BetaTPred: prediction of <b>β</b>-TURNS in a protein
  using statistical algorithms

Kaur, Harpreet; Raghava, Gajendra P. S.

doi:10.1093/bioinformatics/18.3.498

Cited by 24 publications

(22 citation statements)

References 9 publications

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“…On the other hand, type VI were divided into 2 sub-types, that is, VIa [89,117] are nowadays considered as the standard (see Table 1). They are widely analyzed in molecular dynamics [118] and prediction methods have been developed [119][120][121][122][123][124][125][126]. Motifs and conformational analysis of amino acid residues adjoining β-turns in proteins have also been extensively described [127].…”

Section: Secondary Structuresmentioning

confidence: 99%

Local Protein Structures

Offmann¹,

Tyagi²,

Brevern

2007

CBIO

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Protein structures are classically described as composed of two regular states, the α-helices and the β-strands and one non-regular and variable state, the coil. Nonetheless, this simple definition of secondary structures hides numerous limitations. In fact, the rules for secondary structure assignment are complex. Thus, numerous assignment methods based on different criteria have emerged leading to heterogeneous and diverging results. In the same way, 3 states may over-simplify the description of protein structure; 50% of all residues, i.e., the coil, are not genuinely described even when it encompass precise local protein structures.Description of local protein structures have hence focused on the elaboration of complete sets of small prototypes or "structural alphabets", able to analyze local protein structures and to approximate every part of the protein backbone. They have also been used to predict the protein backbone conformation and in ab initio / de novo methods. In this paper, we review different approaches towards the description of local structures, mainly through their description in terms of secondary structures and in terms of structural alphabets. We provide some insights into their potential applications.

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Section: Secondary Structuresmentioning

confidence: 99%

Local Protein Structures

Offmann¹,

Tyagi²,

Brevern

2007

CBIO

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“…Seven-fold cross-validation was applied on datasets GR426, FA547 and FA823. For the former, we utilised the the same subsets used by Kaur and Raghava [55] to evaluate different β -turn prediction methods, whereas the partition of the other two datasets was identical to the one used to train COUDES [28]. …”

Section: Methodsmentioning

confidence: 99%

Predicting β-turns and their types using predicted backbone dihedral angles and secondary structures

Kountouris

Hirst

2010

BMC Bioinformatics

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Backgroundβ-turns are secondary structure elements usually classified as coil. Their prediction is important, because of their role in protein folding and their frequent occurrence in protein chains.ResultsWe have developed a novel method that predicts β-turns and their types using information from multiple sequence alignments, predicted secondary structures and, for the first time, predicted dihedral angles. Our method uses support vector machines, a supervised classification technique, and is trained and tested on three established datasets of 426, 547 and 823 protein chains. We achieve a Matthews correlation coefficient of up to 0.49, when predicting the location of β-turns, the highest reported value to date. Moreover, the additional dihedral information improves the prediction of β-turn types I, II, IV, VIII and "non-specific", achieving correlation coefficients up to 0.39, 0.33, 0.27, 0.14 and 0.38, respectively. Our results are more accurate than other methods.ConclusionsWe have created an accurate predictor of β-turns and their types. Our method, called DEBT, is available online at http://comp.chem.nottingham.ac.uk/debt/.

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“…17 In order to utilize the full potential of above-mentioned methods, a web server for β-turn prediction based on statistical methods and correlation models has been developed and is available at http://imtech.res.in/raghava/betatpred/. 21 Another alternative and an improved approach is to use a set of neural networks to learn appropriate sets of parameters from the available data. BTPRED is a neural network based method developed on a set of 300 non-homologous protein domains by Shepherd et al 9 A neural network is used to predict whether a given residue is a part of a β-turn or not.…”

Section: Existing β-Turn Prediction Methodsmentioning

confidence: 99%

BTEVAL: A Server for Evaluation of β-Turn Prediction Methods

Kaur

Raghava

2003

J. Bioinform. Comput. Biol.

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This paper describes a web server BTEVAL, developed for assessing the performance of newly developed beta-turn prediction method and it's ranking with respect to other existing beta-turn prediction methods. Evaluation of a method can be carried out on a single protein or a number of proteins. It consists of clean data set of 426 non-homologous proteins with seven subsets of these proteins. Users can evaluate their method on any subset or a complete set of data. The method is assessed at amino acid level and performance is evaluated in terms of Qtotal, Qpredicted, Qobserved and MCC measures. The server also compares the performance of the method with other existing beta-turn prediction methods such as Chou-Fasman algorithm, Thornton's algorithm, GORBTURN, 1-4 and 2-3 Correlation model, Sequence coupled model and BTPRED. The server is accessible from http://imtech.res.in/raghava/bteval/

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BetaTPred: prediction of β-TURNS in a protein using statistical algorithms

Abstract: The server is accessible from http://imtech.res.in/raghava/betatpred/

Cited by 24 publications

References 9 publications

Local Protein Structures

Local Protein Structures

Predicting β-turns and their types using predicted backbone dihedral angles and secondary structures

BTEVAL: A Server for Evaluation of β-Turn Prediction Methods

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