Protein Phosphorylation Sites Research Articles

Phosphoproteomic analysis reveals the mechanisms of human umbilical cord mesenchymal stem cell-derived exosomes attenuate renal aging

Aging is a critical biological process, with particularly notable impacts on the kidneys. Exosomes derived from human umbilical cord mesenchymal stem cells (hUC-MSCs) are capable of transferring various bioactive molecules, which exhibit beneficial therapeutic effects on kidney diseases. This study demonstrates that exosomes derived from hUC-MSCs ameliorate cellular senescence in the kidneys of naturally aging mice. These exosomes reduce the protein expression of senescence markers and senescence-associated secretory phenotypes (SASP) leading to fewer DNA damage foci and increased expression of the proliferation indicator Ki67. During the aging process, many proteins undergo phosphorylation modifications. We utilized data-independent acquisition (DIA) phosphoproteomics to study kidneys of naturally aging mice and those treated with hUC-MSC-derived exosomes. We observed elevated phosphorylation levels of the differentially phosphorylated proteins, Lamin A/C, at Ser390 and Ser392 sites, which were subsequently verified by western blotting. Overall, this study provides a new molecular characterization of hUC-MSC-derived exosomes in mitigating cellular senescence in the kidneys. SignificanceDIA phosphoproteomics was employed to investigate phosphorylated proteins in the kidney tissues of naturally aging mice with hUCMSC-exos treated. The results demonstrated that the DIA technique detected a higher abundance of phosphorylated proteins. We identified 24 significantly differentially phosphorylated proteins, and found that the phosphorylation of specific Lamin A/C sites is crucial for preventing cellular senescence. This study will help to better reveal the related phosphorylated proteins involved in hUCMSC-exos intervention in the kidneys of naturally aging mice, providing a foundation for future research on specific phosphorylation sites of proteins as potential therapeutic targets for renal aging-related diseases.

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.

Identifying an Abnormal Phosphorylated Adaptor by Viral Kinase Using Mass Spectrometry.

Mass spectrometers are widely used to identify protein phosphorylation sites. The process usually involves selective isolation of phosphoproteins and subsequent fragmentation to identify both the peptide sequence and phosphorylation site. Immunoprecipitation could capture and purify the protein of interest, greatly reducing sample complexity before submitting it for mass spectrometry analysis. This chapter describes a method to identify an abnormal phosphorylated site of the adaptor protein by a viral kinase through immunoprecipitation followed by LC-MS/MS.

Res-GCN: Identification of protein phosphorylation sites using graph convolutional network and residual network

An essential post-translational modification, phosphorylation is intimately related with a wide range of biological activities. The advancement of effective computational methods for correctly recognizing phosphorylation sites is important for in-depth understanding of various physiological phenomena. However, the traditional method of identifying phosphorylation sites experimentally is time-consuming and laborious, which makes it difficult to meet the processing demands of today's big data. This research proposes the use of a novel model, Res-GCN, to recognize the phosphorylation sites of SARS-CoV-2. Firstly, eight feature extraction strategies are utilized to digitize the protein sequence from multiple viewpoints, including amino acid property encodings (AAindex), pseudo-amino acid composition (PseAAC), adapted normal distribution bi-profile Bayes (ANBPB), dipeptide composition (DC), binary encoding (BE), enhanced amino acid composition (EAAC), Word2Vec, and BLOSUM62 matrices. Secondly, elastic net is utilized to eliminate redundant data in the fused matrix. Finally, a combination of graph convolutional network (GCN) and residual network (ResNet) is used to classify the phosphorylated sites and output predictions using a fully connected layer (FC). The performance of Res-GCN is tested by 5-fold cross-validation and independent testing, and excellent results are obtained on S/T and Y datasets. This demonstrates that the Res-GCN model exhibits exceptional predictive performance and generalizability.

Chromosome-level genome assembly of a deep-sea Venus flytrap sea anemone sheds light upon adaptations to an extremely oligotrophic environment.

The Venus flytrap sea anemone Actinoscyphia liui inhabits the nutrient-limited deep ocean in the tropical western Pacific. Compared with most other sea anemones, it has undergone a distinct modification of body shape similar to that of the botanic flytrap. However, the molecular mechanism by which such a peculiar sea anemone adapts to a deep-sea oligotrophic environment is unknown. Here, we report the chromosomal-level genome of A. liui constructed from PacBio and Hi-C data. The assembled genome is 522 Mb in size and exhibits a continuous scaffold N50 of 58.4 Mb. Different from most other sea anemones, which typically possess 14-18 chromosomes per haplotype, A. liui has only 11. The reduced number of chromosomes is associated with chromosome fusion, which likely represents an adaptive strategy to economize energy in oligotrophic deep-sea environments. Comparative analysis with other deep-sea sea anemones revealed adaptive evolution in genes related to cellular autophagy (TMBIM6, SESN1, SCOCB and RPTOR) and mitochondrial energy metabolism (MDH1B and KAD2), which may aid in A. liui coping with severe food scarcity. Meanwhile, the genome has undergone at least two rounds of expansion in gene families associated with fast synaptic transmission, facilitating rapid responses to water currents and prey. Positive selection was detected on putative phosphorylation sites of muscle contraction-related proteins, possibly further improving feeding efficiency. Overall, the present study provides insights into the molecular adaptation to deep-sea oligotrophic environments and sheds light upon potential effects of a novel morphology on the evolution of Cnidaria.

Calorie restriction and rapamycin distinctly mitigate aging-associated protein phosphorylation changes in mouse muscles

Calorie restriction (CR) and treatment with rapamycin (RM), an inhibitor of the mTORC1 growth-promoting signaling pathway, are known to slow aging and promote health from worms to humans. At the transcriptome and proteome levels, long-term CR and RM treatments have partially overlapping effects, while their impact on protein phosphorylation within cellular signaling pathways have not been compared. Here we measured the phosphoproteomes of soleus, tibialis anterior, triceps brachii and gastrocnemius muscles from adult (10 months) and 30-month-old (aged) mice receiving either a control, a calorie restricted or an RM containing diet from 15 months of age. We reproducibly detected and extensively analyzed a total of 6960 phosphosites, 1415 of which are not represented in standard repositories. We reveal the effect of these interventions on known mTORC1 pathway substrates, with CR displaying greater between-muscle variation than RM. Overall, CR and RM have largely consistent, but quantitatively distinct long-term effects on the phosphoproteome, mitigating age-related changes to different degrees. Our data expands the catalog of protein phosphorylation sites in the mouse, providing important information regarding their tissue-specificity, and revealing the impact of long-term nutrient-sensing pathway inhibition on mouse skeletal muscle.

Location of Phosphorylation Sites within Long Polypeptide Chains by Binder-Assisted Nanopore Detection.

The detection and mapping of protein phosphorylation sites are essential for understanding the mechanisms of various cellular processes and for identifying targets for drug development. The study of biopolymers at the single-molecule level has been revolutionized by nanopore technology. In this study, we detect protein phosphorylation within long polypeptides (>700 amino acids), after the attachment of binders that interact with phosphate monoesters; electro-osmosis is used to drive the tagged chains through engineered protein nanopores. By monitoring the ionic current carried by a nanopore, phosphorylation sites are located within individual polypeptide chains, providing a valuable step toward nanopore proteomics.

More than two components: complexities in bacterial phosphosignaling.

For over 40 years, the two-component systems (TCSs) have taken front and center in our thinking about the signaling mechanisms by which bacteria sense and respond to their environment. In contrast, phosphorylation on Ser/Thr and Tyr (O-phosphorylation) was long thought to be mostly restricted to eukaryotes and a somewhat accessory signaling mechanism in bacteria. Several recent studies exploring systems aspects of bacterial O-phosphorylation, however, now show that it is in fact pervasive, with some bacterial proteomes as highly phosphorylated as those of eukaryotes. Labile, non-canonical protein phosphorylation sites on Asp, Arg, and His are now also being identified in large numbers in bacteria and first cellular functions are discovered. Other phosphomodifications on Cys, Glu, and Lys remain largely unexplored. The surprising breadth and complexity of bacterial phosphosignaling reveals a vast signaling capacity, the full scope of which we may only now be beginning to understand but whose functions are likely to affect all aspects of bacterial physiology and pathogenesis.

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.

The protein phosphorylation landscape in photosystem I of the desert algae Chlorella sp.

The phosphorylation of photosystem II (PSII) and its antenna (LHCII) proteins has been studied, and its involvement in state transitions and PSII repair is known. Yet, little is known about the phosphorylation of photosystem I (PSI) and its antenna (LHCI) proteins. Here, we applied proteomics analysis to generate a map of the phosphorylation sites of the PSI-LHCI proteins in Chlorella ohadii cells that were grown under low or extreme high-light intensities (LL and HL). Furthermore, we analyzed the content of oxidized tryptophans and PSI-LHCI protein degradation products in these cells, to estimate the light-induced damage to PSI-LHCI. Our work revealed the phosphorylation of 17 of 22 PSI-LHCI subunits. The analyses detected the extensive phosphorylation of the LHCI subunits Lhca6 and Lhca7, which is modulated by growth light intensity. Other PSI-LHCI subunits were phosphorylated to a lesser extent, including PsaE, where molecular dynamic simulation proposed that a phosphoserine stabilizes ferredoxin binding. Additionally, we show that HL-grown cells accumulate less oxidative damage and degradation products of PSI-LHCI proteins, compared with LL-grown cells. The significant phosphorylation of Lhca6 and Lhca7 at the interface with other LHCI subunits suggests a physiological role during photosynthesis, possibly by altering light-harvesting characteristics and binding of other subunits.

A resource database for protein kinase substrate sequence-preference motifs based on large-scale mass spectrometry data

BackgroundProtein phosphorylation is one of the most prevalent posttranslational modifications involved in molecular control of cellular processes, and is mediated by over 520 protein kinases in humans and other mammals. Identification of the protein kinases responsible for phosphorylation events is key to understanding signaling pathways. Unbiased phosphoproteomics experiments have generated a wealth of data that can be used to identify protein kinase targets and their preferred substrate sequences.MethodsThis study utilized prior data from mass spectrometry-based studies identifying sites of protein phosphorylation after in vitro incubation of protein mixtures with recombinant protein kinases. PTM-Logo software was used with these data to generate position-dependent Shannon information matrices and sequence motif ‘logos’. Webpages were constructed for facile access to logos for each kinase and a new stand-alone application was written in Python that uses the position-dependent Shannon information matrices to identify kinases most likely to phosphorylate a particular phosphorylation site.ResultsA database of kinase substrate target preference logos allows browsing, searching, or downloading target motif data for each protein kinase (https://esbl.nhlbi.nih.gov/Databases/Kinase_Logos/). These logos were combined with phylogenetic analysis of protein kinase catalytic sequences to reveal substrate preference patterns specific to particular groups of kinases (https://esbl.nhlbi.nih.gov/Databases/Kinase_Logos/KinaseTree.html). A stand-alone program, KinasePredictor, is provided (https://esbl.nhlbi.nih.gov/Databases/Kinase_Logos/KinasePredictor.html). It takes as input, amino-acid sequences surrounding a given phosphorylation site and generates a ranked list of protein kinases most likely to phosphorylate that site.ConclusionsThis study provides three new resources for protein kinase characterization. It provides a tool for prediction of kinase-substrate interactions, which in combination with other types of data (co-localization, etc.), can predict which kinases are likely responsible for a given phosphorylation event in a given tissue.-q3N7pjxaL8wVBVRsJ9wBTVideo

Endothelium-mediated regulation of platelet activation: Involvement of multiple protein kinases.

The endothelial regulation of platelet activity is incompletely understood. Here we describe novel approaches to find molecular pathways implicated on the platelet-endothelium interaction. Using high-shear whole-blood microfluidics, employing coagulant or non-coagulant conditions at physiological temperature, we observed that the presence of human umbilical vein endothelial cells (HUVEC) strongly suppressed platelet adhesion and activation, via the collagen receptor glycoprotein VI (GPVI) and the PAR receptors for thrombin. Real-time monitoring of the cytosolic Ca2+ rises in the platelets indicated no major improvement of inhibition by prostacyclin or nitric oxide. Similarly under stasis, exposure of isolated platelets to HUVEC reduced the Ca2+ responses by collagen-related peptide (CRP-XL, GPVI agonist) and thrombin (PAR agonist). We then analyzed the label-free phosphoproteome of platelets (three donors), exposed to HUVEC, CRP-XL, and/or thrombin. High-resolution mass spectrometry gave 5463 phosphopeptides, corresponding to 1472 proteins, with good correlation between biological and technical replicates (R > .86). Stringent filtering steps revealed 26 regulatory pathways (Reactome) and 143 regulated kinase substrates (PhosphoSitePlus), giving a set of protein phosphorylation sites that was differentially (44) or similarly (110) regulated by HUVEC or agonist exposure. The differential regulation was confirmed by stable-isotope analysis of platelets from two additional donors. Substrate analysis indicated major roles of poorly studied protein kinase classes (MAPK, CDK, DYRK, STK, PKC members). Collectively, these results reveal a resetting of the protein phosphorylation profile in platelets exposed to endothelium or to conventional agonists and to endothelium-promoted activity of a multi-kinase network, beyond classical prostacyclin and nitric oxide actors, that may contribute to platelet inhibition.

CURTAIN—A unique web-based tool for exploration and sharing of MS-based proteomics data

To facilitate analysis and sharing of mass spectrometry (MS)-based proteomics data, we created online tools called CURTAIN (https://curtain.proteo.info) and CURTAIN-PTM (https://curtainptm.proteo.info) with an accompanying series of video tutorials (https://www.youtube.com/@CURTAIN-me6hl). These are designed to enable non-MS experts to interactively peruse volcano plots and deconvolute primary experimental data so that replicates can be visualized in bar charts or violin plots and exported in publication-ready format. They also allow assessment of overall experimental quality by correlation matrix and profile plot analysis. After making a selection of protein "hits", the user can analyze known domain structure, AlphaFold predicted structure, reported interactors, relative expression as well as disease links. CURTAIN-PTM permits analysis of all identified PTM sites on protein(s) of interest with selected databases. CURTAIN-PTM also links with the Kinase Library to predict upstream kinases that may phosphorylate sites of interest. We provide examples of the utility of CURTAIN and CURTAIN-PTM in analyzing how targeted degradation of the PPM1H Rab phosphatase that counteracts the Parkinson's LRRK2 kinase impacts cellular protein levels and phosphorylation sites. We also reanalyzed a ubiquitylation dataset, characterizing the PINK1-Parkin pathway activation in primary neurons, revealing data of interest not highlighted previously. CURTAIN and CURTAIN-PTM are free to use and open source, enabling researchers to share and maximize the impact of their proteomics data. We advocate that MS data published in volcano plot format be reported containing a shareable CURTAIN weblink, thereby allowing readers to better analyze and exploit the data.

Structural characterization of phosphorylation sites of the MLKL protein using molecular dynamics

Structural characterization of phosphorylation sites of the MLKL protein using molecular dynamics

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.

5-HT-treated mouse B cells alleviate ulcerative colitis via RIPK1: Insights from proteomic and phosphoproteomic analyses

5-hydroxytryptamine (5-HT) exerts various physiological effects on the intestine through different signaling pathways and molecular transmission mechanisms, including pro- and anti-inflammatory effects. Adoptive transfer of regulatory B cells (Bregs) into colitis mice has exhibited significant therapeutic benefits. We aimed to elucidate the mechanism through which 5-HT-treated B cells alleviate ulcerative colitis. To this end, we analyzed the proteomic and phosphoproteomic profiles of 5-HT-stimulated B cells from naïve mice. We identified 3124 phosphorylation sites in proteins via tandem mass tagging and found 110 differential peptides after protein phosphorylation. Furthermore, we obtained three differential proteins, RIPK1, ATXN2l, and Q8C5K5 through integration of both proteomic datasets. We discovered and validated that 5-HT binds to 5-HT7R and increases the expression of RIPK1 in B cells. We propose a theoretical and experimental basis for further research on the RIPK1 signaling pathway, kinase prediction, and phosphorylation sites in ulcerative colitis. SignificanceSome researchers demonstrated that 5-HT can effectively suppress colitis through a variety of molecular mechanisms. Our study discovered and consistently validated the 5-HT/5-HT7R/RIPK1 pathway, further clarifying the molecular mechanism through which 5-HT stimulates B cells to alleviate intestinal inflammation.

Phosphate Migration versus the Loss of Phosphoric Acid in Protonated Phosphopeptides: A Computational Study.

Residue-specific phosphorylation is a protein post-translational modification that regulates cellular functions. Experimental determination of the exact sites of protein phosphorylation provides an understanding of the signaling and processes at work for a given cellular state. Any experimental artifact that involves migration of the phosphate group during measurement is a concern, as the outcome can lead to erroneous conclusions that may confound studies on cellular signal transduction. Herein, we examine computationally the mechanism by which a phosphate group migrates from one serine residue to another serine in monoprotonated pentapeptides [BA-pSer-Gly-Ser-BB + H]+ → [BA-Ser-Gly-pSer-BB + H]+ (where BA and BB are different combinations of the three basic amino acids, histidine, lysine, and arginine). In addition to moving the phosphate group, the overall mechanism involves transferring a proton from the N-terminal amino acid, BA, to the C-terminal amino acid, BB. This is not a synchronous process, and there is a key high-energy intermediate, structure C, that is zwitterionic with both the basic amino acids protonated and the phosphate group attached to both serine residues and carrying a negative charge. The barriers to moving the phosphate group are calculated to be in the range of 219-274 kJ mol-1 at the B3LYP/6-31G(d) level. These barriers are systematically slightly lower and in good agreement with single-point energy calculations at both M06-2X/6-311++G(d,p) and MP2/6-31++G(d,p) levels. The competitive reaction, loss of phosphoric acid from the protonated pentapeptides, has a barrier in the range of 176-202 kJ mol-1 at the B3LYP/6-31G(d) level. Extension of the theory to M06-2X/6-311++G(d,p)//B3LYP/6-31G(d) and MP2/6-31++G(d,p)// B3LYP/6-31G(d) gives higher values for the loss of phosphoric acid, falling in the range of 196-226 kJ mol-1; these are comparable to the barriers against phosphate migration at the same levels of theory. For larger peptides His-pSer-(Gly)n-Ser-His, where n has values from 2 to 5, the barriers against the loss of phosphoric acid are higher than those against the phosphate group migration. This difference is most pronounced and significant when n = 4 and 5 (the differences are approximately 80 kJ mol-1 under the single-point energy calculations at the M06-2X and MP2 levels). Energy differences using two more recent functionals, M08-HX and MN15, on His-pSer-(Gly)n-Ser-His, where n = 1 and 5, are in good agreement with the M06-2X and MP2 calculations. These results provide the mechanistic rationale for phosphate migration versus other competing reactions in the gas phase under tandem mass spectrometry conditions.

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.

Phosphorylation of PB2 at serine 181 restricts viral replication and virulence of the highly pathogenic H5N1 avian influenza virus in mice

Influenza A virus (IAV) continues to pose a pandemic threat to public health, resulting a high mortality rate annually and during pandemic years. Posttranslational modification of viral protein plays a substantial role in regulating IAV infection. Here, based on immunoprecipitation (IP)-based mass spectrometry (MS) and purified virus-coupled MS, a total of 89 phosphorylation sites distributed among 10 encoded viral proteins of IAV were identified, including 60 novel phosphorylation sites. Additionally, for the first time, we provide evidence that PB2 can also be acetylated at site K187. Notably, the PB2 S181 phosphorylation site was consistently identified in both IP-based MS and purified virus-based MS. Both S181 and K187 are exposed on the surface of the PB2 protein and are highly conserved in various IAV strains, suggesting their fundamental importance in the IAV life cycle. Bioinformatic analysis results demonstrated that S181E/A and K187Q/R mimic mutations do not significantly alter the PB2 protein structure. While continuous phosphorylation mimicked by the PB2 S181E mutation substantially decreases viral fitness in mice, PB2 K187Q mimetic acetylation slightly enhances viral virulence in mice. Mechanistically, PB2 S181E substantially impairs viral polymerase activity and viral replication, remarkably dampens protein stability and nuclear accumulation of PB2, and significantly weakens IAV-induced inflammatory responses. Therefore, our study further enriches the database of phosphorylation and acetylation sites of influenza viral proteins, laying a foundation for subsequent mechanistic studies. Meanwhile, the unraveled antiviral effect of PB2 S181E mimetic phosphorylation may provide a new target for the subsequent study of antiviral drugs.