Prediction of phosphorylation sites based on Krawtchouk image moments.

Protein phosphorylation represents a ubiquitous control mechanism in living cells. The structural prerequisites and consequences of this important post-translational modification, however, are poorly understood. Oncoprotein 18/stathmin (Op18) is a globally disordered phosphoprotein that is involved in the regulation of the microtubule (MT) filament system. Here we document that phosphorylation of Ser63, which is located within a helix initiation site in Op18, disrupts the transiently formed amphipathic helix. The phosphoryl group reduces tubulin binding 10-fold and suppresses the MT polymerization inhibition activity of Op18's C-terminal domain. Op18 represents an example where phosphorylation occurs within a regular secondary structural element. Together, our findings have implications for the prediction of phosphorylation sites and give insights into the molecular behavior of a globally disordered protein.

Phosphorylation ranks among the most crucial post-translational modifications (PTMs), significantly influencing the conformation, activity, and functionality of proteins, and is intricately associated with numerous pathophysiological processes. Therefore, proposing a scientifically valid computational method for precise prediction of phosphorylation sites carries substantial importance. In this study, we propose and evaluate a novel method, PEGN-PSP, based on graph techniques and language models for the prediction of general phosphorylation sites. This method employs an adaptive feature fusion strategy, combining sequence embeddings and pretrained model embeddings to enhance the model's capability in representing features. The use of graph neural network attention mechanisms not only captures local patterns of protein sequences, but also effectively captures long-range dependency information between residues in protein sequences. Independent test results indicate that, in comparison to the current state-of-the-art methods for general phosphorylation site prediction, PEGN-PSP improves the Matthews correlation coefficient for S/T sites by 4.6% and for Y sites by 6.5%. Additionally, PEGN-PSP has good robustness in predicting lysine crotonylation sites, indicating that our method PEGN-PSP has strong potential in predicting other protein post-translational modification sites.

BackgroundProtein phosphorylation catalyzed by kinases plays crucial regulatory roles in cellular processes. Given the high-throughput mass spectrometry-based experiments, the desire to annotate the catalytic kinases for in vivo phosphorylation sites has motivated. Thus, a variety of computational methods have been developed for performing a large-scale prediction of kinase-specific phosphorylation sites. However, most of the proposed methods solely rely on the local amino acid sequences surrounding the phosphorylation sites. An increasing number of three-dimensional structures make it possible to physically investigate the structural environment of phosphorylation sites.ResultsIn this work, all of the experimental phosphorylation sites are mapped to the protein entries of Protein Data Bank by sequence identity. It resulted in a total of 4508 phosphorylation sites containing the protein three-dimensional (3D) structures. To identify phosphorylation sites on protein 3D structures, this work incorporates support vector machines (SVMs) with the information of linear motifs and spatial amino acid composition, which is determined for each kinase group by calculating the relative frequencies of 20 amino acid types within a specific radial distance from central phosphorylated amino acid residue. After the cross-validation evaluation, most of the kinase-specific models trained with the consideration of structural information outperform the models considering only the sequence information. Furthermore, the independent testing set which is not included in training set has demonstrated that the proposed method could provide a comparable performance to other popular tools.ConclusionThe proposed method is shown to be capable of predicting kinase-specific phosphorylation sites on 3D structures and has been implemented as a web server which is freely accessible at http://csb.cse.yzu.edu.tw/PhosK3D/. Due to the difficulty of identifying the kinase-specific phosphorylation sites with similar sequenced motifs, this work also integrates the 3D structural information to improve the cross classifying specificity.

Post-translational modifications provide the proteins with the possibility to perform functions in addition to those determined by their primary sequence. However, analysis of multifunctional protein structures in the environment of cells and body fluids is made especially difficult by the presence of other interacting proteins. Bioinformatics tools are therefore helpful to predict protein multifunctionality through the identification of serine and threonine residues wherein the hydroxyl group is likely to become modified by phosphorylation or glycosylation. Moreover, serines and threonines where both modifications are likely to occur can also be predicted (YinYang sites), to suggest further functional versatility. Structural modifications of hydroxyl groups of P-, E-, and L-selectins have been predicted and possible functions resulting from such modifications are proposed. Functional changes of the three selectins are based on the assumption that transitory and reversible protein modifications by phosphate and O-GlcNAc cause specific conformational changes and generate binding sites for other proteins. The computer-assisted prediction of glycosylation and phosphorylation sites in selectins should be helpful to assess the contribution of dynamic protein modifications in selectin-mediated inflammatory responses and cell-cell adhesion processes that are difficult to determine experimentally.

Reversible protein phosphorylation is one of the most important post-translational modifications, which regulates various biological cellular processes. Identification of the kinase-specific phosphorylation sites is helpful for understanding the phosphorylation mechanism and regulation processes. Although a number of computational approaches have been developed, currently few studies are concerned about hierarchical structures of kinases, and most of the existing tools use only local sequence information to construct predictive models. In this work, we conduct a systematic and hierarchy-specific investigation of protein phosphorylation site prediction in which protein kinases are clustered into hierarchical structures with four levels including kinase, subfamily, family and group. To enhance phosphorylation site prediction at all hierarchical levels, functional information of proteins, including gene ontology (GO) and protein-protein interaction (PPI), is adopted in addition to primary sequence to construct prediction models based on random forest. Analysis of selected GO and PPI features shows that functional information is critical in determining protein phosphorylation sites for every hierarchical level. Furthermore, the prediction results of Phospho.ELM and additional testing dataset demonstrate that the proposed method remarkably outperforms existing phosphorylation prediction methods at all hierarchical levels. The proposed method is freely available at http://bioinformatics.ustc.edu.cn/phos_pred/.

DeepPPSite: A deep learning-based model for analysis and prediction of phosphorylation sites using efficient sequence information

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 490 762 non-redundant p-sites in 71 407 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 30 043 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.

Protein phosphorylation is one of the most critical post-translational modifications of proteins in eukaryotes, which is essential for a variety of biological processes. Plenty of attempts have been made to improve the performance of computational predictors for phosphorylation site prediction. However, most of them are based on extra domain knowledge or feature selection. In this article, we present a novel deep learning-based predictor, named TransPhos, which is constructed using a transformer encoder and densely connected convolutional neural network blocks, for predicting phosphorylation sites. Data experiments are conducted on the datasets of PPA (version 3.0) and Phospho. ELM. The experimental results show that our TransPhos performs better than several deep learning models, including Convolutional Neural Networks (CNN), Long-term and short-term memory networks (LSTM), Recurrent neural networks (RNN) and Fully connected neural networks (FCNN), and some state-of-the-art deep learning-based prediction tools, including GPS2.1, NetPhos, PPRED, Musite, PhosphoSVM, SKIPHOS, and DeepPhos. Our model achieves a good performance on the training datasets of Serine (S), Threonine (T), and Tyrosine (Y), with AUC values of 0.8579, 0.8335, and 0.6953 using 10-fold cross-validation tests, respectively, and demonstrates that the presented TransPhos tool considerably outperforms competing predictors in general protein phosphorylation site prediction.

We present here the recent update of AutoMotif Server (AMS 2.0) that predicts post-translational modification sites in protein sequences. The support vector machine (SVM) algorithm was trained on data gathered in 2007 from various sets of proteins containing experimentally verified chemical modifications of proteins. Short sequence segments around a modification site were dissected from a parent protein, and represented in the training set as binary or profile vectors. The updated efficiency of the SVM classification for each type of modification and the predictive power of both representations were estimated using leave-one-out tests for model of general phosphorylation and for modifications catalyzed by several specific protein kinases. The accuracy of the method was improved in comparison to the previous version of the service (Plewczynski et al., "AutoMotif server: prediction of single residue post-translational modifications in proteins", Bioinformatics 21: 2525-7, 2005). The precision of the updated version reached over 90% for selected types of phosphorylation and was optimized in trade of lower recall value of the classification model. The AutoMotif Server version 2007 is freely available at http://ams2.bioinfo.pl/ . Additionally, the reference dataset for optimization of prediction of phosphorylation sites, collected from the UniProtKB was also provided and can be accessed at http://ams2.bioinfo.pl/data/ .

In this paper, we propose several active learning strategies to train classifiers for phosphorylation site prediction. When combined with support vector machine, we show that active learning with SVM is able to produce classifiers that give comparable or better phosphorylation site prediction performance than conventional SVM techniques and, at the same time, require a significantly less number of annotated protein training samples. The result has both conceptual and practical implications in protein prediction: it exploits information inherent in the large scale database of non-annotated protein samples and reduces the amount of manual labor required for protein annotation. To the best of our knowledge, active learning has not been explored in phosphorylation sites prediction. Several active learning strategies: single-running mode, batch-running mode with sample and support vector diversity, were investigated for phosphorylation sites prediction in this work. Our experiments have shown that active learning with SVM is able to reduce the effort of protein annotation by 6.6% to 25.7% to yield similar prediction performance as compared with conventional SVM technique.

GPS: a novel group-based phosphorylation predicting and scoring method

Tau is a major microtubule-associated protein of axons and is also the principal component of the paired helical filaments (PHFs) that comprise the neurofibrillary tangles found in Alzheimer's disease and other tauopathies. Besides phosphorylation of tau on serine and threonine residues in both normal tau and tau from neurofibrillary tangles, Tyr-18 was reported to be a site of phosphorylation by the Src-family kinase Fyn. We examined whether tyrosine residues other than Tyr-18 are phosphorylated in tau and whether other tyrosine kinases might phosphorylate tau. Using mass spectrometry, we positively identified phosphorylated Tyr-394 in PHF-tau from an Alzheimer brain and in human fetal brain tau. When wild-type human tau was transfected into fibroblasts or neuroblastoma cells, treatment with pervanadate caused tau to become phosphorylated on tyrosine by endogenous kinases. By replacing each of the five tyrosines in tau with phenylalanine, we identified Tyr-394 as the major site of tyrosine phosphorylation in tau. Tyrosine phosphorylation of tau was inhibited by PP2 (4-amino-5-(4-chlorophenyl-7-(t-butyl)pyrazolo[3,4-d]pyrimidine), which is known to inhibit Src-family kinases and c-Abl. Cotransfection of tau and kinases showed that Tyr-18 was the major site for Fyn phosphorylation, but Tyr-394 was the main residue for Abl. In vitro, Abl phosphorylated tau directly. Abl could be coprecipitated with tau and was present in pretangle neurons in brain sections from Alzheimer cases. These results show that phosphorylation of tau on Tyr-394 is a physiological event that is potentially part of a signal relay and suggest that Abl could have a pathogenic role in Alzheimer's disease.

Accurate identificationof phosphorylation sites is fundamentalfor advancing the study of protein phosphorylation, which plays acritical role in elucidating protein function and facilitating rationaldrug design. While mass spectrometry-based experimental techniquesare considered the effective method for phosphorylation site identification,their widespread application is often hindered by high costs, limitedthroughput, and the requirement for specialized equipment. Computationalprediction methods have become a popular alternative strategy forproviding candidate phosphorylation sites by inferring their presencefrom the entire protein sequence. However, many current methods struggleto extract sufficient contextual features, limiting their predictivereliability in complex biological settings. To address these issues,we present a novel protein large language model (pLLM)-based model,called ProtPSP, for phosphorylation site prediction. By leveraginga pretrained pLLM, ProtPSP effectively captures complex sequence contextsthat conventional models trained on limited phosphorylation data maymiss. Comprehensive benchmarking on curated data sets demonstratesthat ProtPSP achieves reliable phosphorylation site prediction forboth serine/threonine and tyrosine sites, outperforming other commonlyused methods across multiple evaluation metrics. Ablation studiessubstantiate the critical contribution of both pLLM-driven featuresand the fusion model architecture to overall performance improvements.Moreover, case studies demonstrate that ProtPSP consistently identifiesall true phosphorylation sites, underscoring its significant potentialas a complementary approach to mass spectrometry-based techniquesin biomedical research and drug discovery.

Several studies have suggested that the function of glutamate receptor channels can be regulated by protein phosphorylation. Furthermore, a basal level of phosphorylation may be necessary to maintain receptor function. Little is known, however, about the phosphorylation state of glutamate receptor channels in neurons and how it is regulated by synaptic activity. In this study, we have investigated the phosphorylation of the AMPA-preferring glutamate receptor subunit GluR1 in cortical neurons in primary culture. These neurons elaborate extensive processes, form functional synapses, and exhibit spontaneous 4-8 sec bursts of synaptic activity every 15-20 sec. In cultures in which this synaptic activity was suppressed by tetrodotoxin and MK-801, the GluR1 protein was phosphorylated on serine residues within a single tryptic phosphopeptide, as determined by phosphoamino acid analysis and phosphopeptide mapping. This same peptide was basally phosphorylated in recombinant GluR1 receptors transiently expressed in human embryonal kidney 293 cells. Treatment of these synaptically inactive cortical neurons with the adenylyl cyclase activator forskolin resulted in a robust increase in phosphorylation on serine residues on a phosphopeptide distinct from the basally phosphorylated peptide. Again, this same phosphopeptide was observed in recombinant GluR1 receptors isolated from 293 cells coexpressing the catalytic subunit of cAMP-dependent protein kinase. Spontaneous synaptic activity in cultures of cortical neurons resulted in a consistent, rapid (within 10-30 sec) increase in phosphorylation on serine and threonine residues. Interestingly, these phosphopeptides were also phosphorylated when neurons from inactive cultures were stimulated with phorbol esters, which activate protein kinase C. These results indicate that AMPA receptors containing the GluR1 subunit may be regulated by extracellular signals working through the cAMP second messenger system as well as by synaptic activity, possibly acting through protein kinase C. Such regulation by protein phosphorylation may be involved in short-term changes in synaptic efficacy thought to involve the functional modulation of AMPA receptors.

Protein phosphorylation is a posttranslational modification that is essential for normal cellular processes; however, abnormal phosphorylation is one of the prime causes for alteration of many structural, functional, and regulatory proteins in disease conditions. In cancer, changes in the states of protein phosphorylation in tyrosine residues have been more studied than phosphorylation in threonine or serine residues, which also undergo alterations with greater predominance. In general, serine phosphorylation leads to the formation of multimolecular signaling complexes that regulate diverse biological processes, but in pathological conditions such as tumorigenesis, anomalous phosphorylation may result in the deregulation of some signaling pathways. Cervical cancer (CC), the main neoplasm associated with human papillomavirus (HPV) infection, is the fourth most frequent cancer worldwide. Persistent infection of the cervix with high-risk human papillomaviruses produces precancerous lesions starting with low-grade squamous intraepithelial lesions (LSIL), progressing to high-grade squamous intraepithelial lesions (HSIL) until CC is generated. Here, we compared the proteomic profile of phosphorylated proteins in serine residues from healthy, LSIL, HSIL, and CC samples. Our data show an increase in the number of phosphorylated proteins in serine residues as the grade of injury rises. These results provide a support for future studies focused on phosphorylated proteins and their possible correlation with the progression of cervical lesions.