Literature DB >> 24621527

Towards more accurate prediction of protein folding rates: a review of the existing Web-based bioinformatics approaches.

Catherine Ching Han Chang, Beng Ti Tey, Jiangning Song, Ramakrishnan Nagasundara Ramanan.   

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.
© The Author 2014. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Keywords:  in silico prediction; machine learning algorithm; molecular biology; prediction model; prediction tool; statistical analysis

Mesh:

Substances:

Year:  2014        PMID: 24621527     DOI: 10.1093/bib/bbu007

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  8 in total

Review 1.  Critical evaluation of in silico methods for prediction of coiled-coil domains in proteins.

Authors:  Chen Li; Catherine Ching Han Chang; Jeremy Nagel; Benjamin T Porebski; Morihiro Hayashida; Tatsuya Akutsu; Jiangning Song; Ashley M Buckle
Journal:  Brief Bioinform       Date:  2015-07-15       Impact factor: 11.622

Review 2.  Stepwise optimization of recombinant protein production in Escherichia coli utilizing computational and experimental approaches.

Authors:  Kulandai Arockia Rajesh Packiam; Ramakrishnan Nagasundara Ramanan; Chien Wei Ooi; Lakshminarasimhan Krishnaswamy; Beng Ti Tey
Journal:  Appl Microbiol Biotechnol       Date:  2020-02-19       Impact factor: 4.813

3.  Elucidation of Increased Cervical Cancer Risk Due to Polymorphisms in XRCC1 (R399Q and R194W), ERCC5 (D1104H), and NQO1 (P187S).

Authors:  Shrishty Tyagi; Nisha Chaudhary; Agneesh Pratim Das; Sandeep Saini; Subhash Mohan Agarwal
Journal:  Reprod Sci       Date:  2022-10-04       Impact factor: 2.924

4.  In silico screening and heterologous expression of soluble dimethyl sulfide monooxygenases of microbial origin in Escherichia coli.

Authors:  Prasanth Karaiyan; Catherine Ching Han Chang; Eng-Seng Chan; Beng Ti Tey; Ramakrishnan Nagasundara Ramanan; Chien Wei Ooi
Journal:  Appl Microbiol Biotechnol       Date:  2022-06-17       Impact factor: 5.560

5.  On the Upper Bounds of the Real-Valued Predictions.

Authors:  Silvia Benevenuta; Piero Fariselli
Journal:  Bioinform Biol Insights       Date:  2019-08-23

Review 6.  Computational and experimental approaches to reveal the effects of single nucleotide polymorphisms with respect to disease diagnostics.

Authors:  Tugba G Kucukkal; Ye Yang; Susan C Chapman; Weiguo Cao; Emil Alexov
Journal:  Int J Mol Sci       Date:  2014-05-30       Impact factor: 5.923

7.  Network measures for protein folding state discrimination.

Authors:  Giulia Menichetti; Piero Fariselli; Daniel Remondini
Journal:  Sci Rep       Date:  2016-07-28       Impact factor: 4.379

8.  Periscope: quantitative prediction of soluble protein expression in the periplasm of Escherichia coli.

Authors:  Catherine Ching Han Chang; Chen Li; Geoffrey I Webb; BengTi Tey; Jiangning Song; Ramakrishnan Nagasundara Ramanan
Journal:  Sci Rep       Date:  2016-03-02       Impact factor: 4.379

  8 in total

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