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Protein secondary structure assignment using residual networks

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Abstract 

Proteins are constructed from amino acid sequences. Their structural classifications include primary, secondary, tertiary, and quaternary, with tertiary and quaternary structures influencing protein function. Because a protein’s structure is inextricably connected to its biological function, machine learning algorithms that can better anticipate the structures have the potential to lead to new scientific discoveries in human health and improve our capacity to develop new treatments. Protein secondary structure assignment enriches the structural and functional understanding of proteins. It helps in protein structure comparison and classification studies, besides facilitating secondary and tertiary structure prediction systems. Several secondary structure assignment methods have been developed since the 1980s, most of which are based on hydrogen bond analysis and atomic coordinate features. However, the assignment process becomes complex when protein data includes missing atoms. Deep neural networks are often referred to as universal function approximators because they can approximate any function to produce the desired output when properly designed and trained. Optimised deep learning architectures have already proven their ability to increase performance in a wide range of problems. Recently, the ResNet architecture has garnered significant interest due to its applicability in various areas, including image classification and protein contact map prediction. The proposed model, which is based on the ResNet architecture, assigns secondary structures using Cα atom coordinates. The model achieved an accuracy of 94% when evaluated against the benchmark and independent test sets. The findings encourage the development of new deep learning-based methods that are more generalised across various protein learning tasks. Furthermore, it allows computational biologists to delve deeper into integrating these techniques with experimental methods. The model codes are available at: https://github.com/jisnava/ResNet_for_Structure_Assignments/.

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Data availability

The data is available at: https://github.com/jisnava/ResNet_for_Structure_Assignments/.

Code availability

The model codes are made open at: https://github.com/jisnava/ResNet_for_Structure_Assignments/.

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Acknowledgements

The authors thank the Centre for Computational Modelling and Simulation (CCMS) and Central Computer Centre (CCC) at the National Institute of Technology Calicut, for providing the NVIDIA DGX station facility to train the deep neural network architectures.

Funding

It is part of my (V. A. 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, Kattangal, Kerala, 673601, India

    Jisna Vellara Antony, Roosafeed Koya, Pulinthanathu Narayanan Pournami, Gopakumar Gopalakrishnan Nair & Jayaraj Pottekkattuvalappil Balakrishnan

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  1. Jisna Vellara Antony
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  2. Roosafeed Koya
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  3. Pulinthanathu Narayanan Pournami
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  4. Gopakumar Gopalakrishnan Nair
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  5. Jayaraj Pottekkattuvalappil Balakrishnan
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Contributions

Jisna Vellara Antony (JVA) did the conceptualisation and dataset construction. JVA and Roosafeed Koya (RK) implemented the models. Jayaraj Pottekkattuvalappil Balakrishnan (JPB), Pulinthanathu Narayanan Pournami (PNP), and Gopakumar Gopalakrishnan Nair (GGN) 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., Koya, R., Pournami, P.N. et al. Protein secondary structure assignment using residual networks. J Mol Model 28, 269 (2022). https://doi.org/10.1007/s00894-022-05271-z

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