Using CHOU'S 5-Steps Rule to Predict O-Linked Serine Glycosylation Sites by Blending Position Relative Features and Statistical Moment
Glycosylation of proteins in eukaryote cells is an important and complicated post-translation modification due to its pivotal role and association with crucial physiological functions within most of the proteins. Identification of glycosylation sites in a polypeptide chain is not an easy task due to multiple impediments. Analytical identification of these sites is expensive and laborious. There is a dire need to develop a reliable computational method for precise determination of such sites which can help researchers to save time and effort. Herein, we propose a novel predictor namely iGlycoS-PseAAC by integrating the Chou's Pseudo Amino Acid Composition (PseAAC) and relative/absolute position-based features. The self-consistency results show that the accuracy revealed by the model using the benchmark dataset for prediction of O-linked glycosylation having serine sites is 98.8 percent. The overall accuracy of predictor achieved through 10-fold cross validation by combining the positive and negative results is 97.2 percent. The overall accuracy achieved through Jackknife test is 96.195 percent by aggregating of all the prediction results. Thus the proposed predictor can help in predicting the O-linked glycosylated serine sites in an efficient and accurate way. The overall results show that the accuracy of the iGlycoS-PseAAC is higher than the existing tools.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:18 |
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| Enthalten in: |
IEEE/ACM transactions on computational biology and bioinformatics - 18(2021), 5 vom: 22. Sept., Seite 2045-2056 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Akmal, Muhammad Aizaz [VerfasserIn] |
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| Anmerkungen: |
Date Completed 21.01.2022 Date Revised 21.01.2022 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1109/TCBB.2020.2968441 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM305819615 |
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| 100 | 1 | |a Akmal, Muhammad Aizaz |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Using CHOU'S 5-Steps Rule to Predict O-Linked Serine Glycosylation Sites by Blending Position Relative Features and Statistical Moment |
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| 500 | |a Date Revised 21.01.2022 | ||
| 500 | |a published: Print-Electronic | ||
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| 520 | |a Glycosylation of proteins in eukaryote cells is an important and complicated post-translation modification due to its pivotal role and association with crucial physiological functions within most of the proteins. Identification of glycosylation sites in a polypeptide chain is not an easy task due to multiple impediments. Analytical identification of these sites is expensive and laborious. There is a dire need to develop a reliable computational method for precise determination of such sites which can help researchers to save time and effort. Herein, we propose a novel predictor namely iGlycoS-PseAAC by integrating the Chou's Pseudo Amino Acid Composition (PseAAC) and relative/absolute position-based features. The self-consistency results show that the accuracy revealed by the model using the benchmark dataset for prediction of O-linked glycosylation having serine sites is 98.8 percent. The overall accuracy of predictor achieved through 10-fold cross validation by combining the positive and negative results is 97.2 percent. The overall accuracy achieved through Jackknife test is 96.195 percent by aggregating of all the prediction results. Thus the proposed predictor can help in predicting the O-linked glycosylated serine sites in an efficient and accurate way. The overall results show that the accuracy of the iGlycoS-PseAAC is higher than the existing tools | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 7 | |a Serine |2 NLM | |
| 650 | 7 | |a 452VLY9402 |2 NLM | |
| 700 | 1 | |a Hussain, Waqar |e verfasserin |4 aut | |
| 700 | 1 | |a Rasool, Nouman |e verfasserin |4 aut | |
| 700 | 1 | |a Khan, Yaser Daanial |e verfasserin |4 aut | |
| 700 | 1 | |a Khan, Sher Afzal |e verfasserin |4 aut | |
| 700 | 1 | |a Chou, Kuo-Chen |e verfasserin |4 aut | |
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