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A Sequence-Based Antibody Paratope Prediction Model Through Combing Local-Global Information and Partner Features

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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

Antibodies are proteins which play a vital role in the immune system by recognizing and neutralizing antigens. The region on the antibody binding to an antigen, known as paratope, mediates antibody-antigen interaction with high affinity and specificity. And the accurate prediction of those regions from antibody sequence contributes to the design of therapeutic antibodies and remains challenging. However, the experimental methods are time-consuming and expensive. In this article, we propose a sequence-based method for antibody paratope prediction by combing local and global information of antibody sequence and partner features from partner antigen sequence. Convolution Neural Networks (CNNs) and a sliding window approach on antibody sequence are used to extract local information. Attention-based Bidirectional Long Short-Term Memory (Att-BLSTM) on antibody sequence are used to extract global information. Also, the partner antigen is vital for paratope prediction, and we employ Att-BLSTM on the partner antigen sequence as well. The outputs of CNNs and Att-BLSTM networks are combined to predict antibody paratope by fully-connected networks. The experiments show that our proposed method achieves superior performance over the state-of-the-art sequenced-based antibody paratope prediction methods on benchmark datasets.

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Authors and Affiliations

  1. Zhengzhou University, Zhengzhou, 450001, China

    Shuai Lu, Xiaofei Nan & Shoutao Zhang

  2. Zhengzhou University of Light Industry, Zhengzhou, 450001, China

    Yuguang Li

Authors
  1. Shuai Lu
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  2. Yuguang Li
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  3. Xiaofei Nan
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  4. Shoutao Zhang
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Correspondence to Shoutao Zhang .

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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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Lu, S., Li, Y., Nan, X., Zhang, S. (2021). A Sequence-Based Antibody Paratope Prediction Model Through Combing Local-Global Information and Partner Features. 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_16

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  • DOI: https://doi.org/10.1007/978-3-030-91415-8_16

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  • Print ISBN: 978-3-030-91414-1

  • Online ISBN: 978-3-030-91415-8

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