Sequence- and Structure-Based Analysis of Tissue-Specific Phosphorylation Sites.
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ABSTRACT:
SUBMITTER: Karabulut NP
PROVIDER: S-EPMC4917084 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND: Post-translational modifications (PTMs) have a key role in regulating cell functions. Consequently, identification of PTM sites has a significant impact on understanding protein function and revealing cellular signal transductions. Especially, phosphorylation is a ubiquitous process with a large portion of proteins undergoing this modification. Experimental methods to identify phosphorylation sites are labor-intensive and of high-cost. With the exponentially growing protein sequence data, development of computational approaches to predict phosphorylation sites is highly desirable. RESULTS: Here, we present a simple and effective method to recognize phosphorylation sites by combining sequence patterns and evolutionary information and by applying a novel noise-reducing algorithm. We suggested that considering long-range region surrounding a phosphorylation site is important for recognizing phosphorylation peptides. Also, from compared results to AutoMotif in 36 different kinase families, new method outperforms AutoMotif. The mean accuracy, precision, and recall of our method are 0.93, 0.67, and 0.40, respectively, whereas those of AutoMotif with a polynomial kernel are 0.91, 0.47, and 0.17, respectively. Also our method shows better or comparable performance in four main kinase groups, CDK, CK2, PKA, and PKC compared to six existing predictors. CONCLUSION: Our method is remarkable in that it is powerful and intuitive approach without need of a sophisticated training algorithm. Moreover, our method is generally applicable to other types of PTMs.
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Project description:BackgroundRyanodine receptor (RyR), a calcium-release channel located in the sarcoplasmic reticulum membrane of muscles, is the target of insecticides used against a wide range of agricultural pests. Mammalian RyRs have been shown to be under the regulatory control of several kinases and phosphatases, but little is known about the regulation of insect RyRs by phosphorylation.ResultsHere we present the crystal structures of wild-type and phospho-mimetic RyR Repeat34 domain containing PKA phosphorylation sites from diamondback moth (DBM), a major lepidopteran pest of cruciferous vegetables. The structure has unique features, not seen in mammalian RyRs, including an additional α-helix near the phosphorylation loop. Using tandem mass spectrometry, we identify several PKA sites clustering in the phosphorylation loop and the newly identified α-helix. Bioinformatics analysis shows that this α-helix is only present in Lepidoptera, suggesting an insect-specific regulation. Interestingly, the specific phosphorylation pattern is temperature-dependent. The thermal stability of the DBM Repeat34 domain is significantly lower than that of the analogous domain in the three mammalian RyR isoforms, indicating a more dynamic domain structure that can be partially unfolded to facilitate the temperature-dependent phosphorylation. Docking the structure into the cryo-electron microscopy model of full-length RyR reveals that the interface between the Repeat34 and neighboring HD1 domain is more conserved than that of the phosphorylation loop region that might be involved in the interaction with SPRY3 domain. We also identify an insect-specific glycerol-binding pocket that could be potentially targeted by novel insecticides to fight the current resistance crisis.ConclusionsThe crystal structures of the DBM Repeat34 domain reveals insect-specific temperature-dependent phosphorylation sites that may regulate insect ryanodine receptor function. It also reveals insect-specific structural features and a potential ligand-binding site that could be targeted in an effort to develop green pesticides with high species-specificity.
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Project description:BackgroundPost-translational modification (PTM) is a biological process that alters proteins and is therefore involved in the regulation of various cellular activities and pathogenesis. Protein phosphorylation is an essential process and one of the most-studied PTMs: it occurs when a phosphate group is added to serine (Ser, S), threonine (Thr, T), or tyrosine (Tyr, Y) residue. Dysregulation of protein phosphorylation can lead to various diseases-most commonly neurological disorders, Alzheimer's disease, and Parkinson's disease-thus necessitating the prediction of S/T/Y residues that can be phosphorylated in an uncharacterized amino acid sequence. Despite a surplus of sequencing data, current experimental methods of PTM prediction are time-consuming, costly, and error-prone, so a number of computational methods have been proposed to replace them. However, phosphorylation prediction remains limited, owing to substrate specificity, performance, and the diversity of its features.MethodsIn the present study we propose machine-learning-based predictors that use the physicochemical, sequence, structural, and functional information of proteins to classify S/T/Y phosphorylation sites. Rigorous feature selection, the minimum redundancy/maximum relevance approach, and the symmetrical uncertainty method were employed to extract the most informative features to train the models.ResultsThe RF and SVM models generated using diverse feature types in the present study were highly accurate as is evident from good values for different statistical measures. Moreover, independent test sets and benchmark validations indicated that the proposed method clearly outperformed the existing methods, demonstrating its ability to accurately predict protein phosphorylation.ConclusionsThe results obtained in the present work indicate that the proposed computational methodology can be effectively used for predicting putative phosphorylation sites further facilitating discovery of various biological processes mechanisms.
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Project description:Protein methylation plays vital roles in many biological processes and has been implicated in various human diseases. To fully understand the mechanisms underlying methylation for use in drug design and work in methylation-related diseases, an initial but crucial step is to identify methylation sites. The use of high-throughput bioinformatics methods has become imperative to predict methylation sites. In this study, we developed a novel method that is based only on sequence conservation to predict protein methylation sites. Conservation difference profiles between methylated and non-methylated peptides were constructed by the information entropy (IE) in a wider neighbor interval around the methylation sites that fully incorporated all of the environmental information. Then, the distinctive neighbor residues were identified by the importance scores of information gain (IG). The most representative model was constructed by support vector machine (SVM) for Arginine and Lysine methylation, respectively. This model yielded a promising result on both the benchmark dataset and independent test set. The model was used to screen the entire human proteome, and many unknown substrates were identified. These results indicate that our method can serve as a useful supplement to elucidate the mechanism of protein methylation and facilitate hypothesis-driven experimental design and validation.
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Project description:BackgroundStudies of gene regulation often utilize genome-wide predictions of transcription factor (TF) binding sites. Most existing prediction methods are based on sequence information alone, ignoring biological contexts such as developmental stages and tissue types. Experimental methods to study in vivo binding, including ChIP-chip and ChIP-seq, can only study one transcription factor in a single cell type and under a specific condition in each experiment, and therefore cannot scale to determine the full set of regulatory interactions in mammalian transcriptional regulatory networks.ResultsWe developed a new computational approach, PIPES, for predicting tissue-specific TF binding. PIPES integrates in vitro protein binding microarrays (PBMs), sequence conservation and tissue-specific epigenetic (DNase I hypersensitivity) information. We demonstrate that PIPES improves over existing methods on distinguishing between in vivo bound and unbound sequences using ChIP-seq data for 11 mouse TFs. In addition, our predictions are in good agreement with current knowledge of tissue-specific TF regulation.ConclusionsWe provide a systematic map of computationally predicted tissue-specific binding targets for 284 mouse TFs across 55 tissue/cell types. Such comprehensive resource is useful for researchers studying gene regulation.
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