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.

蛋白质是细胞内几乎所有过程中的关键生物分子，如新陈代谢、细胞信号、免疫反应等，而蛋白质功能的知识对于基本了解细胞活动是至关重要的。由于核苷酸测序技术的最新进展，可用的基因组序列的数量大约每12个月翻一番，速度惊人地超过摩尔定律。破译蛋白质功能所需的实验技术进展几乎没有那么快。事实上，尽管在综合性的UniProt数据库中大约有1000万个蛋白质序列，但只有0.2%的蛋白质序列经过实验验证了功能注释。这种序列与功能的差距正在迅速扩大，而计算方法的发展对于有效利用这种海量的序列数据至关重要。在这项工作中，我们开发了一个大规模的系统生物学平台Screter，用于从高通量数据中准确地预测蛋白质功能。在一个有前景的基于系统基因组学的原型的基础上，我们将交互网络纳入到我们的模型中，以提高性能。相互作用数据本质上连接了这样的网络中数以千计到数百万的蛋白质，我们使用变分推理和并行实现来解决这一具有挑战性的计算问题。我们还探索了基于低阶矩阵分解的函数预测技术，并在此过程中引入了新的基于采样的方法来加快计算速度。此外，我们还开发了算法来量化Siefter预测中的不确定性，以帮助指导未来的实验工作。这些新算法是对经典Bootstrap抽样的大规模扩展，一般适用于任何涉及海量数据的问题。最后，我们与实验生物学家合作对SIFTER进行评估，使我们能够准确地确定相关的用例，并产生在生物医学界具有广泛影响的有效方法。