REFOLD: An analytical database of protein refolding methods

Chow, Michelle K M; Amin, Abdullah Al; Fulton, Kate Frances; Whisstock, James C.; Buckle, Ashley M.; Bottomley, Stephen P.

doi:10.1016/j.pep.2005.07.022

Cited by 33 publications

(23 citation statements)

References 14 publications

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“…Notwithstanding, efforts have been made to rationalize the refolding step, the free REFOLD database (http://refold.med.monash.edu.au; Chow et al, 2006a,b) can be a useful tool for the design of procedures for the refolding and purification of recombinant proteins.…”

Section: Methods For the Recovery Of Functional Proteins From Ibsmentioning

confidence: 99%

Inclusion bodies: not that bad…

2014

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The formation of inclusion bodies (IBs) constitute a frequent event during the production of heterologous proteins in bacterial hosts. Although the mechanisms leading to their formation are not completely understood, empirical data have been exploited trying to predict the aggregation propensity of specific proteins while a great number of strategies have been developed to avoid the generation of IBs. However, in many cases, the formation of such aggregates can be considered an advantage for basic research as for protein production. In this review, we focus on this positive side of IBs formation in bacteria. We present a compilation on recent advances on the understanding of IBs formation and their utilization as a model to understand protein aggregation and to explore strategies to control this process. We include recent information about their composition and structure, their use as an attractive approach to produce low cost proteins and other promising applications in Biomedicine.

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Section: Methods For the Recovery Of Functional Proteins From Ibsmentioning

confidence: 99%

Inclusion bodies: not that bad…

2014

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“…Unfortunately, most of the data are back calculated from structures and real experimentally measured densities represent only a fraction of the database. The REFOLD database [15] hosts refolding procedures together with protein sequences. In addition to standard public resources, information can be gained from descriptions of medium or large-scale experiments dispersed in literature or from direct collaborations with experimental groups.…”

Section: Datamentioning

confidence: 99%

Predicting Experimental Properties of Proteins from Sequence by Machine Learning Techniques

Smialowski¹,

Martín-Galiano²,

Cox³

et al. 2007

CPPS

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Efficient target selection methods are an important prerequisite for increasing the success rate and reducing the cost of high-throughput structural genomics efforts. There is a high demand for sequence-based methods capable of predicting experimentally tractable proteins and filtering out potentially difficult targets at different stages of the structural genomic pipeline. Simple empirical rules based on anecdotal evidence are being increasingly superseded by rigorous machine-learning algorithms. Although the simplicity of less advanced methods makes them more human understandable, more sophisticated formalized algorithms possess superior classification power. The quickly growing corpus of experimental success and failure data gathered by structural genomics consortia creates a unique opportunity for retrospective data mining using machine learning techniques and results in increased quality of classifiers. For example, the current solubility prediction methods are reaching the accuracy of over 70%. Furthermore, automated feature selection leads to better insight into the nature of the correlation between amino acid sequence and experimental outcome. In this review we summarize methods for predicting experimental success in cloning, expression, soluble expression, purification and crystallization of proteins with a special focus on publicly available resources. We also describe experimental data repositories and machine learning techniques used for classification and feature selection.

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“…Thus, the REFOLD database established and published by Monash University in 2006 played an important role as the sole information source for refolding experiments [10–13]. This database, however, suspended its updates in 2009.…”

Section: Introductionmentioning

confidence: 99%

REFOLDdb: a new and sustainable gateway to experimental protocols for protein refolding

et al. 2017

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BackgroundMore than 7000 papers related to “protein refolding” have been published to date, with approximately 300 reports each year during the last decade. Whilst some of these papers provide experimental protocols for protein refolding, a survey in the structural life science communities showed a necessity for a comprehensive database for refolding techniques. We therefore have developed a new resource – “REFOLDdb” that collects refolding techniques into a single, searchable repository to help researchers develop refolding protocols for proteins of interest.ResultsWe based our resource on the existing REFOLD database, which has not been updated since 2009. We redesigned the data format to be more concise, allowing consistent representations among data entries compared with the original REFOLD database. The remodeled data architecture enhances the search efficiency and improves the sustainability of the database. After an exhaustive literature search we added experimental refolding protocols from reports published 2009 to early 2017. In addition to this new data, we fully converted and integrated existing REFOLD data into our new resource. REFOLDdb contains 1877 entries as of March 17th, 2017, and is freely available at http://p4d-info.nig.ac.jp/refolddb/.ConclusionREFOLDdb is a unique database for the life sciences research community, providing annotated information for designing new refolding protocols and customizing existing methodologies. We envisage that this resource will find wide utility across broad disciplines that rely on the production of pure, active, recombinant proteins. Furthermore, the database also provides a useful overview of the recent trends and statistics in refolding technology development.

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REFOLD: An analytical database of protein refolding methods

Cited by 33 publications

References 14 publications

Inclusion bodies: not that bad…

Inclusion bodies: not that bad…

Predicting Experimental Properties of Proteins from Sequence by Machine Learning Techniques

REFOLDdb: a new and sustainable gateway to experimental protocols for protein refolding

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