Prediction of ß-turns in proteins from multiple alignment using neural network
Protein Sci
Kaur and Raghava 12 (3): 627.
Supplemental Research Data
Files in this Data Supplement:
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The supplementary information has PDB codes of protein chains used in the present study. Table 1 contains the composition of seven different training sets. Table 2 has secondary structure composition of beta-turn residues in terms of DSSP 8 states. Table 3 contains the beta-turn positional frequencies for all the twenty amino acids. Table 4 contains the results of beta-turn/non-turn predictions with a single network trained on single sequences and its comparisons with other methods tested on the same data set. Figure 1 shows a sample output of beta-turn/non-turn predictions by BetaTPred2 server.
