SIFTER: A Systems Biology Platform for Protein Function Prediction

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Proteins are key biomolecules involved in virtually all processes within cells,e.g., metabolism, cell signaling, immune response, etc., and knowledge ofprotein function is vital to obtain a basic understanding of cellular activity.Due to recent advances in nucleotide sequencing technology, the number ofavailable genomic sequences is doubling in size roughly every 12 months, anincredibly fast pace vastly exceeding Moore's law. Experimental technologiesrequired to decipher protein function have not progressed nearly as fast. Infact, although there are roughly 10 million protein sequences in thecomprehensive Uniprot database, only 0.2% have experimentally validatedfunction annotations. This sequence-function gap is rapidly expanding, and thedevelopment of computational methods is of crucial importance to effectivelyutilize this deluge of sequence data.In this work, we develop SIFTER, a large-scale, systems biology platform toaccurately predict protein function from high-throughput data. Building upon apromising phylogenomic-based prototype, we incorporate interaction networksinto our model to improve performance. Interaction data intrinsically couplesthe thousands to millions of proteins within such networks, and we usevariational inference and parallelized implementations to address thischallenging computational problem. We also explore techniques for functionprediction based on low-rank matrix factorization, and along the way, introducenovel sampling-based approaches to speed up computation. Additionally, wedevelop algorithms to quantify uncertainty in SIFTER's predictions tohelp guide future experimental work. These novel algorithms are large-scaleextensions to classical bootstrap sampling and are generally applicable to anyproblem involving massive data. Finally, we evaluate SIFTER incollaboration with experimental biologists, allowing us to pinpoint relevantuse cases and resulting in an effective method with widespread impact withinthe biomedical community.