A Simple Comparison between Specific Protein Secondary Structure Prediction Tools

A comparative evaluation of five widely used protein secondary structure prediction programs available in World Wide Web was carried out. Secondary structure data of ten proteins containing 190 secondary structure motifs were collected from Protein Data Bank (PDB). The amino acid sequences of the proteins were then evaluated using GOR, PSIPRED, HNN, PROF, and YASPIN secondary structure prediction tools and the results were compared with the structural information obtained from PDB. The study reveals considerable differences between results obtained from each program. Within the limit of this comparative study, PSIPRED showed the highest prediction accuracy with 77 % accuracy in α helix prediction and 70 % accuracy in b strand prediction. Furthermore, the level of accuracy varied with the length of the secondary structure motifs. Highest accuracies were obtained for α helices of 16-20 amino acids and β strands of 7-9 amino acids in length. The results suggest that, among the most frequently used software programs available in World Wide Web, PSIPRED is the tool that gives the best results for secondary structure prediction. Tropical Agricultural Research Vol. 23 (1): 91-98 (2011) DOI: http://dx.doi.org/10.4038/tar.v23i1.4636

[1]  R. King,et al.  Identification and application of the concepts important for accurate and reliable protein secondary structure prediction , 1996, Protein science : a publication of the Protein Society.

[2]  Liam J McGuffin,et al.  Benchmarking secondary structure prediction for fold recognition , 2003, Proteins.

[3]  D. Wishart.  Metabolomics: The Principles and Potential Applications to Transplantation , 2005, American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons.

[4]  M Ouali,et al.  Cascaded multiple classifiers for secondary structure prediction , 2000, Protein science : a publication of the Protein Society.

[5]  Kuang Lin,et al.  A simple and fast secondary structure prediction method using hidden neural networks , 2005, Bioinform..

[6]  Min Huang,et al.  Position‐specific residue preference features around the ends of helices and strands and a novel strategy for the prediction of secondary structures , 2008, Protein science : a publication of the Protein Society.

[7]  Liam J. McGuffin,et al.  The PSIPRED protein structure prediction server , 2000, Bioinform..

[8]  P. Y. Chou,et al.  Prediction of protein conformation. , 1974, Biochemistry.

[9]  K. Yamamori,et al.  Prediction of protein secondary structure by multi-modal neural networks , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).