Protein Molecular Function Prediction by Bayesian Phylogenomics

We present a statistical graphical model to infer specific molecular function for unannotated protein sequences using homology. Based on phylogenomic principles, SIFTER (Statistical Inference of Function Through Evolutionary Relationships) accurately predicts molecular function for members of a protein family given a reconciled phylogeny and available function annotations, even when the data are sparse or noisy. Our method produced specific and consistent molecular function predictions across 100 Pfam families in comparison to the Gene Ontology annotation database, BLAST, GOtcha, and Orthostrapper. We performed a more detailed exploration of functional predictions on the adenosine-5′-monophosphate/adenosine deaminase family and the lactate/malate dehydrogenase family, in the former case comparing the predictions against a gold standard set of published functional characterizations. Given function annotations for 3% of the proteins in the deaminase family, SIFTER achieves 96% accuracy in predicting molecular function for experimentally characterized proteins as reported in the literature. The accuracy of SIFTER on this dataset is a significant improvement over other currently available methods such as BLAST (75%), GeneQuiz (64%), GOtcha (89%), and Orthostrapper (11%). We also experimentally characterized the adenosine deaminase from Plasmodium falciparum, confirming SIFTER's prediction. The results illustrate the predictive power of exploiting a statistical model of function evolution in phylogenomic problems. A software implementation of SIFTER is available from the authors.: New genome sequences continue to be published at a prodigious rate. However, unannotated sequences are of limited use to biologists. To computationally annotate a hypothetical protein for molecular function, researchers generally attempt to carry out some form of information transfer from evolutionarily related proteins. Such transfer is most successfully achieved within the context of phylogenetic relationships, exploiting the comprehensive knowledge that is available regarding molecular evolution within a given protein family. This general approach to molecular function annotation is known as phylogenomics, and it is the best method currently available for providing high-quality annotations. A drawback of phylogenomics, however, is that it is a time-consuming manual process requiring expert knowledge. In the current paper, the authors have developed a statistical approach—referred to as SIFTER (Statistical Inference of Function Through Evolutionary Relationships)—that allows phylogenomic analyses to be carried out automatically.