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Download dataset

To Download previously created dataset and the lastest dataset. We have created two dataset, one is 30% non-redunant and other is 90% non-redunant dataset.

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Beta Turn in proteins

Beta-turns are the most common type of non-repetitive structures, and constitute on average 25% of the residues in all protein chains. In a beta turn, a tight loop is formed when the carbonyl oxygen of one residue forms a hydrogen bond with the amide proton of an amino acid three residues down the chain. This hydrogen bond stabilizes the beta bend structure. A beta turn can reverse the direction of its peptide chain.

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Prediction of Beta Turn

In the past numerous methods were developed to predict the beta turns. But all of this method were trained to predict residue level prediction instead of four residue level. Since, a beta turn is composed of four consecutive amno acids. Using this simple approach we have achieved good prediction accuracy and realistic prediction of beta turns. To predict beta turns in your protein click »Submit »

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Propensity based Beta Turn prediction

In the past statistical methods were developed to predict the beta turns based upon propensity score of beta turn. The propensity score was calculated using few hundered PDBs. We have calculate new propensity score using ~18000 PDBs. Users can predict beta turns based upon various position based propensity score. Click here to »Submit »

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Designing of Beta Turn

For the first time, we have developed a module thats helps user in understanding the positional preference of pairs of amino acids. First, user sequence is mapped and various propensity score are shown for all possible tetrapeptide. Second, the module performs all possible mutation in a tetrapeptide, either to increase or decrease its beta turn formation probability. Click here to »Submit »

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Prediction of Beta Turn Type

In the past numerous methods were developed to predict the beta turn types. Using the turn level approach we have significantly improved the prediction accuracy of beta turn types. To predict beta turns types in your protein click »Submit »

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Algorithm

We have developed a algorithm that predcit complete beta turn, earlier algorithm predict the residue that are present in beta turn. They can predict a residue to be beta turn residue, even its neighbouring residue are non beta turn. Our algorithm has overcome all these limitation and can predict only four consecutive beta turn residues.

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Download Datasets and Analysis

This page allows user to download all the protein datasets which are used in the development of BetaTPred3. It also provides facility to download the propensity values of amino acids falling in betaturns in an excel sheet. Three main datasets are used. 1) Standard datasets (BT426) which are used to compare the performance of new method with the already existing methods of betaturn prediction. 2). Unique dataset (BT20142) consisting of unique protein chains and used to calculate the propensity values of the amin oacids falling in betaturns. 3) Non-redundant updated dataset (BT6376) which is used to develop the final model of BetaTPred3 prediction method.
    Download BT426 dataset
    Download BT6376 dataset
    Download BT20142 dataset
    Analysis of Betaturns on BT20142



BT426 Dataset

The dataset has 426 non-homologus protein chains, as described by Guruprasad and Rajkumar (2000). In this data set, no two protein chains have more than 25% sequence identity. The structure of these proteins is determined by X-ray crystallography at 2.0 resolution or better. Each chain contains minimum one beta turn. The PROMOTIF program has been used to assign beta turns in proteins. For detail information and download of these proteins, Click here. .

BT6376 Dataset

The dataset has 6376 non-homologus protein chains. In this data set, no two protein chains have more than 30% sequence identity. The structure of these proteins is determined by X-ray crystallography at 2.0 resolution or better. Each chain contains minimum one beta turn. The PROMOTIF program has been used to assign beta turns in proteins. For PDB codes of these proteins, Click here

There are two datasets: Complete and Turn Type. The complete dataset has amino acid sequence of all 6376 proteins in fasta format and assigned turns and nonturns. There are nine turn type dataset (I-VIII), each containing different number of proteins. Each of these turn type dataset have amino acid sequence in fasta format along with assigned turns/nonturns.


Dataset NameNumber of PDB Chains having Turns
Complete Dataset 6376
Type I 6039
Type I' 2786
Type II 4750
Type II' 1995
Type IV 5950
Type VIa1 600
Type VIa2 177
Type VIb 914
Type VIII 4257



BT20142 Dataset

The dataset has 20142 protein chains. In this data set, no two protein chains have more than 100% sequence identity. The structure of these proteins is determined by X-ray crystallography at 2.0 resolution or better and nmr. Each chain contains minimum one beta turn. The PROMOTIF program has been used to assign beta turns in proteins. For PDB codes of these proteins, Click here

There are two datasets: Complete and Turn Type. The complete dataset has amino acid sequence of all 20142 proteins in fasta format and assigned turns and nonturns. There are nine turn type dataset (I-VII), each containing different number of proteins. Each of these turn type dataset have amino acid sequence in fasta format along with assigned turns/nonturns.


Dataset NameNumber of PDB Chains having Turns
Complete Dataset 20142
Type I 19482
Type I' 10323
Type II 15838
Type II' 6605
Type IV 19179
Type VIa1 2337
Type VIa2 685
Type VIb 3320
Type VIII 14125



Beta-turn Analysis using BT20142

All the analysis having propensity values and counts of residues in betaturn, are provided in an easily understandable and documented excel sheet which can be downloaded using the following link: ANALYSIS_BETATURN (~20 MB)
This excel sheet contains 7 sheets with following names: "Residue Propensity", "Position wise Propensity", "Pair wise propensity", "Tri-peptide propensity", "Tetra-peptide Propensity", "Never found" and "Always found".