion, fine-tune signalling in several different ways, including through ubiquitination.

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.

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.

Posttranslational Modifications of Pyruvate Kinase M2: Tweaks that Benefit Cancer

Cancer cells rewire metabolism to meet biosynthetic and energetic demands. The characteristic increase in glycolysis, i.e., Warburg effect, now considered as a hallmark, supports cancer in various ways. To attain such metabolic reshuffle, cancer cells preferentially re-express the M2 isoform of pyruvate kinase (PKM2, M2-PK) and alter its quaternary structure to generate less-active PKM2 dimers. The relatively inactive dimers cause the accumulation of glycolytic intermediates that are redirected into anabolic pathways. In addition, dimeric PKM2 also benefits cancer cells through various non-glycolytic moonlight functions, such as gene transcription, protein kinase activity, and redox balance. A large body of data have shown that several distinct posttranslation modifications (PTMs) regulate PKM2 in a way that benefits cancer growth, e.g., formation of PKM2 dimers. This review discusses the recent advancements in our understanding of various PTMs and the benefits they impart to the sustenance of cancer. Understanding the PTMs in PKM2 is crucial to assess their therapeutic potential and to design novel anticancer strategies.

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.

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.