Structural alignment of large—size proteins via lagrangian relaxation
Proceedings of the sixth annual international conference on Computational biology
PROFcon: novel prediction of long-range contacts
Bioinformatics
Clustering Using a Similarity Measure Based on Shared Near Neighbors
IEEE Transactions on Computers
Mining of protein contact maps for protein fold prediction
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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The purpose of this work is to demonstrate that it is possible to cluster contact maps for pairs of alpha helices such that each of the clusters corresponds to a group of pairs of alpha helices with similar properties. The property of the configuration of helix pairs that was chosen for study is the packing attribute. The contact maps are compared to one another using a novel contact map comparison scheme based upon the locations of contacts in the contact maps. A k-nearest neighbours technique is used to perform the clustering, and the cosine between vectors corresponding to contact map regions was the distance metric. The clustering of contact maps to determine whether maps corresponding to similar packing values are placed into the same clusters yielded promising results.