Kinase-substrate Relationships Research Articles

The proteomic landscape and temporal dynamics of mammalian gastruloid development.

Gastrulation is the highly coordinated process by which the early embryo breaks symmetry, establishes germ layers and a body plan, and sets the stage for organogenesis. As early mammalian development is challenging to study in vivo, stem cell-derived models have emerged as powerful surrogates, e.g. human and mouse gastruloids. However, although single cell RNA-seq (scRNA-seq) and high-resolution imaging have been extensively applied to characterize such in vitro embryo models, a paucity of measurements of protein dynamics and regulation leaves a major gap in our understanding. Here, we sought to address this by applying quantitative proteomics to human and mouse gastruloids at four key stages of their differentiation (naïve ESCs, primed ESCs, early gastruloids, late gastruloids). To the resulting data, we perform network analysis to map the dynamics of expression of macromolecular protein complexes and biochemical pathways, including identifying cooperative proteins that associate with them. With matched RNA-seq and phosphosite data from these same stages, we investigate pathway-, stage- and species-specific aspects of translational and post-translational regulation, e.g. finding peri-gastrulation stages of human and mice to be discordant with respect to the mitochondrial transcriptome vs. proteome, and nominating novel kinase-substrate relationships based on phosphosite dynamics. Finally, we leverage correlated dynamics to identify conserved protein networks centered around congenital disease genes. Altogether, our data (https://gastruloid.brotmanbaty.org/) and analyses showcase the potential of intersecting in vitro embryo models and proteomics to advance our understanding of early mammalian development in ways not possible through transcriptomics alone.

Identification of APBB1 as a substrate for anaplastic lymphoma kinase.

Anaplastic lymphoma kinase (ALK) is a well-known oncogene involved in various malignancies such as anaplastic large cell lymphoma, lung cancer and neuroblastoma. Several substrates for fused ALK have been identified and their biological functions have been described. However, the lack of a comprehensive identification of ALK substrates limits our understanding of the biological roles of receptor ALK. Thus, this study aimed to identify novel ALK substrates and characterize their biological functions. We screened the interactors of the kinase domain of receptor ALK using proximity-dependent biotin identification and identified 43 interactors. We narrowed down the candidates by evaluating whether these interactors were downstream of ALK in a neuroblastoma cell line, NB-1. Amongst these, we identified amyloid beta precursor protein-binding family B member 1 (APBB1) as an ALK downstream molecule involved in NB-1 cell viability. Finally, we assessed the kinase-substrate relationship between ALK and APBB1 and found that ALK phosphorylated multiple tyrosine residues in APBB1 both in-cell and in-tube assays, with tyrosine 269 as a major target. In conclusion, we successfully identified a new substrate for receptor ALK. Our results may help further elucidate the molecular mechanism of ALK downstream signalling in neuroblastoma.

DNA-encoded probe-based assay for profiling plant kinase activities.

Elucidating kinase-substrate relationships is pivotal for deciphering cellular signaling mechanisms, yet it remains challenging due to the complexity of kinase networks. Herein, we report the development of a versatile DNA-based kinase assay platform for high-throughput profiling of plant protein kinase activities and substrate preferences. Our approach employs DNA-linked peptide substrates, facilitating quantitative and specific kinase activity detection through next-generation DNA sequencing. Leveraging DNA barcodes as quantitative readouts, our approach establishes a high-throughput, sensitive, and specific platform for dissecting kinase-substrate networks in plants, representing a powerful tool for elucidating signaling mechanisms in plants.

Kinase-catalyzed crosslinking: A comparison of ATP-crosslinker analogs

Protein phosphorylation is catalyzed by kinases to regulate cellular events and disease states. Identifying kinase-substrate relationships represents a powerful strategy to understand cell biology and disease yet remains challenging due to the rapid dynamics of phosphorylation. Over the last decade, several γ-phosphoryl modified ATP analogs containing crosslinkers were developed to covalently conjugate kinases, their substrates, and their associated proteins for subsequent characterization. Here, kinetics and crosslinking experiments demonstrated that the UV-activated analogs, ATP-aryl azide and ATP-benzophenone, offered the most robust crosslinking, whereas electrophilic ATP-aryl fluorosulfate promoted the most effective proximity-enabled crosslinking. The data will guide future applications of kinase-catalyzed crosslinking to study normal and disease biology.

Abstract 1977: ACR-2316: A potentially first-in-class, potent, selective WEE1/PKMYT1 inhibitor rationally designed for superior single agent activity through synergistic disruption of cell cycle checkpoints

Abstract WEE1 and PKMYT1 kinases play essential roles in cell cycle checkpoints and DNA damage response. Genetic interaction has been documented between WEE1 and PKMYT1, and clinical trials have reported clinical activity with inhibitors of WEE1 (adavosertib, azenosertib) and PKMYT1 (lunresertib). Here, we report the discovery and characterization of ACR-2316, a dual inhibitor of WEE1 and PKMYT1 specifically designed for optimal selectivity through co-crystallography and superior single agent activity uniquely enabled by Acrivon Predictive Precision Proteomics (AP3). Mass spectrometry-based AP3 profiling was conducted across several novel WEE1 and PKMYT1 inhibitor leads generated through co-crystallography-based rational drug design. Selective leads originating from one series were further optimized using AP3 for biological structure-activity relationship analysis. AP3 profiling revealed WEE1 inhibitor-upregulated phosphorylation sites across a subset of phosphoproteins that were oppositely regulated (quenched) by PKMYT1 inhibitors. Consensus sites for these included CDK1 T14, a direct PKMYT1 phosphorylation site, as well as CHK1 S296. A particular lead compound, ACR-2316, demonstrated a desirable potent, balanced ratio of cellular WEE1 (IC50 = 2 nM, IC90 = 10 nM) and PKMYT1 (IC20 = 44 nM) inhibition resulting in superior activation of the mitotic kinases CDK1, CDK2, and PLK1 compared to adavosertib and lunresertib, based on annotated kinase substrate relationships. ACR-2316 is more selective than adavosertib, azenosertib, and lunresertib based on >200 kinases profiled by AP3 and 468 kinases assessed by KINOMEscan. Cell cycle analyses demonstrated a drastic ACR-2316-induced S-G2/M accumulation qualitatively distinct from adavosertib or lunresertib. In a 19-cancer cell line proliferation assay (CellTiter-Glo), ACR-2316 demonstrated greater potency in all cell lines tested compared to adavosertib and lunresertib (mean IC50 = 70, 252 and 364 nM, respectively). Superior anti-cancer activity of ACR-2316 was observed in 12 ovarian cancer patient-derived xenograft models tested ex vivo (CellTiter-Glo 3D) compared to azenosertib and lunresertib (mean IC50 = 9, 248 and 1620 nM, respectively). Across human tumor xenograft mouse models, oral administration of ACR-2316 demonstrated superior, durable, dose-dependent efficacy compared to azenosertib and lunresertib and was well tolerated at all doses. Complete responses observed with ACR-2316 were associated with strong WEE1 and intermediate PKMYT1 inhibition in tumors. In conclusion, ACR-2316 is a potent, selective dual WEE1/PKMYT1 inhibitor with superior single-agent activity compared to clinical WEE1 or PKMYT1 inhibitors. ACR-2316 is progressing through IND-enabling studies in preparation for clinical monotherapy development. Citation Format: Caroline Wigerup, Helén Nilsson, Lei Shi, Joon Jung, Joelle Baddour-Sousounis, Ruban Cornelius, Nina Lipjankic, Uthira Muralitharan, Valentina Siino, Ignacio Arribas Diez, Zachary Best, Martina Pasetto, William Dahlberg, Shahrzad Rafiei, Portia Lombardo, Magnus E. Jakobsson, Reina Improgo, Christina Scherer, John van Duzer, David A. Proia, Kristina Masson, Peter Blume-Jensen. ACR-2316: A potentially first-in-class, potent, selective WEE1/PKMYT1 inhibitor rationally designed for superior single agent activity through synergistic disruption of cell cycle checkpoints [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1977.

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.

Using explainable machine learning to uncover the kinase-substrate interaction landscape.

Phosphorylation, a post-translational modification regulated by protein kinase enzymes, plays an essential role in almost all cellular processes. Understanding how each of the nearly 500 human protein kinases selectively phosphorylates their substrates is a foundational challenge in bioinformatics and cell signaling. Although deep learning models have been a popular means to predict kinase-substrate relationships, existing models often lack interpretability and are trained on datasets skewed toward a subset of well-studied kinases. Here we leverage recent peptide library datasets generated to determine substrate specificity profiles of 300 serine/threonine kinases to develop an explainable Transformer model for kinase-peptide interaction prediction. The model, trained solely on primary sequences, achieved state-of-the-art performance. Its unique multitask learning paradigm built within the model enables predictions on virtually any kinase-peptide pair, including predictions on 139 kinases not used in peptide library screens. Furthermore, we employed explainable machine learning methods to elucidate the model's inner workings. Through analysis of learned embeddings at different training stages, we demonstrate that the model employs a unique strategy of substrate prediction considering both substrate motif patterns and kinase evolutionary features. SHapley Additive exPlanation (SHAP) analysis reveals key specificity determining residues in the peptide sequence. Finally, we provide a web interface for predicting kinase-substrate associations for user-defined sequences and a resource for visualizing the learned kinase-substrate associations. All code and data are available at https://github.com/esbgkannan/Phosformer-ST. Web server is available at https://phosformer.netlify.app.

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.

Pharmacological approaches to understanding protein kinase signaling networks.

Protein kinases play vital roles in controlling cell behavior, and an array of kinase inhibitors are used successfully for treatment of disease. Typical drug development pipelines involve biological studies to validate a protein kinase target, followed by the identification of small molecules that effectively inhibit this target in cells, animal models, and patients. However, it is clear that protein kinases operate within complex signaling networks. These networks increase the resilience of signaling pathways, which can render cells relatively insensitive to inhibition of a single kinase, and provide the potential for pathway rewiring, which can result in resistance to therapy. It is therefore vital to understand the properties of kinase signaling networks in health and disease so that we can design effective multi-targeted drugs or combinations of drugs. Here, we outline how pharmacological and chemo-genetic approaches can contribute to such knowledge, despite the known low selectivity of many kinase inhibitors. We discuss how detailed profiling of target engagement by kinase inhibitors can underpin these studies; how chemical probes can be used to uncover kinase-substrate relationships, and how these tools can be used to gain insight into the configuration and function of kinase signaling networks.

Kinase regulation by liquid-liquid phase separation.

Liquid-liquid phase separation (LLPS) is emerging as a mechanism of spatiotemporal regulation that could answer long-standing questions about how order is achieved in biochemical signaling. In this review we discuss how LLPS orchestrates kinase signaling, either by creating condensate structures that are sensed by kinases or by direct LLPS of kinases, cofactors, and substrates - thereby acting as a mechanism to compartmentalize kinase-substrate relationships, and in some cases also sequestering the kinase away from inhibitory factors. We also examine the possibility that selective pressure promotes genomic rearrangements that fuse pro-growth kinases to LLPS-prone protein sequences, which in turn drives aberrant kinase activation through LLPS.

Abstract 3127: Comprehensive proteogenomic characterization of rare kidney tumors

Abstract Non-clear cell renal cell carcinomas (non-ccRCCs) represent ~15-20% RCCs cases comprising nearly 20 different disease subtypes and a wide spectrum of clinical behavior from benign to highly aggressive course. Clinically, metastatic non-ccRCC patients, regardless of subtypes with distinct genomic aberrations, are all treated with the same standard of care therapies, underscoring the need for precision therapeutic strategies. Diagnostic challenges also exist as benign and malignant entities often display overlapping histomorphologies that current diagnostic cytokeratin markers cannot resolve. Therefore, identification of more reliable diagnostic and prognostic non-ccRCC biomarkers remains an unmet need in this field. As part of the Clinical Tumor Analysis Consortium (CPTAC), we performed integrative analysis of multi-omics data including genomic next generation sequencing-based whole exome, whole genome, RNAseq, snRNAseq and mass spectrometry-based proteomics, post translational modifications (glycosylation and phosphorylation) and metabolomic profiles generated by CPTAC. The composition of the kidney tumor cohort (n=151) included 103 ccRCC, 15 oncocytomas, 13 papillary RCC (PRCC), 11 other rare tumors and 8 unclassified RCCs. Our multi-omic analysis revealed both unique and shared molecular features of RCC subtypes. We characterized proteogenomic, PTM and glycoproteome impact of genome instability (GI), a feature that is associated with poor prognosis in both ccRCC and non-ccRCC and affects 10-15% of cases. These analyses identified new prognostic signatures, outlier targetable kinase expression patterns, kinase-substrate relationships and differential protein glycosylation events. Glycoproteome analysis also revealed variation in cell-type specific marker expression among RCC subtypes such as FUT8 (core-fucosyltransferase) associated protein glycosylation in PRCC. Integrative analysis of snRNA-seq data predicted diverse tumor cell-of-origin and stratified RCC subtype specific proteogenomic signatures. Differential expression analysis revealed several novel diagnostic makers including MAPRE3, GPNMB, PIGR, SOSTDC1. These biomarkers were validated by IHC and their addition to existing panels results in improved diagnostic specificity. Metabolic characterization revealed RCC subtype-specific differences and increased oncometabolite SAICAR in oncocytomas that may have functional significance. The valuable proteogenomic data resource we generated contains several rare tumor types that are hard to obtain for proteogenomic characterization at the scale described here, and will certainly aid in future pan-RCC studies. Citation Format: Ginny Xiaohe Li, Yi Hsiao, Lijun Chen, Rahul Mannan, Yuping Zhang, Francesca Petralia, Hanbyul Cho, Noshad Hosseini, Anna Calinawan, Yize Li, Shankara Anand, Aniket Dagar, Yifat Geffen, Felipe V. Leprevost, Anne Le, Sean Ponce, Michael Schnaubelt, Nataly Naser Al Deen, Wagma Caravan, Andrew Houston, Chandan Kumar-Sinha, Xiaoming Wang, Seema Chugh, Gilbert S. Omenn, Daniel W. Chan, Christopher Ricketts, Rohit Mehra, Arul Chinnaiyan, Li Ding, Marcin Cieslik, Hui Zhang, Saravana M. Dhanasekaran, Alexey I. Nesvizhskii. Comprehensive proteogenomic characterization of rare kidney tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3127.

CDK6 activity in a recurring convergent kinase network motif.

In humans, more than 500 kinases phosphorylate ~15% of all proteins in an emerging phosphorylation network. Convergent local interaction motifs, in which ≥two kinases phosphorylate the same substrate, underlie feedback loops and signal amplification events but have not been systematically analyzed. Here, we first report a network-wide computational analysis of convergent kinase-substrate relationships (cKSRs). In experimentally validated phosphorylation sites, we find that cKSRs are common and involve >80% of all human kinases and >24% of all substrates. We show that cKSRs occur over a wide range of stoichiometries, in many instances harnessing co-expressed kinases from family subgroups. We then experimentally demonstrate for the prototypical convergent CDK4/6 kinase pair how multiple inputs phosphorylate the tumor suppressor retinoblastoma protein (RB) and thereby hamper in situ analysis of the individual kinases. We hypothesize that overexpression of one kinase combined with a CDK4/6 inhibitor can dissect convergence. In breast cancer cells expressing high levels of CDK4, we confirm this hypothesis and develop a high-throughput compatible assay that quantifies genetically modified CDK6 variants and inhibitors. Collectively, our work reveals the occurrence, topology, and experimental dissection of convergent interactions toward a deeper understanding of kinase networks and functions.

In-Depth Characterization of the Clostridioides difficile Phosphoproteome to Identify Ser/Thr Kinase Substrates

Clostridioides difficile is the leading cause of postantibiotic diarrhea in adults. During infection, the bacterium must rapidly adapt to the host environment by using survival strategies. Protein phosphorylation is a reversible post-translational modification employed ubiquitously for signal transduction and cellular regulation. Hanks-type serine/threonine kinases (STKs) and serine/threonine phosphatases have emerged as important players in bacterial cell signaling and pathogenicity. C. difficile encodes two STKs (PrkC and CD2148) and one phosphatase. We optimized a titanium dioxide phosphopeptide enrichment approach to determine the phosphoproteome of C. difficile. We identified and quantified 2500 proteins representing 63% of the theoretical proteome. To identify STK and serine/threonine phosphatase targets, we then performed comparative large-scale phosphoproteomics of the WT strain and isogenic ΔprkC, CD2148, Δstp, and prkC CD2148 mutants. We detected 635 proteins containing phosphorylated peptides. We showed that PrkC is phosphorylated on multiple sites in vivo and autophosphorylates in vitro. We were unable to detect a phosphorylation for CD2148 in vivo, whereas this kinase was phosphorylated in vitro only in the presence of PrkC. Forty-one phosphoproteins were identified as phosphorylated under the control of CD2148, whereas 114 proteins were phosphorylated under the control of PrkC including 27 phosphoproteins more phosphorylated in the ∆stp mutant. We also observed enrichment for phosphothreonine among the phosphopeptides more phosphorylated in the Δstp mutant. Both kinases targeted pathways required for metabolism, translation, and stress response, whereas cell division and peptidoglycan metabolism were more specifically controlled by PrkC-dependent phosphorylation in agreement with the phenotypes of the ΔprkC mutant. Using a combination of approaches, we confirmed that FtsK was phosphorylated in vivo under the control of PrkC and that Spo0A was a substrate of PrkC in vitro. This study provides a detailed mapping of kinase–substrate relationships in C. difficile, paving the way for the identification of new biomarkers and therapeutic targets.

Chemical Genetic Validation of CSNK2 Substrates Using an Inhibitor-Resistant Mutant in Combination with Triple SILAC Quantitative Phosphoproteomics.

Casein Kinase 2 (CSNK2) is an extremely pleiotropic, ubiquitously expressed protein kinase involved in the regulation of numerous key biological processes. Mapping the CSNK2-dependent phosphoproteome is necessary for better characterization of its fundamental role in cellular signalling. While ATP-competitive inhibitors have enabled the identification of many putative kinase substrates, compounds targeting the highly conserved ATP-binding pocket often exhibit off-target effects limiting their utility for definitive kinase-substrate assignment. To overcome this limitation, we devised a strategy combining chemical genetics and quantitative phosphoproteomics to identify and validate CSNK2 substrates. We engineered U2OS cells expressing exogenous wild type CSNK2A1 (WT) or a triple mutant (TM, V66A/H160D/I174A) with substitutions at residues important for inhibitor binding. These cells were treated with CX-4945, a clinical-stage inhibitor of CSNK2, and analyzed using large-scale triple SILAC (Stable Isotope Labelling of Amino Acids in Cell Culture) quantitative phosphoproteomics. In contrast to wild-type CSNK2A1, CSNK2A1-TM retained activity in the presence of CX-4945 enabling identification and validation of several CSNK2 substrates on the basis of their increased phosphorylation in cells expressing CSNK2A1-TM. Based on high conservation within the kinase family, we expect that this strategy can be broadly adapted for identification of other kinase-substrate relationships.

Systematic identification of ALK substrates by integrated phosphoproteome and interactome analysis.

The sensitivity of phosphorylation site identification by mass spectrometry has improved markedly. However, the lack of kinase-substrate relationship (KSR) data hinders the improvement of the range and accuracy of kinase activity prediction. In this study, we aimed to develop a method for acquiring systematic KSR data on anaplastic lymphoma kinase (ALK) using mass spectrometry and to apply this method to the prediction of kinase activity. Thirty-seven ALK substrate candidates, including 34 phosphorylation sites not annotated in the PhosphoSitePlus database, were identified by integrated analysis of the phosphoproteome and crosslinking interactome of HEK 293 cells with doxycycline-induced ALK overexpression. Furthermore, KSRs of ALK were validated by an in vitro kinase assay. Finally, using phosphoproteomic data from ALK mutant cell lines and patient-derived cells treated with ALK inhibitors, we found that the prediction of ALK activity was improved when the KSRs identified in this study were used instead of the public KSR dataset. Our approach is applicable to other kinases, and future identification of KSRs will facilitate more accurate estimations of kinase activity and elucidation of phosphorylation signals.

A human kinase yeast array for the identification of kinases modulating phosphorylation‐dependent protein–protein interactions

Protein kinases play an important role in cellular signaling pathways and their dysregulation leads to multiple diseases, making kinases prime drug targets. While more than 500 human protein kinases are known to collectively mediate phosphorylation of over 290,000 S/T/Y sites, the activities have been characterized only for a minor, intensively studied subset. To systematically address this discrepancy, we developed a human kinase array in Saccharomyces cerevisiae as a simple readout tool to systematically assess kinase activities. For this array, we expressed 266 human kinases in four different S. cerevisiae strains and profiled ectopic growth as a proxy for kinase activity across 33 conditions. More than half of the kinases showed an activity‐dependent phenotype across many conditions and in more than one strain. We then employed the kinase array to identify the kinase(s) that can modulate protein–protein interactions (PPIs). Two characterized, phosphorylation‐dependent PPIs with unknown kinase–substrate relationships were analyzed in a phospho‐yeast two‐hybrid assay. CK2α1 and SGK2 kinases can abrogate the interaction between the spliceosomal proteins AAR2 and PRPF8, and NEK6 kinase was found to mediate the estrogen receptor (ERα) interaction with 14‐3‐3 proteins. The human kinase yeast array can thus be used for a variety of kinase activity‐dependent readouts.

Interrogating Kinase-Substrate Relationships with Proximity Labeling and Phosphorylation Enrichment.

Kinases govern many cellular responses through the reversible transfer of a phosphate moiety to their substrates. However, pairing a substrate with a kinase is challenging. In proximity labeling experiments, proteins proximal to a target protein are marked by biotinylation, and mass spectrometry can be used for their identification. Here, we combine ascorbate peroxidase (APEX) proximity labeling and a phosphorylation enrichment-based workflow, Phospho-APEX (pAPEX), to rapidly identify phosphorylated and biotinylated neighbor proteins which can be considered for candidate substrates. The pAPEX strategy enriches and quantifies differences in proximity for proteins and phosphorylation sites proximal to an APEX2-tagged kinase under the kinase "ON" and kinase "OFF" conditions. As a proof of concept, we identified candidate substrates of MAPK1 in HEK293T and HCT116 cells and candidate substrates of PKA in HEK293T cells. In addition to many known substrates, C15orf39 was identified and confirmed as a novel MAPK1 substrate. In all, we adapted the proximity labeling-based platform to accommodate phosphorylation analysis for kinase substrate identification.

Motif-centric phosphoproteomics to target kinase-mediated signaling pathways

SummaryIdentifying cellular phosphorylation pathways based on kinase-substrate relationships is a critical step to understanding the regulation of physiological functions in cells. Mass spectrometry-based phosphoproteomics workflows have made it possible to comprehensively collect information on individual phosphorylation sites in a variety of samples. However, there is still no generic approach to uncover phosphorylation networks based on kinase-substrate relationships in rare cell populations. Here, we describe a motif-centric phosphoproteomics approach combined with multiplexed isobaric labeling, in which in vitro kinase reactions are used to generate targeted phosphopeptides, which are spiked into one of the isobaric channels to increase detectability. Proof-of-concept experiments demonstrate selective and comprehensive quantification of targeted phosphopeptides by using multiple kinases for motif-centric channels. More than 7,000 tyrosine phosphorylation sites were quantified from several tens of micrograms of starting materials. This approach enables the quantification of multiple phosphorylation pathways under physiological or pathological regulation in a motif-centric manner.

PKSPS: a novel method for predicting kinase of specific phosphorylation sites based on maximum weighted bipartite matching algorithm and phosphorylation sequence enrichment analysis.

With the development of biotechnology, a large number of phosphorylation sites have been experimentally confirmed and collected, but only a few of them have kinase annotations. Since experimental methods to detect kinases at specific phosphorylation sites are expensive and accidental, some computational methods have been proposed to predict the kinase of these sites, but most methods only consider single sequence information or single functional network information. In this study, a new method Predicting Kinase of Specific Phosphorylation Sites (PKSPS) is developed to predict kinases of specific phosphorylation sites in human proteins by combining PKSPS-Net with PKSPS-Seq, which considers protein-protein interaction (PPI) network information and sequence information. For PKSPS-Net, kinase-kinase and substrate-substrate similarity are quantified based on the topological similarity of proteins in the PPI network, and maximum weighted bipartite matching algorithm is proposed to predict kinase-substrate relationship. In PKSPS-Seq, phosphorylation sequence enrichment analysis is used to analyze the similarity of local sequences around phosphorylation sites and predict the kinase of specific phosphorylation sites (KSP). PKSPS has been proved to be more effective than the PKSPS-Net or PKSPS-Seq on different sets of kinases. Further comparison results show that the PKSPS method performs better than existing methods. Finally, the case study demonstrates the effectiveness of the PKSPS in predicting kinases of specific phosphorylation sites. The open source code and data of the PKSPS can be obtained from https://github.com/guoxinyunncu/PKSPS.

Abstract 16: Patterns and regulation of post translational modifications in cancer

Abstract Post-translational modifications (PTMs) (e.g., ubiquitylation, phosphorylation, acetylation) have been studied for their key role in cell signaling and in regulating cell physiology. Advances in mass spectrometry now enable measuring PTMs and studying their role and prevalence in cancer. Although most studies have focused on a single PTM and its impact on downstream signaling in a single cancer type, understanding PTM-driven commonalities, disparities, and their crosstalk across cancer types will be critical in understanding fundamental oncogenic principles governed by PTMs. Thus, we aim to understand the underlying patterns of PTMs in molecular signaling pathways shared across multiple cancer types by studying changes in protein acetylation and phosphorylation within the largest collection of in-depth genomic, transcriptomic, proteomic, and PTM profiles compiled from 11 cancer types generated across the CPTAC projects.We used matrix factorization methods to extract expression signatures jointly from transcriptomic, proteomic, and PTM data across multiple cancer types. Preliminary analysis of 6 tumor types identified 18 pan-cancer, multi-omic expression signatures. Characterization of these signatures using ranked gene-set enrichment analysis (GSEA) and site-specific PTM signature enrichment analysis (PTM-SEA) highlighted pathways including DNA damage response, immune inactivation/deactivation, tumor invasiveness, and metabolic pathways. To investigate the effect of driver alterations on PPIs, we (i) compute rank correlations of pan-cancer mutational signatures to both expression levels and our multi-omic expression signatures, and (ii) map PTMs and mutations to available crystal structures. We additionally investigate mutations that recur across cancer types (e.g., TP53, PIK3CA, CTNNB1) and impact expression profiles of downstream PTMs. We further connect pan-cancer phosphoproteomic data to a library of biochemical specificities of the human kinome using a novel, unbiased computational platform that builds on extensive and experimentally validated kinase-substrate relationships. We identified activated and deactivated kinases/acetylases based on their altered PTM sites within and across the multi-omic subtypes and compared the effects on different targets. We also characterize the crosstalk between acetylation and phosphorylation across cancer, and, systematically identify acetylation events that prime and enhance kinase activity. Finally, we define kinases, acetylases, and PTM outliers as potential druggable targets (using The Drug Gene Interaction Database and DepMap). Overall, this study presents the landscape of PTMs across cancer and will serve as a community resource to promote a deeper understanding of PTM-governed processes leading to cancer progression, with the potential to improve current therapies or help design new treatment approaches against cancer. Citation Format: Yifat Geffen, Shankara Anand, Yo Akiyama, Tommer M. Yaron, Alexander Kerelsky, Jared L. Johnson, Karsten Krug, David Heiman, Shankha Satpathy, Karl Clauser, Michael Gillette, D. R. Mani, Chet Birger, Steven Carr, Lewis C. Cantley, Francois Aguet, Gad Getz, Oncogenic Drivers and Pathways Group, Clinical Proteomic Tumor Analysis Consortium (CPTAC). Patterns and regulation of post translational modifications in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 16.