Abstract
Amino acid indices are useful tools in bioinformatics. With the appearance of novel theory and technology, and the rapid increase of experimental data, building new indices to cope with new or unsolved old problems is still necessary. In this study, residue networks are constructed from the PDB structures of 640 representative proteins based on the distance between Cα atoms with an 8 Åcutoff. All these networks show typical small world features. New amino acid indices, termed relative connectivity, clustering coefficient, closeness and betweenness, are derived from the corresponding topological parameters of amino acids in the residue networks. The 4 new network based indices are closely clustered together and related to hydrophobicity and β propensity. When compared with related amino acid indices, the new indices show better or comparable performance in protein surface residue prediction. Relative connectivity is the best index and can reach a useful performance with an area under the curve about 0.75. It indicates that the network property based amino acid indices can be useful complements to the existing physicochemical property based amino acid indices
