Protein ß-turn prediction using nearest-neighbor method

Motivation: With the emerging success of protein secondary structure prediction through the applications of various statistical and machine learning techniques, similar techniques have been applied to protein β-turn prediction. In this study, we perform protein β-turn prediction using a k -nearest neighbor method, which is combined with a filter that uses predicted protein secondary structure information. Traditional β-turn prediction from k -nearest neighbor method is modified to account for the unbalanced ratio of the natural occurrence of β-turns and non-β-turns. Results: Our prediction scheme is tested on a set of 426 non-homologous protein sequences. The prediction scheme consists of two stages: k -nearest neighbor method stage and filtering stage. Variations of the k -nearest neighbor method were used to take property of β-turns into consideration. Our filtering method uses β-turn/non-β-turn estimates from the k -nearest neighbor method stage and predicted protein secondary structure information from PSI-PRED in order to get new β-turn/non-β-turn estimate. Our result is compared with the previously best known β-turn prediction method on the dataset of 426 non-homologous protein sequences and is shown to give slightly superior performance at significantly lower computational complexity. Availability: Contact the author for information on the source code of the programs used. Contact: [email protected]