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Planning Combinatorial Disulfide Cross-Links For Protein Fold Determination, Fei Xiong, Alan M Friedman, Chris Bailey-Kellogg
Planning Combinatorial Disulfide Cross-Links For Protein Fold Determination, Fei Xiong, Alan M Friedman, Chris Bailey-Kellogg
Dartmouth Scholarship
Fold recognition techniques take advantage of the limited number of overall structural organizations, and have become increasingly effective at identifying the fold of a given target sequence. However, in the absence of sufficient sequence identity, it remains difficult for fold recognition methods to always select the correct model. While a native-like model is often among a pool of highly ranked models, it is not necessarily the highest-ranked one, and the model rankings depend sensitively on the scoring function used. Structure elucidation methods can then be employed to decide among the models based on relatively rapid biochemical/biophysical experiments.
Computational Prediction Of Heme-Binding Residues By Exploiting Residue Interaction Network, R. Liu, Jianjun Hu
Computational Prediction Of Heme-Binding Residues By Exploiting Residue Interaction Network, R. Liu, Jianjun Hu
Faculty Publications
Computational identification of heme-binding residues is beneficial for predicting and designing novel heme proteins. Here we proposed a novel method for heme-binding residue prediction by exploiting topological properties of these residues in the residue interaction networks derived from three-dimensional structures. Comprehensive analysis showed that key residues located in heme-binding regions are generally associated with the nodes with higher degree, closeness and betweenness, but lower clustering coefficient in the network. HemeNet, a support vector machine (SVM) based predictor, was developed to identify heme-binding residues by combining topological features with existing sequence and structural features. The results showed that incorporation of network-based …