QAUST:Protein Function Prediction Using Structure Similarity,Protein Interaction,and Functional Motifs

The number of available protein sequences in public databases is increasing exponentially.However,a sig-nificant percentage of these sequences lack functional annotation,which is essential for the understanding of how bio-logical systems operate.Here,we propose a novel method,Quantitative Annotation of Unknown STructure(QAUST),to infer protein functions,specifically Gene Ontology(GO)terms and Enzyme Commission(EC)numbers.QAUST uses three sources of information:structure information encoded by global and local structure similarity search,biological network information inferred by protein–protein interaction data,and sequence information extracted from functionally discriminative sequence motifs.These three pieces of information are combined by consensus averaging to make the final prediction.Our approach has been tested on 500 protein targets from the Critical Assessment of Functional Annotation(CAFA)benchmark set.The results show that our method provides accurate functional annotation and outperforms other prediction methods based on sequence similarity search or threading.We further demonstrate that a previously unknown function of human tripartite motif-containing 22(TRIM22)protein predicted by QAUST can be experimentally validated.

Cytochrome P450 BM3 of Bacillus megaterium-A possible endosulfan biotransforming gene

Computing chemistry was applied to understand biotransforrnation mechanism of an organochlorine pesticide, endosulfan. The stereo specific metabolic activity of human CYP-2B6 （cytochrome P450） on endosulfan has been well demonstrated. Sequence and structural similarity search revealed that the bacterium Bacillus megaterium encodes CYP-BM3, which is similar to CYP-2B6. The functional similarity was studied at organism level by batch-scale studies and it was proved that B. megaterium could metabolize endosulfan to endosulfan sulfate, as CYP-2B6 does in human system. The gene expression analyses also confirmed the possible role of CYP-BM3 in endosulfan metabolism. Thus, our results show that the protein structure based in-silico approach can help us to understand and identify microbes for remediation strategy development. To the best of our knowledge this is the first report which has extrapolated the bacterial gene for endosulfan biotransformation through in silico prediction approach for metabolic gene identification.