Abstract
Protein is a complex molecule of amino acid and carries out a variety of crucial functions in the organism. In contrast to other macromolecules, proteins are the most prevalent organic molecules in natural systems and have more significant structural and functional variability. The protein’s functionalities depend on the 3D structure, which further depends on the sequence of amino acids. Each serves a different purpose and can be located in a single cell. Each molecule comprises one or more amino acid chains, even though their structures and activities differ significantly. This is the most significant optimization problem that computational biologists face continuously. Traditional experimental approaches are time-consuming and relatively expensive. Based on amino acid sequence, advanced approaches can be created by using artificial intelligence such as machine learning and deep learning that can train a machine to efficiently predict the structure and functionalities of the protein. This paper reviews the different approaches used for predicting protein sequences, their structures, and their functionalities. We also included a section in this review to present the available dataset and the most suitable deep learning approaches used for predicting the protein sequences along with its structures.
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Singh, P., Tripathi, S., Bihari, A. (2023). A Comprehensive Review of the Works of Literature for the Prediction of Protein Structure—Perceptions on Traditional and Deep Learning Approaches. In: Swaroop, A., Kansal, V., Fortino, G., Hassanien, A.E. (eds) Proceedings of Fourth Doctoral Symposium on Computational Intelligence . DoSCI 2023. Lecture Notes in Networks and Systems, vol 726. Springer, Singapore. https://doi.org/10.1007/978-981-99-3716-5_19
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DOI: https://doi.org/10.1007/978-981-99-3716-5_19
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