Bioinformatics Advance Access originally published online on April 6, 2005
Bioinformatics 2005 21(12):2844-2849; doi:10.1093/bioinformatics/bti423
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Prediction of protein solvent accessibility using fuzzy k-nearest neighbor method
1Department of Bioinformatics and Life Science, Bioinformatics and Molecular Design Technology Innovation Center, and Computer Aided Molecular Design Research Center, Soongsil University Seoul 156-743, South Korea
2School of Computational Sciences, Korea Institute for Advanced Study Seoul 130-722, South Korea
*To whom correspondence should be addressed.
Motivation: The solvent accessibility of amino acid residues plays an important role in tertiary structure prediction, especially in the absence of significant sequence similarity of a query protein to those with known structures. The prediction of solvent accessibility is less accurate than secondary structure prediction in spite of improvements in recent researches. The k-nearest neighbor method, a simple but powerful classification algorithm, has never been applied to the prediction of solvent accessibility, although it has been used frequently for the classification of biological and medical data.
Results: We applied the fuzzy k-nearest neighbor method to the solvent accessibility prediction, using PSI-BLAST profiles as feature vectors, and achieved high prediction accuracies. With leave-one-out cross-validation on the ASTRAL SCOP reference dataset constructed by sequence clustering, our method achieved 64.1% accuracy for a 3-state (buried/intermediate/exposed) prediction (thresholds of 9% for buried/intermediate and 36% for intermediate/exposed) and 86.7, 82.0, 79.0 and 78.5% accuracies for 2-state (buried/exposed) predictions (thresholds of each 0, 5, 16 and 25% for buried/exposed), respectively. Our method also showed slightly better accuracies than other methods by about 2–5% on the RS126 dataset and a benchmarking dataset with 229 proteins.
Availability: Program and datasets are available at http://biocom1.ssu.ac.kr/FKNNacc/
Contact: jul{at}ssu.ac.kr
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