Prediction of Tight Turns In Protein Protein Sequence + G.P.S. Raghava, Ph.D., F.N.A.Sc. Scientist and Head Bioinformatics Centre Institute of Microbial Technology, Sector-39 A, Chandigarh, India Email:email@example.com Web: http://www.imtech.res.in/raghava/ Structure
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G.P.S. Raghava, Ph.D., F.N.A.Sc.
Scientist and Head Bioinformatics Centre
Institute of Microbial Technology, Sector-39 A, Chandigarh, India
Energy Minimization based methods in their pure form, make no priori assumptions and attempt to locate global minma.
Intermidiate structures are predicted, instead of predicting tertiary structure of protein from amino acids sequence
Accuracy is only 75-80 %
Only three state prediction
Random coils, bulges)
A -turn is defined by four consecutive residues i, i+1, i+2 and i+3 that do not form a helix and have a C(i)-C(i+3) distance less than 7Å and the turn lead to reversal in the protein chain. (Richardson, 1981).
The conformation of -turn is defined in terms of and of two central residues, i+1 and i+2 and can be classified into different types on the basis of and .
a:Ramachandran plot showing the characteristic region where
-sheet and -helices are found.
b: Ramachandran plot showing Type I and II turns represented by a vector
Harpreet Kaur and G P S Raghava (2002) BetaTPred: Prediction of -turns in a protein using statistical algorithms.Bioinformatics18(3), 498-499.
We have evaluated the performance of six methods of -turn prediction. All the methods have been tested on a set of 426 non-homologous protein chains. In this study, both threshold dependent (Qtotal, Qpred., Qobs. And MCC) and independent (ROC) measures have been used for evaluation.
Harpreet Kaur and G.P.S Raghava (2002) An evaluation of -turn prediction methods.Bioinformatics18(11), 1508-1514.
Performance of existing -turn methods
Harpreet Kaur and G P S Raghava (2003)BTEVAL: A server for evaluation of -turn prediction methods.Journal of Bioinformatics and Computational Biology(in press).
Harpreet Kaur and G P S Raghava (2003) Prediction of -turns in proteins from multiple alignment using neural network. Protein Science 12, 627-634.
BetaTPred2 prediction results using single sequence and multiple alignment.
Harpreet Kaur and G P S Raghava (2003) Prediction of -turns in proteins from multiple alignment using neural network.Protein Science12, 627-634.
Harpreet Kaur and G P S Raghava (2003) A neural network based method for prediction of -turns in proteins from multiple sequence alignment. Protein Science12, 923-929.
Harpreet Kaur and G P S Raghava (2003) A neural network based method for prediction of -turns in proteins from multiple sequence alignment.Protein Science12, 923-929.
Harpreet Kaur and G P S Raghava (2003) Prediction of -turn types in proteins from evolutionary information using neural network. Bioinformatics (In Press)
Harpreet Kaur and G P S Raghava (2003) Prediction of -turns in proteins using PSI-BLAST profiles and secondary structure information.Proteins(in press).
3 models have been studied for each peptide. The first model has been ( = = 180o). The second model is build up by constructed by taking all the peptide residues in the extended conformation assigning the peptide residues the , angles of the secondary structure states predicted by PSIPRED. The third model has been constructed with , angles corresponding to the secondary states predicted by PSIPRED and -turns predicted by BetaTPred2.
( = = 180o).
Root Mean Square Deviation has been calculated…….
Averaged backbone root mean deviation before and after energy minimization and dynamics simulations.