Prediction of Protein Solvent Accessibility Using Fuzzy k-Nearest Neighbor Method ABSTRACT Motivation: Solvent accessibility of amino acid residues plays an important role in the 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/.