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Curr Drug Metab. 2019;20(3):224-228. doi: 10.2174/1389200219666181031105916.

Recent Advances in Machine Learning Methods for Predicting Heat Shock Proteins.

Author information

1
Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611730, China.
2
School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan 063000, China.
3
Hebei Province Key Laboratory of Occupational Health and Safety for Coal Industry, School of Public Health, North China University of Science and Technology, Tangshan 063000, China.

Abstract

BACKGROUND:

As molecular chaperones, Heat Shock Proteins (HSPs) not only play key roles in protein folding and maintaining protein stabilities, but are also linked with multiple kinds of diseases. Therefore, HSPs have been regarded as the focus of drug design. Since HSPs from different families play distinct functions, accurately classifying the families of HSPs is the key step to clearly understand their biological functions. In contrast to laborintensive and cost-ineffective experimental methods, computational classification of HSP families has emerged to be an alternative approach.

METHODS:

We reviewed the paper that described the existing datasets of HSPs and the representative computational approaches developed for the identification and classification of HSPs.

RESULTS:

The two benchmark datasets of HSPs, namely HSPIR and sHSPdb were introduced, which provided invaluable resources for computationally identifying HSPs. The gold standard dataset and sequence encoding schemes for building computational methods of classifying HSPs were also introduced. The three representative web-servers for identifying HSPs and their families were described.

CONCLUSION:

The existing machine learning methods for identifying the different families of HSPs indeed yielded quite encouraging results and did play a role in promoting the research on HSPs. However, the number of HSPs with known structures is very limited. Therefore, determining the structure of the HSPs is also urgent, which will be helpful in revealing their functions.

KEYWORDS:

Heat shock protein; drug target; machine learning; n-peptide composition; reduced amino acid composition; web server.

[Indexed for MEDLINE]

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