Protein Phosphorylation Sites Research Articles

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.

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.

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.

IDPpub: Illuminating the Dark Phosphoproteome Through PubMed Mining

Global phosphoproteomics experiments quantify tens of thousands of phosphorylation sites. However, data interpretation is hampered by our limited knowledge on functions, biological contexts, or precipitating enzymes of the phosphosites. This study establishes a repository of phosphosites with associated evidence in biomedical abstracts, using deep learning-based natural language processing techniques. Our model for illuminating the dark phosphoproteome through PubMed mining (IDPpub) was generated by fine-tuning BioBERT, a deep learning tool for biomedical text mining. Trained using sentences containing protein substrates and phosphorylation site positions from 3000 abstracts, the IDPpub model was then used to extract phosphorylation sites from all MEDLINE abstracts. The extracted proteins were normalized to gene symbols using the National Center for Biotechnology Information gene query, and sites were mapped to human UniProt sequences using ProtMapper and mouse UniProt sequences by direct match. Precision and recall were calculated using 150 curated abstracts, and utility was assessed by analyzing the CPTAC (Clinical Proteomics Tumor Analysis Consortium) pan-cancer phosphoproteomics datasets and the PhosphoSitePlus database. Using 10-fold cross validation, pairs of correct substrates and phosphosite positions were extracted with an average precision of 0.93 and recall of 0.94. After entity normalization and site mapping to human reference sequences, an independent validation achieved a precision of 0.91 and recall of 0.77. The IDPpub repository contains 18,458 unique human phosphorylation sites with evidence sentences from 58,227 abstracts and 5918 mouse sites in 14,610 abstracts. This included evidence sentences for 1803 sites identified in CPTAC studies that are not covered by manually curated functional information in PhosphoSitePlus. Evaluation results demonstrate the potential of IDPpub as an effective biomedical text mining tool for collecting phosphosites. Moreover, the repository (http://idppub.ptmax.org), which can be automatically updated, can serve as a powerful complement to existing resources.

DeepMPSF: A Deep Learning Network for Predicting General Protein Phosphorylation Sites Based on Multiple Protein Sequence Features.

Phosphorylation, as one of the most important post-translational modifications, plays a key role in various cellular physiological processes and disease occurrences. In recent years, computer technology has been gradually applied to the prediction of protein phosphorylation sites. However, most existing methods rely on simple protein sequence features that provide limited contextual information. To overcome this limitation, we propose DeepMPSF, a phosphorylation site prediction model based on multiple protein sequence features. There are two types of features: sequence semantic features, which comprise protein residue type information and relative position information within protein sequence, and protein background biophysical features, which include global semantic information containing more comprehensive protein background information obtained from pretrained models. To extract these features, DeepMPSF employs two separate subnetworks: the S71SFE module and the BBFE module, which automatically extract high-level semantic features. Our model incorporates a learning strategy for handling imbalanced datasets through ensemble learning during training and prediction. DeepMPSF is trained and evaluated on a well-established dataset of human proteins. Comparing the analysis with other benchmark methods reveals that DeepMPSF outperforms in predicting both S/T residues and Y residues. In particular, DeepMPSF showed excellent generalization performance in cross-species blind test performance, with an average improvement of 5.63%/5.72%, 22.28%/25.94%, 20.11%/17.49%, and 26.40%/28.33% for Mus musculus/Rattus norvegicus test sets in area under curves (AUCs) of ROC curve, AUC of the PR curve, F1-score, and MCC metrics, respectively. Furthermore, it also shows excellent performance in the latest updated case of natural proteins with functional phosphorylation sites. Through an ablation study and visual analysis, we uncover that the design of different feature modules significantly contributes to the accurate classification of DeepMPSF, which provides valuable insights for predicting phosphorylation sites and offers effective support for future downstream research.

Quantitative mapping of the in vivo O-GalNAc glycoproteome in mouse tissues identifies GalNAc-T2 O-glycosites in metabolic disorder.

The family of GalNAc-Ts (GalNAcpolypeptide:N-Acetylgalactosaminyl transferases) catalyzes the first committed step in the synthesis of O-glycans, which is an abundant and biologically important protein modification. Abnormalities in the activity of individual GalNAc-Ts can result in congenital disorders of O-glycosylation (CDG) and influence a broad array of biological functions. How site-specific O-glycans regulate biology is unclear. Compiling in vivo O-glycosites would be an invaluable step in determining the function of site-specific O-glycans. We integrated chemical and enzymatic conditions that cleave O-glycosites, a higher-energy dissociation product ions-triggered electron-transfer/higher-energy collision dissociation mass spectrometry (MS) workflow and software to study nine mouse tissues and whole blood. We identified 2,154 O-glycosites from 595 glycoproteins. The O-glycosites and glycoproteins displayed consensus motifs and shared functions as classified by Gene Ontology terms. Limited overlap of O-glycosites was observed with protein O-GlcNAcylation and phosphorylation sites. Quantitative glycoproteomics and proteomics revealed a tissue-specific regulation of O-glycosites that the differential expression of Galnt isoenzymes in tissues partly contributes to. We examined the Galnt2-null mouse model, which phenocopies congenital disorder of glycosylation involving GALNT2 and revealed a network of glycoproteins that lack GalNAc-T2-specific O-glycans. The known direct and indirect functions of these glycoproteins appear consistent with the complex metabolic phenotypes observed in the Galnt2-null animals. Through this study and interrogation of databases and the literature, we have compiled an atlas of experimentally identified mouse O-glycosites consisting of 2,925 O-glycosites from 758 glycoproteins.

Genome-Wide Identification and Evolution of the GRF Gene Family and Functional Characterization of PbGRF18 in Pear.

Proteins encoded by the G-box regulating factor (GRF, also called 14-3-3) gene family are involved in protein-protein interactions and mediate signaling transduction, which play important roles in plant growth, development, and stress responses. However, there were no detailed investigations of the GRF gene family in pear at present. In this study, we identified 25 GRF family members in the pear genome. Based on a phylogenetic analysis, the 25 GRF genes were clustered into two groups; the ε group and the non-ε group. Analyses of the exon-intron structures and motifs showed that the gene structures were conserved within each of the ε and non-ε groups. Gene duplication analysis indicated that most of the PbGRF gene expansion that occurred in both groups was due to WGD/segmental duplication. Phosphorylation sites analysis showed that the main phosphorylation sites of PbGRF proteins were serine residues. For gene expression, five PbGRF genes (PbGRF7, PbGRF11, PbGRF16, PbGRF21, and PbGRF23) were highly expressed in fruits, and PbGRF18 was highly expressed in all tissues. Further analysis revealed that eight PbGRF genes were significantly differentially expressed after treatment with different sugars; the expression of PbGRF7, PbGRF8, and PbGRF11 significantly increased, implying the involvement of these genes in sugar signaling. In addition, subcellular localization studies showed that the tested GRF proteins localize to the plasma membrane, and transgenic analysis showed that PbGRF18 can increase the sugar content in tomato leaves and fruit. The results of our research establish a foundation for functional determination of PbGRF proteins, and will help to promote a further understanding of the regulatory network in pear fruit development.

An Inherent Difference between Serine and Threonine Phosphorylation: Phosphothreonine Strongly Prefers a Highly Ordered, Compact, Cyclic Conformation.

Phosphorylation and dephosphorylation of proteins by kinases and phosphatases are central to cellular responses and function. The structural effects of serine and threonine phosphorylation were examined in peptides and in proteins, by circular dichroism, NMR spectroscopy, bioinformatics analysis of the PDB, small-molecule X-ray crystallography, and computational investigations. Phosphorylation of both serine and threonine residues induces substantial conformational restriction in their physiologically more important dianionic forms. Threonine exhibits a particularly strong disorder-to-order transition upon phosphorylation, with dianionic phosphothreonine preferentially adopting a cyclic conformation with restricted ϕ (ϕ ∼ -60°) stabilized by three noncovalent interactions: a strong intraresidue phosphate-amide hydrogen bond, an n → π* interaction between consecutive carbonyls, and an n → σ* interaction between the phosphate Oγ lone pair and the antibonding orbital of C-Hβ that restricts the χ2 side-chain conformation. Proline is unique among the canonical amino acids for its covalent cyclization on the backbone. Phosphothreonine can mimic proline's backbone cyclization via noncovalent interactions. The preferred torsions of dianionic phosphothreonine are ϕ,ψ = polyproline II helix > α-helix (ϕ ∼ -60°); χ1 = g-; χ2 ∼ +115° (eclipsed C-H/O-P bonds). This structural signature is observed in diverse proteins, including in the activation loops of protein kinases and in protein-protein interactions. In total, these results suggest a structural basis for the differential use and evolution of threonine versus serine phosphorylation sites in proteins, with serine phosphorylation typically inducing smaller, rheostat-like changes, versus threonine phosphorylation promoting larger, step function-like switches, in proteins.

Identification genetic variations in some heat shock protein genes of Tali goat breed and study their structural and functional effects on relevant proteins.

Animals of different regions have adapted to adverse environmental conditions by modifying their phenotypic and genotypic characteristics in the long run. In this study, the effect of genetic variations of 10 heat shock protein (HSP) genes (HSP70A4, HSP70A9, HSP40C17, HSP40C27, HSP90AA1, HSP90AB1, HSPB7, HSPB11, HSPD1 and HSPE1) on the three-dimensional protein structure and function of proteins in Tali goat (a tropical breed) were studied and were compared with Saanen goat (as a sensitive breed). A pooled DNA of 15 samples from blood was sequenced and mapped to the goat reference sequence. The bioinformatics analysis was used to identify nsSNPs in the Tali breed and was compared with the Saanen goat. Four online bioinformatics tools (Sorting Intolerant from Tolerant, Protein Variation Effect Analyzer, Polymorphism Phenotyping version2 and Single Nucleotide Polymorphism Database and Gene Ontology) showed three deleterious missense nsSNPs and seven natural missense SNPs in these HSPs genes of Tali goat. Out of 10 reported nsSNPs, 5 nsSNPs in HSP70A4, 1 nsSNP inHSP70A9, 2 nsSNPs in HSP40C17, 1 nsSNP in HSP40C27 and 1 nsSNP in HSPD1 were detected. ConSurf tools showed that the majority of the predicted nsSNPs occur in conserved sites. Moreover, several post-translational modification (PTM) predictors computed the probability of post-translation change of nsSNPs. The putative phosphorylation and glycosylation sites in HSPs proteins were substitutions rs669769139 and rs666336692 of the Tali goat breed. These results on the effect of type of genetic variants on the function of HSP proteins will assist to predict the resistance to hard conditions in goat breeds. Considering that the identified SNPid rs669769139 (S248) which is located on the N-terminal ATPase domain of HSP70A4 is a PTM site with a highly conserved score and a natural substitution on changing the stability and benign protein that can affect the functional and structural characterization of HSPs protein for adaptation to the local climate.

A phospho-dawn of protein modification anticipates light onset in the picoeukaryote Ostreococcus tauri.

Diel regulation of protein levels and protein modification had been less studied than transcript rhythms. Here, we compare transcriptome data under light-dark cycles with partial proteome and phosphoproteome data, assayed using shotgun MS, from the alga Ostreococcus tauri, the smallest free-living eukaryote. A total of 10% of quantified proteins but two-thirds of phosphoproteins were rhythmic. Mathematical modelling showed that light-stimulated protein synthesis can account for the observed clustering of protein peaks in the daytime. Prompted by night-peaking and apparently dark-stable proteins, we also tested cultures under prolonged darkness, where the proteome changed less than under the diel cycle. Among the dark-stable proteins were prasinophyte-specific sequences that were also reported to accumulate when O. tauri formed lipid droplets. In the phosphoproteome, 39% of rhythmic phospho-sites reached peak levels just before dawn. This anticipatory phosphorylation suggests that a clock-regulated phospho-dawn prepares green cells for daytime functions. Acid-directed and proline-directed protein phosphorylation sites were regulated in antiphase, implicating the clock-related casein kinases 1 and 2 in phase-specific regulation, alternating with the CMGC protein kinase family. Understanding the dynamic phosphoprotein network should be facilitated by the minimal kinome and proteome of O. tauri. The data are available from ProteomeXchange, with identifiers PXD001734, PXD001735, and PXD002909.