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

蛋白质是在各种细胞区室和亚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，包括植物的功能性特征差，或人类和动物的疾病。蛋白质定位是一个受多种因素控制的复杂生物学过程。因此，对于大多数蛋白质，它们的定位机制还不清楚。此外，用于测量蛋白质定位程度的实验方法是费时费力的。因此，研究蛋白质定位分析方法具有重要意义。目前的计算方法往往缺乏准确性，以量化蛋白质定位在亚细胞器水平。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，或揭示靶信号并提供对阐明该过程的机制重要的信息。该项目将通过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来帮助解决方法中的这一差距。该结果不仅可以提高蛋白质定位预测的精度和分辨率，而且可以揭示蛋白质定位的机理。此外，深度学习框架可以应用于其他几个生物信息学问题，例如mRNA定位预测，酶分类和活性位点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。该项目将开发一个深度学习框架，结合基于序列的神经网络和图神经网络用于亚细胞器蛋白定位预测，以及机器学习注意力机制的应用。该框架的可解释性将使高清晰度的定位机制的研究，包括潜在的新的靶向信号识别和预测的突变或监管改变驱动的错误定位。此外，该框架将被扩展到研究组织特异性或细胞类型特异性定位通过合并单细胞数据。将开发一个用于蛋白质定位预测的一体化门户网站，为蛋白质定位分析提供无代码环境。该平台将提供本项目中使用和生成的所有功能和相关数据。该网络资源也将成为各级人工智能学习和实践的教育工具，例如高中生物学，以及许多可视化和操场功能。预测网络服务以及项目进展和培训材料将在www.example.com上提供https://www.mu-loc.org/.This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。

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.