Current Bioinformatics

Yi-Ping Phoebe Chen
Department of Computer Science and Information Technology
La Trobe University


Predicting Protein Metal Binding Sites with RBF Networks based on PSSM Profiles and Additional Properties

Author(s): Yu-Yen Ou.


Background: Metal atoms are involved in many biological mechanisms, such as protein structure stability, apoptosis and aging. Therefore, identifying metal-binding sites in proteins is an important issue in helping biologists better understand the workings of these mechanisms.

Methods: We propose a method based on Position Specific Scoring Matrix (PSSM) profiles and additional information (conservation score and solvent accessible surface area (ASA)) to identify the metal-binding residues in proteins.

Results: We have selected a non-redundant set of 262 metal-binding proteins and 617 disulfide proteins as the independent test set. The proposed method can predict metal-binding sites at 51.0% recall and 73.4% precision. Comparing with the previous work of A. Passerini et al., the proposed method can improve over 7% of precision with the same level of recall on the independent dataset.

Conclusions: We have developed a novel approach based on PSSM profiles and additional properties for identifying metal-binding sites from proteins. The proposed approach achieved a significant improvement with newly discovered metal-binding proteins and disulfide proteins.

Keywords: Metal-binding proteins, PSSM, RBF networks, accessible surface area, metal-binding sites, frequency, MetalDetector, β-barrel proteins, non-redundant, PSI-BLAST

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Article Details

Year: 2012
Page: [180 - 186]
Pages: 7
DOI: 10.2174/157489312800604417
Price: $58