CAREER: Physics-Constrained Modeling of Molecular Texts, Graphs, and Images for Deciphering Protein-Protein Interactions

职业：分子文本、图形和图像的物理约束建模，用于破译蛋白质-蛋白质相互作用

基本信息

批准号：
1943008
负责人：
Yang Shen
金额：
$ 50万
依托单位：
Texas A&M Engineering Experiment Station
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2025-06-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1943008&HistoricalAwards=false
关键词：
CAREER Physics Constrained Modeling Molecular

项目摘要

Proteins are essential parts of biological systems that often function through interactions. Toward understanding and engineering biological systems, data are rapidly accumulating on what proteins and what protein-protein interactions (PPIs) are present in such systems, but a major barrier remains as knowledge is limited on how proteins interact in 3-dimensional (3D) space. This project is designed to help fill the knowledge gap by developing computational methods that predict mechanism-revealing 3D structures formed by PPIs. While developing such methods, a data-focused yet physics-rationalized approach will be pursued, which is expected to advance the state of the knowledge across natural science and artificial intelligence. The outcome of the project will facilitate deciphering and engineering genome-wide PPIs for wide applications such as novel therapeutics, clean energy, and smart materials. The project is also designed with educational activities to promote the awareness, participation, training, and communication of data-driven science discovery for students, educators, domain scientists, and general public. The highly interdisciplinary research and education activities will be integrated to foster a diverse globally-competitive workforce, including historically underrepresented groups, to be ready for the era of big data. The research goal of this project is to advance the state of the art for structural PPI prediction and re-think and tackle the problem as explaining how pairs of proteins, represented in various data forms such as texts, graphs, or images, interact under governing physics. In pursuit of the goal, the research objectives of the project involve three levels of PPI structural prediction of increasing resolutions and challenges: residue-level contact maps, residue-level distance distributions, and atom-level 3D structures. Initiated by these objectives, novel machine learning algorithms will be developed and contribute to foundational algorithm research, including the effective integration and learning from heterogeneous data as well as the flexible representation and incorporation of domain knowledge. Such advance in foundational algorithm research will expand the applicability of PPI structural prediction to genome-scale and learn physical principles underlying diverse PPIs rather than “memorizing” patterns in similar PPIs. Moreover, such methodological advance is expected to impact broad application fields beyond PPI structural prediction. The proposed research is integrated with an educational plan by feeding research results and trained personnel to multi-scale education and outreach activities, involving educated students in research, and engaging general public in citizen science. New curricular and co-curricular activities will be developed to enhance the accessibility to interdisciplinary data-science training for a diverse student body and domain scientists. Also, multi-level outreach activities in collaboration with existing programs will be used to foster the awareness of and interest in interdisciplinary data science among diverse middle- and high-school students as well as the general public.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.

蛋白质是通常通过相互作用起作用的生物系统的重要部分。为了理解和工程生物系统，数据迅速积累了这种系统中存在的蛋白质和哪些蛋白质蛋白质相互作用（PPI），但是由于知识在3维（3D）空间中相互作用的知识限制，因此存在一个主要的障碍。该项目旨在通过开发预测PPIS形成的机理3D结构的计算方法来帮助填补知识差距。在开发此类方法的同时，将采用以数据为重点但物理理性化的方法，这有望促进自然科学和人工智能的知识状态。该项目的结果将促进诸如新颖疗法，清洁能源和智能材料等广泛应用的解密和工程全基因组PPI。该项目还采用教育活动设计，以促进针对学生，教育者，领域科学家和公众的数据驱动科学发现的认识，参与，培训和交流。高度跨学科的研究和教育活动将集成，以促进全球竞争力的劳动力，包括历史上代表性不足的群体，为大数据时代做好准备。该项目的研究目的是推进结构PPI预测的最新技术，并重新考虑并解决该问题，以解释如何在各种数据形式（例如文本，图形或图像）中表示的蛋白质如何在理性物理学下进行交互。为了实现目标，该项目的研究目标涉及分辨率和挑战的三个级别的PPI结构性预测：保留级接触图，保留级别距离分布和原子级3D结构。由这些目标发起的，将开发新的机器学习算法并为基础算法研究做出贡献，包括从异质数据中的有效整合和学习，以及灵活的代表和域知识公司。基础算法研究中的这种进步将扩大PPI结构预测到基因组规模的适用性，并学习潜水员PPI的物理原理，而不是在类似的PPI中“记住”模式。此外，预计这种方法论将影响PPI结构预测以外的广泛应用领域。拟议的研究与一项教育计划结合在一起，通过为研究结果提供喂养和培训的人员进行多规模的教育和外展活动，涉及教育的学生参与研究，并参与公众参与公民科学。将开发新的课程和课外活动，以增强针对大型学生团体和领域科学家的跨学科数据科学培训的可访问性。此外，与现有计划合作的多层次外展活动将用于提高潜水员中学和高中生以及公众对跨学科数据科学的认识和兴趣。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子和更广泛的影响来评估NSF的法定任务，并被认为是珍贵的支持。