Recent Advances in the Prediction of Protein Structural Classes: Feature Descriptors and Machine Learning Algorithms
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 s...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Crystals |
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| Online Access: | https://www.mdpi.com/2073-4352/11/4/324 |
| _version_ | 1797540072662237184 |
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| author | Lin Zhu Mehdi D. Davari Wenjin Li |
| author_facet | Lin Zhu Mehdi D. Davari Wenjin Li |
| author_sort | Lin Zhu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T12:55:53Z |
| format | Article |
| id | doaj.art-33eb0e769b3b4a3faa579613dd00b859 |
| institution | Directory Open Access Journal |
| issn | 2073-4352 |
| language | English |
| last_indexed | 2024-03-10T12:55:53Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Crystals |
| spelling | doaj.art-33eb0e769b3b4a3faa579613dd00b8592023-11-21T11:55:06ZengMDPI AGCrystals2073-43522021-03-0111432410.3390/cryst11040324Recent Advances in the Prediction of Protein Structural Classes: Feature Descriptors and Machine Learning AlgorithmsLin Zhu0Mehdi D. Davari1Wenjin Li2Institute for Advanced Study, Shenzhen University, Shenzhen 518060, ChinaInstitute of Biotechnology, RWTH Aachen University, Worringerweg 3, 52074 Aachen, GermanyInstitute for Advanced Study, Shenzhen University, Shenzhen 518060, ChinaIn 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.https://www.mdpi.com/2073-4352/11/4/324machine learningdeep learningprotein structure classrepresenting proteinsfeature selection |
| spellingShingle | Lin Zhu Mehdi D. Davari Wenjin Li Recent Advances in the Prediction of Protein Structural Classes: Feature Descriptors and Machine Learning Algorithms Crystals machine learning deep learning protein structure class representing proteins feature selection |
| title | Recent Advances in the Prediction of Protein Structural Classes: Feature Descriptors and Machine Learning Algorithms |
| title_full | Recent Advances in the Prediction of Protein Structural Classes: Feature Descriptors and Machine Learning Algorithms |
| title_fullStr | Recent Advances in the Prediction of Protein Structural Classes: Feature Descriptors and Machine Learning Algorithms |
| title_full_unstemmed | Recent Advances in the Prediction of Protein Structural Classes: Feature Descriptors and Machine Learning Algorithms |
| title_short | Recent Advances in the Prediction of Protein Structural Classes: Feature Descriptors and Machine Learning Algorithms |
| title_sort | recent advances in the prediction of protein structural classes feature descriptors and machine learning algorithms |
| topic | machine learning deep learning protein structure class representing proteins feature selection |
| url | https://www.mdpi.com/2073-4352/11/4/324 |
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