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Year : 2017  |  Volume : 1  |  Issue : 1  |  Page : 29-36

Evaluation of in silico protein secondary structure prediction methods by employing statistical techniques

Department of Bioinformatics, Bharathiar University, Coimbatore, Tamil Nadu, India

Correspondence Address:
Kandavelmani Angamuthu
Department of Bioinformatics, Bharathiar University, Coimbatore - 641 046, Tamil Nadu
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/bbrj.bbrj_28_17

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Background: With the advent of many new advanced techniques, sequences of a number of proteins have been made available. But the relative paucity of the experimentally determined three-dimensional structures of these proteins has paved way for the development of computational structure prediction methods. Protein secondary structure prediction is an essential step in modeling the tertiary structure. Among the various secondary structure prediction methods available, three different methods with unique working principles, namely, GOR, HNN, and SOPMA were evaluated for their efficiency to predict secondary structures. Methods: A set of 90 different proteins with known secondary structures from three major classes namely, mainly alpha, mainly beta, and mainly alpha beta was used as reference. Secondary structure data of these proteins obtained through experimental methods were compared with that of predictions made by GOR, HNN, and SOPMA respectively by employing various statistical analyses, namely paired sample test, correlation coefficient, standard deviation, standard error mean and scatter plots. Results: The secondary structure prediction tools namely, GOR and HNN were found to predict helical structures more accurately than the sheets. SOPMA was observed to predict sheets more accurately than helices. Conclusion: Based on the observed results, it could be concluded that there is no single tool that consistently predicts all the secondary structures accurately. It could also be anticipated that a combined use of these secondary prediction tools could further enhance the efficacy of in silico protein secondary structure prediction methods.

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