SIFTER: A Systems Biology Platform for Protein Function Prediction

SIFTER：蛋白质功能预测的系统生物学平台

• 批准号: 1122732
• 负责人: Ameet Talwalkar
• 金额: $ 24万
• 依托单位: Talwalkar Ameet S
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1122732&HistoricalAwards=false
• 关键词: SIFTER; Systems; Biology; Platform; Protein

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.

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.将重新推出蛋白质功能的实验技术的进展几乎没有那么快。实际上，尽管在综合的Uniprot数据库中大约有1000万个蛋白质序列，但只有0.2％的蛋白质序列具有实验验证的函数注释。该序列功能差距正在迅速扩大，计算方法的开发对于有效地利用了这一序列数据的泛滥至关重要。在这项工作中，我们开发了SIFTER，这是一个大规模的系统生物学平台，可以准确地预测高通量数据的蛋白质功能。 在基于系统基因组的原型中，我们结合了相互作用网络，以提高性能。交互数据本质上是在此类网络中数千个蛋白质的数千蛋白，我们使用不同的推断和并行化的实现来解决策划的计算问题。 我们还基于低级别矩阵分解的功能预测技术，并在此过程中，基于引入采样的方法来加快计算的速度。 此外，在Sifter的预测中量化不确定性的介绍算法TOHELP指导未来的实验工作。 这些新颖的算法是对经典的引导抽样的大规模X骨，通常适用于涉及大量数据的任何问题。 最后，我们评估了Sifter与实验生物学家的不散热器，使我们能够查明相关病例，并在生物医学界产生有效的方法，并产生广泛影响。