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Comprehensive Analysis of Computational Methods for Predicting Anti-inflammatory Peptides

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Abstract

Inflammation is a biological resistant response to harmful stimuli, such as infection, damaged cells, toxic chemicals, or tissue injuries. Inflammation eradicates pathogenic microorganisms or irritants and facilitates tissue repair. Prolonged inflammation can result in chronic inflammatory diseases. In the last decade, peptide therapeutics have gained significant attention due to their high specificity in targeting affected cells without affecting healthy cells. Motivated by the significance of peptide-based therapies, Anti-inflammatory peptides (AIPs) related models have become increasingly popular due to their significant characteristics. The systematic experimental identifying methods for AIPs deals with various problems of the existing in-vitro methods. Hence, to develop an accurate and reliable prediction of AIPs, numerous computational models have been developed. These models demonstrate high diversity in their datasets, feature representation, feature selection, model training, and evaluation methods. In this study, we conducted an extensive survey of the existing models for discriminating AIPs and point out the differences between these approaches. We evaluate the prediction results of the models based on independent tests and AUC values. Moreover, we emphasize the challenges and prospects in this field, paving the way for developing more effective prediction models that can enhance the predictive outcomes in predicting AIPs.

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Authors and Affiliations

  1. Department of Physical and Numerical Sciences, Qurtuba University of Science and Information Technology, Peshawar, Pakistan

    Ali Raza & Jamal Uddin

  2. Department of Computer Science, MY University, Islamabad, Pakistan

    Ali Raza & Ashfaq Ahmad

  3. Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Shahid Akbar & Quan Zou

  4. Department of Computer Science, Abdul Wali Khan University Mardan, Mardan, KP, 23200, Pakistan

    Shahid Akbar

  5. Department of Management Information Systems (MIS), School of Business, King Faisal University (KFU), Al-Ahsa, 31982, Saudi Arabia

    Fawaz Khaled Alarfaj

  6. Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, 324000, China

    Quan Zou

  7. Riphah School of Computing and Innovation, Riphah International University Lahore, Lahore, Pakistan

    Jamal Uddin

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  1. Ali Raza
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  2. Jamal Uddin
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Raza, A., Uddin, J., Akbar, S. et al. Comprehensive Analysis of Computational Methods for Predicting Anti-inflammatory Peptides. Arch Computat Methods Eng (2024). https://doi.org/10.1007/s11831-024-10078-7

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