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.

蛋白质是参与细胞内几乎所有过程（例如新陈代谢、细胞信号传导、免疫反应等）的关键生物分子，并且蛋白质功能的知识对于获得对细胞活动的基本了解至关重要。由于核苷酸测序技术的最新进展，可用基因组序列的数量大约每 12 个月就会增加一倍，速度快得令人难以置信，远远超过了摩尔定律。破译蛋白质功能所需的实验技术的进展却没有那么快。事实上，尽管 Uniprot 综合数据库中约有 1000 万个蛋白质序列，但只有 0.2% 具有经过实验验证的功能注释。这种序列与功能的差距正在迅速扩大，计算方法的发展对于有效利用大量的序列数据至关重要。在这项工作中，我们开发了 SIFTER，一个大规模的系统生物学平台，可以通过高通量准确预测蛋白质功能数据。 基于有前途的基于系统发育学的原型，我们将交互网络纳入我们的模型中以提高性能。交互数据本质上耦合了此类网络中的数千到数百万个蛋白质，我们使用变分推理和并行实现来解决这个具有挑战性的计算问题。 我们还探索基于低秩矩阵分解的函数预测技术，并在此过程中引入基于采样的新颖方法来加速计算。 此外，我们开发了算法来量化 SIFTER 预测的不确定性，以帮助指导未来的实验工作。 这些新颖的算法是经典引导采样的大规模扩展，通常适用于涉及海量数据的任何问题。 最后，我们与实验生物学家合作评估 SIFTER，使我们能够查明相关用例，并形成一种在生物医学界产生广泛影响的有效方法。