Decoy clustering through graded possibilistic c-medoids

Modern methods for ab initio prediction of protein structures typically explore multiple simulated conformations, called decoys, to find the best native-like conformations. To limit the search space, clustering algorithms are routinely used to group similar decoys, based on the hypothesis that the largest group of similar decoys will be the closest to the native state. In this paper a novel clustering algorithm, called Graded Possibilistic c-medoids, is proposed and applied to a decoy selection problem. As it will be shown, the added flexibility of the graded possibilistic framework allows an effective selection of the best decoys with respect to similar methods based on medoids - that is on the most central points belonging to each cluster. The proposed algorithm has been compared with other c-medoids algorithms and also with SPICKER on real data, the large majority of times outperforming both

Decoy Meta–Clustering Through Rough Graded Possibilistic C-Medoids

Current ab initio methods for structure–prediction of proteins explore multiple simulated conformations, called de- coys, to generate families of folds, one of which is the closest to the native one. To limit the exploration of the conformational space, clustering algorithms are routinely applied to group similar decoys and then finding the most plausible cluster centroid, based on the hypothesis that there are more low–energy conformations surrounding the native fold than the others; nevertheless different clustering algorithms, or different parameters, are likely to output different partitions of the input data and choosing only one of the possible solutions can be too restrictive and unreliable. meta–clustering algorithms allow to reconcile multiple clustering solutions by grouping them into meta-clusters (i.e. clusters of clusterings), so that similar partitions are grouped in the same meta–cluster. In this paper the use of meta–clustering is proposed for the selection of lowest energy decoys, testing the Rough Graded Possibilistic c-medoids clustering algorithm for both baseline clustering and meta–clustering. Preliminary tests on real data suggest that meta–clustering is effective in reducing the sensitivity to parameters of the clustering algorithm and to expand the explored space