Profiles and fuzzy K-nearest neighbor algorithm for protein secondary structure prediction

We introduce a new approach for predicting the secondary structure of proteins using profiles and the Fuzzy K-Nearest Neighbor algorithm. K-Nearest Neighbor methods give relatively better performance than Neural Networks or Hidden Markov models when the query protein has few homologs in the sequence database to build sequence profile. Although the traditional K-Nearest Neighbor algorithms are a good choice for this situation, one of the difficulties in utilizing these techniques is that all the labeled samples are given equal importance while deciding the secondary structure class of the protein residue and once a class has been assigned to a residue, there is no indication of its confidence in a particular class. In this paper, we propose a system based on the Fuzzy K-Nearest Neighbor Algorithm that addresses the above-mentioned issues and the system outperforms earlier K-Nearest neighbor methods that use multiple sequence alignments. We also introduce a new distance measure to calculate the distance between two protein sequences, a new method to assign membership values to the Nearest Neighbors in each of the Helix, Strand and Coil classes. We also propose a novel heuristic based filter to smoothen the prediction. Particularly attractive feature of our filter is that it does not require retraining when new structures are added to the database. We have achieved a sustained three-state overall accuracy of 75.75% with our system. The software is available upon request.