期刊
BRIEFINGS IN BIOINFORMATICS
卷 16, 期 2, 页码 314-324出版社
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbu007
关键词
prediction tool; in silico prediction; machine learning algorithm; prediction model; statistical analysis; molecular biology
资金
- Ministry of Higher Education (MOHE) of Malaysia [FRGS/1/2012/TK05/MUSM/03/2]
- Ministry of Science, Technology and Innovation (MOSTI) of Malaysia [e-Science: 02-02-10-SF0088]
- National Natural Science Foundation of China [61202167, 61303169]
- Knowledge Innovative Program of the CAS [KSCX2-EW-G-8]
- National Health and Medical Research Council of Australia (NHMRC) [490989]
- Australian Research Council [LP110200333]
- Major Inter-disciplinary Research (IDR) Project Grant by Monash University
- Hundred Talents Program of CAS
- Australian Research Council [LP110200333] Funding Source: Australian Research Council
The understanding of protein-folding mechanisms is often considered to be an important goal that will enable structural biologists to discover the mysterious relationship between the sequence, structure and function of proteins. The ability to predict protein-folding rates without the need for actual experimental work will assist the research work of structural biologists in many ways. Many bioinformatics tools have emerged in the past decade, and each has showcased different features. In this article, we review and compare eight web-based prediction tools that are currently available and that predominantly predict the protein-folding rate. The prediction performance, usability and utility, together with the prediction tool development and validation methodologies for these tools, are critically reviewed. This article is presented in a comprehensible manner to assist readers in the process of selecting the most appropriate bioinformatics tools to meet their needs.
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