Prediction of gamma-turns from amino acid sequences

Predicting turns in proteins with a unified model

MOTIVATION

Turns are a critical element of the structure of a protein; turns play a crucial role in loops, folds, and interactions. Current prediction methods are well developed for the prediction of individual turn types, including α-turn, β-turn, and γ-turn, etc. However, for further protein structure and function prediction it is necessary to develop a uniform model that can accurately predict all types of turns simultaneously.

RESULTS

In this study, we present a novel approach, TurnP, which offers the ability to investigate all the turns in a protein based on a unified model. The main characteristics of TurnP are: (i) using newly exploited features of structural evolution information (secondary structure and shape string of protein) based on structure homologies, (ii) considering all types of turns in a unified model, and (iii) practical capability of accurate prediction of all turns simultaneously for a query. TurnP utilizes predicted secondary structures and predicted shape strings, both of which have greater accuracy, based on innovative technologies which were both developed by our group. Then, sequence and structural evolution features, which are profile of sequence, profile of secondary structures and profile of shape strings are generated by sequence and structure alignment. When TurnP was validated on a non-redundant dataset (4,107 entries) by five-fold cross-validation, we achieved an accuracy of 88.8% and a sensitivity of 71.8%, which exceeded the most state-of-the-art predictors of certain type of turn. Newly determined sequences, the EVA and CASP9 datasets were used as independent tests and the results we achieved were outstanding for turn predictions and confirmed the good performance of TurnP for practical applications.

Beta-hairpin prediction with quadratic discriminant analysis using diversity measure

On the basis of the features of protein sequential pattern, we used the method of increment of diversity combined with quadratic discriminant analysis (IDQD) to predict beta-hairpins motifs in protein sequences. Three rules are used to extract the raw beta-beta motifs sequential patterns for fixed-length. Amino acid basic compositions, dipeptide components, and amino acid composition distribution are combined to represent the compositional features. Eighteen feature variables on a sequential pattern to be predicted are defined in terms of ID. They are integrated in a single formal framework given by IDQD. The method is trained and tested on ArchDB40 dataset containing 3088 proteins. The overall accuracy of prediction and Matthew's correlation coefficient for the independent testing dataset are 81.7% and 0.60, respectively. In addition, a higher accuracy of 84.5% and Matthew's correlation coefficient of 0.68 for the independent testing dataset are obtained on a dataset previously used by Kumar et al. (Nucleic Acids Res 2005, 33, 154), which contains 2088 proteins. For a fair assessment of our method, the performance is also evaluated on all 63 proteins used in CASP6. The overall accuracy of prediction is 74.2% for the independent testing dataset.

gamma-Turn types prediction in proteins using the support vector machines

Recently, two different models have been developed for predicting gamma-turns in proteins by Kaur and Raghava [2002. An evaluation of beta-turn prediction methods. Bioinformatics 18, 1508-1514; 2003. A neural-network based method for prediction of gamma-turns in proteins from multiple sequence alignment. Protein Sci. 12, 923-929]. However, the major limitation of previous methods is inability in predicting gamma-turns types. Thus, there is a need to predict gamma-turn types using an approach which will be useful in overall tertiary structure prediction. In this work, support vector machines (SVMs), a powerful model is proposed for predicting gamma-turn types in proteins. The high rates of prediction accuracy showed that the formation of gamma-turn types is evidently correlated with the sequence of tripeptides, and hence can be approximately predicted based on the sequence information of the tripeptides alone.