PSEA: Kinase-specific prediction and analysis of human phosphorylation substrates

Hepatic kynurenic acid mediates phosphorylation of Nogo-A in the medial prefrontal cortex to regulate chronic stress-induced anxiety-like behaviors in mice

Exercise training effectively relieves anxiety disorders via modulating specific brain networks. The role of post-translational modification of proteins in this process, however, has been underappreciated. Here we performed a mouse study in which chronic restraint stress-induced anxiety-like behaviors can be attenuated by 14-day persistent treadmill exercise, in association with dramatic changes of protein phosphorylation patterns in the medial prefrontal cortex (mPFC). In particular, exercise was proposed to modulate the phosphorylation of Nogo-A protein, which drives the ras homolog family member A (RhoA)/ Rho-associated coiled-coil-containing protein kinases 1(ROCK1) signaling cascade. Further mechanistic studies found that liver-derived kynurenic acid (KYNA) can affect the kynurenine metabolism within the mPFC, to modulate this RhoA/ROCK1 pathway for conferring stress resilience. In sum, we proposed that circulating KYNA might mediate stress-induced anxiety-like behaviors via protein phosphorylation modification within the mPFC, and these findings shed more insights for the liver-brain communications in responding to both stress and physical exercise.

Deep Learning in Phosphoproteomics: Methods and Application in Cancer Drug Discovery

Protein phosphorylation is a key post-translational modification (PTM) that is a central regulatory mechanism of many cellular signaling pathways. Several protein kinases and phosphatases precisely control this biochemical process. Defects in the functions of these proteins have been implicated in many diseases, including cancer. Mass spectrometry (MS)-based analysis of biological samples provides in-depth coverage of phosphoproteome. A large amount of MS data available in public repositories has unveiled big data in the field of phosphoproteomics. To address the challenges associated with handling large data and expanding confidence in phosphorylation site prediction, the development of many computational algorithms and machine learning-based approaches have gained momentum in recent years. Together, the emergence of experimental methods with high resolution and sensitivity and data mining algorithms has provided robust analytical platforms for quantitative proteomics. In this review, we compile a comprehensive collection of bioinformatic resources used for the prediction of phosphorylation sites, and their potential therapeutic applications in the context of cancer.

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.

Computational Phosphorylation Network Reconstruction: An Update on Methods and Resources

Most proteins undergo some form of modification after translation, and phosphorylation is one of the most relevant and ubiquitous post-translational modifications. The succession of protein phosphorylation and dephosphorylation catalyzed by protein kinase and phosphatase, respectively, constitutes a key mechanism of molecular information flow in cellular systems. The protein interactions of kinases, phosphatases, and their regulatory subunits and substrates are the main part of phosphorylation networks. To elucidate the landscape of phosphorylation events has been a central goal pursued by both experimental and computational approaches. Substrate specificity (e.g., sequence, structure) or the phosphoproteome has been utilized in an array of different statistical learning methods to infer phosphorylation networks. In this chapter, different computational phosphorylation network inference-related methods and resources are summarized and discussed.

Robust inference of kinase activity using functional networks

Mass spectrometry enables high-throughput screening of phosphoproteins across a broad range of biological contexts. When complemented by computational algorithms, phospho-proteomic data allows the inference of kinase activity, facilitating the identification of dysregulated kinases in various diseases including cancer, Alzheimer’s disease and Parkinson’s disease. To enhance the reliability of kinase activity inference, we present a network-based framework, RoKAI, that integrates various sources of functional information to capture coordinated changes in signaling. Through computational experiments, we show that phosphorylation of sites in the functional neighborhood of a kinase are significantly predictive of its activity. The incorporation of this knowledge in RoKAI consistently enhances the accuracy of kinase activity inference methods while making them more robust to missing annotations and quantifications. This enables the identification of understudied kinases and will likely lead to the development of novel kinase inhibitors for targeted therapy of many diseases. RoKAI is available as web-based tool at http://rokai.io.

Kinases drive fundamental changes in cell state, but predicting kinase activity based on substrate-level changes can be challenging. Here the authors introduce a computational framework that utilizes similarities between substrates to robustly infer kinase activity.

Computational Analysis of Phosphoproteomics Data in Multi-Omics Cancer Studies

Multiple types of molecular data for the same set of clinical samples are increasingly available and may be analyzed jointly in an integrative analysis to maximize comprehensive biological insight. This analysis is important as separate analyses of individual omics data types usually do not fully explain disease phenotypes. An increasing number of studies have now been focusing on multi-omics data integration, yet not many studies have included phosphoproteomics data, an important layer for understanding signaling pathways. Multi-omics integration methods with phosphoproteomics data are reviewed in the context of cancer research as well as multi-omics methods papers that would be promising to apply to phosphoproteomics data. Analysis of individual data types is still the major approach even in large cohort proteogenomics studies. Hence, a section is dedicated on possible integrative methods for multi-omics and phosphoproteomics data. In summary, this review provides the readers with both currently used integrative methods previously applied to phosphoproteomics and multi-omics data integration and other algorithms for multi-omics data integration promising for future application to phosphoproteomics data.

EPSD: a well-annotated data resource of protein phosphorylation sites in eukaryotes

As an important post-translational modification (PTM), protein phosphorylation is involved in the regulation of almost all of biological processes in eukaryotes. Due to the rapid progress in mass spectrometry-based phosphoproteomics, a large number of phosphorylation sites (p-sites) have been characterized but remain to be curated. Here, we briefly summarized the current progresses in the development of data resources for the collection, curation, integration and annotation of p-sites in eukaryotic proteins. Also, we designed the eukaryotic phosphorylation site database (EPSD), which contained 1 616 804 experimentally identified p-sites in 209 326 phosphoproteins from 68 eukaryotic species. In EPSD, we not only collected 1 451 629 newly identified p-sites from high-throughput (HTP) phosphoproteomic studies, but also integrated known p-sites from 13 additional databases. Moreover, we carefully annotated the phosphoproteins and p-sites of eight model organisms by integrating the knowledge from 100 additional resources that covered 15 aspects, including phosphorylation regulator, genetic variation and mutation, functional annotation, structural annotation, physicochemical property, functional domain, disease-associated information, protein-protein interaction, drug-target relation, orthologous information, biological pathway, transcriptional regulator, mRNA expression, protein expression/proteomics and subcellular localization. We anticipate that the EPSD can serve as a useful resource for further analysis of eukaryotic phosphorylation. With a data volume of 14.1 GB, EPSD is free for all users at http://epsd.biocuckoo.cn/.

A Curated Resource for Phosphosite-specific Signature Analysis

Signaling pathways are orchestrated by post-translational modifications (PTMs) such as phosphorylation. However, pathway analysis of PTM data sets generated by mass spectrometry (MS)-based proteomics is typically performed at a gene-centric level because of the lack of appropriately curated PTM signature databases and bioinformatic tools that leverage PTM site-specific information. Here we present the first version of PTMsigDB, a database of modification site-specific signatures of perturbations, kinase activities and signaling pathways curated from more than 2,500 publications. We adapted the widely used single sample Gene Set Enrichment Analysis approach to utilize PTMsigDB, enabling PTMSignature Enrichment Analysis (PTM-SEA) of quantitative MS data. We used a well-characterized data set of epidermal growth factor (EGF)-perturbed cancer cells to evaluate our approach and demonstrated better representation of signaling events compared with gene-centric methods. We then applied PTM-SEA to analyze the phosphoproteomes of cancer cells treated with cell-cycle inhibitors and detected mechanism-of-action specific signatures of cell cycle kinases. We also applied our methods to analyze the phosphoproteomes of PI3K-inhibited human breast cancer cells and detected signatures of compounds inhibiting PI3K as well as targets downstream of PI3K (AKT, MAPK/ERK) covering a substantial fraction of the PI3K pathway. PTMsigDB and PTM-SEA can be freely accessed at https://github.com/broadinstitute/ssGSEA2.0.

Computational prediction and analysis of species-specific fungi phosphorylation via feature optimization strategy

Protein phosphorylation is a reversible and ubiquitous post-translational modification that primarily occurs at serine, threonine and tyrosine residues and regulates a variety of biological processes. In this paper, we first briefly summarized the current progresses in computational prediction of eukaryotic protein phosphorylation sites, which mainly focused on animals and plants, especially on human, with a less extent on fungi. Since the number of identified fungi phosphorylation sites has greatly increased in a wide variety of organisms and their roles in pathological physiology still remain largely unknown, more attention has been paid on the identification of fungi-specific phosphorylation. Here, experimental fungi phosphorylation sites data were collected and most of the sites were classified into different types to be encoded with various features and trained via a two-step feature optimization method. A novel method for prediction of species-specific fungi phosphorylation-PreSSFP was developed, which can identify fungi phosphorylation in seven species for specific serine, threonine and tyrosine residues (http://computbiol.ncu.edu.cn/PreSSFP). Meanwhile, we critically evaluated the performance of PreSSFP and compared it with other existing tools. The satisfying results showed that PreSSFP is a robust predictor. Feature analyses exhibited that there have some significant differences among seven species. The species-specific prediction via two-step feature optimization method to mine important features for training could considerably improve the prediction performance. We anticipate that our study provides a new lead for future computational analysis of fungi phosphorylation.

Cucurbitacin B exerts anti-cancer activities in human multiple myeloma cells in vitro and in vivo by modulating multiple cellular pathways

Cucurbitacin B (CuB), a triterpenoid compound isolated from the stems of Cucumis melo, has long been used to treat hepatitis and hepatoma in China. Although its remarkable anti-cancer activities have been reported, the mechanism by which it achieves this therapeutic activity remains unclear. This study was designed to investigate the molecular mechanisms by which CuB inhibits cancer cell proliferation. Our results indicate that CuB is a novel inhibitor of Aurora A in multiple myeloma (MM) cells, arresting cells in the G2/M phase. CuB also inhibited IL-10-induced STAT3 phosphorylation, synergistically increasing the anti-tumor activity of Adriamycin in vitro. CuB induced dephosphorylation of cofilin, resulting in the loss of mitochondrial membrane potential, release of cytochrome c, and activation of caspase-8. CuB inhibited MM tumor growth in a murine MM model, without host toxicity. In conclusion, these results indicate that CuB interferes with multiple cellular pathways in MM cells. CuB thus represents a promising therapeutic tool for the treatment of MM.

Mechanisms regulating phosphatase specificity and the removal of individual phosphorylation sites during mitotic exit

Entry into mitosis is driven by the activity of kinases, which phosphorylate over 7000 proteins on multiple sites. For cells to exit mitosis and segregate their genome correctly, these phosphorylations must be removed in a specific temporal order. This raises a critical and important question: how are specific phosphorylation sites on an individual protein removed? Traditionally, the temporal order of dephosphorylation was attributed to decreasing kinase activity. However, recent evidence in human cells has identified unique patterns of dephosphorylation during mammalian mitotic exit that cannot be fully explained by the loss of kinase activity. This suggests that specificity is determined in part by phosphatases. In this review, we explore how the physicochemical properties of an individual phosphosite and its surrounding amino acids can affect interactions with a phosphatase. These positive and negative interactions in turn help determine the specific pattern of dephosphorylation required for correct mitotic exit.

Crk Tyrosine Phosphorylation Regulates PDGF-BB-inducible Src Activation and Breast Tumorigenicity and Metastasis

The activity of Src family kinases (Src being the prototypical member) is tightly regulated by differential phosphorylation on Tyr416 (positive) and Tyr527 (negative), a duet that reciprocally regulates kinase activity. The latter negative regulation of Src on Tyr527 is mediated by C-terminal Src kinase (CSK) that phosphorylates Tyr527 and maintains Src in a clamped negative regulated state by promoting an intramolecular association. Here it is demonstrated that the SH2- and SH3-domain containing adaptor protein CrkII, by virtue of its phosphorylation on Tyr239, regulates the Csk/Src signaling axis to control Src activation. Once phosphorylated, the motif (PIpYARVIQ) forms a consensus sequence for the SH2 domain of CSK to form a pTyr239-CSK complex. Functionally, when expressed in Crk^-/- MEFs or in Crk^+/+ HS683 cells, Crk Y239F delayed PDGF-BB-inducible Src Tyr416 phosphorylation. Moreover, expression of Crk Y239F in HS683 cells delayed Src kinase activation and suppressed the cell-invasive and -transforming phenotypes. Finally, through loss-of-function and epistasis experiments using CRISPR-Cas9-engineered 4T1 murine breast cancer cells, Crk Tyr239 is implicated in breast cancer tumor growth and metastasis in orthotopic immunocompetent 4T1 mice model of breast adenocarcinoma. These findings delineate a novel role for Crk Tyr239 phosphorylation in the regulation of Src kinases, as well as a potential molecular explanation for a long-standing question as to how Crk regulates the activation of Src kinases.Implications: These findings provide new perspectives on the versatility of Crk in cancer by demonstrating how Crk mechanistically drives, through a tyrosine phosphorylation-dependent manner, tumor growth, and metastasis. Mol Cancer Res; 16(1); 173-83. ©2017 AACR.

PhosphoPredict: A bioinformatics tool for prediction of human kinase-specific phosphorylation substrates and sites by integrating heterogeneous feature selection

Protein phosphorylation is a major form of post-translational modification (PTM) that regulates diverse cellular processes. In silico methods for phosphorylation site prediction can provide a useful and complementary strategy for complete phosphoproteome annotation. Here, we present a novel bioinformatics tool, PhosphoPredict, that combines protein sequence and functional features to predict kinase-specific substrates and their associated phosphorylation sites for 12 human kinases and kinase families, including ATM, CDKs, GSK-3, MAPKs, PKA, PKB, PKC, and SRC. To elucidate critical determinants, we identified feature subsets that were most informative and relevant for predicting substrate specificity for each individual kinase family. Extensive benchmarking experiments based on both five-fold cross-validation and independent tests indicated that the performance of PhosphoPredict is competitive with that of several other popular prediction tools, including KinasePhos, PPSP, GPS, and Musite. We found that combining protein functional and sequence features significantly improves phosphorylation site prediction performance across all kinases. Application of PhosphoPredict to the entire human proteome identified 150 to 800 potential phosphorylation substrates for each of the 12 kinases or kinase families. PhosphoPredict significantly extends the bioinformatics portfolio for kinase function analysis and will facilitate high-throughput identification of kinase-specific phosphorylation sites, thereby contributing to both basic and translational research programs.

Inferring Disease Associated Phosphorylation Sites via Random Walk on Multi-Layer Heterogeneous Network

As protein phosphorylation plays an important role in numerous cellular processes, many studies have been undertaken to analyze phosphorylation-related activities for drug design and disease treatment. However, although progresses have been made in illustrating the relationship between phosphorylation and diseases, no existing method focuses on disease-associated phosphorylation sites prediction. In this work, we proposed a multi-layer heterogeneous network model that makes use of the kinase information to infer disease-phosphorylation site relationship and implemented random walk on the heterogeneous network. Experimental results reveal that multi-layer heterogeneous network model with kinase layer is superior in inferring disease-phosphorylation site relationship when comparing with existing random walk model and common used classification methods.

Sequence- and Structure-Based Analysis of Tissue-Specific Phosphorylation Sites

Phosphorylation is the most widespread and well studied reversible posttranslational modification. Discovering tissue-specific preferences of phosphorylation sites is important as phosphorylation plays a role in regulating almost every cellular activity and disease state. Here we present a comprehensive analysis of global and tissue-specific sequence and structure properties of phosphorylation sites utilizing recent proteomics data. We identified tissue-specific motifs in both sequence and spatial environments of phosphorylation sites. Target site preferences of kinases across tissues indicate that, while many kinases mediate phosphorylation in all tissues, there are also kinases that exhibit more tissue-specific preferences which, notably, are not caused by tissue-specific kinase expression. We also demonstrate that many metabolic pathways are differentially regulated by phosphorylation in different tissues.

Revisiting protein kinase-substrate interactions: Toward therapeutic development

Despite the efforts of pharmaceutical companies to develop specific kinase modulators, few drugs targeting kinases have been completely successful in the clinic. This is primarily due to the conserved nature of kinases, especially in the catalytic domains. Consequently, many currently available inhibitors lack sufficient selectivity for effective clinical application. Kinases phosphorylate their substrates to modulate their activity. One of the important steps in the catalytic reaction of protein phosphorylation is the correct positioning of the target residue within the catalytic site. This positioning is mediated by several regions in the substrate binding site, which is typically a shallow crevice that has critical subpockets that anchor and orient the substrate. The structural characterization of this protein-protein interaction can aid in the elucidation of the roles of distinct kinases in different cellular processes, the identification of substrates, and the development of specific inhibitors. Because the region of the substrate that is recognized by the kinase can be part of a linear consensus motif or a nonlinear motif, advances in technology beyond simple linear sequence scanning for consensus motifs were needed. Cost-effective bioinformatics tools are already frequently used to predict kinase-substrate interactions for linear consensus motifs, and new tools based on the structural data of these interactions improve the accuracy of these predictions and enable the identification of phosphorylation sites within nonlinear motifs. In this Review, we revisit kinase-substrate interactions and discuss the various approaches that can be used to identify them and analyze their binding structures for targeted drug development.

A grammar inference approach for predicting kinase specific phosphorylation sites

Kinase mediated phosphorylation site detection is the key mechanism of post translational mechanism that plays an important role in regulating various cellular processes and phenotypes. Many diseases, like cancer are related with the signaling defects which are associated with protein phosphorylation. Characterizing the protein kinases and their substrates enhances our ability to understand the mechanism of protein phosphorylation and extends our knowledge of signaling network; thereby helping us to treat such diseases. Experimental methods for predicting phosphorylation sites are labour intensive and expensive. Also, manifold increase of protein sequences in the databanks over the years necessitates the improvement of high speed and accurate computational methods for predicting phosphorylation sites in protein sequences. Till date, a number of computational methods have been proposed by various researchers in predicting phosphorylation sites, but there remains much scope of improvement. In this communication, we present a simple and novel method based on Grammatical Inference (GI) approach to automate the prediction of kinase specific phosphorylation sites. In this regard, we have used a popular GI algorithm Alergia to infer Deterministic Stochastic Finite State Automata (DSFA) which equally represents the regular grammar corresponding to the phosphorylation sites. Extensive experiments on several datasets generated by us reveal that, our inferred grammar successfully predicts phosphorylation sites in a kinase specific manner. It performs significantly better when compared with the other existing phosphorylation site prediction methods. We have also compared our inferred DSFA with two other GI inference algorithms. The DSFA generated by our method performs superior which indicates that our method is robust and has a potential for predicting the phosphorylation sites in a kinase specific manner.

Progress and challenges in predicting protein methylation sites

We review the progress in the prediction of protein methylation sites in the past 10 years and discuss the challenges that are faced while developing novel predictors in the future.