Prediction of protein folding rates from primary sequence by fusing multiple sequential features

Shen, Hong‐Bin; Song, Jiangning; Chou, Kuo‐Chen

doi:10.4236/jbise.2009.23024

Cited by 107 publications

(54 citation statements)

References 53 publications

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“…Even for a two-state protein folding system when the reverse effect needs to be considered, i.e., the system described by the following scheme and equation 12 21 unfold fold where 21 k represents the reverse rate constant converting fold P back to unfold P . With the similar derivation by using the non-steady state graphic rule [245,246] as described above, we can get the following equivalent relation [249]       12 12 21 f 1 2 2 1 21 12 12 21 exp exp…”

Section: Pred-pfrmentioning

confidence: 98%

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REVIEW : Recent advances in developing web-servers for predicting protein attributes

Chou¹,

Shen²

2009

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277

173

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Recent advance in large-scale genome sequencing has generated a huge volume of protein sequences. In order to timely utilize the information hidden in these newly discovered sequences, it is highly desired to develop computational methods for efficiently identifying their various attributes because the information thus obtained will be very useful for both basic research and drug development. Particularly, it would be even more useful and welcome if a user-friendly web-server could be provided for each of these methods. In this minireview, a systematic introduction is presented to highlight the development of these web-servers by our group during the last three years.

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Section: Pred-pfrmentioning

confidence: 98%

“…Recently, a web-server, called "Pred-PFR" (Predicting Protein Folding Rate), was developed for predicting the folding rate of a protein [249]. The predictor is featured by fusing multiple individual predictors, each of which is established based on one special feature derived from the protein sequence.…”

Section: Pred-pfrmentioning

confidence: 99%

REVIEW : Recent advances in developing web-servers for predicting protein attributes

Chou¹,

Shen²

2009

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277

173

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“…A variety of programs that predict the folding rate based on amino acid sequence integrated with other information have been published [8][9][10][11][12][13]. Recently, using Delaunay tessellation (DT) Ouyang and Liang [14] proposed a geometric contact (Nα) to replace the previous contact order.…”

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confidence: 99%

The dynamical contact order: Protein folding rate parameters based on quantum conformational transitions

Zhang

Luo

2011

Sci. China Life Sci.

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Protein folding is regarded as a quantum transition between the torsion states of a polypeptide chain. According to the quantum theory of conformational dynamics, we propose the dynamical contact order (DCO) defined as a characteristic of the contact described by the moment of inertia and the torsion potential energy of the polypeptide chain between contact residues. Consequently, the protein folding rate can be quantitatively studied from the point of view of dynamics. By comparing theoretical calculations and experimental data on the folding rate of 80 proteins, we successfully validate the view that protein folding is a quantum conformational transition. We conclude that (i) a correlation between the protein folding rate and the contact inertial moment exists; (ii) multi-state protein folding can be regarded as a quantum conformational transition similar to that of two-state proteins but with an intermediate delay. We have estimated the order of magnitude of the time delay; (iii) folding can be classified into two types, exergonic and endergonic. Most of the two-state proteins with higher folding rate are exergonic and most of the multi-state proteins with low folding rate are endergonic. The folding speed limit is determined by exergonic folding. moment of inertia, dynamical contact order (DCO), protein folding rate Citation:Zhang Y, Luo L F. The dynamical contact order: Protein folding rate parameters based on quantum conformational transitions. Sci China Life Sci, 2011Sci, , 54: 386 -392, doi: 10.1007 Protein folding is a complex kinetic process by which a polypeptide changes from the denatured state to the native folding state. Although a great deal of theoretical work has been carried out, the fundamental physics underlying the folding remains unclear. The folding kinetics of a large number of proteins has been studied experimentally [1][2][3]. For many of these proteins, folding has been shown to be an all-or-none process with no clear intermediate state. These are called two-state proteins. However, some proteins require the accumulation of intermediates to complete the folding process. These are referred to as three-state or multi-state proteins. The rate of folding varies from milliseconds to hours. Some small proteins fold much faster, at rates in microsecond [2,3]. Experimental data has indicated that most of the ultrafast folders show a significant decrease in folding rates as the temperature is increased [4]. The wide range of folding rates and a possible folding speed limit are likely to be closely related to the inherent physics behind the phenomena. A deeper understanding of the folding mechanism and the accurate prediction of protein folding rates is an important topic in protein science. Since 1998, when the relative contact order (RCO) was proposed by Plaxco et al. [5], it has been widely accepted that folding rates and mechanisms are largely determined by the topology of the native state. Much theoretical work based on this concept has been proposed. For example, Ivankov et al. [6] pr...

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“…The fusion approaches are a well known technique for improving the performance of the standalone methods, remarkable successes of using fusion approaches are: (Chou and Shen, 2008b) protein subcellular location prediction, (Chou and Shen, 2008a) protease type prediction, (Chou and A c c e p t e d m a n u s c r i p t 5 Shen, 2009b;Shen et al, 2009) protein folding rate prediction, (Chou and Shen, 2007b) signal peptide prediction, (Chou and Shen, 2007a) membrane protein type prediction.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Protein classification using texture descriptors extracted from the protein backbone image

Nanni

Shi

Brahnam

et al. 2010

Journal of Theoretical Biology

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Prediction of protein folding rates from primary sequence by fusing multiple sequential features

Cited by 107 publications

References 53 publications

REVIEW : Recent advances in developing web-servers for predicting protein attributes

REVIEW : Recent advances in developing web-servers for predicting protein attributes

The dynamical contact order: Protein folding rate parameters based on quantum conformational transitions

Protein classification using texture descriptors extracted from the protein backbone image

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