Literature DB >> 28425870

iPreny-PseAAC: Identify C-terminal Cysteine Prenylation Sites in Proteins by Incorporating Two Tiers of Sequence Couplings into PseAAC.

Yan Xu1, Zu Wang1, Chunhui Li2, Kuo-Chen Chou3.   

Abstract

BACKGROUND: Occurring at the cysteine residue in the C-terminal of a protein, prenylation is a special kind of post-translational modification (PTM), which may play a key role for statin in altering immune function. Therefore, knowledge of the prenylation sites in proteins is important for drug development as well as for in-depth understanding the biological process concerned.
OBJECTIVE: Given a query protein whose C-terminal contains some cysteine residues, which one can be of prenylation or none of them can be prenylated?
METHODS: To address this problem, we have developed a new predictor, called "iPreny-PseAAC", by incorporating two tiers of sequence pair coupling effects into the general form of PseAAC (pseudo amino acid composition).
RESULTS: It has been observed by four different cross-validation approaches that all the important indexes in reflecting its prediction quality are quite high and fully consistent to each other.
CONCLUSION: It is anticipated that the iPreny-PseAAC predictor holds very high potential to become a useful high throughput tool in identifying protein C-terminal cysteine prenylation sites and the other relevant areas. To maximize the convenience for most experimental biologists, the webserver for the new predictor has been established at http://app.aporc.org/iPreny-PseAAC/, by which users can easily get their desired results without needing to go through the mathematical details involved in this paper. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Entities:  

Keywords:  Autoimmune disease; PseAAC; SVM; cysteine prenylation; protein C-terminal; web-server

Mesh:

Substances:

Year:  2017        PMID: 28425870     DOI: 10.2174/1573406413666170419150052

Source DB:  PubMed          Journal:  Med Chem        ISSN: 1573-4064            Impact factor:   2.745


  21 in total

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Journal:  Mol Biol Rep       Date:  2018-09-20       Impact factor: 2.316

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5.  Assessing the Performances of Protein Function Prediction Algorithms from the Perspectives of Identification Accuracy and False Discovery Rate.

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Journal:  Int J Mol Sci       Date:  2018-01-08       Impact factor: 5.923

6.  iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition.

Authors:  Wang-Ren Qiu; Shi-Yu Jiang; Zhao-Chun Xu; Xuan Xiao; Kuo-Chen Chou
Journal:  Oncotarget       Date:  2017-06-20

7.  Small molecular floribundiquinone B derived from medicinal plants inhibits acetylcholinesterase activity.

Authors:  Bing Niu; Mengying Zhang; Pu Du; Li Jiang; Rui Qin; Qiang Su; Fuxue Chen; Dongshu Du; Yilai Shu; Kuo-Chen Chou
Journal:  Oncotarget       Date:  2017-07-11

8.  iDNAProt-ES: Identification of DNA-binding Proteins Using Evolutionary and Structural Features.

Authors:  Shahana Yasmin Chowdhury; Swakkhar Shatabda; Abdollah Dehzangi
Journal:  Sci Rep       Date:  2017-11-02       Impact factor: 4.379

9.  Prediction of HIV-1 and HIV-2 proteins by using Chou's pseudo amino acid compositions and different classifiers.

Authors:  Juan Mei; Ji Zhao
Journal:  Sci Rep       Date:  2018-02-05       Impact factor: 4.379

10.  Improving succinylation prediction accuracy by incorporating the secondary structure via helix, strand and coil, and evolutionary information from profile bigrams.

Authors:  Abdollah Dehzangi; Yosvany López; Sunil Pranit Lal; Ghazaleh Taherzadeh; Abdul Sattar; Tatsuhiko Tsunoda; Alok Sharma
Journal:  PLoS One       Date:  2018-02-12       Impact factor: 3.240

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