SysPTM 2.0: an updated systematic resource for post-translational modification

Li, Jing; Jia, Jia; Li, Hong; Jian, Yu; Sun, Han; He, Ying; Lv, Daqing; Yang, Xiaojuan; Glocker, Michael O.; Ma, Liangxiao; Yang, Jiabei; Li, Ling; Li, Wei; Zhang, Guoqing; Liu, Qian; Li, Yixue; Xie, Lu

doi:10.1093/database/bau025

Cited by 64 publications

(48 citation statements)

References 63 publications

(75 reference statements)

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“…Thus, to investigate whether proteins modified by major PTM types tend to be a target of drugs, we compared the proportions of proteins with PTMs between hDP + and hDP 0 . Considering major PTM types, such as phosphorylation, ubiquitination, and acetylation [37, 38], hDP + contained relatively higher number of PTM residues than hDP 0 . This type of pattern was likewise observed in all three PTM types (Fig.…”

Section: Resultsmentioning

confidence: 99%

In silico re-identification of properties of drug target proteins

Kim

Han

et al. 2017

BMC Bioinformatics

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BackgroundComputational approaches in the identification of drug targets are expected to reduce time and effort in drug development. Advances in genomics and proteomics provide the opportunity to uncover properties of druggable genomes. Although several studies have been conducted for distinguishing drug targets from non-drug targets, they mainly focus on the sequences and functional roles of proteins. Many other properties of proteins have not been fully investigated.MethodsUsing the DrugBank (version 3.0) database containing nearly 6,816 drug entries including 760 FDA-approved drugs and 1822 of their targets and human UniProt/Swiss-Prot databases, we defined 1578 non-redundant drug target and 17,575 non-drug target proteins. To select these non-redundant protein datasets, we built four datasets (A, B, C, and D) by considering clustering of paralogous proteins.ResultsWe first reassessed the widely used properties of drug target proteins. We confirmed and extended that drug target proteins (1) are likely to have more hydrophobic, less polar, less PEST sequences, and more signal peptide sequences higher and (2) are more involved in enzyme catalysis, oxidation and reduction in cellular respiration, and operational genes. In this study, we proposed new properties (essentiality, expression pattern, PTMs, and solvent accessibility) for effectively identifying drug target proteins. We found that (1) drug targetability and protein essentiality are decoupled, (2) druggability of proteins has high expression level and tissue specificity, and (3) functional post-translational modification residues are enriched in drug target proteins. In addition, to predict the drug targetability of proteins, we exploited two machine learning methods (Support Vector Machine and Random Forest). When we predicted drug targets by combining previously known protein properties and proposed new properties, an F-score of 0.8307 was obtained.ConclusionsWhen the newly proposed properties are integrated, the prediction performance is improved and these properties are related to drug targets. We believe that our study will provide a new aspect in inferring drug-target interactions.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1639-3) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

In silico re-identification of properties of drug target proteins

Kim

Han

et al. 2017

BMC Bioinformatics

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“…Phosphorylation sites can also occur as local clusters (“hotspots”) of several sites on a given protein, very likely also functioning as such; i.e. collectively as “clouds” rather than individual sites …”

Section: Introductionmentioning

confidence: 99%

Spatial proximity statistics suggest a regulatory role of protein phosphorylation on compound binding

Korkuć

Walther

2016

Proteins

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Phosphorylation is an important post-translational modification that regulates protein function by the attachment of negatively charged phosphate groups to phosphorylatable amino acid residues. As a mode of action, an influence of phosphorylation on the binding of compounds to proteins has been discussed and described for a number of proteins in the literature. However, a systematic statistical survey probing for enriched phosphorylation sites close to compound binding sites in support of this notion and with properly chosen random reference distributions has not been presented yet. Using high-resolution protein structures from the Protein Data Bank including their co-crystallized non-covalently bound compounds and experimentally determined phosphorylation sites, we analyzed the pairwise distance distributions of phosphorylation and compound binding sites on protein surfaces. We found that phosphorylation sites are indeed located at significantly closer distances to compounds than expected by chance holding true specifically also for the subset of compound binding sites serving as catalytic sites of metabolic reactions. This tendency was particularly evident when treating phosphorylation sites as collective sets supporting the relevance of phosphorylation hotspots. Interestingly, phosphorylation sites were found to be closer to negatively charged than to positively charged compounds suggesting a stronger modulation of the binding of negatively charged compounds in dependence on phosphorylation status than on positively charged compounds. The enrichment of phosphorylation sites near compound binding sites confirms a regulatory role of phosphorylation in compound binding and provides a solid statistical basis for the literature-reported selected events.

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“…Only experimentally validated methylated proteins. 78 Given the above, developing species-specific methylation site predictor is necessary and feasible.…”

Section: Future Perspectivesmentioning

confidence: 99%

Progress and challenges in predicting protein methylation sites

Shi

Wen

et al. 2015

Mol. BioSyst.

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Protein methylation catalyzed by methyltransferases carries many important biological functions. Methylation and their regulatory enzymes are involved in a variety of human disease states, raising the possibility that abnormally methylated proteins can be disease markers and methyltransferases are potential therapeutic targets. Identification of methylation sites is a prerequisite for decoding methylation regulatory networks in living cells and understanding their physiological roles that have been implicated in the pathological processes. Due to various limitations of experimental methods, in silico approaches for identifying novel methylation sites have become increasingly popular. In this review, we summarize the progress in the prediction of protein methylation sites from the dataset, feature representation, prediction algorithm and online resources in the past ten years. We also discuss the challenges that are faced while developing novel predictors in the future. The development and application of methylation site prediction is a promising field of systematic biology, provided that protein methyltransferases, species and functional information will be taken into account.

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SysPTM 2.0: an updated systematic resource for post-translational modification

Cited by 64 publications

References 63 publications

In silico re-identification of properties of drug target proteins

In silico re-identification of properties of drug target proteins

Spatial proximity statistics suggest a regulatory role of protein phosphorylation on compound binding

Progress and challenges in predicting protein methylation sites

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