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AFP-SRC: identification of antifreeze proteins using sparse representation classifier

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Abstract

Species living in the extreme cold environment fight against the harsh conditions using antifreeze proteins (AFPs), which manipulate the freezing mechanism of water in more than one way. This amazing nature of AFPs turns out to be extremely useful in several industrial and medical applications. The lack of similarity in their structure and sequence makes their prediction an arduous task, and identifying them experimentally in the wet laboratory is time-consuming and expensive. In this research, we propose a computational framework for the prediction of AFPs, which is essentially based on a sample-specific classification method using sparse reconstruction. A linear model and an over-complete dictionary matrix (ODM) of known AFPs are used to predict a sparse class-label vector that provides a sample-association score. Delta rule is applied for the reconstruction of two pseudo-samples using lower and upper parts of the sample-association vector and based on the minimum recovery score, class labels are assigned. We compare our approach with contemporary methods on a standard dataset. The proposed method outperforms the contemporary methods in terms of balanced accuracy and Youden’s index. The MATLAB implementation of the proposed method is available at the author’s GitHub page (https://github.com/Shujaat123/AFP-SRC).

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Acknowledgements

The authors would like to thank Misbah Tariq for helping in visualization. We would also like to thank learned referees and editor for their valuable and useful suggestions.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

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Authors and Affiliations

  1. Department of Computer Engineering, College of IT Convergence Engineering, Chosun University, Gwangju, 61452, Republic of Korea

    Muhammad Usman

  2. Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea

    Shujaat Khan & Seongyong Park

  3. Department of Mathematics, School of Sciences and Humanities, Nazarbayev University, 53 Kabanbay Batyr Avenue, Nur-Sultan, 010000, Kazakhstan

    Abdul Wahab

Authors
  1. Muhammad Usman
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  2. Shujaat Khan
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  4. Abdul Wahab
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Contributions

MU and SK contributed equally, they designed the research, conducted the experiments and wrote the manuscript, SP conceived the DeepSRC experiments and performed analysis, AW supervised the study and analyzed the results. All authors discussed the results and reviewed the manuscript.

Corresponding author

Correspondence to Shujaat Khan.

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Conflict of Interest

The authors reported no conflict of interest.

Code Availability

All the data used in this study along with the MATLAB and python implementations of the SRC and DeepSRC methods are available at the author’s GitHub page (https://github.com/Shujaat123/AFP-SRC).

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Usman, M., Khan, S., Park, S. et al. AFP-SRC: identification of antifreeze proteins using sparse representation classifier. Neural Comput & Applic 34, 2275–2285 (2022). https://doi.org/10.1007/s00521-021-06558-7

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  • DOI: https://doi.org/10.1007/s00521-021-06558-7

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