A multi-purpose, regenerable, proteome-scale, human phosphoserine resource for phosphoproteomics

Profiling Proteins and Phosphorylation Sites During T Cell Activation Using an Integrated Thermal Shift Assay

T cell activation is a complex biological process of naïve cells maturing into effector cells. Proteomic and phospho-proteomic approaches have provided critical insights into this process, yet it is not always clear how changes in individual proteins or phosphorylation sites have functional significance. Here, we developed the Phosphorylation Integrated Thermal Shift Assay (PITSA) that combines the measurement of protein or phosphorylation site abundance and thermal stability into a single TMT experiment and apply this method to study T cell activation. We quantified the abundance and thermal stability of over 7,500 proteins and 5,000 phosphorylation sites, and identified significant differences in chromatin-related, TCR signaling, DNA repair, and proliferative phosphoproteins. PITSA may be applied to a wide range of biological contexts to generate hypotheses as to which proteins or phosphorylation sites are functionally regulated in a given system, as well as the mechanisms by which this regulation may occur.

The ribotoxic stress response drives UV-mediated cell death

While ultraviolet (UV) radiation damages DNA, eliciting the DNA damage response (DDR), it also damages RNA, triggering transcriptome-wide ribosomal collisions and eliciting a ribotoxic stress response (RSR). However, the relative contributions, timing, and regulation of these pathways in determining cell fate is unclear. Here we use time-resolved phosphoproteomic, chemical-genetic, single-cell imaging, and biochemical approaches to create a chronological atlas of signaling events activated in cells responding to UV damage. We discover that UV-induced apoptosis is mediated by the RSR kinase ZAK and not through the DDR. We identify two negative-feedback modules that regulate ZAK-mediated apoptosis: (1) GCN2 activation limits ribosomal collisions and attenuates ZAK-mediated RSR and (2) ZAK activity leads to phosphodegron autophosphorylation and its subsequent degradation. These events tune ZAK's activity to collision levels to establish regimes of homeostasis, tolerance, and death, revealing its key role as the cellular sentinel for nucleic acid damage.

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.

Inserting Pre-analytical Chromatographic Priming Runs Significantly Improves Targeted Pathway Proteomics with Sample Multiplexing

GoDig, a platform for targeted pathway proteomics without the need for manual assay scheduling or synthetic standards, is a powerful, flexible, and easy-to-use method that uses tandem mass tags to increase sample throughput up to 18-fold relative to label-free methods. Though the protein-level success rates of GoDig are high, the peptide-level success rates are more limited, hampering assays of harder-to-quantify proteins and site-specific phenomena. To guide the optimization of GoDig assays as well as improvements to the GoDig platform, we created GoDigViewer, a new stand-alone software that provides detailed visualizations of GoDig runs. GoDigViewer guided the implementation of "priming runs," an acquisition mode with significantly higher success rates. In this mode, two or more chromatographic priming runs are automatically performed to improve the accuracy and precision of target elution orders, followed by analytical runs which quantify targets. Using priming runs, success rates exceeded 97% for a list of 400 peptide targets and 95% for a list of 200 targets that are usually not quantified using untargeted mass spectrometry. We used priming runs to establish a quantitative assay of 125 macroautophagy proteins that had a >95% success rate and revealed differences in macroautophagy expression profiles across four human cell lines.

Computational approaches to identify sites of phosphorylation

Due to their oftentimes ambiguous nature, phosphopeptide positional isomers can present challenges in bottom-up mass spectrometry-based workflows as search engine scores alone are often not enough to confidently distinguish them. Additional scoring algorithms can remedy this by providing confidence metrics in addition to these search results, reducing ambiguity. Here we describe challenges to interpreting phosphoproteomics data and review several different approaches to determine sites of phosphorylation for both data-dependent and data-independent acquisition-based workflows. Finally, we discuss open questions regarding neutral losses, gas-phase rearrangement, and false localization rate estimation experienced by both types of acquisition workflows and best practices for managing ambiguity in phosphosite determination.

Accelerating multiplexed profiling of protein-ligand interactions: High-throughput plate-based reactive cysteine profiling with minimal input

Chemoproteomics has made significant progress in investigating small-molecule-protein interactions. However, the proteome-wide profiling of cysteine ligandability remains challenging to adapt for high-throughput applications, primarily due to a lack of platforms capable of achieving the desired depth using low input in 96- or 384-well plates. Here, we introduce a revamped, plate-based platform which enables routine interrogation of either ∼18,000 or ∼24,000 reactive cysteines based on starting amounts of 10 or 20 μg, respectively. This represents a 5-10X reduction in input and 2-3X improved coverage. We applied the platform to screen 192 electrophiles in the native HEK293T proteome, mapping the ligandability of 38,450 reactive cysteines from 8,274 human proteins. We further applied the platform to characterize new cellular targets of established drugs, uncovering that ARS-1620, a KRASG12C inhibitor, binds to and inhibits an off-target adenosine kinase ADK. The platform represents a major step forward to high-throughput proteome-wide evaluation of reactive cysteines.

Inserting Pre-Analytical Chromatographic Priming Runs Significantly Improves Targeted Pathway Proteomics With Sample Multiplexing

GoDig, a recent platform for targeted pathway proteomics without the need for manual assay scheduling or synthetic standard peptides, is a relatively flexible and easy-to-use method that uses tandem mass tags (TMT) to increase sample throughput up to 18-fold relative to label-free targeted proteomics. Though the protein quantification success rate of GoDig is generally high, the peptide-level success rate is more limited, hampering the extension of GoDig to assays of harder-to-quantify proteins and site-specific phenomena. In order to guide the optimization of GoDig assays as well as improvements to the GoDig platform, we created GoDigViewer, a new stand-alone software that provides detailed visualizations of GoDig runs. GoDigViewer guided the implementation of "priming runs," an acquisition mode with significantly higher success rates due to improved elution order calibration. In this mode, one or more chromatographic priming runs are automatically performed to determine accurate and precise target elution orders, followed by analytical runs which quantify targets. Using priming runs, peptide-level quantification success rates exceeded 97% for a list of 400 peptide targets and 95% for a list of 200 targets that are usually not quantified using untargeted mass spectrometry. We used priming runs to establish a quantitative assay of 125 macroautophagy proteins that had a >95% success rate and revealed differences in macroautophagy protein expression profiles across four human cell lines.

NAK-associated protein 1/NAP1 activates TBK1 to ensure accurate mitosis and cytokinesis

Subcellular location and activation of Tank Binding Kinase 1 (TBK1) govern precise progression through mitosis. Either loss of activated TBK1 or its sequestration from the centrosomes causes errors in mitosis and growth defects. Yet, what regulates its recruitment and activation on the centrosomes is unknown. We identified that NAK-associated protein 1 (NAP1) is essential for mitosis, binding to and activating TBK1, which both localize to centrosomes. Loss of NAP1 causes several mitotic and cytokinetic defects due to inactivation of TBK1. Our quantitative phosphoproteomics identified numerous TBK1 substrates that are not only confined to the centrosomes but are also associated with microtubules. Substrate motifs analysis indicates that TBK1 acts upstream of other essential cell cycle kinases like Aurora and PAK kinases. We also identified NAP1 as a TBK1 substrate phosphorylating NAP1 at S318 to promote its degradation by the ubiquitin proteasomal system. These data uncover an important distinct function for the NAP1-TBK1 complex during cell division.

Covalent inhibition of pro-apoptotic BAX

BCL-2-associated X protein (BAX) is a promising therapeutic target for activating or restraining apoptosis in diseases of pathologic cell survival or cell death, respectively. In response to cellular stress, BAX transforms from a quiescent cytosolic monomer into a toxic oligomer that permeabilizes the mitochondria, releasing key apoptogenic factors. The mitochondrial lipid trans-2-hexadecenal (t-2-hex) sensitizes BAX activation by covalent derivatization of cysteine 126 (C126). In this study, we performed a disulfide tethering screen to discover C126-reactive molecules that modulate BAX activity. We identified covalent BAX inhibitor 1 (CBI1) as a compound that selectively derivatizes BAX at C126 and inhibits BAX activation by triggering ligands or point mutagenesis. Biochemical and structural analyses revealed that CBI1 can inhibit BAX by a dual mechanism of action: conformational constraint and competitive blockade of lipidation. These data inform a pharmacologic strategy for suppressing apoptosis in diseases of unwanted cell death by covalent targeting of BAX C126.

Mapping the proteogenomic landscape enables prediction of drug response in acute myeloid leukemia

Acute myeloid leukemia is a poor-prognosis cancer commonly stratified by genetic aberrations, but these mutations are often heterogeneous and fail to consistently predict therapeutic response. Here, we combine transcriptomic, proteomic, and phosphoproteomic datasets with ex vivo drug sensitivity data to help understand the underlying pathophysiology of AML beyond mutations. We measure the proteome and phosphoproteome of 210 patients and combine them with genomic and transcriptomic measurements to identify four proteogenomic subtypes that complement existing genetic subtypes. We build a predictor to classify samples into subtypes and map them to a "landscape" that identifies specific drug response patterns. We then build a drug response prediction model to identify drugs that target distinct subtypes and validate our findings on cell lines representing various stages of quizartinib resistance. Our results show how multiomics data together with drug sensitivity data can inform therapy stratification and drug combinations in AML.

Phosphorylation Sites of the Gastric Inhibitory Polypeptide Receptor (GIPR) Revealed by Trapped-Ion-Mobility Spectrometry Coupled to Time-of-Flight Mass Spectrometry (TIMS-TOF MS)

The gastric inhibitory polypeptide receptor (GIPR), a G protein-coupled receptor (GPCR) that regulates glucose metabolism and insulin secretion, is a target for the development of therapeutic agents to address type 2 diabetes and obesity. Signal transduction processes mediated by GPCR activation typically result in receptor phosphorylation, but very little is known about GIPR phosphorylation. Mass spectrometry (MS) is a powerful tool for detecting phosphorylation and other post-translational modifications of proteins and for identifying modification sites. However, applying MS methods to GPCRs is challenging because the native expression levels are low and the hydrophobicity of these proteins complicates isolation and enrichment. Here we use a widely available technique, trapped-ion-mobility spectrometry coupled to time-of-flight mass spectrometry (TIMS-TOF MS), to characterize the phosphorylation status of the GIPR. We identified eight serine residues that are phosphorylated, one in an intracellular loop and the remainder in the C-terminal domain. Stimulation with the native agonist GIP enhanced phosphorylation at four of these sites. For comparison, we evaluated tirzepatide (TZP), a dual agonist of the glucagon-like peptide-1 (GLP-1) receptor and the GIPR that has recently been approved for the treatment of type 2 diabetes. Stimulation with TZP enhanced phosphorylation at the same four sites that were enhanced with GIP; however, TZP also enhanced phosphorylation at a fifth site that is unique to this synthetic agonist. This work establishes an important and accessible tool for the characterization of signal transduction via the GIPR and reveals an unanticipated functional difference between GIP and TZP.

The anaphase-promoting complex controls a ubiquitination-phosphoprotein axis in chromatin during neurodevelopment

Mutations in the degradative ubiquitin ligase anaphase-promoting complex (APC) alter neurodevelopment by impairing proteasomal protein clearance, but our understanding of their molecular and cellular pathogenesis remains limited. Here, we employ the proteomic-based discovery of APC substrates in APC mutant mouse brain and human cell lines and identify the chromosome-passenger complex (CPC), topoisomerase 2a (Top2a), and Ki-67 as major chromatin factors targeted by the APC during neuronal differentiation. These substrates accumulate in phosphorylated form, suggesting that they fail to be eliminated after mitosis during terminal differentiation. The accumulation of the CPC kinase Aurora B within constitutive heterochromatin and hyperphosphorylation of its target histone 3 are corrected in the mutant brain by pharmacologic Aurora B inhibition. Surprisingly, the reduction of Ki-67, but not H3S10ph, rescued the function of constitutive heterochromatin in APC mutant neurons. These results expand our understanding of how ubiquitin signaling regulates chromatin during neurodevelopment and identify potential therapeutic targets in APC-related disorders.

A Multipathway Phosphopeptide Standard for Rapid Phosphoproteomics Assay Development

Recent advances in methodology have made phosphopeptide analysis a tractable problem for many proteomics researchers. There are now a wide variety of robust and accessible enrichment strategies to generate phosphoproteomes while free or inexpensive software tools for quantitation and site localization have simplified phosphoproteome analysis workflow tremendously. As a research group under the Association for Biomolecular Resource Facilities umbrella, the Proteomics Standards Research Group has worked to develop a multipathway phosphopeptide standard based on a mixture of heavy-labeled phosphopeptides designed to enable researchers to rapidly develop assays. This mixture contains 131 mass spectrometry vetted phosphopeptides specifically chosen to cover as many known biologically interesting phosphosites as possible from seven different signaling networks: AMPK signaling, death and apoptosis signaling, ErbB signaling, insulin/insulin-like growth factor-1 signaling, mTOR signaling, PI3K/AKT signaling, and stress (p38/SAPK/JNK) signaling. Here, we describe a characterization of this mixture spiked into a HeLa tryptic digest stimulated with both epidermal growth factor and insulin-like growth factor-1 to activate the MAPK and PI3K/AKT/mTOR pathways. We further demonstrate a comparison of phosphoproteomic profiling of HeLa performed independently in five labs using this phosphopeptide mixture with data-independent acquisition. Despite different experimental and instrumentation processes, we found that labs could produce reproducible, harmonized datasets by reporting measurements as ratios to the standard, while intensity measurements showed lower consistency between labs even after normalization. Our results suggest that widely available, biologically relevant phosphopeptide standards can act as a quantitative "yardstick" across laboratories and sample preparations enabling experimental designs larger than a single laboratory can perform. Raw data files are publicly available in the MassIVE dataset MSV000090564.

DeepFLR facilitates false localization rate control in phosphoproteomics

Protein phosphorylation is a post-translational modification crucial for many cellular processes and protein functions. Accurate identification and quantification of protein phosphosites at the proteome-wide level are challenging, not least because efficient tools for protein phosphosite false localization rate (FLR) control are lacking. Here, we propose DeepFLR, a deep learning-based framework for controlling the FLR in phosphoproteomics. DeepFLR includes a phosphopeptide tandem mass spectrum (MS/MS) prediction module based on deep learning and an FLR assessment module based on a target-decoy approach. DeepFLR improves the accuracy of phosphopeptide MS/MS prediction compared to existing tools. Furthermore, DeepFLR estimates FLR accurately for both synthetic and biological datasets, and localizes more phosphosites than probability-based methods. DeepFLR is compatible with data from different organisms, instruments types, and both data-dependent and data-independent acquisition approaches, thus enabling FLR estimation for a broad range of phosphoproteomics experiments.

Spatial probabilistic mapping of metabolite ensembles in mass spectrometry imaging

Mass spectrometry imaging vows to enable simultaneous spatially resolved investigation of hundreds of metabolites in tissues, but it primarily relies on traditional ion images for non-data-driven metabolite visualization and analysis. The rendering and interpretation of ion images neither considers nonlinearities in the resolving power of mass spectrometers nor does it yet evaluate the statistical significance of differential spatial metabolite abundance. Here, we outline the computational framework moleculaR ( https://github.com/CeMOS-Mannheim/moleculaR ) that is expected to improve signal reliability by data-dependent Gaussian-weighting of ion intensities and that introduces probabilistic molecular mapping of statistically significant nonrandom patterns of relative spatial abundance of metabolites-of-interest in tissue. moleculaR also enables cross-tissue statistical comparisons and collective molecular projections of entire biomolecular ensembles followed by their spatial statistical significance evaluation on a single tissue plane. It thereby fosters the spatially resolved investigation of ion milieus, lipid remodeling pathways, or complex scores like the adenylate energy charge within the same image.

Spin column-based peptide fractionation alternatives for streamlined tandem mass tag (SL-TMT) sample processing

Fractionation is essential to achieving deep proteome coverage for sample multiplexing experiments where currently up to 18 samples can be analyzed concurrently. However, peptide fractionation (i.e., upstream of LC-MS/MS analysis) with a liquid chromatography system constrains sample processing as only a single sample can be fractionated at once. Here, we highlight the use of spin column-based methods which permit multiple multiplexed samples to be fractionated simultaneously. These methods require only a centrifuge and eliminate the need for a dedicated liquid chromatography system. We investigate peptide fractionation with strong anion exchange (SAX) and high-pH reversed phase (HPRP) spin columns, as well as a combination of both. In two separate experiments, we acquired deep proteome coverage (>8000 quantified proteins), while starting with <25 μg of protein per channel. Our datasets showcase the proteome alterations in two human cell lines resulting from treatment with inhibitors acting on the ubiquitin-proteasome system. We recommend this spin column-based peptide fractionation strategy for high-throughput screening applications or whenever a liquid chromatograph is not readily available. SIGNIFICANCE: Fractionation is a means to achieve deep proteome coverage for global proteomics analysis. Typical liquid chromatography systems may be a prohibitive expense for many laboratories. Here, we investigate prefractionation with strong anion exchange (SAX) and high-pH reversed phase (HPRP) spin columns, as well as a combination of both, as peptide fractionation methods. These spin columns have advantages over liquid chromatography systems, which include relative affordability, higher throughput capability, no carry over, and fewer potential instrument-related malfunctions. In two separate experiments, we acquired deep proteome coverage (>8000 quantified proteins), thereby showing the utility of each or a combination of both spin columns for global proteome analysis.

Leukemia core transcriptional circuitry is a sparsely interconnected hierarchy stabilized by incoherent feed-forward loops

Lineage-defining transcription factors form densely interconnected circuits in chromatin occupancy assays, but the functional significance of these networks remains underexplored. We reconstructed the functional topology of a leukemia cell transcription network from the direct gene-regulatory programs of eight core transcriptional regulators established in pre-steady state assays coupling targeted protein degradation with nascent transcriptomics. The core regulators displayed narrow, largely non-overlapping direct transcriptional programs, forming a sparsely interconnected functional hierarchy stabilized by incoherent feed-forward loops. BET bromodomain and CDK7 inhibitors disrupted the core regulators' direct programs, acting as mixed agonists/antagonists. The network is predictive of dynamic gene expression behaviors in time-resolved assays and clinically relevant pathway activity in patient populations.

Deep learning prediction boosts phosphoproteomics-based discoveries through improved phosphopeptide identification

Shotgun phosphoproteomics enables high-throughput analysis of phosphopeptides in biological samples, but low phosphopeptide identification rate in data analysis limits the potential of this technology. Here we present DeepRescore2, a computational workflow that leverages deep learning-based retention time and fragment ion intensity predictions to improve phosphopeptide identification and phosphosite localization. Using a state-of-the-art computational workflow as a benchmark, DeepRescore2 increases the number of correctly identified peptide-spectrum matches by 17% in a synthetic dataset and identifies 19%-46% more phosphopeptides in biological datasets. In a liver cancer dataset, 30% of the significantly altered phosphosites between tumor and normal tissues and 60% of the prognosis-associated phosphosites identified from DeepRescore2-processed data could not be identified based on the state-of-the-art workflow. Notably, DeepRescore2-processed data uniquely identifies EGFR hyperactivation as a new target in poor-prognosis liver cancer, which is validated experimentally. Integration of deep learning prediction in DeepRescore2 improves phosphopeptide identification and facilitates biological discoveries.