Literature DB >> 17138584

K-Fold: a tool for the prediction of the protein folding kinetic order and rate.

E Capriotti1, R Casadio.   

Abstract

UNLABELLED: K-Fold is a tool for the automatic prediction of the protein folding kinetic order and rate. The tool is based on a support vector machine (SVM) that was trained on a data set of 63 proteins, whose 3D structure and folding mechanism are known from experiments already described in the literature. The method predicts whether a protein of known atomic structure folds according to a two-state or a multi-state kinetics and correctly classifies 81% of the folding mechanisms when tested over the training set of the 63 proteins. It also predicts as a further option the logarithm of the folding rate. To the best of our knowledge, the tool discriminates for the first time whether a protein is characterized by a two state or a multiple state kinetics, during the folding process, and concomitantly estimates also the value of the constant rate of the process. When used to predict the logarithm of the folding rate, K-Fold scores with a correlation value to the experimental data of 0.74 (with a SE of 1.2). AVAILABILITY: http://gpcr.biocomp.unibo.it/cgi/predictors/K-Fold/K-Fold.cgi. SUPPLEMENTARY INFORMATION: http://gpcr.biocomp.unibo.it/~emidio/K-Fold/K-Fold_help.html.

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Substances:

Year:  2006        PMID: 17138584     DOI: 10.1093/bioinformatics/btl610

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  15 in total

1.  Real value prediction of protein folding rate change upon point mutation.

Authors:  Liang-Tsung Huang; M Michael Gromiha
Journal:  J Comput Aided Mol Des       Date:  2012-03-18       Impact factor: 3.686

2.  Slow and bimolecular folding of a de novo designed monomeric protein DS119.

Authors:  Cheng Zhu; Ziwei Dai; Huanhuan Liang; Tao Zhang; Feng Gai; Luhua Lai
Journal:  Biophys J       Date:  2013-11-05       Impact factor: 4.033

Review 3.  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

4.  VIPdb, a genetic Variant Impact Predictor Database.

Authors:  Zhiqiang Hu; Changhua Yu; Mabel Furutsuki; Gaia Andreoletti; Melissa Ly; Roger Hoskins; Aashish N Adhikari; Steven E Brenner
Journal:  Hum Mutat       Date:  2019-08-17       Impact factor: 4.878

5.  Turning Failures into Applications: The Problem of Protein ΔΔG Prediction.

Authors:  Rita Casadio; Castrense Savojardo; Piero Fariselli; Emidio Capriotti; Pier Luigi Martelli
Journal:  Methods Mol Biol       Date:  2022

6.  PERISCOPE-Opt: Machine learning-based prediction of optimal fermentation conditions and yields of recombinant periplasmic protein expressed in Escherichia coli.

Authors:  Kulandai Arockia Rajesh Packiam; Chien Wei Ooi; Fuyi Li; Shutao Mei; Beng Ti Tey; Huey Fang Ong; Jiangning Song; Ramakrishnan Nagasundara Ramanan
Journal:  Comput Struct Biotechnol J       Date:  2022-06-03       Impact factor: 6.155

7.  Coupling between properties of the protein shape and the rate of protein folding.

Authors:  Dmitry N Ivankov; Natalya S Bogatyreva; Michail Yu Lobanov; Oxana V Galzitskaya
Journal:  PLoS One       Date:  2009-08-03       Impact factor: 3.240

8.  SeqRate: sequence-based protein folding type classification and rates prediction.

Authors:  Guan Ning Lin; Zheng Wang; Dong Xu; Jianlin Cheng
Journal:  BMC Bioinformatics       Date:  2010-04-29       Impact factor: 3.169

9.  Thermal unfolding simulations of bacterial flagellin: insight into its refolding before assembly.

Authors:  Choon-Peng Chng; Akio Kitao
Journal:  Biophys J       Date:  2008-02-08       Impact factor: 4.033

10.  Machine Learning: How Much Does It Tell about Protein Folding Rates?

Authors:  Marc Corrales; Pol Cuscó; Dinara R Usmanova; Heng-Chang Chen; Natalya S Bogatyreva; Guillaume J Filion; Dmitry N Ivankov
Journal:  PLoS One       Date:  2015-11-25       Impact factor: 3.240

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