Protein Molecular Function Prediction by Bayesian Phylogenomics

By Barbara E. Engelhardt, Michael I. Jordan, Kathryn E. Muratore, Steven E. Brenner. 2005

209 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Jinbo Xu
2. Sheng Wang
3. Jianzhu Ma
4. Paul D. Thomas
5. Zhiyong Wang
6. Pascale Gaudet
7. Morgan N. Price
8. Huaiyu Mi

Citations in the corpus

• MRFalign: Protein Homology Detection through Alignment of Markov Random Fields (2014, background): “Sequence-based protein alignment and homology detection has been extensively studied and widely applied to many biological problems such as homology modeling [1-4], phylogeny inference [5-7] and protein function prediction [8-10].”
• Phylogenetic and Functional Assessment of Orthologs Inference Projects and Methods (2009, unknown): “[26,41].”
• FastTree: Computing Large Minimum Evolution Trees with Profiles instead of a Distance Matrix (2009, background): “Analyzing all the sequences is important for taxonomy, for predicting gene function, for classifying environmental DNA sequences, and for identifying functional residues (Eisen 1998; Lichtarge et al. 2003; Engelhardt et al. 2005; von Mering et al. 2007).”
• Phylogenetic-based propagation of functional annotations within the Gene Ontology consortium (2011, methodology): “Our general approach is similar to the ‘phyloge-nomic’ method proposed by Eisen [6] and further developed into a probabilistic form by Engelhardt et al. [7], but with important differences.”