Genome-scale phylogenetic function annotation of large and diverse protein families

Abstract

The Statistical Inference of Function Through Evolutionary Relationships (SIFTER) framework uses a statistical graphical model that applies phylogenetic principles to automate precise protein function prediction. Here we present a revised approach (SIFTER version 2.0) that enables annotations on a genomic scale. SIFTER 2.0 produces equivalently precise predictions compared to the earlier version on a carefully studied family and on a collection of 100 protein families. We have added an approximation method to SIFTER 2.0 and show a 500-fold improvement in speed with minimal impact on prediction results in the functionally diverse sulfotransferase protein family. On the Nudix protein family, previously inaccessible to the SIFTER framework because of the 66 possible molecular functions, SIFTER achieved 47.4% accuracy on experimental data (where BLAST achieved 34.0%). Finally, we used SIFTER to annotate all of the Schizosaccharomyces pombe proteins with experimental functional characterizations, based on annotations from proteins in 46 fungal genomes. SIFTER precisely predicted molecular function for 45.5% of the characterized proteins in this genome, as compared with four current function prediction methods that precisely predicted function for 62.6%, 30.6%, 6.0%, and 5.7% of these proteins. We use both precision-recall curves and ROC analyses to compare these genome-scale predictions across the different methods and to assess performance on different types of applications. SIFTER 2.0 is capable of predicting protein molecular function for large and functionally diverse protein families using an approximate statistical model, enabling phylogenetics-based protein function prediction for genome-wide analyses. The code for SIFTER and protein family data are available at http://sifter.berkeley.edu. [Supplemental material is available for this article.]Automated protein function prediction is an important challenge for computational biology because protein function is difficult to describe and represent, protein databases are littered with annotation errors, and our understanding of how molecular functions arise and mutate over evolutionary time is far from complete. Because biologists depend on protein function annotations for insight and analysis, automated methods have been used extensively to compensate for the relative dearth of experimental characterizations. Although there are 10 7 protein sequences in the comprehensive UniProt database (The UniProt Consortium 2010), <5% have annotations from the Gene Ontology Annotation (GOA) database (Barrell et al. 2009). Far fewer (0.2%) have been manually annotated, and only 0.25% of those manual annotations are from the molecular function ontology in Gene Ontology (GO) (The Gene Ontology Consortium 2010) and are based on experimental evidence. Because of the need for so many annotations, function prediction methods are often assessed based on annotation quantity rather than quality, increasing the number of false positive function annotations and polluting databases (Galper...

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A Bayesian Sampler for Optimization of Protein Domain Hierarchies

“…The omcBPPS sampler differs from existing protein classification methods that cluster sequences either based on pairwise similarity (Remm et al, 2001;Abascal and Valencia, 2002;Li and Godzik, 2006) or by cutting phylogenetic trees (Wicker et al, 2001;Storm and Sonnhammer, 2002;Zmasek and Eddy, 2002;Engelhardt et al, 2011), which are also constructed based on sequence similarity scores. Condensing detailed similarities and differences between sequences into single overall alignment scores in this way discards valuable biological information.…”

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“…The propagation algorithm captures the notion that functional transitions are more likely to occur after duplication than after speciation events, and when the terms are similar-i.e., the corresponding nodes are close in the GO graph. In order to speed up the computation, the authors have recently suggested limiting the number of GO term annotations that can be assigned to each protein [ 42 ], and they are providing pre-calculated predictions for a vast set of sequences from different species, including multi-domain proteins [ 43 ]. The semiautomated Phylogenetic Annotation and Inference Tool (PAINT) [ 44 ] recently adopted by the GO consortium provides a more fl exible framework, which tries to keep functional change events uncoupled, so that the gain of one function does not imply the loss of another and vice versaa desirable feature for annotating biological processes and for dealing with multifunctional proteins in general.…”