title: Towards more accurate prediction of protein folding rates: a review of the existing Web-based bioinformatics approaches.
abstract: 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.
authors.forename: Catherine Ching Han
authors.initials: CC
authors.lastname: Chang
authors.forename: Beng Ti
authors.initials: BT
authors.lastname: Tey
authors.forename: Jiangning
authors.initials: J
authors.lastname: Song
authors.forename: Ramakrishnan Nagasundara
authors.initials: RN
authors.lastname: Ramanan
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D030562:Databases, Protein / Q000706:statistics & numerical data; D020407:Internet; D007700:Kinetics; D000069550:Machine Learning; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry / Q000378:metabolism; D012984:Software
chemical_list: D011506:Proteins
keywords: in silico prediction; machine learning algorithm; molecular biology; prediction model; prediction tool; statistical analysis