Illuminating the dark phosphoproteome

Natural hydrogen gas and engineered microalgae prevent acute lung injury in sepsis

Background

Hydrogen gas and microalgae both exist in the natural environment. We aimed to integrate hydrogen gas and biology nano microalgae together to expand the treatment options in sepsis.

Methods

Phosphoproteomics, metabolomics and proteomics data were obtained from mice undergoing cecum ligation and puncture (CLP) and inhalation of hydrogen gas. All omics analysis procedure were accordance with standards. Multi R packages were used in single cell and spatial transcriptomics analysis to identify primary cells expressing targeted genes, and the genes' co-expression relationships in sepsis related lung landscape. Then, network pharmacology method was used to identify candidate drugs. We used hydrophobic-force-driving self-assembly method to construct dihydroquercetin (DQ) nanoparticle. To cooperate with molecular hydrogen, ammonia borane (B) was added to DQ surface. Then, Chlorella vulgaris (C) was used as biological carrier to improve self-assembly nanoparticle. Vivo and vitro experiments were both conducted to evaluate anti-inflammation, anti-ferroptosis, anti-infection and organ protection capability.

Results

As a result, we identified Esam and Zo-1 were target phosphorylation proteins for molecular hydrogen treatment in lung. Ferroptosis and glutathione metabolism were two target pathways. Chlorella vulgaris improved the dispersion of DQB and reconstructed morphological features of DQB, formed DQB@C nano-system (size = 307.3 nm, zeta potential = -22mv), with well infection-responsive hydrogen release capability and biosafety. In addition, DQB@C was able to decrease oxidative stress and inflammation factors accumulation in lung cells. Through increasing expression level of Slc7a11/xCT and decreasing Cox2 level to participate with the regulation of ferroptosis. Also, DQB@C played lung and multi organ protection and anti-inflammation roles on CLP mice.

Conclusion

Our research proposed DQB@C as a novel biology nano-system with enormous potential on treatment for sepsis related acute lung injury to solve the limitation of hydrogen gas utilization in clinics.

Cellular thermal shift assay: an approach to identify and assess protein target engagement

INTRODUCTION

A comprehensive and global knowledge of protein target engagement is of vital importance for mechanistic studies and in drug development. Since its initial introduction, the cellular thermal shift assay (CETSA) has proven to be a reliable and flexible technique that can be widely applied to multiple contexts and has profound applications in facilitating the identification and assessment of protein target engagement.

AREAS COVERED

This review introduces the principle of CETSA, elaborates on western blot-based CETSA and MS-based thermal proteome profiling (TPP) as well as the major applications and prospects of these approaches.

EXPERT OPINION

CETSA primarily evaluates a given ligand binding to a particular target protein in cells and tissues with the protein thermal stabilities analyzed by western blot. When coupling mass spectrometry with CETSA, thermal proteome profiling allows simultaneous proteome-wide experiment that greatly increased the efficiency of target engagement evaluation, and serves as a promising strategy to identify protein targets and off-targets as well as protein-protein interactions to uncover the biological effects. The CETSA approaches have broad applications and potentials in drug development and clinical research.

Luminescent lanthanide metallopeptides for biomolecule sensing and cellular imaging

Lanthanide ions display unique luminescent properties that make them particularly attractive for the development of bioprobes, including long-lived excited states that allow the implementation of time-gated experiments and the elimination of background fluorescence associated with biological media, as well as narrow emission bands in comparison with typical organic fluorophores, which allow ratiometric and multiplex assays. These luminescent complexes can be combined with peptide ligands to endow them with additional targeting, responsiveness, and selectivity, thus multiplying the opportunities for creative probe design. In this feature article we will present some of the main strategies that researchers have used to develop lanthanide metallopeptide probes for the detection of proteins and nucleic acids, as well as for monitoring enzymatic activity and cellular imaging.

Functional profiling of serine, threonine and tyrosine sites

Systematic perturbation of amino acids at endogenous loci provides diverse insights into protein function. Here, we performed a genome-wide screen to globally assess the cell fitness dependency of serine, threonine and tyrosine residues. Using an adenine base editor, we designed a whole-genome library comprising 817,089 single guide RNAs to perturb 584,337 S, T and Y sites. We identified 3,467 functional substitutions affecting cell fitness and 677 of them involving phosphorylation, including numerous phosphorylation-mediated gain-of-function substitutions that regulate phosphorylation levels of itself or downstream factors. Furthermore, our findings highlight that specific substitution types, notably serine to proline, are crucial for maintaining domain structure broadly. Lastly, we demonstrate that 309 enriched hits capable of initiating cell overproliferation might be potential cancer driver mutations. This study represents an extensive functional profiling of S, T and Y residues and provides insights into the distinctive roles of these amino acids in biological mechanisms and tumor progression.

"Phosphoproteomic quantification based on phosphopeptide intensity or occupancy? An evaluation based on casein kinase 2 downstream effects"

Quantitative phosphoproteomic data has mostly been reported from experiments comparing relative phosphopeptides intensities in two or more different conditions, while the ideal parameter to compare is phosphopeptides occupancies. This term is scarcely used and therefore barely implemented in phosphoproteomics studies, and this should be of concern for the scientific journals. In order to demonstrate the relevance of this issue, here we show how the method of choice affects the interpretation of the data. The phosphoproteomic profile modulated in two AML cell lines after CK2 inhibition with CIGB-300 or CX-4945 is shown. Following the downstream action of CK2 the phosphosite intensity and occupancy results were compared to validate the best approach for quantitative phosphoproteomic studies. Even when the total number of quantified phosphopeptides was higher by using the intensity calculation, in all the cases the percent of CK2 consensus sequences which were down-regulated in response to CK2 inhibition was higher using the phosphosite occupancy quantification. To note, a high number of CK2 consensus sequences was found down-regulated with at least a 10% or 15% of phosphosite occupancy variation illustrating that low thresholds of occupancy modulation might be indicative of biological effect. Additionally, several biological processes only appear significantly over-represented in the phosphoproteome quantified by occupancy. The functional enrichment analysis per ranges of occupancy variations also illustrated clear differences among AML cell lines subjected to CK2 inhibition by CX-4945. A low overlap between the phosphoproteomes quantified by intensity and occupancy was obtained illustrating that new developments in proteomics techniques are needed to improve the performance of the occupancy approach. Even in such context, results indicate that occupancy quantification performs better than phosphorylation quantification based on intensity reinforcing the importance of such quantification approach to describe phosphoproteomic data.

The fitness cost of spurious phosphorylation

The fidelity of signal transduction requires the binding of regulatory molecules to their cognate targets. However, the crowded cell interior risks off-target interactions between proteins that are functionally unrelated. How such off-target interactions impact fitness is not generally known. Here, we use Saccharomyces cerevisiae to inducibly express tyrosine kinases. Because yeast lacks bona fide tyrosine kinases, the resulting tyrosine phosphorylation is biologically spurious. We engineered 44 yeast strains each expressing a tyrosine kinase, and quantitatively analysed their phosphoproteomes. This analysis resulted in ~30,000 phosphosites mapping to ~3500 proteins. The number of spurious pY sites generated correlates strongly with decreased growth, and we predict over 1000 pY events to be deleterious. However, we also find that many of the spurious pY sites have a negligible effect on fitness, possibly because of their low stoichiometry. This result is consistent with our evolutionary analyses demonstrating a lack of phosphotyrosine counter-selection in species with tyrosine kinases. Our results suggest that, alongside the risk for toxicity, the cell can tolerate a large degree of non-functional crosstalk as interaction networks evolve.

Molecular biomarkers identification and applications in CHO bioprocessing

Biomarkers are valuable tools in clinical research where they allow to predict susceptibility to diseases, or response to specific treatments. Likewise, biomarkers can be extremely useful in the biomanufacturing of therapeutic proteins. Indeed, constraints such as short timelines and the need to find hyper-productive cells could benefit from a data-driven approach during cell line and process development. Many companies still rely on large screening capacities to develop productive cell lines, but as they reach a limit of production, there is a need to go from empirical to rationale procedures. Similarly, during bioprocessing runs, substrate consumption and metabolism wastes are commonly monitored. None of them possess the ability to predict the culture behavior in the bioreactor. Big data driven approaches are being adapted to the study of industrial mammalian cell lines, enabled by the publication of Chinese hamster and CHO genome assemblies which allowed the use of next-generation sequencing with these cells, as well as continuous proteome and metabolome annotation. However, if these different -omics technologies contributed to the characterization of CHO cells, there is a significant effort remaining to apply this knowledge to biomanufacturing methods. The correlation of a complex phenotype such as high productivity or rapid growth to the presence or expression level of a specific biomarker could save time and effort in the screening of manufacturing cell lines or culture conditions. In this review we will first discuss the different biological molecules that can be identified and quantified in cells, their detection techniques, and associated challenges. We will then review how these markers are used during the different steps of cell line and bioprocess development, and the inherent limitations of this strategy.

ERK synchronizes embryonic cleavages in Drosophila

Extracellular-signal-regulated kinase (ERK) signaling controls development and homeostasis and is genetically deregulated in human diseases, including neurocognitive disorders and cancers. Although the list of ERK functions is vast and steadily growing, the full spectrum of processes controlled by any specific ERK activation event remains unknown. Here, we show how ERK functions can be systematically identified using targeted perturbations and global readouts of ERK activation. Our experimental model is the Drosophila embryo, where ERK signaling at the embryonic poles has thus far only been associated with the transcriptional patterning of the future larva. Through a combination of live imaging and phosphoproteomics, we demonstrated that ERK activation at the poles is also critical for maintaining the speed and synchrony of embryonic cleavages. The presented approach to interrogating phosphorylation networks identifies a hidden function of a well-studied signaling event and sets the stage for similar studies in other organisms.

Current and future directions in network biology

Summary

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology.

Availability and implementation

Not applicable.

Deep PIM kinase substrate profiling reveals new rational cotherapeutic strategies for acute myeloid leukemia

ABSTRACT

Provirus integration site for Moloney murine leukemia virus (PIM) family serine/threonine kinases perform protumorigenic functions in hematologic malignancies and solid tumors by phosphorylating substrates involved in tumor metabolism, cell survival, metastasis, inflammation, and immune cell invasion. However, a comprehensive understanding of PIM kinase functions is currently lacking. Multiple small-molecule PIM kinase inhibitors are currently being evaluated as cotherapeutics in patients with cancer. To further illuminate PIM kinase functions in cancer, we deeply profiled PIM1 substrates using the reverse in-gel kinase assay to identify downstream cellular processes targetable with small molecules. Pathway analyses of putative PIM substrates nominated RNA splicing and ribosomal RNA (rRNA) processing as PIM-regulated cellular processes. PIM inhibition elicited reproducible splicing changes in PIM-inhibitor-responsive acute myeloid leukemia (AML) cell lines. PIM inhibitors synergized with splicing modulators targeting splicing factor 3b subunit 1 (SF3B1) and serine-arginine protein kinase 1 (SRPK1) to kill AML cells. PIM inhibition also altered rRNA processing, and PIM inhibitors synergized with an RNA polymerase I inhibitor to kill AML cells and block AML tumor growth. These data demonstrate that deep kinase substrate knowledge can illuminate unappreciated kinase functions, nominating synergistic cotherapeutic strategies. This approach may expand the cotherapeutic armamentarium to overcome kinase inhibitor-resistant disease that limits durable responses in malignant disease.

Novel CDKL5 targets identified in human iPSC-derived neurons

CDKL5 Deficiency Disorder (CDD) is a debilitating epileptic encephalopathy disorder affecting young children with no effective treatments. CDD is caused by pathogenic variants in Cyclin-Dependent Kinase-Like 5 (CDKL5), a protein kinase that regulates key phosphorylation events in neurons. For therapeutic intervention, it is essential to understand molecular pathways and phosphorylation targets of CDKL5. Using an unbiased phosphoproteomic approach we identified novel targets of CDKL5, including GTF2I, PPP1R35, GATAD2A and ZNF219 in human iPSC-derived neuronal cells. The phosphoserine residue in the target proteins lies in the CDKL5 consensus motif. We validated direct phosphorylation of GTF2I and PPP1R35 by CDKL5 using complementary approaches. GTF2I controls axon guidance, cell cycle and neurodevelopment by regulating expression of neuronal genes. PPP1R35 is critical for centriole elongation and cilia morphology, processes that are impaired in CDD. PPP1R35 interacts with CEP131, a known CDKL5 phospho-target. GATAD2A and ZNF219 belong to the Nucleosome Remodelling Deacetylase (NuRD) complex, which regulates neuronal activity-dependent genes and synaptic connectivity. In-depth knowledge of molecular pathways regulated by CDKL5 will allow a better understanding of druggable disease pathways to fast-track therapeutic development.

A Rapid One-Pot Workflow for Sensitive Microscale Phosphoproteomics

Compared to advancements in single-cell proteomics, phosphoproteomics sensitivity has lagged behind due to low abundance, complex sample preparation, and substantial sample input requirements. We present a simple and rapid one-pot phosphoproteomics workflow (SOP-Phos) integrated with data-independent acquisition mass spectrometry (DIA-MS) for microscale phosphoproteomic analysis. SOP-Phos adapts sodium deoxycholate based one-step lysis, reduction/alkylation, direct trypsinization, and phosphopeptide enrichment by TiO2 beads in a single-tube format. By reducing surface adsorptive losses via utilizing n-dodecyl β-d-maltoside precoated tubes and shortening the digestion time, SOP-Phos is completed within 3-4 h with a 1.4-fold higher identification coverage. SOP-Phos coupled with DIA demonstrated >90% specificity, enhanced sensitivity, lower missing values (<1%), and improved reproducibility (8%-10% CV). With a sample size-comparable spectral library, SOP-Phos-DIA identified 33,787 ± 670 to 22,070 ± 861 phosphopeptides from 5 to 0.5 μg cell lysate and 30,433 ± 284 to 6,548 ± 21 phosphopeptides from 50,000 to 2,500 cells. Such sensitivity enabled mapping key lung cancer signaling sites, such as EGFR autophosphorylation sites Y1197/Y1172 and drug targets. The feasibility of SOP-Phos-DIA was demonstrated on EGFR-TKI sensitive and resistant cells, revealing the interplay of multipathway Hippo-EGFR-ERBB signaling cascades underlying the mechanistic insight into EGFR-TKI resistance. Overall, SOP-Phos-DIA is an efficient and robust protocol that can be easily adapted in the community for microscale phosphoproteomic analysis.

Molecular causality in the advent of foundation models

Correlation is not causation: this simple and uncontroversial statement has far-reaching implications. Defining and applying causality in biomedical research has posed significant challenges to the scientific community. In this perspective, we attempt to connect the partly disparate fields of systems biology, causal reasoning, and machine learning to inform future approaches in the field of systems biology and molecular medicine.

µPhos: a scalable and sensitive platform for high-dimensional phosphoproteomics

Mass spectrometry has revolutionized cell signaling research by vastly simplifying the analysis of many thousands of phosphorylation sites in the human proteome. Defining the cellular response to perturbations is crucial for further illuminating the functionality of the phosphoproteome. Here we describe µPhos ('microPhos'), an accessible phosphoproteomics platform that permits phosphopeptide enrichment from 96-well cell culture and small tissue amounts in <8 h total processing time. By greatly minimizing transfer steps and liquid volumes, we demonstrate increased sensitivity, >90% selectivity, and excellent quantitative reproducibility. Employing highly sensitive trapped ion mobility mass spectrometry, we quantify ~17,000 Class I phosphosites in a human cancer cell line using 20 µg starting material, and confidently localize ~6200 phosphosites from 1 µg. This depth covers key signaling pathways, rendering sample-limited applications and perturbation experiments with hundreds of samples viable. We employ µPhos to study drug- and time-dependent response signatures in a leukemia cell line, and by quantifying 30,000 Class I phosphosites in the mouse brain we reveal distinct spatial kinase activities in subregions of the hippocampal formation.

Unravelling G protein-coupled receptor signalling networks using global phosphoproteomics

G protein-coupled receptor (GPCR) activation initiates signalling via a complex network of intracellular effectors that combine to produce diverse cellular and tissue responses. Although we have an advanced understanding of the proximal events following receptor stimulation, the molecular detail of GPCR signalling further downstream often remains obscure. Unravelling these GPCR-mediated signalling networks has important implications for receptor biology and drug discovery. In this context, phosphoproteomics has emerged as a powerful approach for investigating global GPCR signal transduction. Here, we provide a brief overview of the phosphoproteomic workflow and discuss current limitations and future directions for this technology. By highlighting some of the novel insights into GPCR signalling networks gained using phosphoproteomics, we demonstrate the utility of global phosphoproteomics to dissect GPCR signalling networks and to accelerate discovery of new targets for therapeutic development. LINKED ARTICLES: This article is part of a themed issue Therapeutic Targeting of G Protein-Coupled Receptors: hot topics from the Australasian Society of Clinical and Experimental Pharmacologists and Toxicologists 2021 Virtual Annual Scientific Meeting. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v181.14/issuetoc.

Inferring kinase activity from phosphoproteomic data: Tool comparison and recent applications

Aberrant cellular signaling pathways are a hallmark of cancer and other diseases. One of the most important signaling mechanisms involves protein phosphorylation/dephosphorylation. Protein phosphorylation is catalyzed by protein kinases, and over 530 protein kinases have been identified in the human genome. Aberrant kinase activity is one of the drivers of tumorigenesis and cancer progression and results in altered phosphorylation abundance of downstream substrates. Upstream kinase activity can be inferred from the global collection of phosphorylated substrates. Mass spectrometry-based phosphoproteomic experiments nowadays routinely allow identification and quantitation of >10k phosphosites per biological sample. This substrate phosphorylation footprint can be used to infer upstream kinase activities using tools like Kinase Substrate Enrichment Analysis (KSEA), Posttranslational Modification Substrate Enrichment Analysis (PTM-SEA), and Integrative Inferred Kinase Activity Analysis (INKA). Since the topic of kinase activity inference is very active with many new approaches reported in the past 3 years, we would like to give an overview of the field. In this review, an inventory of kinase activity inference tools, their underlying algorithms, statistical frameworks, kinase-substrate databases, and user-friendliness is presented. The most widely-used tools are compared in-depth. Subsequently, recent applications of the tools are described focusing on clinical tissues and hematological samples. Two main application areas for kinase activity inference tools can be discerned. (1) Maximal biological insights can be obtained from large data sets with group comparisons using multiple complementary tools (e.g., PTM-SEA and KSEA or INKA). (2) In the oncology context where personalized treatment requires analysis of single samples, INKA for example, has emerged as tool that can prioritize actionable kinases for targeted inhibition.

The substrate quality of CK2 target sites has a determinant role on their function and evolution

Most biological processes are regulated by signaling modules that bind to short linear motifs. For protein kinases, substrates may have full or only partial matches to the kinase recognition motif, a property known as "substrate quality." However, it is not clear whether differences in substrate quality represent neutral variation or if they have functional consequences. We examine this question for the kinase CK2, which has many fundamental functions. We show that optimal CK2 sites are phosphorylated at maximal stoichiometries and found in many conditions, whereas minimal substrates are more weakly phosphorylated and have regulatory functions. Optimal CK2 sites tend to be more conserved, and substrate quality is often tuned by selection. For intermediate sites, increases or decreases in substrate quality may be deleterious, as we demonstrate for a CK2 substrate at the kinetochore. The results together suggest a strong role for substrate quality in phosphosite function and evolution. A record of this paper's transparent peer review process is included in the supplemental information.

Post-translational modification-centric base editor screens to assess phosphorylation site functionality in high throughput

Signaling pathways that drive gene expression are typically depicted as having a dozen or so landmark phosphorylation and transcriptional events. In reality, thousands of dynamic post-translational modifications (PTMs) orchestrate nearly every cellular function, and we lack technologies to find causal links between these vast biochemical pathways and genetic circuits at scale. Here we describe the high-throughput, functional assessment of phosphorylation sites through the development of PTM-centric base editing coupled to phenotypic screens, directed by temporally resolved phosphoproteomics. Using T cell activation as a model, we observe hundreds of unstudied phosphorylation sites that modulate NFAT transcriptional activity. We identify the phosphorylation-mediated nuclear localization of PHLPP1, which promotes NFAT but inhibits NFκB activity. We also find that specific phosphosite mutants can alter gene expression in subtle yet distinct patterns, demonstrating the potential for fine-tuning transcriptional responses. Overall, base editor screening of PTM sites provides a powerful platform to dissect PTM function within signaling pathways.

Proline-directed yeast and human MAP kinases phosphorylate the Dot1p/DOT1L histone H3K79 methyltransferase

Disruptor of telomeric silencing 1 (Dot1p) is an exquisitely conserved histone methyltransferase and is the sole enzyme responsible for H3K79 methylation in the budding yeast Saccharomyces cerevisiae. It has been shown to be highly phosphorylated in vivo; however, the upstream kinases that act on Dot1p are almost entirely unknown in yeast and all other eukaryotes. Here, we used in vitro and in vivo kinase discovery approaches to show that mitogen-activated protein kinase HOG1 (Hog1p) is a bona fide kinase of the Dot1p methyltransferase. In vitro kinase assays showed that Hog1p phosphorylates Dot1p at multiple sites, including at several proline-adjacent sites that are consistent with known Hog1p substrate preferences. The activity of Hog1p was specifically enhanced at these proline-adjacent sites on Dot1p upon Hog1p activation by the osmostress-responsive MAP kinase kinase PBS2 (Pbs2p). Genomic deletion of HOG1 reduced phosphorylation at specific sites on Dot1p in vivo, providing further evidence for Hog1p kinase activity on Dot1p in budding yeast cells. Phenotypic analysis of knockout and phosphosite mutant yeast strains revealed the importance of Hog1p-catalysed phosphorylation of Dot1p for cellular responses to ultraviolet-induced DNA damage. In mammalian systems, this kinase-substrate relationship was found to be conserved: human DOT1L (the ortholog of yeast Dot1p) can be phosphorylated by the proline-directed kinase p38β (also known as MAPK11; the ortholog of yeast Hog1p) at multiple sites in vitro. Taken together, our findings establish Hog1p and p38β as newly identified upstream kinases of the Dot1p/DOT1L H3K79 methyltransferase enzymes in eukaryotes.

Genetic Encoding of Phosphorylated Amino Acids into Proteins

Reversible phosphorylation is a fundamental mechanism for controlling protein function. Despite the critical roles phosphorylated proteins play in physiology and disease, our ability to study individual phospho-proteoforms has been hindered by a lack of versatile methods to efficiently generate homogeneous proteins with site-specific phosphoamino acids or with functional mimics that are resistant to phosphatases. Genetic code expansion (GCE) is emerging as a transformative approach to tackle this challenge, allowing direct incorporation of phosphoamino acids into proteins during translation in response to amber stop codons. This genetic programming of phospho-protein synthesis eliminates the reliance on kinase-based or chemical semisynthesis approaches, making it broadly applicable to diverse phospho-proteoforms. In this comprehensive review, we provide a brief introduction to GCE and trace the development of existing GCE technologies for installing phosphoserine, phosphothreonine, phosphotyrosine, and their mimics, discussing both their advantages as well as their limitations. While some of the technologies are still early in their development, others are already robust enough to greatly expand the range of biologically relevant questions that can be addressed. We highlight new discoveries enabled by these GCE approaches, provide practical considerations for the application of technologies by non-GCE experts, and also identify avenues ripe for further development.

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.

The intrinsic substrate specificity of the human tyrosine kinome

Phosphorylation of proteins on tyrosine (Tyr) residues evolved in metazoan organisms as a mechanism of coordinating tissue growth1. Multicellular eukaryotes typically have more than 50 distinct protein Tyr kinases that catalyse the phosphorylation of thousands of Tyr residues throughout the proteome1-3. How a given Tyr kinase can phosphorylate a specific subset of proteins at unique Tyr sites is only partially understood4-7. Here we used combinatorial peptide arrays to profile the substrate sequence specificity of all human Tyr kinases. Globally, the Tyr kinases demonstrate considerable diversity in optimal patterns of residues surrounding the site of phosphorylation, revealing the functional organization of the human Tyr kinome by substrate motif preference. Using this information, Tyr kinases that are most compatible with phosphorylating any Tyr site can be identified. Analysis of mass spectrometry phosphoproteomic datasets using this compendium of kinase specificities accurately identifies specific Tyr kinases that are dysregulated in cells after stimulation with growth factors, treatment with anti-cancer drugs or expression of oncogenic variants. Furthermore, the topology of known Tyr signalling networks naturally emerged from a comparison of the sequence specificities of the Tyr kinases and the SH2 phosphotyrosine (pTyr)-binding domains. Finally we show that the intrinsic substrate specificity of Tyr kinases has remained fundamentally unchanged from worms to humans, suggesting that the fidelity between Tyr kinases and their protein substrate sequences has been maintained across hundreds of millions of years of evolution.

PhosMap: An ensemble bioinformatic platform to empower interactive analysis of quantitative phosphoproteomics

BACKGROUND

Liquid chromatography-mass spectrometry (LC-MS)-based quantitative phosphoproteomics has been widely used to detect thousands of protein phosphorylation modifications simultaneously from the biological specimens. However, the complicated procedures for analyzing phosphoproteomics data has become a bottleneck to widening its application.

METHODS

Here, we develop PhosMap, a versatile and scalable tool to accomplish phosphoproteomics data analysis. A standardized phosphorylation data format was created for data analyses, from data preprocessing to downstream bioinformatic analyses such as dimension reduction, differential phosphorylation analysis, kinase activity, survival analysis, and so on. For better usability, we distribute PhosMap as a Docker image for easy local deployment upon any of Windows, Linux, and Mac system.

RESULTS

The source code is deposited at https://github.com/BADD-XMU/PhosMap. A free PhosMap webserver (https://huggingface.co/spaces/Bio-Add/PhosMap), with easy-to-follow fashion of dashboards, is curated for interactive data analysis.

CONCLUSIONS

PhosMap fills the technical gap of large-scale phosphorylation research by empowering researchers to process their own phosphoproteomics data expediently and efficiently, and facilitates better data interpretation.

Multiomics of early epileptogenesis in mice reveals phosphorylation and dephosphorylation-directed growth and synaptic weakening

To investigate the phosphorylation-based signaling and protein changes occurring early in epileptogenesis, the hippocampi of mice treated with pilocarpine were examined by quantitative mass spectrometry at 4 and 24 h post-status epilepticus at vast depth. Hundreds of posttranscriptional regulatory proteins were the major early targets of increased phosphorylation. At 24 h, many protein level changes were detected and the phosphoproteome continued to be perturbed. The major targets of decreased phosphorylation at 4 and 24 h were a subset of postsynaptic density scaffold proteins, ion channels, and neurotransmitter receptors. Many proteins targeted by dephosphorylation at 4 h also had decreased protein abundance at 24 h, indicating a phosphatase-mediated weakening of synapses. Increased translation was indicated by protein changes at 24 h. These observations, and many additional indicators within this multiomic resource, suggest that early epileptogenesis is characterized by signaling that stimulates both growth and a homeostatic response that weakens excitability.

Multilevel Regulation of Membrane Proteins in Response to Metal and Metalloid Stress: A Lesson from Yeast

In the face of flourishing industrialization and global trade, heavy metal and metalloid contamination of the environment is a growing concern throughout the world. The widespread presence of highly toxic compounds of arsenic, antimony, and cadmium in nature poses a particular threat to human health. Prolonged exposure to these toxins has been associated with severe human diseases, including cancer, diabetes, and neurodegenerative disorders. These toxins are known to induce analogous cellular stresses, such as DNA damage, disturbance of redox homeostasis, and proteotoxicity. To overcome these threats and improve or devise treatment methods, it is crucial to understand the mechanisms of cellular detoxification in metal and metalloid stress. Membrane proteins are key cellular components involved in the uptake, vacuolar/lysosomal sequestration, and efflux of these compounds; thus, deciphering the multilevel regulation of these proteins is of the utmost importance. In this review, we summarize data on the mechanisms of arsenic, antimony, and cadmium detoxification in the context of membrane proteome. We used yeast Saccharomyces cerevisiae as a eukaryotic model to elucidate the complex mechanisms of the production, regulation, and degradation of selected membrane transporters under metal(loid)-induced stress conditions. Additionally, we present data on orthologues membrane proteins involved in metal(loid)-associated diseases in humans.

Spectra without stories: reporting 94% dark and unidentified ancient proteomes

Background

Data-dependent, bottom-up proteomics is widely used for identifying proteins and peptides. However, one key challenge is that 70% of fragment ion spectra consistently fail to be assigned by conventional database searching. This 'dark matter' of bottom-up proteomics seems to affect fields where non-model organisms, low-abundance proteins, non-tryptic peptides, and complex modifications may be present. While palaeoproteomics may appear as a niche field, understanding and reporting unidentified ancient spectra require collaborative innovation in bioinformatics strategies. This may advance the analysis of complex datasets.

Methods

14.97 million high-impact ancient spectra published in Nature and Science portfolios were mined from public repositories. Identification rates, defined as the proportion of assigned fragment ion spectra, were collected as part of deposited database search outputs or parsed using open-source python packages.

Results and Conclusions

We report that typically 94% of the published ancient spectra remain unidentified. This phenomenon may be caused by multiple factors, notably the limitations of database searching and the selection of user-defined reference data with advanced modification patterns. These 'spectra without stories' highlight the need for widespread data sharing to facilitate methodological development and minimise the loss of often irreplaceable ancient materials. Testing and validating alternative search strategies, such as open searching and de novo sequencing, may also improve overall identification rates. Hence, lessons learnt in palaeoproteomics may benefit other fields grappling with challenging data.

Pathogenic mutations of human phosphorylation sites affect protein-protein interactions

Despite their lack of a defined 3D structure, intrinsically disordered regions (IDRs) of proteins play important biological roles. Many IDRs contain short linear motifs (SLiMs) that mediate protein-protein interactions (PPIs), which can be regulated by post-translational modifications like phosphorylation. 20% of pathogenic missense mutations are found in IDRs, and understanding how such mutations affect PPIs is essential for unraveling disease mechanisms. Here, we employ peptide-based interaction proteomics to investigate 36 disease-associated mutations affecting phosphorylation sites. Our results unveil significant differences in interactomes between phosphorylated and non-phosphorylated peptides, often due to disrupted phosphorylation-dependent SLiMs. We focused on a mutation of a serine phosphorylation site in the transcription factor GATAD1, which causes dilated cardiomyopathy. We find that this phosphorylation site mediates interaction with 14-3-3 family proteins. Follow-up experiments reveal the structural basis of this interaction and suggest that 14-3-3 binding affects GATAD1 nucleocytoplasmic transport by masking a nuclear localisation signal. Our results demonstrate that pathogenic mutations of human phosphorylation sites can significantly impact protein-protein interactions, offering insights into potential molecular mechanisms underlying pathogenesis.

Illuminating the Dark Cancer Phosphoproteome Through a Machine-Learned Co-Regulation Map of 26,280 Phosphosites

Mass spectrometry-based phosphoproteomics offers a comprehensive view of protein phosphorylation, but limited knowledge about the regulation and function of most phosphosites restricts our ability to extract meaningful biological insights from phosphoproteomics data. To address this, we combine machine learning and phosphoproteomic data from 1,195 tumor specimens spanning 11 cancer types to construct CoPheeMap, a network mapping the co-regulation of 26,280 phosphosites. Integrating network features from CoPheeMap into a machine learning model, CoPheeKSA, we achieve superior performance in predicting kinase-substrate associations. CoPheeKSA reveals 24,015 associations between 9,399 phosphosites and 104 serine/threonine kinases, including many unannotated phosphosites and under-studied kinases. We validate the accuracy of these predictions using experimentally determined kinase-substrate specificities. By applying CoPheeMap and CoPheeKSA to phosphosites with high computationally predicted functional significance and cancer-associated phosphosites, we demonstrate the effectiveness of these tools in systematically illuminating phosphosites of interest, revealing dysregulated signaling processes in human cancer, and identifying under-studied kinases as putative therapeutic targets.

An artificial protein modulator reprogramming neuronal protein functions

Reversible protein phosphorylation, regulated by protein phosphatases, fine-tunes target protein function and plays a vital role in biological processes. Dysregulation of this process leads to aberrant post-translational modifications (PTMs) and contributes to disease development. Despite the widespread use of artificial catalysts as enzyme mimetics, their direct modulation of proteins remains largely unexplored. To address this gap and enable the reversal of aberrant PTMs for disease therapy, we present the development of artificial protein modulators (APROMs). Through atomic-level engineering of heterogeneous catalysts with asymmetric catalytic centers, these modulators bear structural similarities to protein phosphatases and exhibit remarkable ability to destabilize the bridging μ3-hydroxide. This activation of catalytic centers enables spontaneous hydrolysis of phospho-substrates, providing precise control over PTMs. Notably, APROMs, with protein phosphatase-like characteristics, catalytically reprogram the biological function of α-synuclein by directly hydrolyzing hyperphosphorylated α-synuclein. Consequently, synaptic function is reinforced in Parkinson's disease. Our findings offer a promising avenue for reprogramming protein function through de novo PTMs strategy.

Taking Me away: the function of phosphorylation on histone lysine demethylases

Histone lysine demethylases (KDMs) regulate eukaryotic gene transcription by catalysing the removal of methyl groups from histone proteins. These enzymes are intricately regulated by the kinase signalling system in response to internal and external stimuli. Here, we review the mechanisms by which kinase-mediated phosphorylation influence human histone KDM function. These include the changing of histone KDM subcellular localisation or chromatin binding, the altering of protein half-life, changes to histone KDM complex formation that result in histone demethylation, non-histone demethylation or demethylase-independent effects, and effects on histone KDM complex dissociation. We also explore the structural context of phospho-sites on histone KDMs and evaluate how this relates to function.

The genetic and dietary landscape of the muscle insulin signalling network

Metabolic disease is caused by a combination of genetic and environmental factors, yet few studies have examined how these factors influence signal transduction, a key mediator of metabolism. Using mass spectrometry-based phosphoproteomics, we quantified 23,126 phosphosites in skeletal muscle of five genetically distinct mouse strains in two dietary environments, with and without acute in vivo insulin stimulation. Almost half of the insulin-regulated phosphoproteome was modified by genetic background on an ordinary diet, and high-fat high-sugar feeding affected insulin signalling in a strain-dependent manner. Our data revealed coregulated subnetworks within the insulin signalling pathway, expanding our understanding of the pathway's organisation. Furthermore, associating diverse signalling responses with insulin-stimulated glucose uptake uncovered regulators of muscle insulin responsiveness, including the regulatory phosphosite S469 on Pfkfb2, a key activator of glycolysis. Finally, we confirmed the role of glycolysis in modulating insulin action in insulin resistance. Our results underscore the significance of genetics in shaping global signalling responses and their adaptability to environmental changes, emphasising the utility of studying biological diversity with phosphoproteomics to discover key regulatory mechanisms of complex traits.

Dual-ligand hydrogen-bonded organic framework: Tailored for mono-phosphopeptides and glycopeptides analysis

Hydrogen-bonded organic frameworks (HOFs) have emerged as a promising class of materials for applications of separation and enrichment. Utilizing multiple-ligands to construct HOFs is a promising avenue towards the development of structurally stable and functionally diverse frameworks, offering opportunities to create customized binding sites for selective recognition of biomolecules. In recent years, due to the crucial role that protein post-translational modifications (PTMs) play in maintaining protein function and regulating signaling pathways, and the growing recognition of the extensive cross-talk that can occur between PTMs, simultaneous analysis of different types of PTMs represents a requirement of a new generation of enrichment materials. Here, for the first attempt, we report a dual-ligand HOF constructed from borate anion and guanidinium cation for the simultaneous identification of glycopeptides and phosphopeptides, especially mono-phosphopeptides. According to theoretical calculations, the HOF functional sites display a synergistic "matching" effect with mono-phosphopeptides, resulting in a stronger enrichment effect for mono-phosphopeptides as compared to multi-phosphopeptides. Also, due to its high hydrophilicity and boronate affinity, this material can efficiently capture glycoproteins. HOF is set to become an active research direction in the development of highly efficient simultaneous protein enrichment materials, and offers a new approach for comprehensive PTMs analysis.

Systematic Identification of Kinase-Substrate Relationship by Integrated Phosphoproteome and Interactome Analysis

The sensitivity of phosphorylation site identification by mass spectrometry (MS)-based phosphoproteomics has improved significantly. However, the lack of kinase-substrate relationship (KSR) data has hindered improvement of the range and accuracy of kinase activity prediction using phosphoproteome data. We herein describe the application of a systematic identification of KSR by integrated phosphoproteome and interactome analysis using doxycycline (Dox)-induced target kinase-overexpressing HEK-293 cells.

A split-luciferase lysate-based approach to identify small-molecule modulators of phosphatase subunit interactions

An emerging strategy for the therapeutic targeting of protein phosphatases involves the use of compounds that interfere with the binding of regulatory subunits or substrates. However, high-throughput screening strategies for such interfering molecules are scarce. Here, we report on the conversion of the NanoBiT split-luciferase system into a robust assay for the quantification of phosphatase subunit and substrate interactions in cell lysates. The assay is suitable to screen small-molecule libraries for interfering compounds. We designed and validated split-luciferase sensors for a broad range of PP1 and PP2A holoenzymes, including sensors that selectively report on weak interaction sites. To facilitate efficient hit triaging in large-scale screening campaigns, deselection procedures were developed to eliminate assay-interfering molecules with high fidelity. As a proof-of-principle, we successfully applied the split-luciferase screening tool to identify small-molecule disruptors of the interaction between the C-terminus of PP1β and the ankyrin-repeat domain of the myosin-phosphatase targeting subunit MYPT1.

SnapKin: a snapshot deep learning ensemble for kinase-substrate prediction from phosphoproteomics data

A major challenge in mass spectrometry-based phosphoproteomics lies in identifying the substrates of kinases, as currently only a small fraction of substrates identified can be confidently linked with a known kinase. Machine learning techniques are promising approaches for leveraging large-scale phosphoproteomics data to computationally predict substrates of kinases. However, the small number of experimentally validated kinase substrates (true positive) and the high data noise in many phosphoproteomics datasets together limit their applicability and utility. Here, we aim to develop advanced kinase-substrate prediction methods to address these challenges. Using a collection of seven large phosphoproteomics datasets, and both traditional and deep learning models, we first demonstrate that a 'pseudo-positive' learning strategy for alleviating small sample size is effective at improving model predictive performance. We next show that a data resampling-based ensemble learning strategy is useful for improving model stability while further enhancing prediction. Lastly, we introduce an ensemble deep learning model ('SnapKin') by incorporating the above two learning strategies into a 'snapshot' ensemble learning algorithm. We propose SnapKin, an ensemble deep learning method, for predicting substrates of kinases from large-scale phosphoproteomics data. We demonstrate that SnapKin consistently outperforms existing methods in kinase-substrate prediction. SnapKin is freely available at https://github.com/PYangLab/SnapKin.

Proteome-wide base editor screens to assess phosphorylation site functionality in high-throughput

Signaling pathways that drive gene expression are typically depicted as having a dozen or so landmark phosphorylation and transcriptional events. In reality, thousands of dynamic post-translational modifications (PTMs) orchestrate nearly every cellular function, and we lack technologies to find causal links between these vast biochemical pathways and genetic circuits at scale. Here, we describe "signaling-to-transcription network" mapping through the development of PTM-centric base editing coupled to phenotypic screens, directed by temporally-resolved phosphoproteomics. Using T cell activation as a model, we observe hundreds of unstudied phosphorylation sites that modulate NFAT transcriptional activity. We identify the phosphorylation-mediated nuclear localization of the phosphatase PHLPP1 which promotes NFAT but inhibits NFκB activity. We also find that specific phosphosite mutants can alter gene expression in subtle yet distinct patterns, demonstrating the potential for fine-tuning transcriptional responses. Overall, base editor screening of PTM sites provides a powerful platform to dissect PTM function within signaling pathways.

Loss of succinyl-CoA synthetase in mouse forebrain results in hypersuccinylation with perturbed neuronal transcription and metabolism

Lysine succinylation is a subtype of protein acylation associated with metabolic regulation of succinyl-CoA in the tricarboxylic acid cycle. Deficiency of succinyl-CoA synthetase (SCS), the tricarboxylic acid cycle enzyme catalyzing the interconversion of succinyl-CoA to succinate, results in mitochondrial encephalomyopathy in humans. This report presents a conditional forebrain-specific knockout (KO) mouse model of Sucla2, the gene encoding the ATP-specific beta isoform of SCS, resulting in postnatal deficiency of the entire SCS complex. Results demonstrate that accumulation of succinyl-CoA in the absence of SCS leads to hypersuccinylation within the murine cerebral cortex. Specifically, increased succinylation is associated with functionally significant reduced activity of respiratory chain complex I and widescale alterations in chromatin landscape and gene expression. Integrative analysis of the transcriptomic data also reveals perturbations in regulatory networks of neuronal transcription in the KO forebrain. Together, these findings provide evidence that protein succinylation plays a significant role in the pathogenesis of SCS deficiency.

The fitness cost of spurious phosphorylation

The fidelity of signal transduction requires the binding of regulatory molecules to their cognate targets. However, the crowded cell interior risks off-target interactions between proteins that are functionally unrelated. How such off-target interactions impact fitness is not generally known, but quantifying this is required to understand the constraints faced by cell systems as they evolve. Here, we use the model organism S. cerevisiae to inducibly express tyrosine kinases. Because yeast lacks bona fide tyrosine kinases, most of the resulting tyrosine phosphorylation is spurious. This provides a suitable system to measure the impact of artificial protein interactions on fitness. We engineered 44 yeast strains each expressing a tyrosine kinase, and quantitatively analysed their phosphoproteomes. This analysis resulted in ~30,000 phosphosites mapping to ~3,500 proteins. Examination of the fitness costs in each strain revealed a strong correlation between the number of spurious pY sites and decreased growth. Moreover, the analysis of pY effects on protein structure and on protein function revealed over 1000 pY events that we predict to be deleterious. However, we also find that a large number of the spurious pY sites have a negligible effect on fitness, possibly because of their low stoichiometry. This result is consistent with our evolutionary analyses demonstrating a lack of phosphotyrosine counter-selection in species with bona fide tyrosine kinases. Taken together, our results suggest that, alongside the risk for toxicity, the cell can tolerate a large degree of non-functional crosstalk as interaction networks evolve.

Phosphoproteome reveals long-term potentiation deficit following treatment of ultra-low dose soman exposure in mice

Soman, a warfare nerve agent, poses a significant threat by inducing severe brain damage that often results in death. Nonetheless, our understanding of the biological changes underlying persistent neurocognitive dysfunction caused by low dosage of soman remains limited. This study used mice to examine the effects of different doses of soman over time. Phosphoproteomic analysis of the mouse brain is the first time to be used to detect toxic effects of soman at such low or ultra-low doses, which were undetectable based on measuring the activity of acetylcholinesterase at the whole-animal level. We also found that phosphoproteome alterations could accurately track the soman dose, irrespective of the sampling time. Moreover, phosphoproteome revealed a rapid and adaptive cellular response to soman exposure, with the points of departure 8-38 times lower than that of acetylcholinesterase activity. Impaired long-term potentiation was identified in phosphoproteomic studies, which was further validated by targeted quantitative proteomics, immunohistochemistry, and immunofluorescence analyses, with significantly increased levels of phosphorylation of protein phosphatase 1 in the hippocampus following soman exposure. This increase in phosphorylation inhibits long-term potentiation, ultimately leading to long-term memory dysfunction in mice.

Validity of the cell-extracted proteome as a substrate pool for exploring phosphorylation motifs of kinases

Three representative protein kinases with different substrate preferences, ERK1 (Pro-directed), CK2 (acidophilic), and PKA (basophilic), were used to investigate phosphorylation sequence motifs in substrate pools consisting of the proteomes from three different cell lines, MCF7 (human mammary carcinoma), HeLa (human cervical carcinoma), and Jurkat (human acute T-cell leukemia). Specifically, recombinant kinases were added to the cell-extracted proteomes to phosphorylate the substrates in vitro. After trypsin digestion, the phosphopeptides were enriched and subjected to nanoLC/MS/MS analysis to identify their phosphorylation sites on a large scale. By analyzing the obtained phosphorylation sites and their surrounding sequences, phosphorylation motifs were extracted for each kinase-substrate proteome pair. We found that each kinase exhibited the same set of phosphorylation motifs, independently of the substrate pool proteome. Furthermore, the identified motifs were also consistent with those found using a completely randomized peptide library. These results indicate that cell-extracted proteomes can provide kinase phosphorylation motifs with sufficient accuracy, even though their sequences are not completely random, supporting the robustness of phosphorylation motif identification based on phosphoproteome analysis of cell extracts as a substrate pool for a kinase of interest.

A proteogenomics data-driven knowledge base of human cancer

By combining mass-spectrometry-based proteomics and phosphoproteomics with genomics, epi-genomics, and transcriptomics, proteogenomics provides comprehensive molecular characterization of cancer. Using this approach, the Clinical Proteomic Tumor Analysis Consortium (CPTAC) has characterized over 1,000 primary tumors spanning 10 cancer types, many with matched normal tissues. Here, we present LinkedOmicsKB, a proteogenomics data-driven knowledge base that makes consistently processed and systematically precomputed CPTAC pan-cancer proteogenomics data available to the public through ∼40,000 gene-, protein-, mutation-, and phenotype-centric web pages. Visualization techniques facilitate efficient exploration and reasoning of complex, interconnected data. Using three case studies, we illustrate the practical utility of LinkedOmicsKB in providing new insights into genes, phosphorylation sites, somatic mutations, and cancer phenotypes. With precomputed results of 19,701 coding genes, 125,969 phosphosites, and 256 genotypes and phenotypes, LinkedOmicsKB provides a comprehensive resource to accelerate proteogenomics data-driven discoveries to improve our understanding and treatment of human cancer. A record of this paper's transparent peer review process is included in the supplemental information.

Exploring Temporal and Sex-Linked Dysregulation in Alzheimer's Disease Phospho-Proteome

This study aims to characterize dysregulation of phosphorylation for the 5XFAD mouse model of Alzheimer's disease (AD). Employing global phosphoproteome measurements, we analyze temporal (3, 6, 9 months) and sex-dependent effects on mouse hippocampus tissue to unveil molecular signatures associated with AD initiation and progression. Our results indicate 1.9 to 4.4 times higher phosphorylation prevalence compared to protein expression across all time points, with approximately 4.5 times greater prevalence in females compared to males at 3 and 9 months. Moreover, our findings reveal consistent phosphorylation of known AD biomarkers APOE and GFAP in 5XFAD mice, alongside novel candidates BIG3, CLCN6 and STX7, suggesting their potential as biomarkers for AD pathology. In addition, we identify PDK1 as a significantly dysregulated kinase at 9 months in females, and the regulation of gap junction activity as a key pathway associated with Alzheimer's disease across all time points. AD-Xplorer, the interactive browser of our dataset, enables exploration of AD-related changes in phosphorylation, protein expression, kinase activities, and pathways. AD-Xplorer aids in biomarker discovery and therapeutic target identification, emphasizing temporal and sex-specific nature of significant phosphoproteomic signatures. Available at: https://yilmazs.shinyapps.io/ADXplorer.

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

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

De Novo Linear Phosphorylation Site Motifs for BCR-ABL Kinase Revealed by Phospho-Proteomics in Yeast

BCR-ABL is the oncogenic fusion product of tyrosine kinase ABL1 and a highly frequent driver of acute lymphocytic leukemia (ALL) and chronic myeloid leukemia (CML). The kinase activity of BCR-ABL is strongly elevated; however, changes of substrate specificity in comparison to wild-type ABL1 kinase are less well characterized. Here, we heterologously expressed full-length BCR-ABL kinases in yeast. We exploited the proteome of living yeast as an in vivo phospho-tyrosine substrate for assaying human kinase specificity. Phospho-proteomic analysis of ABL1 and BCR-ABL isoforms p190 and p210 yielded a high-confidence data set of 1127 phospho-tyrosine sites on 821 yeast proteins. We used this data set to generate linear phosphorylation site motifs for ABL1 and the oncogenic ABL1 fusion proteins. The oncogenic kinases yielded a substantially different linear motif when compared to ABL1. Kinase set enrichment analysis with human pY-sites that have high linear motif scores well-recalled BCR-ABL driven cancer cell lines from human phospho-proteome data sets.

A basic phosphoproteomic-DIA workflow integrating precise quantification of phosphosites in systems biology

Phosphorylation is one of the most important post-translational modifications (PTMs) of proteins, governing critical protein functions. Most human proteins have been shown to undergo phosphorylation, and phosphoproteomic studies have been widely applied due to recent advancements in high-resolution mass spectrometry technology. Although the experimental workflow for phosphoproteomics has been well-established, it would be useful to optimize and summarize a detailed, feasible protocol that combines phosphoproteomics and data-independent acquisition (DIA), along with follow-up data analysis procedures due to the recent instrumental and bioinformatic advances in measuring and understanding tens of thousands of site-specific phosphorylation events in a single experiment. Here, we describe an optimized Phos-DIA protocol, from sample preparation to bioinformatic analysis, along with practical considerations and experimental configurations for each step. The protocol is designed to be robust and applicable for both small-scale phosphoproteomic analysis and large-scale quantification of hundreds of samples for studies in systems biology and systems medicine.

Recent advances in kinase signaling network profiling by mass spectrometry

Mass spectrometry-based phosphoproteomics is currently the leading methodology for the study of global kinase signaling. The scientific community is continuously releasing technological improvements for sensitive and fast identification of phosphopeptides, and their accurate quantification. To interpret large-scale phosphoproteomics data, numerous bioinformatic resources are available that help understanding kinase network functional role in biological systems upon perturbation. Some of these resources are databases of phosphorylation sites, protein kinases and phosphatases; others are bioinformatic algorithms to infer kinase activity, predict phosphosite functional relevance and visualize kinase signaling networks. In this review, we present the latest experimental and bioinformatic tools to profile protein kinase signaling networks and provide examples of their application in biomedicine.

Principles of phosphoproteomics and applications in cancer research

Phosphorylation constitutes the most common and best-studied regulatory post-translational modification in biological systems and archetypal signalling pathways driven by protein and lipid kinases are disrupted in essentially all cancer types. Thus, the study of the phosphoproteome stands to provide unique biological information on signalling pathway activity and on kinase network circuitry that is not captured by genetic or transcriptomic technologies. Here, we discuss the methods and tools used in phosphoproteomics and highlight how this technique has been used, and can be used in the future, for cancer research. Challenges still exist in mass spectrometry phosphoproteomics and in the software required to provide biological information from these datasets. Nevertheless, improvements in mass spectrometers with enhanced scan rates, separation capabilities and sensitivity, in biochemical methods for sample preparation and in computational pipelines are enabling an increasingly deep analysis of the phosphoproteome, where previous bottlenecks in data acquisition, processing and interpretation are being relieved. These powerful hardware and algorithmic innovations are not only providing exciting new mechanistic insights into tumour biology, from where new drug targets may be derived, but are also leading to the discovery of phosphoproteins as mediators of drug sensitivity and resistance and as classifiers of disease subtypes. These studies are, therefore, uncovering phosphoproteins as a new generation of disruptive biomarkers to improve personalised anti-cancer therapies.

MIND-S is a deep-learning prediction model for elucidating protein post-translational modifications in human diseases

We present a deep-learning-based platform, MIND-S, for protein post-translational modification (PTM) predictions. MIND-S employs a multi-head attention and graph neural network and assembles a 15-fold ensemble model in a multi-label strategy to enable simultaneous prediction of multiple PTMs with high performance and computation efficiency. MIND-S also features an interpretation module, which provides the relevance of each amino acid for making the predictions and is validated with known motifs. The interpretation module also captures PTM patterns without any supervision. Furthermore, MIND-S enables examination of mutation effects on PTMs. We document a workflow, its applications to 26 types of PTMs of two datasets consisting of ∼50,000 proteins, and an example of MIND-S identifying a PTM-interrupting SNP with validation from biological data. We also include use case analyses of targeted proteins. Taken together, we have demonstrated that MIND-S is accurate, interpretable, and efficient to elucidate PTM-relevant biological processes in health and diseases.