Genome-wide analysis to predict protein sequence variations that change phosphorylation sites or their corresponding kinases

We define phosphovariants as genetic variations that change phosphorylation sites or their interacting kinases. Considering the essential role of phosphorylation in protein functions, it is highly likely that phosphovariants change protein functions and may constitute a proportion of the mechanisms by which genetic variations cause individual differences or diseases. We categorized phosphovariants into three subtypes and developed a system that predicts them. Our method can be used to screen important polymorphisms and help to identify the mechanisms of genetic diseases

Using multitask classification methods to investigate the kinase-specific phosphorylation sites

<p>Abstract</p> <p>Background</p> <p>Identification of phosphorylation sites by computational methods is becoming increasingly important because it reduces labor-intensive and costly experiments and can improve our understanding of the common properties and underlying mechanisms of protein phosphorylation.</p> <p>Methods</p> <p>A multitask learning framework for learning four kinase families simultaneously, instead of studying each kinase family of phosphorylation sites separately, is presented in the study. The framework includes two multitask classification methods: the Multi-Task Least Squares Support Vector Machines (MTLS-SVMs) and the Multi-Task Feature Selection (MT-Feat3).</p> <p>Results</p> <p>Using the multitask learning framework, we successfully identify 18 common features shared by four kinase families of phosphorylation sites. The reliability of selected features is demonstrated by the consistent performance in two multi-task learning methods.</p> <p>Conclusions</p> <p>The selected features can be used to build efficient multitask classifiers with good performance, suggesting they are important to protein phosphorylation across 4 kinase families.</p

SiteSeek: Post-translational modification analysis using adaptive locality-effective kernel methods and new profiles

<p>Abstract</p> <p>Background</p> <p>Post-translational modifications have a substantial influence on the structure and functions of protein. Post-translational phosphorylation is one of the most common modification that occur in intracellular proteins. Accurate prediction of protein phosphorylation sites is of great importance for the understanding of diverse cellular signalling processes in both the human body and in animals. In this study, we propose a new machine learning based protein phosphorylation site predictor, SiteSeek. SiteSeek is trained using a novel compact evolutionary and hydrophobicity profile to detect possible protein phosphorylation sites for a target sequence. The newly proposed method proves to be more accurate and exhibits a much stable predictive performance than currently existing phosphorylation site predictors.</p> <p>Results</p> <p>The performance of the proposed model was compared to nine existing different machine learning models and four widely known phosphorylation site predictors with the newly proposed PS-Benchmark_1 dataset to contrast their accuracy, sensitivity, specificity and correlation coefficient. SiteSeek showed better predictive performance with 86.6% accuracy, 83.8% sensitivity, 92.5% specificity and 0.77 correlation-coefficient on the four main kinase families (CDK, CK2, PKA, and PKC).</p> <p>Conclusion</p> <p>Our newly proposed methods used in SiteSeek were shown to be useful for the identification of protein phosphorylation sites as it performed much better than widely known predictors on the newly built PS-Benchmark_1 dataset.</p

Identifying Human Kinase-Specific Protein Phosphorylation Sites by Integrating Heterogeneous Information from Various Sources

Phosphorylation is an important type of protein post-translational modification. Identification of possible phosphorylation sites of a protein is important for understanding its functions. Unbiased screening for phosphorylation sites by in vitro or in vivo experiments is time consuming and expensive; in silico prediction can provide functional candidates and help narrow down the experimental efforts. Most of the existing prediction algorithms take only the polypeptide sequence around the phosphorylation sites into consideration. However, protein phosphorylation is a very complex biological process in vivo. The polypeptide sequences around the potential sites are not sufficient to determine the phosphorylation status of those residues. In the current work, we integrated various data sources such as protein functional domains, protein subcellular location and protein-protein interactions, along with the polypeptide sequences to predict protein phosphorylation sites. The heterogeneous information significantly boosted the prediction accuracy for some kinase families. To demonstrate potential application of our method, we scanned a set of human proteins and predicted putative phosphorylation sites for Cyclin-dependent kinases, Casein kinase 2, Glycogen synthase kinase 3, Mitogen-activated protein kinases, protein kinase A, and protein kinase C families (avaiable at http://cmbi.bjmu.edu.cn/huphospho). The predicted phosphorylation sites can serve as candidates for further experimental validation. Our strategy may also be applicable for the in silico identification of other post-translational modification substrates

PhosTryp: a phosphorylation site predictor specific for parasitic protozoa of the family trypanosomatidae

<p>Abstract</p> <p>Background</p> <p>Protein phosphorylation modulates protein function in organisms at all levels of complexity. Parasites of the <it>Leishmania </it>genus undergo various developmental transitions in their life cycle triggered by changes in the environment. The molecular mechanisms that these organisms use to process and integrate these external cues are largely unknown. However <it>Leishmania </it>lacks transcription factors, therefore most regulatory processes may occur at a post-translational level and phosphorylation has recently been demonstrated to be an important player in this process. Experimental identification of phosphorylation sites is a time-consuming task. Moreover some sites could be missed due to the highly dynamic nature of this process or to difficulties in phospho-peptide enrichment.</p> <p>Results</p> <p>Here we present PhosTryp, a phosphorylation site predictor specific for trypansomatids. This method uses an SVM-based approach and has been trained with recent <it>Leishmania </it>phosphosproteomics data. PhosTryp achieved a 17% improvement in prediction performance compared with Netphos, a non organism-specific predictor. The analysis of the peptides correctly predicted by our method but missed by Netphos demonstrates that PhosTryp captures <it>Leishmania</it>-specific phosphorylation features. More specifically our results show that <it>Leishmania </it>kinases have sequence specificities which are different from their counterparts in higher eukaryotes. Consequently we were able to propose two possible <it>Leishmania</it>-specific phosphorylation motifs.</p> <p>We further demonstrate that this improvement in performance extends to the related trypanosomatids <it>Trypanosoma brucei </it>and <it>Trypanosoma cruzi</it>. Finally, in order to maximize the usefulness of PhosTryp, we trained a predictor combining all the peptides from <it>L. infantum, T. brucei and T. cruzi</it>.</p> <p>Conclusions</p> <p>Our work demonstrates that training on organism-specific data results in an improvement that extends to related species. PhosTryp is freely available at <url>http://phostryp.bio.uniroma2.it</url></p

Predicting post-translational lysine acetylation using support vector machines

Motivation: Lysine acetylation is a post-translational protein modification and a primary regulatory mechanism that controls many cell signaling processes. Lysine acetylation sites are recognized by acetyltransferases and deacetylases through sequence patterns (motifs). Recently, we used high-resolution mass spectrometry to identify 3600 lysine acetylation sites on 1750 human proteins covering most of the previously annotated sites and providing the most comprehensive acetylome so far. This dataset should provide an excellent source to train support vector machines (SVMs) allowing the high accuracy in silico prediction of acetylated lysine residues

Charge environments around phosphorylation sites in proteins

Background: Phosphorylation is a central feature in many biological processes. Structural analyses have identified the importance of charge-charge interactions, for example mediating phosphorylation-driven allosteric change and protein binding to phosphopeptides. Here, we examine computationally the prevalence of charge stabilisation around phosphorylated sites in the structural database, through comparison with locations that are not phosphorylated in the same structures. Results: A significant fraction of phosphorylated sites appear to be electrostatically stabilised, largely through interaction with sidechains. Some examples of stabilisation across a subunit interface are evident from calculations with biological units. When considering the immediately surrounding environment, in many cases favourable interactions are only apparent after conformational change that accompanies phosphorylation. A simple calculation of potential interactions at longer-range, applied to non-phosphorylated structures, recovers the separation exhibited by phosphorylated structures. In a study of sites in the Phospho.ELM dataset, for which structural annotation is provided by non-phosphorylated proteins, there is little separation of the known phospho-acceptor sites relative to background, even using the wider interaction radius. However, there are differences in the distributions of patch polarity for acceptor and background sites in the Phospho.ELM dataset. Conclusion: In this study, an easy to implement procedure is developed that could contribute to the identification of phospho-acceptor sites associated with charge-charge interactions and conformational change. Since the method gives information about potential anchoring interactions subsequent to phosphorylation, it could be combined with simulations that probe conformational change. Our analysis of the Phospho.ELM dataset also shows evidence for mediation of phosphorylation effects through (i) conformational change associated with making a solvent inaccessible phospho-acceptor site accessible, and (ii) modulation of protein-protein interactions

Computational Prediction of O-linked Glycosylation Sites That Preferentially Map on Intrinsically Disordered Regions of Extracellular Proteins

O-glycosylation of mammalian proteins is one of the important posttranslational modifications. We applied a support vector machine (SVM) to predict whether Ser or Thr is glycosylated, in order to elucidate the O-glycosylation mechanism. O-glycosylated sites were often found clustered along the sequence, whereas other sites were located sporadically. Therefore, we developed two types of SVMs for predicting clustered and isolated sites separately. We found that the amino acid composition was effective for predicting the clustered type, whereas the site-specific algorithm was effective for the isolated type. The highest prediction accuracy for the clustered type was 74%, while that for the isolated type was 79%. The existence frequency of amino acids around the O-glycosylation sites was different in the two types: namely, Pro, Val and Ala had high existence probabilities at each specific position relative to a glycosylation site, especially for the isolated type. Independent component analyses for the amino acid sequences around O-glycosylation sites showed the position-specific existences of the identified amino acids as independent components. The O-glycosylation sites were preferentially located within intrinsically disordered regions of extracellular proteins: particularly, more than 90% of the clustered O-GalNAc glycosylation sites were observed in intrinsically disordered regions. This feature could be the key for understanding the non-conservation property of O-glycosylation, and its role in functional diversity and structural stability