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SuccSPred: Succinylation Sites Prediction Using Fused Feature Representation and Ranking Method

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Bioinformatics Research and Applications (ISBRA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13064))

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Abstract

Protein succinylation is a novel type of post-translational modification in recent decade years. Experiments verified that it played an important role in biological structure and functions. However, experimental identification of succinylation sites is time-consuming and laborious. Traditional technology cannot meet the rapid growth of the sequence data sets. Therefore, we proposed a new computational method named SuccSPred to predict succinylation sites in a given protein sequence by fusing many kinds of feature representation and ranking method. SuccSPred was implemented based on a two-step strategy. Firstly, linear discriminant analysis was used to reduce feature dimensions to prevent overfitting. Subsequently, the predictor was built based on incrementing features selection binding classifiers to identify succinylation sites. After the comparison of the classifiers using ten-fold cross-validation experiment, the selected model achieved promising improvement. Comparative experiments showed that SuccSPred significantly outperformed previous tools and had the great ability to identify the succinylation sites in given proteins.

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Acknowledgment

This research was supported in part by the National Natural Science Foundation of China (No. 61702146, 61841104), National key research and development program of China (No. 2019YFC0118404), Joint Funds of the Zhejiang Provincial Natural Science Foundation of China (No. U1909210, U20A20386), Zhejiang Provincial Natural Science Foundation of China (No. LY21F020017) and Zhejiang Provincial Science and Technology Program in China (No. 2021C01108).

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Authors and Affiliations

  1. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China

    Ruiquan Ge, Yizhang Luo, Gangyong Jia, Hua Zhang, Chong Xu & Gang Xu

  2. Xi’an Key Laboratory of Big Data and Intelligent Vision, School of Computer Science and Technology, Xidian University, Xi’an, 710071, China

    Guanwen Feng

  3. Computer School, Hubei University of Arts and Science, Xiangyang, 441053, China

    Pu Wang

Authors
  1. Ruiquan Ge
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  2. Yizhang Luo
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  3. Guanwen Feng
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  4. Gangyong Jia
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  5. Hua Zhang
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  6. Chong Xu
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  7. Gang Xu
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  8. Pu Wang
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Correspondence to Pu Wang .

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Editors and Affiliations

  1. Shenzhen Institutes of Advanced Technology, Shenzhen, China

    Yanjie Wei

  2. Central South University, Changsha, China

    Min Li

  3. Georgia State University, Atlanta, GA, USA

    Pavel Skums

  4. Georgia State University, Atlanta, GA, USA

    Zhipeng Cai

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Ge, R. et al. (2021). SuccSPred: Succinylation Sites Prediction Using Fused Feature Representation and Ranking Method. In: Wei, Y., Li, M., Skums, P., Cai, Z. (eds) Bioinformatics Research and Applications. ISBRA 2021. Lecture Notes in Computer Science(), vol 13064. Springer, Cham. https://doi.org/10.1007/978-3-030-91415-8_17

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  • DOI: https://doi.org/10.1007/978-3-030-91415-8_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-91414-1

  • Online ISBN: 978-3-030-91415-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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