Beta-turn is an important element of protein structure. In past three decades numerous beta-turn prediction methods have been developed based on various strategies. At present, it is difficult to say which method is better. This is because these methods were developed on different sets of data. Thus, it is important to evaluate the performance of beta-turn prediction methods.

We evaluate the performance of six methods of beta-turn prediction. Original parameters available in the literature are used to test the methods on a set of 426 non-homologus protein chains. It is observed that the performance of neural network based method BTPRED is significantly better than the statistical methods. We also train, test and evaluate the performance of all methods except BTPRED and GORBTURN, on a new data set using sevenfold cross-validation technique. There is a siginficant improvement in performance of all methods when secondary structure information is incorporated. In this study, both threshold dependent and independent (ROC) measures are used for evaluation.

DATASET

The representative protein chains are selected so that no two chains have more than 25% sequence identity. Protein chains determined by X-ray crystallography at 2.0 resolution or better and containing atleast one beta-turn are used in the analysis. Following are the pdb codes of 426 protein chains used for analysis. It is the same data set as described by K.Guruprasad.

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PREDICTION METHODS EVALUATED

ORIGINAL PARAMETERS AND THRESHOLDS

• Chou-Fasman algorithm

Original conformational parameters and positional frequencies for helix,ß-sheet and ß-turn residues.

Original Threshold = 0.000075

• Thornton's algorithm

Original conformational parameters and positional frequencies:

Type I beta-turns

Type II beta-turns

Original Threshold for Type I turn = 4.0

Original Threshold for Type II turn = 2.7

• 1-4 & 2-3 Correlation Model

Original probabilities and conditional probabilities:

P1(X1)

P2(X2)

P3(X3|X2)

P4(X4|X1)

Original Threshold = 0.1875

• Sequence Coupled Model

Original probabilities and conditional probabilities for turns:

Pi+(Ri)

Pi+1+(Ri+1|Ri)

Pi+2+(Ri+2|Ri+1 )

P+i+3 (Ri+3|Ri+2)

Original probabilitis and conditional probabilities for non-turns:

P-i(Ri)

P-i+1 (Ri+1 |Ri)

P-i+2(Ri+2|Ri+1)

P-i+3(Ri+3|Ri+2)

Original Threshold = 0

• GORBTURN

Original positional frequencies for Type I turns

Original positional frequencies for Type II turns

New Threshold : A new threshold value is chosen at which the sensitivity and specificity are nearly equal.

THRESHOLD DEPENDENT MEASURES

Following four different parameters were used to measure the performance of prediction methods:

Qtotal, the percentage of correctly predicted residues, defined as

MCC, the Matthews Correlation Coefficient, defined as

Qpredicted, probability of correct prediction, defined as

Qobserved, percent coverage, defined as

RESULTS

Table 1 Results of 7-fold cross-validation at original thresholds.

*values in brackets are calculated by using original parameters of methods.

Following are the results of cross-validation in terms of Qtotal, Qpredicted, Qobserved and MCC.

(a) Qtotal

(b) Qpredicted

(c) Qobserved

(d) MCC

Effect of different secondary structure methods on performance of BTPRED

Table 2 ROC values for different methods without cross-validation.

Table 3 ROC values for different methods with cross validation.

ROC with and without cross-validation

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