Mining Calcium-binding Sites From Protein Structure Graphs
Abstract
Identifying protein calcium-binding sites is an important problem in proteomics. To this end, we construct a graph containing only oxygen information to represent protein partial structures. In this graph, each vertex represents an oxygen atom. Edges are given to any two vertex-atoms based on a simple distance threshold between contact atoms. Applying a clique-finding algorithm to a set of graphs representing a group of calcium-binding proteins, we obtain several hundred oxygen clique-clusters with size four possibly around calcium-binding sites. We then use geometric and chemic properties of four co-spherical vertices to exclude some clique-clusters. We finally use support vector machines (SVM) to do binary classification with vertex-atom coordinates as the input variables for distinguishing calcium-binding clique-clusters and non calcium-binding clique-clusters. The results show the site selectivity reaches 80% with 91% site sensitivity. This new protein graph mining and geometric classification model can be used for rapid and automated annotation of protein function-calcium binding
Department(s)
Computer Science
Document Type
Conference Proceeding
DOI
https://doi.org/10.1109/icnnb.2005.1615012
Publication Date
2005
Recommended Citation
Deng, Hai, Hui Liu, and Yanqing Zhang. "Mining calcium-binding sites from protein structure graphs." In 2005 International Conference on Neural Networks and Brain. IEEE, 2005.
Journal Title
Proceedings of 2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05