DephosSite: a machine learning approach for discovering phosphotase-specific dephosphorylation sites

Wang, Xiaofeng; Yan, Renxiang; Song, Jiangning

doi:10.1038/srep23510

Cited by 15 publications

(13 citation statements)

References 61 publications

(60 reference statements)

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“…Both Shp1 and Shp2 show a preference for acidic and aromatic residues on either side of the target pTyr residue, with just subtle differences observed between the two phosphatases. More recent computational methods have confirmed these general principles for Shp1 and Shp2 substrate preferences [39]. These observations match well with known substrates of Shp1 found in myeloid cells, including p62Dok [40], Syk [41], Vav The Ptpn6 gene is found on mouse chromosome 6 and on human chromosome 12p13.…”

Section: Myeloid Substrates Of Shp1supporting

confidence: 75%

Shp1 function in myeloid cells

Abram

Lowell

2017

Journal of Leukocyte Biology

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The mouse was first described in 1975 as a model of systemic inflammation and autoimmunity, as a result of immune system dysregulation. The phenotype was later ascribed to mutations in the cytoplasmic tyrosine phosphatase Shp1. This phosphatase is expressed widely throughout the hematopoietic system and has been shown to impact a multitude of cell signaling pathways. The determination of which cell types contribute to the different aspects of the phenotype caused by global Shp1 loss or mutation and which pathways within these cell types are regulated by Shp1 is important to further our understanding of immune system regulation. In this review, we focus on the role of Shp1 in myeloid cells and how its dysregulation affects immune function, which can impact human disease.

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Section: Myeloid Substrates Of Shp1supporting

confidence: 75%

Shp1 function in myeloid cells

Abram

Lowell

2017

Journal of Leukocyte Biology

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“…The PseAAC employed in apolipoprotein prediction model has been widely used in various fields of computational proteomics ( Feng et al, 2013 ; Yang et al, 2016 ; Long et al, 2017 ). Another feature expression of CKSAAP used for subfamily classification is also a convenient tool in bioinformatics ( Wang et al, 2016 ; Li et al, 2017 ; Cheng, 2019 ). The prediction models based on these features achieved encouraging results in 10-fold cross validation, which are demonstrated by cluster analysis via t-distributed stochastic neighbor embedding (t-SNE).…”

Section: Discussionmentioning

confidence: 99%

ApoPred: Identification of Apolipoproteins and Their Subfamilies With Multifarious Features

Liu

Chen

Zhang

et al. 2021

Front. Cell Dev. Biol.

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Apolipoprotein is a group of plasma proteins that are associated with a variety of diseases, such as hyperlipidemia, atherosclerosis, Alzheimer’s disease, and diabetes. In order to investigate the function of apolipoproteins and to develop effective targets for related diseases, it is necessary to accurately identify and classify apolipoproteins. Although it is possible to identify apolipoproteins accurately through biochemical experiments, they are expensive and time-consuming. This work aims to establish a high-efficiency and high-accuracy prediction model for recognition of apolipoproteins and their subfamilies. We firstly constructed a high-quality benchmark dataset including 270 apolipoproteins and 535 non-apolipoproteins. Based on the dataset, pseudo-amino acid composition (PseAAC) and composition of k-spaced amino acid pairs (CKSAAP) were used as input vectors. To improve the prediction accuracy and eliminate redundant information, analysis of variance (ANOVA) was used to rank the features. And the incremental feature selection was utilized to obtain the best feature subset. Support vector machine (SVM) was proposed to construct the classification model, which could produce the accuracy of 97.27%, sensitivity of 96.30%, and specificity of 97.76% for discriminating apolipoprotein from non-apolipoprotein in 10-fold cross-validation. In addition, the same process was repeated to generate a new model for predicting apolipoprotein subfamilies. The new model could achieve an overall accuracy of 95.93% in 10-fold cross-validation. According to our proposed model, a convenient webserver called ApoPred was established, which can be freely accessed at http://tang-biolab.com/server/ApoPred/service.html. We expect that this work will contribute to apolipoprotein function research and drug development in relevant diseases.

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“…The KSNPF frequencies are nucleotide pairs separated by k arbitrary nucleotides and have been successfully employed for the prediction of mucin-type O-glycosylation sites 39 and phosphotase-specific dephosphorylation sites 40 . The KSNPF can be calculated using the following equation:where n 1 and n 2 represent a pair of sequence elements.…”

Section: Methodsmentioning

confidence: 99%

csDMA: an improved bioinformatics tool for identifying DNA 6 mA modifications via Chou’s 5-step rule

Liu

Dong

Jiang

et al. 2019

Sci Rep

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DNA N6-methyldeoxyadenosine (6 mA) modifications were first found more than 60 years ago but were thought to be only widespread in prokaryotes and unicellular eukaryotes. With the development of high-throughput sequencing technology, 6 mA modifications were found in different multicellular eukaryotes by using experimental methods. However, the experimental methods were time-consuming and costly, which makes it is very necessary to develop computational methods instead. In this study, a machine learning-based prediction tool, named csDMA, was developed for predicting 6 mA modifications. Firstly, three feature encoding schemes, Motif, Kmer, and Binary, were used to generate the feature matrix. Secondly, different algorithms were selected into the prediction model and the ExtraTrees model received the best AUC of 0.878 by using 5-fold cross-validation on the training dataset. Besides, the ExtraTrees model also received the best AUC of 0.893 on the independent testing dataset. Finally, we compared our method with state-of-the-art predictors and the results shown that our model achieved better performance than existing tools.

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DephosSite: a machine learning approach for discovering phosphotase-specific dephosphorylation sites

Cited by 15 publications

References 61 publications

Shp1 function in myeloid cells

Shp1 function in myeloid cells

ApoPred: Identification of Apolipoproteins and Their Subfamilies With Multifarious Features

csDMA: an improved bioinformatics tool for identifying DNA 6 mA modifications via Chou’s 5-step rule

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