Feature amplified voting algorithm for functional analysis of protein superfamily

Che-Lun Hung,Chun-Yuan Lin,Chih-Hung Chang,Chihan Lee,Yeh-Ching Chung,Chuan Yi Tang

doi:10.1186/1471-2164-11-s3-s14

Abstract

BackgroundIdentifying the regions associated with protein function is a singularly important task in the post-genomic era. Biological studies often identify functional enzyme residues by amino acid sequences, particularly when related structural information is unavailable. In some cases of protein superfamilies, functional residues are difficult to detect by current alignment tools or evolutionary strategies when phylogenetic relationships do not parallel their protein functions. The solution proposed in this study is Feature Amplified Voting Algorithm with Three-profile alignment (FAVAT). The core concept of FAVAT is to reveal the desired features of a target enzyme or protein by voting on three different property groups aligned by three-profile alignment method. Functional residues of a target protein can then be retrieved by FAVAT analysis. In this study, the amidohydrolase superfamily was an interesting case for verifying the proposed approach because it contains divergent enzymes and proteins.ResultsThe FAVAT was used to identify critical residues of mammalian imidase, a member of the amidohydrolase superfamily. Members of this superfamily were first classified by their functional properties and sources of original organisms. After FAVAT analysis, candidate residues were identified and compared to a bacterial hydantoinase in which the crystal structure (1GKQ) has been fully elucidated. One modified lysine, three histidines and one aspartate were found to participate in the coordination of metal ions in the active site. The FAVAT analysis also redressed the misrecognition of metal coordinator Asp57 by the multiple sequence alignment (MSA) method. Several other amino acid residues known to be related to the function or structure of mammalian imidase were also identified.ConclusionsThe FAVAT is shown to predict functionally important amino acids in amidohydrolase superfamily. This strategy effectively identifies functionally important residues by analyzing the discrepancy between the sequence and functional properties of related proteins in a superfamily, and it should be applicable to other protein families.

Highlights

Identifying the regions associated with protein function is a singularly important task in the postgenomic era
The clustered sequences were subjected to Feature Amplified Voting Algorithm with Three-profile alignment (FAVAT) analysis, and two sets of scores were obtained for each residue of the target sequence
The T-score suggests the importance of each residue of the target sequence after accumulating the total voting scores (V-scores), each V-score is calculated from the target sequence, an A protein sequence and a ~A protein sequence

Summary

Introduction

Identifying the regions associated with protein function is a singularly important task in the postgenomic era. Biological studies often identify functional enzyme residues by amino acid sequences, when related structural information is unavailable. In some cases of protein superfamilies, functional residues are difficult to detect by current alignment tools or evolutionary strategies when phylogenetic relationships do not parallel their protein functions. The solution proposed in this study is Feature Amplified Voting Algorithm with Three-profile alignment (FAVAT). The core concept of FAVAT is to reveal the desired features of a target enzyme or protein by voting on three different property groups aligned by three-profile alignment method. Functional residues of a target protein can be retrieved by FAVAT analysis. The amidohydrolase superfamily was an interesting case for verifying the proposed approach because it contains divergent enzymes and proteins

Methods

Results

Conclusion