BetaTPred 3.0 Home Page: Prediction and Design of Beta turns

Beta Turn in proteins

Beta-turns are most common type of non-repetitive structures in a protein. It 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 protein backbone.

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 »

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 »

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 »

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 »

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.

Welcome to BetaTPred 3.0

If you are using BetaTPred3, please cite Singh H, Singh S and Raghava GPS (2015). Proteins: Structure, Function, and Bioinformatics 10.1002/prot.24783

Previously, our group developed methods for predicting beta turns and their types, called BetaTPred (based on statistical algorithm), BetaTPred2 (based on neural network using PSSM profile) and BetaTurns (prediction of turn types). BetaTPred3 is a new and improved version developed for predicting beta turns and their types. Following are major salient features of BetaTPred3 (module wise).

Accuracy of Prediction: Our previous method BetaTPred2 was developed on 426 proteins. In BetaTPred3, models have been trained, tested and evaluated on a large data set containinig more than 5000 protein chains. The prediction models in this version are more reliable and accurate than previous version.

Prediction of Whole Beta-turn: In past methods have been developed to predict beta-turn at residue level where they predict probability of a residue to be part of beta-turn. In contrast, BetaTPred3 predict whole beta-turn; basically probability of four-consecutive residues to form a beta-turn.

Prediction of Type of Beta-turns: BetaTPred3 allows one to predict type of beta-turn. In this study models have been trained on large data set as well as for nine types of beta-turn. In our previous version BetaTurns models were trained on small dataset as well it only predict four types of beta-turns.

Propensity-based Prediction: These methods have advatage over machine learning techniques as they proviode propensity. Thus we also developed statistical method for predicting propensity of residues (e.g., residue, dipeptide, tripeptides) to be part of beta turn. These propensities help user to identify regions in a protein that have beta-turn forming or breaking potential

Inducing or Breaking Beta-Turns: This module allow users to indentify mutation required to induce or break (increase or decrease propensity) beta-turn in a tetra-peptide. This module will be useful for researchers working in the field of protein engineering. As they may design protein with beta-turns at desired locations with the help of this module.

In summary, BetaTPred3 is a comprehensive web server that provides most of information related to beta-turns required for a researcher.