IIBR Informatics: Development of Multimodal approaches for protein function prediction

IIBR 信息学：蛋白质功能预测多模式方法的开发

• 批准号: 2003635
• 负责人: Daisuke Kihara
• 金额: $ 42.75万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2003635&HistoricalAwards=false
• 关键词: IIBR; Informatics; Development; Multimodal; approaches

Proteins are working molecules, playing crucial roles in almost all activities of a living cell. Therefore, elucidating the biological function of proteins is fundamental in any modern molecular biology, biochemistry, medical science, and drug development. In the post-genomics era, when a vast quantity of genomics and proteomics data are awaiting biological interpretation, substantial improvement of computational function prediction methods is essential to achieve the scale and reliability required for practical use by experimental biologists. Computational prediction is crucially useful in biological studies for designing experiments and for interpreting experimental data. In this project, a comprehensive framework for protein function prediction will be built that effectively integrates various aspects of protein features that are indicative of function. Moreover, a web-based portal will be developed, which will provide biologists with easy-to-access function prediction, visualization, and analysis tools as well as pre-computed genome function annotation. The project will train next generation interdisciplinary students through course work and direct involvement with research. Interdisciplinary proteomics approaches will be learned through local and national workshops. The framework will integrate several different types of state-of-the-art deep neural networks. Multiple relationships of proteins, including physical similarities and proteomics data similarities, will be represented as similarity graphs centered at the target proteins, where the functional inference will be performed using deep convolutional neural networks. Among the protein features to be considered, we incorporate three-dimensional structure similarity of proteins, which will be measured through encoded local protein structures detected from protein sequence information using deep convolutional neural network. The developed methods will be used for functional analysis of photosynthesis and nitrogen fixation pathways of photosynthetic cyanobacteria, Cyanothece ATCC51142, which provides promising platforms for light-driven biofuel production. Proteins involved in photosynthesis and nitrogen fixation cycles will be experimentally identified using a new protein complex profiling method that combines chromatography separation techniques with quantitative mass spectrometry. Then, we will apply the developed prediction methods to determine their function and validate the predicted functions with the expressed proteome. All project outputs will be available at http://kiharalab.org/software.phpThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

蛋白质是工作分子，在活细胞的几乎所有活动中起着至关重要的作用。因此，阐明蛋白质的生物学功能是任何现代分子生物学、生物化学、医学科学和药物开发的基础。在后基因组时代，当大量的基因组学和蛋白质组学数据等待生物学解释时，计算功能预测方法的实质性改进对于实现实验生物学家实际使用所需的规模和可靠性是必不可少的。计算预测在生物学研究中对于设计实验和解释实验数据至关重要。在这个项目中，将建立一个全面的蛋白质功能预测框架，有效地整合蛋白质功能的各个方面。此外，将开发一个基于网络的门户网站，这将为生物学家提供易于访问的功能预测，可视化和分析工具以及预先计算的基因组功能注释。该项目将通过课程工作和直接参与研究来培养下一代跨学科学生。跨学科的蛋白质组学方法将通过地方和国家讲习班学习。该框架将集成几种不同类型的最先进的深度神经网络。蛋白质的多种关系，包括物理相似性和蛋白质组学数据相似性，将被表示为以目标蛋白质为中心的相似性图，其中功能推理将使用深度卷积神经网络进行。在要考虑的蛋白质特征中，我们结合了蛋白质的三维结构相似性，这将通过使用深度卷积神经网络从蛋白质序列信息中检测到的编码的局部蛋白质结构来测量。所开发的方法将用于光合作用蓝藻，蓝杆藻ATCC 51142，这为光驱动的生物燃料生产提供了有前途的平台的光合作用和固氮途径的功能分析。参与光合作用和固氮循环的蛋白质将使用一种新的蛋白质复合物分析方法进行实验鉴定，该方法将色谱分离技术与定量质谱法相结合。然后，我们将应用开发的预测方法来确定它们的功能，并与表达的蛋白质组验证预测的功能。所有项目产出将在www.example.com上提供http://kiharalab.org/software.phpThis奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

项目成果

Protein contact map refinement for improving structure prediction using generative adversarial networks

• DOI: 10.1093/bioinformatics/btab220
• 发表时间: 2021-03
• 期刊: Bioinformatics
• 影响因子: 5.8
• 作者: Sai Raghavendra Maddhuri Venkata Subramaniya;Genki Terashi;Aashish Jain;Yuki Kagaya;D. Kihara

ContactPFP: Protein Function Prediction Using Predicted Contact Information

• DOI: 10.3389/fbinf.2022.896295
• 发表时间: 2022-06-02
• 期刊: FRONTIERS IN BIOINFORMATICS
• 作者: Kagaya，Yuki;Flannery，Sean T.;Kihara，Daisuke
• 通讯作者: Kihara，Daisuke

Bioinformatic Approaches for Characterizing Molecular Structure and Function of Food Proteins

表征食品蛋白质分子结构和功能的生物信息学方法

• DOI: 10.1146/annurev-food-060721-022222
• 发表时间: 2023
• 期刊: Annual Review of Food Science and Technology
• 影响因子: 12.4
• 作者: Helmick, Harrison;Jain, Anika;Terashi, Genki;Liceaga, Andrea;Bhunia, Arun K.;Kihara, Daisuke;Kokini, Jozef L.
• 通讯作者: Kokini, Jozef L.