Improved Species-Specific Lysine Acetylation Site Prediction Based on a Large Variety of Features Set.
Lysine acetylation is a major post-translational modification. It plays a vital role in numerous essential biological processes, such as gene expression and metabolism, and is related to some human diseases. To fully understand the regulatory mechanism of acetylation, identification of acetylation s...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2016-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0155370&type=printable |
| _version_ | 1826584014628061184 |
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| author | Qiqige Wuyun Wei Zheng Yanping Zhang Jishou Ruan Gang Hu |
| author_facet | Qiqige Wuyun Wei Zheng Yanping Zhang Jishou Ruan Gang Hu |
| author_sort | Qiqige Wuyun |
| collection | DOAJ |
| description | Lysine acetylation is a major post-translational modification. It plays a vital role in numerous essential biological processes, such as gene expression and metabolism, and is related to some human diseases. To fully understand the regulatory mechanism of acetylation, identification of acetylation sites is first and most important. However, experimental identification of protein acetylation sites is often time consuming and expensive. Therefore, the alternative computational methods are necessary. Here, we developed a novel tool, KA-predictor, to predict species-specific lysine acetylation sites based on support vector machine (SVM) classifier. We incorporated different types of features and employed an efficient feature selection on each type to form the final optimal feature set for model learning. And our predictor was highly competitive for the majority of species when compared with other methods. Feature contribution analysis indicated that HSE features, which were firstly introduced for lysine acetylation prediction, significantly improved the predictive performance. Particularly, we constructed a high-accurate structure dataset of H.sapiens from PDB to analyze the structural properties around lysine acetylation sites. Our datasets and a user-friendly local tool of KA-predictor can be freely available at http://sourceforge.net/p/ka-predictor. |
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| id | doaj.art-abfe524641a847d184b70981a8d61e8a |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2025-03-14T15:31:01Z |
| publishDate | 2016-01-01 |
| publisher | Public Library of Science (PLoS) |
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| spelling | doaj.art-abfe524641a847d184b70981a8d61e8a2025-02-25T05:36:19ZengPublic Library of Science (PLoS)PLoS ONE1932-62032016-01-01115e015537010.1371/journal.pone.0155370Improved Species-Specific Lysine Acetylation Site Prediction Based on a Large Variety of Features Set.Qiqige WuyunWei ZhengYanping ZhangJishou RuanGang HuLysine acetylation is a major post-translational modification. It plays a vital role in numerous essential biological processes, such as gene expression and metabolism, and is related to some human diseases. To fully understand the regulatory mechanism of acetylation, identification of acetylation sites is first and most important. However, experimental identification of protein acetylation sites is often time consuming and expensive. Therefore, the alternative computational methods are necessary. Here, we developed a novel tool, KA-predictor, to predict species-specific lysine acetylation sites based on support vector machine (SVM) classifier. We incorporated different types of features and employed an efficient feature selection on each type to form the final optimal feature set for model learning. And our predictor was highly competitive for the majority of species when compared with other methods. Feature contribution analysis indicated that HSE features, which were firstly introduced for lysine acetylation prediction, significantly improved the predictive performance. Particularly, we constructed a high-accurate structure dataset of H.sapiens from PDB to analyze the structural properties around lysine acetylation sites. Our datasets and a user-friendly local tool of KA-predictor can be freely available at http://sourceforge.net/p/ka-predictor.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0155370&type=printable |
| spellingShingle | Qiqige Wuyun Wei Zheng Yanping Zhang Jishou Ruan Gang Hu Improved Species-Specific Lysine Acetylation Site Prediction Based on a Large Variety of Features Set. PLoS ONE |
| title | Improved Species-Specific Lysine Acetylation Site Prediction Based on a Large Variety of Features Set. |
| title_full | Improved Species-Specific Lysine Acetylation Site Prediction Based on a Large Variety of Features Set. |
| title_fullStr | Improved Species-Specific Lysine Acetylation Site Prediction Based on a Large Variety of Features Set. |
| title_full_unstemmed | Improved Species-Specific Lysine Acetylation Site Prediction Based on a Large Variety of Features Set. |
| title_short | Improved Species-Specific Lysine Acetylation Site Prediction Based on a Large Variety of Features Set. |
| title_sort | improved species specific lysine acetylation site prediction based on a large variety of features set |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0155370&type=printable |
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