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Review

Recent Advances in the Prediction of Protein Structural Classes: Feature Descriptors and Machine Learning Algorithms

1
Institute for Advanced Study, Shenzhen University, Shenzhen 518060, China
2
Institute of Biotechnology, RWTH Aachen University, Worringerweg 3, 52074 Aachen, Germany
*
Author to whom correspondence should be addressed.
Crystals 2021, 11(4), 324; https://doi.org/10.3390/cryst11040324
Submission received: 1 March 2021 / Revised: 20 March 2021 / Accepted: 21 March 2021 / Published: 24 March 2021

Abstract

In the postgenomic age, rapid growth in the number of sequence-known proteins has been accompanied by much slower growth in the number of structure-known proteins (as a result of experimental limitations), and a widening gap between the two is evident. Because protein function is linked to protein structure, successful prediction of protein structure is of significant importance in protein function identification. Foreknowledge of protein structural class can help improve protein structure prediction with significant medical and pharmaceutical implications. Thus, a fast, suitable, reliable, and reasonable computational method for protein structural class prediction has become pivotal in bioinformatics. Here, we review recent efforts in protein structural class prediction from protein sequence, with particular attention paid to new feature descriptors, which extract information from protein sequence, and the use of machine learning algorithms in both feature selection and the construction of new classification models. These new feature descriptors include amino acid composition, sequence order, physicochemical properties, multiprofile Bayes, and secondary structure-based features. Machine learning methods, such as artificial neural networks (ANNs), support vector machine (SVM), K-nearest neighbor (KNN), random forest, deep learning, and examples of their application are discussed in detail. We also present our view on possible future directions, challenges, and opportunities for the applications of machine learning algorithms for prediction of protein structural classes.
Keywords: machine learning; deep learning; protein structure class; representing proteins; feature selection machine learning; deep learning; protein structure class; representing proteins; feature selection

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MDPI and ACS Style

Zhu, L.; Davari, M.D.; Li, W. Recent Advances in the Prediction of Protein Structural Classes: Feature Descriptors and Machine Learning Algorithms. Crystals 2021, 11, 324. https://doi.org/10.3390/cryst11040324

AMA Style

Zhu L, Davari MD, Li W. Recent Advances in the Prediction of Protein Structural Classes: Feature Descriptors and Machine Learning Algorithms. Crystals. 2021; 11(4):324. https://doi.org/10.3390/cryst11040324

Chicago/Turabian Style

Zhu, Lin, Mehdi D. Davari, and Wenjin Li. 2021. "Recent Advances in the Prediction of Protein Structural Classes: Feature Descriptors and Machine Learning Algorithms" Crystals 11, no. 4: 324. https://doi.org/10.3390/cryst11040324

APA Style

Zhu, L., Davari, M. D., & Li, W. (2021). Recent Advances in the Prediction of Protein Structural Classes: Feature Descriptors and Machine Learning Algorithms. Crystals, 11(4), 324. https://doi.org/10.3390/cryst11040324

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