KinasePhos 3.0: Redesign and Expansion of the Prediction on Kinase-specific Phosphorylation Sites

Deciphering the phospho-signature induced by hepatitis B virus in primary human hepatocytes

Phosphorylation is a major post-translation modification (PTM) of proteins which is finely tuned by the activity of several hundred kinases and phosphatases. It controls most if not all cellular pathways including anti-viral responses. Accordingly, viruses often induce important changes in the phosphorylation of host factors that can either promote or counteract viral replication. Among more than 500 kinases constituting the human kinome only few have been described as important for the hepatitis B virus (HBV) infectious cycle, and most of them intervene during early or late infectious steps by phosphorylating the viral Core (HBc) protein. In addition, little is known on the consequences of HBV infection on the activity of cellular kinases. The objective of this study was to investigate the global impact of HBV infection on the cellular phosphorylation landscape early after infection. For this, primary human hepatocytes (PHHs) were challenged or not with HBV, and a mass spectrometry (MS)-based quantitative phosphoproteomic analysis was conducted 2- and 7-days post-infection. The results indicated that while, as expected, HBV infection only minimally modified the cell proteome, significant changes were observed in the phosphorylation state of several host proteins at both time points. Gene enrichment and ontology analyses of up- and down-phosphorylated proteins revealed common and distinct signatures induced by infection. In particular, HBV infection resulted in up-phosphorylation of proteins involved in DNA damage signaling and repair, RNA metabolism, in particular splicing, and cytoplasmic cell-signaling. Down-phosphorylated proteins were mostly involved in cell signaling and communication. Validation studies carried out on selected up-phosphorylated proteins, revealed that HBV infection induced a DNA damage response characterized by the appearance of 53BP1 foci, the inactivation of which by siRNA increased cccDNA levels. In addition, among up-phosphorylated RNA binding proteins (RBPs), SRRM2, a major scaffold of nuclear speckles behaved as an antiviral factor. In accordance with these findings, kinase prediction analysis indicated that HBV infection upregulates the activity of major kinases involved in DNA repair. These results strongly suggest that HBV infection triggers an intrinsic anti-viral response involving DNA repair factors and RBPs that contribute to reduce HBV replication in cell culture models.

In Vitro Identification of Phosphorylation Sites on TcPolβ by Protein Kinases TcCK1, TcCK2, TcAUK1, and TcPKC1 and Effect of Phorbol Ester on Activation by TcPKC of TcPolβ in Trypanosoma cruzi Epimastigotes

Chagas disease is caused by the single-flagellated protozoan Trypanosoma cruzi, which affects several million people worldwide. Understanding the signal transduction pathways involved in this parasite's growth, adaptation, and differentiation is crucial. Understanding the basic mechanisms of signal transduction in T. cruzi could help to develop new drugs to treat the disease caused by these protozoa. In the present work, we have demonstrated that Fetal Calf Serum (FCS) can quickly increase the levels of both phosphorylated and unphosphorylated forms of T. cruzi DNA polymerase beta (TcPolβ) in tissue-cultured trypomastigotes. The in vitro phosphorylation sites on TcPolβ by protein kinases TcCK1, TcCK2, TcAUK1, and TcPKC1 have been identified by Mass Spectrometry (MS) analysis and with antibodies against phosphor Ser-Thr-Tyr. MS analysis indicated that these protein kinases can phosphorylate Ser and Thr residues on several sites on TcPolβ. Unexpectedly, it was found that TcCK1 and TcPKC1 can phosphorylate a different Tyr residue on TcPolβ. By using a specific anti-phosphor Tyr monoclonal antibody, it was determined that TcCK1 can be in vitro autophosphorylated on Tyr residues. In vitro and in vivo studies showed that phorbol 12-myristate 13-acetate (PMA) can activate the PKC to stimulate the TcPolβ phosphorylation and enzymatic activity in T. cruzi epimastigotes.

KinPred-RNA-kinase activity inference and cancer type classification using machine learning on RNA-seq data

Kinases as important enzymes can transfer phosphate groups from high-energy and phosphate-donating molecules to specific substrates and play essential roles in various cellular processes. Existing algorithms for kinase activity from phosphorylated proteomics data are often costly, requiring valuable samples. Moreover, methods to extract kinase activities from bulk RNA sequencing data remain undeveloped. In this study, we propose a computational framework KinPred-RNA to derive kinase activities from bulk RNA-sequencing data in cancer samples. KinPred-RNA framework, using the extreme gradient boosting (XGBoost) regression model, outperforms random forest regression, multiple linear regression, and support vector machine regression models in predicting kinase activities from cancer-related RNA sequencing data. Efficient gene signatures from the LINCS-L1000 dataset were used as inputs for KinPred-RNA. The results highlight its potential to be related to biological function. In conclusion, KinPred RNA constitutes a significant advance in cancer research by potentially facilitating the identification of cancer.

Unveiling orphan receptor-like kinases in plants: novel client discovery using high-confidence library predictions in the Kinase-Client (KiC) assay

Plants are remarkable in their ability to adapt to changing environments, with receptor-like kinases (RLKs) playing a pivotal role in perceiving and transmitting environmental cues into cellular responses. Despite extensive research on RLKs from the plant kingdom, the function and activity of many kinases, i.e., their substrates or "clients", remain uncharted. To validate a novel client prediction workflow and learn more about an important RLK, this study focuses on P2K1 (DORN1), which acts as a receptor for extracellular ATP (eATP), playing a crucial role in plant stress resistance and immunity. We designed a Kinase-Client (KiC) assay library of 225 synthetic peptides, incorporating previously identified P2K phosphorylated peptides and novel predictions from a deep-learning phosphorylation site prediction model (MUsite) and a trained hidden Markov model (HMM) based tool, HMMER. Screening the library against purified P2K1 cytosolic domain (CD), we identified 46 putative substrates, including 34 novel clients, 27 of which may be novel peptides, not previously identified experimentally. Gene Ontology (GO) analysis among phosphopeptide candidates revealed proteins associated with important biological processes in metabolism, structure development, and response to stress, as well as molecular functions of kinase activity, catalytic activity, and transferase activity. We offer selection criteria for efficient further in vivo experiments to confirm these discoveries. This approach not only expands our knowledge of P2K1's substrates and functions but also highlights effective prediction algorithms for identifying additional potential substrates. Overall, the results support use of the KiC assay as a valuable tool in unraveling the complexities of plant phosphorylation and provide a foundation for predicting the phosphorylation landscape of plant species based on peptide library results.

Interpretable Multi-Scale Deep Learning for RNA Methylation Analysis across Multiple Species

RNA modification plays a crucial role in cellular regulation. However, traditional high-throughput sequencing methods for elucidating their functional mechanisms are time-consuming and labor-intensive, despite extensive research. Moreover, existing methods often limit their focus to specific species, neglecting the simultaneous exploration of RNA modifications across diverse species. Therefore, a versatile computational approach is necessary for interpretable analysis of RNA modifications across species. A multi-scale biological language-based deep learning model is proposed for interpretable, sequential-level prediction of diverse RNA modifications. Benchmark comparisons across species demonstrate the model's superiority in predicting various RNA methylation types over current state-of-the-art methods. The cross-species validation and attention weight visualization also highlight the model's capability to capture sequential and functional semantics from genomic backgrounds. Our analysis of RNA modifications helps us find the potential existence of "biological grammars" in each modification type, which could be effective for mapping methylation-related sequential patterns and understanding the underlying biological mechanisms of RNA modifications.

Experimental measurement and computational prediction of bacterial Hanks-type Ser/Thr signaling system regulatory targets

Bacteria possess diverse classes of signaling systems that they use to sense and respond to their environments and execute properly timed developmental transitions. One widespread and evolutionarily ancient class of signaling systems are the Hanks-type Ser/Thr kinases, also sometimes termed "eukaryotic-like" due to their homology with eukaryotic kinases. In diverse bacterial species, these signaling systems function as critical regulators of general cellular processes such as metabolism, growth and division, developmental transitions such as sporulation, biofilm formation, and virulence, as well as antibiotic tolerance. This multifaceted regulation is due to the ability of a single Hanks-type Ser/Thr kinase to post-translationally modify the activity of multiple proteins, resulting in the coordinated regulation of diverse cellular pathways. However, in part due to their deep integration with cellular physiology, to date, we have a relatively limited understanding of the timing, regulatory hierarchy, the complete list of targets of a given kinase, as well as the potential regulatory overlap between the often multiple kinases present in a single organism. In this review, we discuss experimental methods and curated datasets aimed at elucidating the targets of these signaling pathways and approaches for using these datasets to develop computational models for quantitative predictions of target motifs. We emphasize novel approaches and opportunities for collecting data suitable for the creation of new predictive computational models applicable to diverse species.

DeepAFP: An effective computational framework for identifying antifungal peptides based on deep learning

Fungal infections have become a significant global health issue, affecting millions worldwide. Antifungal peptides (AFPs) have emerged as a promising alternative to conventional antifungal drugs due to their low toxicity and low propensity for inducing resistance. In this study, we developed a deep learning-based framework called DeepAFP to efficiently identify AFPs. DeepAFP fully leverages and mines composition information, evolutionary information, and physicochemical properties of peptides by employing combined kernels from multiple branches of convolutional neural network with bi-directional long short-term memory layers. In addition, DeepAFP integrates a transfer learning strategy to obtain efficient representations of peptides for improving model performance. DeepAFP demonstrates strong predictive ability on carefully curated datasets, yielding an accuracy of 93.29% and an F1-score of 93.45% on the DeepAFP-Main dataset. The experimental results show that DeepAFP outperforms existing AFP prediction tools, achieving state-of-the-art performance. Finally, we provide a downloadable AFP prediction tool to meet the demands of large-scale prediction and facilitate the usage of our framework by the public or other researchers. Our framework can accurately identify AFPs in a short time without requiring significant human and material resources, and hence can accelerate the development of AFPs as well as contribute to the treatment of fungal infections. Furthermore, our method can provide new perspectives for other biological sequence analysis tasks.

Site-Specific Phosphorylation of ZYG-1 Regulates ZYG-1 Stability and Centrosome Number

Spindle bipolarity is critical for genomic integrity. Given that centrosome number often dictates mitotic bipolarity, tight control of centrosome assembly is vital for the fidelity of cell division. The kinase ZYG-1/Plk4 is a master centrosome factor that is integral for controlling centrosome number and is modulated by protein phosphorylation. While autophosphorylation of Plk4 has been extensively studied in other systems, the mechanism of ZYG-1 phosphorylation in C. elegans remains largely unexplored. In C. elegans, Casein Kinase II (CK2) negatively regulates centrosome duplication by controlling centrosome-associated ZYG-1 levels. In this study, we investigated ZYG-1 as a potential substrate of CK2 and the functional impact of ZYG-1 phosphorylation on centrosome assembly. First, we show that CK2 directly phosphorylates ZYG-1 in vitro and physically interacts with ZYG-1 in vivo. Intriguingly, depleting CK2 or blocking ZYG-1 phosphorylation at putative CK2 target sites leads to centrosome amplification. In the non-phosphorylatable (NP)-ZYG-1 mutant embryo, the overall levels of ZYG-1 are elevated, leading to an increase in centrosomal ZYG-1 and downstream factors, providing a possible mechanism of the NP-ZYG-1 mutation to drive centrosome amplification. Moreover, inhibiting the 26S proteasome blocks degradation of the phospho-mimetic (PM)-ZYG-1, while the NP-ZYG-1 mutant shows partial resistance to proteasomal degradation. Our findings suggest that site-specific phosphorylation of ZYG-1, partly mediated by CK2, controls ZYG-1 levels via proteasomal degradation, limiting centrosome number. We provide a mechanism linking CK2 kinase activity to centrosome duplication through direct phosphorylation of ZYG-1, which is critical for the integrity of centrosome number.

Activity-dependent post-translational regulation of palmitoylating and depalmitoylating enzymes in the hippocampus

Activity-induced changes in protein palmitoylation can regulate the plasticity of synaptic connections, critically impacting learning and memory. Palmitoylation is a reversible post-translational modification regulated by both palmitoyl-acyl transferases that mediate palmitoylation and palmitoyl thioesterases that depalmitoylate proteins. However, it is not clear how fluctuations in synaptic activity can mediate the dynamic palmitoylation of neuronal proteins. Using primary hippocampal cultures, we demonstrate that synaptic activity does not impact the transcription of palmitoylating and depalmitoylating enzymes, changes in thioesterase activity, or post-translational modification of the depalmitoylating enzymes of the ABHD17 family and APT2 (also known as LYPLA2). In contrast, synaptic activity does mediate post-translational modification of the palmitoylating enzymes ZDHHC2, ZDHHC5 and ZDHHC9 (but not ZDHHC8) to influence protein-protein interactions, enzyme stability and enzyme function. Post-translational modifications of the ZDHHC enzymes were also observed in the hippocampus following fear conditioning. Taken together, our findings demonstrate that signaling events activated by synaptic activity largely impact activity of the ZDHHC family of palmitoyl-acyl transferases with less influence on the activity of palmitoyl thioesterases.

Mapping the Protein Kinome: Current Strategy and Future Direction

The kinome includes over 500 different protein kinases, which form an integrated kinase network that regulates cellular phosphorylation signals. The kinome plays a central role in almost every cellular process and has strong linkages with many diseases. Thus, the evaluation of the cellular kinome in the physiological environment is essential to understand biological processes, disease development, and to target therapy. Currently, a number of strategies for kinome analysis have been developed, which are based on monitoring the phosphorylation of kinases or substrates. They have enabled researchers to tackle increasingly complex biological problems and pathological processes, and have promoted the development of kinase inhibitors. Additionally, with the increasing interest in how kinases participate in biological processes at spatial scales, it has become urgent to develop tools to estimate spatial kinome activity. With multidisciplinary efforts, a growing number of novel approaches have the potential to be applied to spatial kinome analysis. In this paper, we review the widely used methods used for kinome analysis and the challenges encountered in their applications. Meanwhile, potential approaches that may be of benefit to spatial kinome study are explored.

Accelerating the Discovery of Anticancer Peptides through Deep Forest Architecture with Deep Graphical Representation

Cancer is one of the leading diseases threatening human life and health worldwide. Peptide-based therapies have attracted much attention in recent years. Therefore, the precise prediction of anticancer peptides (ACPs) is crucial for discovering and designing novel cancer treatments. In this study, we proposed a novel machine learning framework (GRDF) that incorporates deep graphical representation and deep forest architecture for identifying ACPs. Specifically, GRDF extracts graphical features based on the physicochemical properties of peptides and integrates their evolutionary information along with binary profiles for constructing models. Moreover, we employ the deep forest algorithm, which adopts a layer-by-layer cascade architecture similar to deep neural networks, enabling excellent performance on small datasets but without complicated tuning of hyperparameters. The experiment shows GRDF exhibits state-of-the-art performance on two elaborate datasets (Set 1 and Set 2), achieving 77.12% accuracy and 77.54% F1-score on Set 1, as well as 94.10% accuracy and 94.15% F1-score on Set 2, exceeding existing ACP prediction methods. Our models exhibit greater robustness than the baseline algorithms commonly used for other sequence analysis tasks. In addition, GRDF is well-interpretable, enabling researchers to better understand the features of peptide sequences. The promising results demonstrate that GRDF is remarkably effective in identifying ACPs. Therefore, the framework presented in this study could assist researchers in facilitating the discovery of anticancer peptides and contribute to developing novel cancer treatments.

Kinase-Catalyzed Crosslinking and Immunoprecipitation (K-CLIP) to Explore Kinase-Substrate Pairs

Kinases are responsible for phosphorylation of proteins and are involved in many biological processes, including cell signaling. Identifying the kinases that phosphorylate specific phosphoproteins is critical to augment the current understanding of cellular events. Herein, we report a general protocol to study the kinases of a target substrate phosphoprotein using kinase-catalyzed crosslinking and immunoprecipitation (K-CLIP). K-CLIP uses a photocrosslinking γ-phosphoryl-modified ATP analog, such as ATP-arylazide, to covalently crosslink substrates to kinases with UV irradiation. Crosslinked kinase-substrate complexes can then be enriched by immunoprecipitating the target substrate phosphoprotein, with bound kinase(s) identified using Western blot or mass spectrometry analysis. K-CLIP is an adaptable chemical tool to investigate and discover kinase-substrate pairs, which will promote characterization of complex phosphorylation-mediated cell biology. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Kinase-catalyzed crosslinking of lysates Basic Protocol 2: Kinase-catalyzed crosslinking and immunoprecipitation (K-CLIP).