Prediction of kinase-specific phosphorylation sites through an integrative model of protein context and sequence

IFI16 phase separation via multi-phosphorylation drives innate immune signaling

The interferon inducible protein 16 (IFI16) is a prominent sensor of nuclear pathogenic DNA, initiating innate immune signaling and suppressing viral transcription. However, little is known about mechanisms that initiate IFI16 antiviral functions or its regulation within the host DNA-filled nucleus. Here, we provide in vitro and in vivo evidence to establish that IFI16 undergoes liquid-liquid phase separation (LLPS) nucleated by DNA. IFI16 binding to viral DNA initiates LLPS and induction of cytokines during herpes simplex virus type 1 (HSV-1) infection. Multiple phosphorylation sites within an intrinsically disordered region (IDR) function combinatorially to activate IFI16 LLPS, facilitating filamentation. Regulated by CDK2 and GSK3β, IDR phosphorylation provides a toggle between active and inactive IFI16 and the decoupling of IFI16-mediated cytokine expression from repression of viral transcription. These findings show how IFI16 switch-like phase transitions are achieved with temporal resolution for immune signaling and, more broadly, the multi-layered regulation of nuclear DNA sensors.

KSTAR: An algorithm to predict patient-specific kinase activities from phosphoproteomic data

Kinase inhibitors as targeted therapies have played an important role in improving cancer outcomes. However, there are still considerable challenges, such as resistance, non-response, patient stratification, polypharmacology, and identifying combination therapy where understanding a tumor kinase activity profile could be transformative. Here, we develop a graph- and statistics-based algorithm, called KSTAR, to convert phosphoproteomic measurements of cells and tissues into a kinase activity score that is generalizable and useful for clinical pipelines, requiring no quantification of the phosphorylation sites. In this work, we demonstrate that KSTAR reliably captures expected kinase activity differences across different tissues and stimulation contexts, allows for the direct comparison of samples from independent experiments, and is robust across a wide range of dataset sizes. Finally, we apply KSTAR to clinical breast cancer phosphoproteomic data and find that there is potential for kinase activity inference from KSTAR to complement the current clinical diagnosis of HER2 status in breast cancer patients.

In Vitro Kinase-to-Phosphosite Database (iKiP-DB) Predicts Kinase Activity in Phosphoproteomic Datasets

Phosphoproteomics routinely quantifies changes in the levels of thousands of phosphorylation sites, but functional analysis of such data remains a major challenge. While databases like PhosphoSitePlus contain information about many phosphorylation sites, the vast majority of known sites is not assigned to any protein kinase. Assigning changes in the phosphoproteome to the activity of individual kinases therefore remains a key challenge. A recent large-scale study systematically identified in vitro substrates for most human protein kinases. Here, we reprocessed and filtered these data to generate an in vitro Kinase-to-Phosphosite database (iKiP-DB). We show that iKiP-DB can accurately predict changes in kinase activity in published phosphoproteomic data sets for both well-studied and poorly characterized kinases. We apply iKiP-DB to a newly generated phosphoproteomic analysis of SARS-CoV-2 infected human lung epithelial cells and provide evidence for coronavirus-induced changes in host cell kinase activity. In summary, we show that iKiP-DB is widely applicable to facilitate the functional analysis of phosphoproteomic data sets.

Mitochondrial Aurora kinase A induces mitophagy by interacting with MAP1LC3 and Prohibitin 2

The multifunctional Ser/Thr kinase AURKA uses the Inner Mitochondrial Membrane receptor PHB2 and MAP1LC3 as a signalling platform to orchestrate the elimination of dysfunctional mitochondria.

Epithelial and haematologic tumours often show the overexpression of the serine/threonine kinase AURKA. Recently, AURKA was shown to localise at mitochondria, where it regulates mitochondrial dynamics and ATP production. Here we define the molecular mechanisms of AURKA in regulating mitochondrial turnover by mitophagy. AURKA triggers the degradation of Inner Mitochondrial Membrane/matrix proteins by interacting with core components of the autophagy pathway. On the inner mitochondrial membrane, the kinase forms a tripartite complex with MAP1LC3 and the mitophagy receptor PHB2, which triggers mitophagy in a PARK2/Parkin–independent manner. The formation of the tripartite complex is induced by the phosphorylation of PHB2 on Ser39, which is required for MAP1LC3 to interact with PHB2. Last, treatment with the PHB2 ligand xanthohumol blocks AURKA-induced mitophagy by destabilising the tripartite complex and restores normal ATP production levels. Altogether, these data provide evidence for a role of AURKA in promoting mitophagy through the interaction with PHB2 and MAP1LC3. This work paves the way to the use of function-specific pharmacological inhibitors to counteract the effects of the overexpression of AURKA in cancer.

KinPred: A unified and sustainable approach for harnessing proteome-level human kinase-substrate predictions

Tyrosine and serine/threonine kinases are essential regulators of cell processes and are important targets for human therapies. Unfortunately, very little is known about specific kinase-substrate relationships, making it difficult to infer meaning from dysregulated phosphoproteomic datasets or for researchers to identify possible kinases that regulate specific or novel phosphorylation sites. The last two decades have seen an explosion in algorithms to extrapolate from what little is known into the larger unknown-predicting kinase relationships with site-specific substrates using a variety of approaches that include the sequence-specificity of kinase catalytic domains and various other factors, such as evolutionary relationships, co-expression, and protein-protein interaction networks. Unfortunately, a number of limitations prevent researchers from easily harnessing these resources, such as loss of resource accessibility, limited information in publishing that results in a poor mapping to a human reference, and not being updated to match the growth of the human phosphoproteome. Here, we propose a methodological framework for publishing predictions in a unified way, which entails ensuring predictions have been run on a current reference proteome, mapping the same substrates and kinases across resources to a common reference, filtering for the human phosphoproteome, and providing methods for updating the resource easily in the future. We applied this framework on three currently available resources, published in the last decade, which provide kinase-specific predictions in the human proteome. Using the unified datasets, we then explore the role of study bias, the emergent network properties of these predictive algorithms, and comparisons within and between predictive algorithms. The combination of the code for unification and analysis, as well as the unified predictions are available under the resource we named KinPred. We believe this resource will be useful for a wide range of applications and establishes best practices for long-term usability and sustainability for new and existing predictive algorithms.

Multiple Site-Specific Phosphorylation of IDPs Monitored by NMR

In line with their high accessibility, disordered proteins are exquisite targets of kinases. Eukaryotic organisms use the so-called intrinsically disordered proteins (IDPs) or intrinsically disordered regions of proteins (IDRs) as molecular switches carrying intracellular information tuned by reversible phosphorylation schemes. Solvent-exposed serines and threonines are abundant in IDPs, and, consistently, kinases often modify disordered regions of proteins at multiple sites. In this context, nuclear magnetic resonance (NMR) spectroscopy provides quantitative, residue-specific information that permits mapping of phosphosites and monitoring of their individual kinetics. Hence, NMR monitoring emerges as an in vitro approach, complementary to mass-spectrometry or immuno-blotting, to characterize IDP phosphorylation comprehensively. Here, we describe in detail generic protocols for carrying out NMR monitoring of IDP phosphorylation, and we provide a number of practical insights that improve handiness and reproducibility of this method.

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.

PhosphoPICK-SNP: quantifying the effect of amino acid variants on protein phosphorylation

Motivation

Genome-wide association studies are identifying single nucleotide variants (SNVs) linked to various diseases, however the functional effect caused by these variants is often unknown. One potential functional effect, the loss or gain of protein phosphorylation sites, can be induced through variations in key amino acids that disrupt or introduce valid kinase binding patterns. Current methods for predicting the effect of SNVs on phosphorylation operate on the sequence content of reference and variant proteins. However, consideration of the amino acid sequence alone is insufficient for predicting phosphorylation change, as context factors determine kinase-substrate selection.

Results

We present here a method for quantifying the effect of SNVs on protein phosphorylation through an integrated system of motif analysis and context-based assessment of kinase targets. By predicting the effect that known variants across the proteome have on phosphorylation, we are able to use this background of proteome-wide variant effects to quantify the significance of novel variants for modifying phosphorylation. We validate our method on a manually curated set of phosphorylation change-causing variants from the primary literature, showing that the method predicts known examples of phosphorylation change at high levels of specificity. We apply our approach to data-sets of variants in phosphorylation site regions, showing that variants causing predicted phosphorylation loss are over-represented among disease-associated variants.

Availability and Implementation

The method is freely available as a web-service at the website http://bioinf.scmb.uq.edu.au/phosphopick/snp.

Contact

m.boden@uq.edu.au.

Supplementary information

Supplementary data are available at Bioinformatics online.