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

蛋白质是生物系统的重要组成部分，通常通过相互作用发挥作用。为了理解和工程生物系统，关于蛋白质和蛋白质-蛋白质相互作用（PPIs）在这些系统中存在的数据正在迅速积累，但由于对蛋白质在三维空间（3D）中如何相互作用的知识有限，仍然存在一个主要障碍。该项目旨在通过开发计算方法来预测ppi形成的揭示机制的3D结构，从而帮助填补知识空白。在开发这些方法的同时，将追求以数据为中心但物理合理化的方法，这有望推动自然科学和人工智能领域的知识水平。该项目的成果将促进全基因组PPIs的破译和工程，用于新疗法、清洁能源和智能材料等广泛应用。该项目还设计了一些教育活动，以促进学生、教育工作者、领域科学家和公众对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整合在一起，以培养多样化的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。该项目的研究目标是推进结构PPI预测的最新技术，并重新思考和解决这一问题，以解释以各种数据形式（如文本、图形或图像）表示的蛋白质对如何在控制物理下相互作用。为了实现这一目标，本项目的研究目标涉及三个层次的PPI结构预测：残差级接触图、残差级距离分布和原子级三维结构。在这些目标的推动下，新的机器学习算法将被开发出来，并为基础算法研究做出贡献，包括有效地集成和学习异构数据，以及灵活地表示和整合领域知识。这种基础算法研究的进步将扩大PPI结构预测到基因组规模的适用性，并学习不同PPI背后的物理原理，而不是在类似PPI中“记忆”模式。此外，这种方法上的进步有望影响到PPI结构预测以外的广泛应用领域。拟议的研究与教育计划相结合，将研究成果和训练有素的人员提供给多规模的教育和推广活动，让受过教育的学生参与研究，并让公众参与公民科学。将开发新的课程和课外活动，以提高不同学生群体和领域科学家获得跨学科数据科学培训的机会。此外，与现有项目合作的多层次外展活动将用于培养不同中学生和高中生以及公众对跨学科数据科学的认识和兴趣。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

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.