Image Title 1

Developers of BetaTpred 3.0

Dr. G. P. S. Raghava
Coordinator and Scientist
Bioinformatics Centre
Institute of Microbial Technology
Sector 39-A Chandigarh-160036, INDIA
Phone: +91-11-26907444, 2690908
Fax: +91-172-2690585, 2690632
Web: http://webs.iiitd.edu.in/raghava/
E-mail:raghava@iiitd.ac.in  

Image Title 1

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.

Image Title 2

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 »

Image Title 2

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 »

Image Title 2

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 »

Image Title 2

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 »

Image Title 3

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.

More »
Image Title 4

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.

More »

Developers of BetaTPred 3.0



Dr. G. P. S. Raghava

Coordinator and Scientist,
Bioinformatics Centre,
Institute of Microbial Technology,
Sector 39-A Chandigarh-160036.
INDIA.
Phone: +91-11-26907444, 2690908
Fax: +91-172-2690585, 2690632
Web: http://webs.iiitd.edu.in/raghava/
E-mail:raghava@iiitd.ac.in

 

Harinder Singh

Senior Research Fellow
Bioinformatics Centre,
Institute of Microbial Technology,
Sector 39-A Chandigarh-160036. INDIA.

Sandeep Singh

Senior Research Fellow
Bioinformatics Centre,
Institute of Microbial Technology,
Sector 39-A Chandigarh-160036. INDIA.