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Protein Interresidue Contact Prediction Based on Deep Learning and Massive Features from Multi-sequence Alignment

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Parallel and Distributed Computing, Applications and Technologies (PDCAT 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12606))

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Abstract

Predicting the corresponding 3D structure from the protein’s sequence is one of the most challenging tasks in computational biology, and a confident interresdiue contact map serves as the main driver towards ab initio protein structure prediction. Benefiting from the ever-increasing sequence databases, residue contact prediction has been revolutionized recently by the introduction of direct coupling analysis and deep learning techniques. However, existing deep learning contact prediction methods often rely on a number of external programs and are therefore computationally expensive. Here, we introduce a novel contact prediction method based on fully convolutional neural networks and extensively extracted evolutionary features from multi-sequence alignment. The results show that our deep learning model based on a highly optimized feature extraction mechanism is very effective in interresidue contact prediction.

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Acknowledgment

This work is partly supported by Strategic Priority CAS Project XDB38000000, National Science Foundation of China under Grant No. U1813203, the Shenzhen Basic Research Fund under Grant No. JCYJ20180507182818013, JCYJ20200109114818703 and JCYJ20170413093358429, Hong Kong Research Grant Council under Grant No. GRF-17208019 and CAS Key Lab under Grant No. 2011DP173015. We would also like to thank the Outstanding Youth Innovation Fund (CAS-SIAT to Huiling Zhang).

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Authors and Affiliations

  1. University of Chinese Academy of Sciences, No.19(A) Yuquan Road, Shijingshan District, Beijing, 100049, China

    Huiling Zhang & Yanjie Wei

  2. Shenzhen Institutes of Advanced Technology, Centre for High Performance Computing, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, China

    Huiling Zhang, Hao Wu & Yanjie Wei

  3. Department of Computer Science, The University of Hong Kong, Hong Kong, 999077, China

    Hing-Fung Ting

Authors
  1. Huiling Zhang
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  2. Hao Wu
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  3. Hing-Fung Ting
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  4. Yanjie Wei
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Corresponding author

Correspondence to Yanjie Wei .

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Editors and Affiliations

  1. Shenzhen Institutes of Advanced Technology, Shenzhen, China

    Yong Zhang

  2. Shenzhen Institutes of Advanced Technology, Shenzhen, China

    Yicheng Xu

  3. Griffith University, Gold Coast, QLD, Australia

    Hui Tian

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Zhang, H., Wu, H., Ting, HF., Wei, Y. (2021). Protein Interresidue Contact Prediction Based on Deep Learning and Massive Features from Multi-sequence Alignment. In: Zhang, Y., Xu, Y., Tian, H. (eds) Parallel and Distributed Computing, Applications and Technologies. PDCAT 2020. Lecture Notes in Computer Science(), vol 12606. Springer, Cham. https://doi.org/10.1007/978-3-030-69244-5_19

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  • DOI: https://doi.org/10.1007/978-3-030-69244-5_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-69243-8

  • Online ISBN: 978-3-030-69244-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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