A deep learning framework for high-definition prediction and interpretation of protein localization

蛋白质是在不同的细胞室和亚细胞细胞器中执行不同功能的分子。蛋白质的异常定位可能导致对细胞的有害影响，包括植物功能不佳的特征，或者人类和动物的疾病。蛋白质定位是一个受多种因素控制的复杂生物过程。因此，对于大多数蛋白质来说，它们的定位机制还不是很清楚。此外，测量蛋白质局部化程度的实验方法既费时又费力。因此，开展蛋白质定位分析方法的研究具有重要意义。目前的计算方法通常缺乏在亚细胞器水平上量化蛋白质定位的准确性。此外，大多数方法缺乏预测突变对蛋白质定位的影响的能力，或者揭示靶信号并提供对阐明这一过程的机制至关重要的信息。该项目将通过为蛋白质定位研究开发一种可解释的深度学习方法和相关的信息学基础设施，帮助解决方法上的这一差距。这一结果不仅将提高蛋白质定位预测的准确性和分辨率，而且还将有助于阐明定位机制。此外，深度学习框架还可以应用于其他几个生物信息学问题，如mRNA定位预测、酶分类和活性部位预测以及蛋白质功能预测。该项目还将为各种学生，特别是代表性不足的少数民族，提供机器学习和现实世界软件开发方面的培训和研究经验。每年将举办虚拟工作坊和社区比赛，为不同背景的学生提供机器学习培训。该项目将开发一个深度学习框架，其中包括用于细胞器下蛋白质定位预测的基于序列的神经网络和图形神经网络，以及机器学习注意机制的应用。该框架的可解释性将使高清晰度定位机制的研究成为可能，包括潜在的新靶向信号识别和预测由突变或调控改变驱动的定位错误。此外，该框架将扩展到通过结合单细胞数据来研究特定组织或特定细胞类型的定位。将开发一个一体式蛋白质定位预测门户网站，为蛋白质定位分析提供一个免码的环境。将在该项目中使用和生成的所有功能以及相关数据将在该平台上提供。该网络资源还将成为不同层次的人工智能学习和实践的教育工具，如高中生物，以及许多可视化和操场功能。预测网络服务以及项目进度和培训材料将在https://www.mu-loc.org/.This颁奖典礼上提供，这反映了国家科学基金会的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Proteins are molecules that perform diverse functions in various cell compartments and subcellular organelles. Aberrant localization of proteins may lead to harmful effects on cells, including poorly functional traits in plants, or disease in humans and animals. Protein localization is a complicated biological process controlled by many factors. Thus, for most proteins, their localization mechanisms are not well understood. Moreover, experimental methods for measuring the degree of protein localization are time- and labor-consuming. Therefore, it is of great significance to develop protein localization analysis methods. Current computational methods are often lacking in accuracy to quantify protein localization at the suborganelle level. In addition, most methods lack capacities to predict the effects of mutations on protein localization, or reveal target signals and provide information important for elucidating the mechanism of this process. This project will help address this gap in methos by developing an interpretable deep-learning approach and related informatics infrastructure for protein localization studies. The outcome will not only improve the protein localization prediction accuracy and resolution, but also shed light on localization mechanisms. Furthermore, the deep-learning framework can be applied to several other bioinformatics problems, such as mRNA localization prediction, enzyme classification and active site prediction, and protein function prediction. The project will also provide training and research experience in machine learning and real-world software development for various students, especially underrepresented minorities. Virtual workshops and community-wide competitions will be hosted annually to provide machine learning training for students with different backgrounds.The project will develop a deep-learning framework that incorporates a sequence-based neural network and graph neural network for suborganellar protein localization prediction, together with applications of machine-learning attention mechanisms. The framework’s interpretability will enable studies of high-definition localization mechanisms, including potential novel targeting signal identification and prediction of mislocalization driven by mutation or regulatory alteration. In addition, the framework will be extended to study tissue-specific or cell-type-specific localization by incorporating single-cell data. An all-in-one web portal for protein localization prediction will be developed to provide a code-free environment for protein localization analysis. All the functionalities, and related data to be used and generated in this project will be provided on the platform. The web resource will also be an educational tool for AI learning and practices at various levels, such as high-school biology, together with many visualization and playground features. The prediction web services, as well as project progress and training materials will be provided at https://www.mu-loc.org/.This 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.