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Assigning secondary structure in proteins using AI

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Abstract

Knowledge about protein structure assignment enriches the structural and functional understanding of proteins. Accurate and reliable structure assignment data is crucial for secondary structure prediction systems. Since the 1980s, various methods based on hydrogen bond analysis and atomic coordinate geometry, followed by machine learning, have been employed in protein structure assignment. However, the assignment process becomes challenging when missing atoms are present in the protein files. Our method proposed a multi-class classifier program named DLFSA for assigning protein secondary structure elements (SSE) using convolutional neural networks (CNNs). A fast and efficient GPU-based parallel procedure extracts fragments from protein files. The model implemented in this work is trained with a subset of the protein fragments and achieves 88.1% and 82.5% train and test accuracy, respectively. The model uses only Cα coordinates for secondary structure assignments. The model has been successfully tested on a few full-length proteins also. Results from the fragment-based studies demonstrate the feasibility of applying deep learning solutions for structure assignment problems.

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Data availability

DLFSA is made available to the public through the Web portal—www.proteinallinfo.in. The datasets generated during and/or analyzed during the current study are not publicly available due to their large size but are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to thank the Central Computing Centre, National Institute of Technology Calicut (NITC), for providing GPU servers for this work. The authors would like to acknowledge the valuable suggestions from Jinto Antony for developing the Web portal.

Code availability

The library generation source codes and the model codes are made open at https://github.com/jisnava/DLFSA/.

Funding

It is part of my (VA Jisna) PhD work at the National Institute of Technology Calicut, India. The research is funded by the Ministry of Human Resource Development, India.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Calicut, Kerala, 673601, India

    Jisna Vellara Antony, Jayaraj Pottekkattuvalappil Balakrishnan & Hemant Yadav

  2. Computer Science and Engineering Dept., Rajiv Gandhi Institute of Technology, Kottayam, India

    Prayagh Madhu

Authors
  1. Jisna Vellara Antony
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  2. Prayagh Madhu
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  3. Jayaraj Pottekkattuvalappil Balakrishnan
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  4. Hemant Yadav
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Contributions

Jisna Vellara Antony (JVA) did the conceptualization. Hemant Yadav developed the fragment library. Prayagh Madhu did the model coding. JVA developed the Web portal and wrote the manuscript. Jayaraj Pottekkattuvalappil Balakrishnan supervised the project. All authors read and approved the final manuscript.

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Correspondence to Jisna Vellara Antony.

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Antony, J.V., Madhu, P., Balakrishnan, J.P. et al. Assigning secondary structure in proteins using AI. J Mol Model 27, 252 (2021). https://doi.org/10.1007/s00894-021-04825-x

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