Prediction Of Phosphorylation Sites Research Articles

MFPSP: Identification of fungal species-specific phosphorylation site using offspring competition-based genetic algorithm

Protein phosphorylation is essential in various signal transduction and cellular processes. To date, most tools are designed for model organisms, but only a handful of methods are suitable for predicting task in fungal species, and their performance still leaves much to be desired. In this study, a novel tool called MFPSP is developed for phosphorylation site prediction in multi-fungal species. The amino acids sequence features were derived from physicochemical and distributed information, and an offspring competition-based genetic algorithm was applied for choosing the most effective feature subset. The comparison results shown that MFPSP achieves a more advanced and balanced performance to several state-of-the-art available toolkits. Feature contribution and interaction exploration indicating the proposed model is efficient in uncovering concealed patterns within sequence. We anticipate MFPSP to serve as a valuable bioinformatics tool and benefiting practical experiments by pre-screening potential phosphorylation sites and enhancing our functional understanding of phosphorylation modifications in fungi. The source code and datasets are accessible at https://github.com/AI4HKB/MFPSP/.

GPS-pPLM: A Language Model for Prediction of Prokaryotic Phosphorylation Sites

In the prokaryotic kingdom, protein phosphorylation serves as one of the most important posttranslational modifications (PTMs) and is involved in orchestrating a broad spectrum of biological processes. Here, we report an updated online server named the group-based prediction system for prokaryotic phosphorylation language model (GPS-pPLM), used for predicting phosphorylation sites (p-sites) in prokaryotes. For model training, two deep learning methods, a transformer and a deep neural network, were employed, and a total of 10 sequence features and contextual features were integrated. Using 44,839 nonredundant p-sites in 16,041 proteins from 95 prokaryotes, two general models for the prediction of O-phosphorylation and N-phosphorylation were first pretrained and then fine-tuned to construct 6 predictors specific for each phosphorylatable residue type as well as 134 species-specific predictors. Compared with other existing tools, the GPS-pPLM exhibits higher accuracy in predicting prokaryotic O-phosphorylation p-sites. Protein sequences in FASTA format or UniProt accession numbers can be submitted by users, and the predicted results are displayed in tabular form. In addition, we annotate the predicted p-sites with knowledge from 22 public resources, including experimental evidence, 3D structures, and disorder tendencies. The online service of the GPS-pPLM is freely accessible for academic research.

Genome-Scale Identification of Wild Soybean Serine/Arginine-Rich Protein Family Genes and Their Responses to Abiotic Stresses.

Serine/arginine-rich (SR) proteins mostly function as splicing factors for pre-mRNA splicing in spliceosomes and play critical roles in plant development and adaptation to environments. However, detailed study about SR proteins in legume plants is still lacking. In this report, we performed a genome-wide investigation of SR protein genes in wild soybean (Glycine soja) and identified a total of 31 GsSR genes from the wild soybean genome. The analyses of chromosome location and synteny show that the GsSRs are unevenly distributed on 15 chromosomes and are mainly under the purifying selection. The GsSR proteins can be phylogenetically classified into six sub-families and are conserved in evolution. Prediction of protein phosphorylation sites indicates that GsSR proteins are highly phosphorylated proteins. The protein-protein interaction network implies that there exist numerous interactions between GsSR proteins. We experimentally confirmed their physical interactions with the representative SR proteins of spliceosome-associated components such as U1-70K or U2AF35 by yeast two-hybrid assays. In addition, we identified various stress-/hormone-responsive cis-acting elements in the promoter regions of these GsSR genes and verified their expression patterns by RT-qPCR analyses. The results show most GsSR genes are highly expressed in root and stem tissues and are responsive to salt and alkali stresses. Splicing analysis showed that the splicing patterns of GsSRs were in a tissue- and stress-dependent manner. Overall, these results will help us to further investigate the biological functions of leguminous plant SR proteins and shed new light on uncovering the regulatory mechanisms of plant SR proteins in growth, development, and stress responses.

Computational prediction of phosphorylation sites of SARS-CoV-2 infection using feature fusion and optimization strategies

SARS-CoV-2′s global spread has instigated a critical health and economic emergency, impacting countless individuals. Understanding the virus's phosphorylation sites is vital to unravel the molecular intricacies of the infection and subsequent changes in host cellular processes. Several computational methods have been proposed to identify phosphorylation sites, typically focusing on specific residue (S/T) or Y phosphorylation sites. Unfortunately, current predictive tools perform best on these specific residues and may not extend their efficacy to other residues, emphasizing the urgent need for enhanced methodologies. In this study, we developed a novel predictor that integrated all the residues (STY) phosphorylation sites information. We extracted ten different feature descriptors, primarily derived from composition, evolutionary, and position-specific information, and assessed their discriminative power through five classifiers. Our results indicated that Light Gradient Boosting (LGB) showed superior performance, and five descriptors displayed excellent discriminative capabilities. Subsequently, we identified the top two integrated features have high discriminative capability and trained with LGB to develop the final prediction model, LGB-IPs. The proposed approach shows an excellent performance on 10-fold cross-validation with an ACC, MCC, and AUC values of 0.831, 0.662, 0.907, respectively. Notably, these performances are replicated in the independent evaluation. Consequently, our approach may provide valuable insights into the phosphorylation mechanisms in SARS-CoV-2 infection for biomedical researchers.

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.

PhosAF: An integrated deep learning architecture for predicting protein phosphorylation sites with AlphaFold2 predicted structures

Phosphorylation is indispensable in comprehending biological processes, while biological experimental methods for identifying phosphorylation sites are tedious and arduous. With the rapid growth of biotechnology, deep learning methods have made significant progress in site prediction tasks. Nevertheless, most existing predictors only consider protein sequence information, that limits the capture of protein spatial information. Building upon the latest advancement in protein structure prediction by AlphaFold2, a novel integrated deep learning architecture PhosAF is developed to predict phosphorylation sites in human proteins by integrating CMA-Net and MFC-Net, which considers sequence and structure information predicted by AlphaFold2. Here, CMA-Net module is composed of multiple convolutional neural network layers and multi-head attention is appended to obtaining the local and long-term dependencies of sequence features. Meanwhile, the MFC-Net module composed of deep neural network layers is used to capture the complex representations of evolutionary and structure features. Furthermore, different features are combined to predict the final phosphorylation sites. In addition, we put forward a new strategy to construct reliable negative samples via protein secondary structures. Experimental results on independent test data and case study indicate that our model PhosAF surpasses the current most advanced methods in phosphorylation site prediction.

Attenphos: General Phosphorylation Site Prediction Model Based on Attention Mechanism.

Phosphorylation site prediction has important application value in the field of bioinformatics. It can act as an important reference and help with protein function research, protein structure research, and drug discovery. So, it is of great significance to propose scientific and effective calculation methods to accurately predict phosphorylation sites. In this study, we propose a new method, Attenphos, based on the self-attention mechanism for predicting general phosphorylation sites in proteins. The method not only captures the long-range dependence information of proteins but also better represents the correlation between amino acids through feature vector encoding transformation. Attenphos takes advantage of the one-dimensional convolutional layer to reduce the number of model parameters, improve model efficiency and prediction accuracy, and enhance model generalization. Comparisons between our method and existing state-of-the-art prediction tools were made using balanced datasets from human proteins and unbalanced datasets from mouse proteins. We performed prediction comparisons using independent test sets. The results showed that Attenphos demonstrated the best overall performance in the prediction of Serine (S), Threonine (T), and Tyrosine (Y) sites on both balanced and unbalanced datasets. Compared to current state-of-the-art methods, Attenphos has significantly higher prediction accuracy. This proves the potential of Attenphos in accelerating the identification and functional analysis of protein phosphorylation sites and provides new tools and ideas for biological research and drug discovery.

PhosHSGN: Deep Neural Networks Combining Sequence and Protein Spatial Information to Improve Protein Phosphorylation Site Prediction

PhosHSGN: Deep Neural Networks Combining Sequence and Protein Spatial Information to Improve Protein Phosphorylation Site Prediction

DF-Phos: Prediction of Protein Phosphorylation Sites by Deep Forest.

Phosphorylation is the most important and studied post-translational modification (PTM), which plays a crucial role in protein function studies and experimental design. Many significant studies have been performed to predict phosphorylation sites using various machine-learning methods. Recently, several studies have claimed that deep learning-based methods are the best way to predict the phosphorylation sites because deep learning as an advanced machine learning method can automatically detect complex representations of phosphorylation patterns from raw sequences and thus offers a powerful tool to improve phosphorylation site prediction. In this study, we report DF-Phos, a new phosphosite predictor based on the Deep Forest to predict phosphorylation sites. In DF-Phos, the feature vector taken from the CkSAApair method is as input for a Deep Forest framework for predicting phosphorylation sites. The results of 10-fold cross-validation show that the Deep Forest method has the highest performance among other available methods. We implemented a Python program of DF-Phos, which is freely available for non-commercial use at https://github.com/zahiriz/DF-Phos Moreover, users can use it for various PTM predictions.

Advancing the accuracy of SARS-CoV-2 phosphorylation site detection via meta-learning approach.

The worldwide appearance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has generated significant concern and posed a considerable challenge to global health. Phosphorylation is a common post-translational modification that affects many vital cellular functions and is closely associated with SARS-CoV-2 infection. Precise identification of phosphorylation sites could provide more in-depth insight into the processes underlying SARS-CoV-2 infection and help alleviate the continuing COVID-19 crisis. Currently, available computational tools for predicting these sites lack accuracy and effectiveness. In this study, we designed an innovative meta-learning model, Meta-Learning for Serine/Threonine Phosphorylation (MeL-STPhos), to precisely identify protein phosphorylation sites. We initially performed a comprehensive assessment of 29 unique sequence-derived features, establishing prediction models for each using 14 renowned machine learning methods, ranging from traditional classifiers to advanced deep learning algorithms. We then selected the most effective model for each feature by integrating the predicted values. Rigorous feature selection strategies were employed to identify the optimal base models and classifier(s) for each cell-specific dataset. To the best of our knowledge, this is the first study to report two cell-specific models and a generic model for phosphorylation site prediction by utilizing an extensive range of sequence-derived features and machine learning algorithms. Extensive cross-validation and independent testing revealed that MeL-STPhos surpasses existing state-of-the-art tools for phosphorylation site prediction. We also developed a publicly accessible platform at https://balalab-skku.org/MeL-STPhos. We believe that MeL-STPhos will serve as a valuable tool for accelerating the discovery of serine/threonine phosphorylation sites and elucidating their role in post-translational regulation.

Elucidating the structural and functional prophecy of the Rv2326c gene, an ABC transporter of Mycobacterium tuberculosis H37Rv through computational approach.

Tuberculosis is a fatal disease caused by Mycobacterium tuberculosis. M. tuberculosis becoming drug-resistant day by day, necessitating to know the mechanism behind the drug resistance and how to overcome this deadly malady. Drug resistance and reduced drug bioavailability are caused by a class of transporter proteins called the ATP-binding cassette (ABC) transporters, which pump a range of medicines out of cells at the price of ATP hydrolysis. By using computational approaches, we tried to elaborate the probable function of the Rv2326c gene of M. tuberculosis, perhaps involved in drug resistance mechanism. The presence of the signature motif of ABC transporters (LSGGELQRLALAAAL and LSGGQMRRVVLAGLL) and ATP binding motif (GXXXXGKT and GXXXXGKS) in the protein sequence signifying its importance in the ATP binding and transportation of molecules. Further, this manuscript elaborated about tertiary structure and validation, functional category, localization, phosphorylation site prediction, mutational analysis of conserved motifs. Ligand docking study shows the highest affinity with ATP than GTP justified its function as an ATP binding protein. The Rv2326c protein is present in the inner membrane and working as an ATP binding protein and might be playing a dynamic role in transportation. In this study, we found that Rv2326c protein might be working as an ABC transporter by which the drugs and other molecules are imported or exported into the bacterium. As a result, the current study provides a means to better understand its normal functioning and basic biology, which can help in the development of novel therapeutic targeting approaches for Rv2326c protein.

LMPhosSite: A Deep Learning-Based Approach for General Protein Phosphorylation Site Prediction Using Embeddings from the Local Window Sequence and Pretrained Protein Language Model.

Phosphorylation is one of the most important post-translational modifications and plays a pivotal role in various cellular processes. Although there exist several computational tools to predict phosphorylation sites, existing tools have not yet harnessed the knowledge distilled by pretrained protein language models. Herein, we present a novel deep learning-based approach called LMPhosSite for the general phosphorylation site prediction that integrates embeddings from the local window sequence and the contextualized embedding obtained using global (overall) protein sequence from a pretrained protein language model to improve the prediction performance. Thus, the LMPhosSite consists of two base-models: one for capturing effective local representation and the other for capturing global per-residue contextualized embedding from a pretrained protein language model. The output of these base-models is integrated using a score-level fusion approach. LMPhosSite achieves a precision, recall, Matthew's correlation coefficient, and F1-score of 38.78%, 67.12%, 0.390, and 49.15%, for the combined serine and threonine independent test data set and 34.90%, 62.03%, 0.298, and 44.67%, respectively, for the tyrosine independent test data set, which is better than the compared approaches. These results demonstrate that LMPhosSite is a robust computational tool for the prediction of the general phosphorylation sites in proteins.

Identification and characterization of RNA polymerase II (RNAP) C-Terminal domain phosphatase-like 3 (SlCPL3) in tomato under biotic stress.

Bacterial diseases are a huge threat to the production of tomatoes. During infection intervals, pathogens affect biochemical, oxidant and molecular properties of tomato. Therefore, it is necessary to study the antioxidant enzymes, oxidation state and genes involved during bacterial infection in tomato. Different bioinformatic analyses were performed to conduct homology, gene promoter analysis and determined protein structure. Antioxidant, MDA and H2O2 response was measured in Falcon, Rio grande and Sazlica tomato cultivars. In this study, RNA Polymerase II (RNAP) C-Terminal Domain Phosphatase-like 3 (SlCPL-3) gene was identified and characterized. It contained 11 exons, and encoded for two protein domains i.e., CPDCs and BRCT. SOPMA and Phyre2, online bioinformatic tools were used to predict secondary structure. For the identification of protein pockets CASTp web-based tool was used. Netphos and Pondr was used for prediction of phosphorylation sites and protein disordered regions. Promoter analysis revealed that the SlCPL-3 is involved in defense-related mechanisms. We further amplified two different regions of SlCPL-3 and sequenced them. It showed homology respective to the reference tomato genome. Our results showed that SlCPL-3 gene was triggered during bacterial stress. SlCPL-3 expression was upregulated in response to bacterial stress during different time intervals. Rio grande showed a high level of SICPL-3 gene expression after 72 hpi. Biochemical and gene expression analysis showed that under biotic stress Rio grande cultivar is more sensitive to Pst DC 3000 bacteria. This study lays a solid foundation for the functional characterization of SlCPL-3 gene in tomato cultivars. All these findings would be beneficial for further analysis of SlCPL-3 gene and may be helpful for the development of resilient tomato cultivars.

The Regulation Network of Glycerolipid Metabolism as Coregulators of Immunotherapy-Related Myocarditis.

To date, immunotherapy for patients with malignant tumors has shown a significant association with myocarditis. However, the mechanism of metabolic reprogramming changes for immunotherapy-related cardiotoxicity is still not well understood. The CD45+ single-cell RNA sequencing (scRNA-seq) of the Pdcd1-/-Ctla4+/- and wild-type mouse heart in GSE213486 was downloaded to demonstrate the heterogeneity of immunocyte atlas in immunotherapy-related myocarditis. The liquid chromatography-tandem mass spectrometry (LC-MS/MS) spectrum metabolomics analysis detects the metabolic network differences. The drug prediction, organelle level interaction, mitochondrial level regulatory network, and phosphorylation site prediction for key regulators have also been screened via multibioinformatics analysis methods. The scRNA analysis shows that the T cell is the main regulatory cell subpopulation in the pathological progress of immunotherapy-related myocarditis. Mitochondrial regulation pathway significantly participated in pseudotime trajectory- (PTT-) related differential expressed genes (DEGs) in the T cell subpopulation. Additionally, both the gene set enrichment analysis (GSEA) of PTT-related DEGs and LC-MS/MS metabolomics analysis showed that mitochondrial-regulated glycerolipid metabolism plays a central role in metabolic reprogramming changes for immunotherapy-related cardiotoxicity. Finally, the hub-regulated protease of diacylglycerol kinase zeta (Dgkz) was significantly identified and widely played various roles in glycerolipid metabolism, oxidative phosphorylation, and lipid kinase activation. Mitochondrial-regulated glycerolipid metabolism, especially the DGKZ protein, plays a key role in the metabolic reprogramming of immunotherapy-related myocarditis.

GPS 6.0: an updated server for prediction of kinase-specific phosphorylation sites in proteins.

Protein phosphorylation, catalyzed by protein kinases (PKs), is one of the most important post-translational modifications (PTMs), and involved in regulating almost all of biological processes. Here, we report an updated server, Group-based Prediction System (GPS) 6.0, for prediction of PK-specific phosphorylation sites (p-sites) in eukaryotes. First, we pre-trained a general model using penalized logistic regression (PLR), deep neural network (DNN), and Light Gradient Boosting Machine (LightGMB) on 490762 non-redundant p-sites in 71407 proteins. Then, transfer learning was conducted to obtain 577 PK-specific predictors at the group, family and single PK levels, using a well-curated data set of 30043 known site-specific kinase-substrate relations in 7041 proteins. Together with the evolutionary information, GPS 6.0 could hierarchically predict PK-specific p-sites for 44046 PKs in 185 species. Besides the basic statistics, we also offered the knowledge from 22 public resources to annotate the prediction results, including the experimental evidence, physical interactions, sequence logos, and p-sites in sequences and 3D structures. The GPS 6.0 server is freely available at https://gps.biocuckoo.cn. We believe that GPS 6.0 could be a highly useful service for further analysis of phosphorylation.

PKA mediates modality-specific modulation of the mechanically gated ion channel PIEZO2

PKA is a downstream effector of many inflammatory mediators that induce pain hypersensitivity by increasing the mechanosensitivity of nociceptive sensory afferent. Here, we examine the molecular mechanism underlying PKA-dependent modulation of the mechanically activated ion channel PIEZO2, which confers mechanosensitivity to many nociceptors. Using phosphorylation site prediction algorithms, we identified multiple putative and highly conserved PKA phosphorylation sites located on intracellular intrinsically disordered regions of PIEZO2. Site-directed mutagenesis and patch-clamp recordings showed that substitution of one or multiple putative PKA sites within a single intracellular domain does not alter PKA-induced PIEZO2 sensitization, whereas mutation of a combination of nine putative sites located on four different intracellular regions completely abolishes PKA-dependent PIEZO2 modulation, though it remains unclear whether all or just some of these nine sites are required. By demonstrating that PIEZO1 is not modulated by PKA, our data also reveal a previously unrecognized functional difference between PIEZO1 and PIEZO2. Moreover, by demonstrating that PKA only modulates PIEZO2 currents evoked by focal mechanical indentation of the cell, but not currents evoked by pressure-induced membrane stretch, we provide evidence suggesting that PIEZO2 is a polymodal mechanosensor that engages different protein domains for detecting different types of mechanical stimuli.

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.

Cloning and Molecular Characterization of the Recombinant CVB4E2 Immunogenic Viral Protein (rVP1), as a Potential Subunit Protein for Vaccine and Immunodiagnostic Reagent Candidate.

The aim of the present study was, first, to clone the VP1 gene of the human coxsackievirus B4 strain E2 (CVB4E2) in the prokaryotic pUC19 plasmid expression vector then to compare it with the structural capsid proteins of the same strain using bioinformatic tools. PCR colony amplification followed through a restriction digestion analysis and sequencing process which affirmed the success of the cloning process. SDS-PAGE and Western Blotting were used to characterize the purified recombinant viral protein expressed in bacteria cells. The BLASTN tool revealed that the nucleotide sequence of the recombinant VP1 (rVP1) expressed by pUC19 highly matched the target nucleotide sequence of the diabetogenic CVB4E2 strain. Secondary structure and three-dimension structure prediction suggested that rVP1, such as wild-type VP1, is chiefly composed of random coils and a high percentage of exposed amino acids. Linear B-cell epitope prediction showed that several antigenic epitopes are likely present in rVP1 and CVB4E2 VP1 capsid protein. Additionally, phosphorylation site prediction revealed that both proteins may affect the signal transduction of host cells and can be involved in virus virulence. The present work highlights the usefulness of cloning and bioinformatics characterizations for gene investigation. Furthermore, the collected data are helpful for future experimental research related to the development of immunodiagnostic reagents and subunit vaccines based on the expression of immunogenic viral capsid proteins.

GPS 6.0: an updated server for prediction of kinase-specific phosphorylation sites in proteins

GPS 6.0: an updated server for prediction of kinase-specific phosphorylation sites in proteins

IPs-GRUAtt: An attention-based bidirectional gated recurrent unit network for predicting phosphorylation sites of SARS-CoV-2 infection

The global pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has generated tremendous concern and poses a serious threat to international public health. Phosphorylation is a common post-translational modification affecting many essential cellular processes and is inextricably linked to SARS-CoV-2 infection. Hence, accurate identification of phosphorylation sites will be helpful to understand the mechanisms of SARS-CoV-2 infection and mitigate the ongoing COVID-19 pandemic. In the present study, an attention-based bidirectional gated recurrent unit network, called IPs-GRUAtt, was proposed to identify phosphorylation sitesin SARS-CoV-2-infected host cells. Comparative results demonstrated that IPs-GRUAtt surpassed both state-of-the-art machine-learning methods and existing models for identifying phosphorylation sites. Moreover, the attention mechanism made IPs-GRUAtt able to extract the key features from protein sequences. These results demonstrated that the IPs-GRUAtt is a powerful tool for identifying phosphorylation sites. For facilitating its academic use, a freely available online web server for IPs-GRUAtt is provided at http://cbcb.cdutcm.edu.cn/phosphory/.