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

• 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

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。 为了理解和工程化生物系统，有关此类系统中存在哪些蛋白质以及哪些蛋白质-蛋白质相互作用（PPI）的数据正在迅速积累，但由于对蛋白质如何在3维（3D）空间中相互作用的了解有限，因此仍然存在一个主要障碍。空间。 该项目旨在通过开发预测PPI形成的揭示机制的3D结构的计算方法来帮助填补知识空白。 在开发这些方法的同时，将采用一种以数据为中心但物理合理化的方法，预计这将推动自然科学和人工智能领域的知识发展。 该项目的成果将有助于破译和工程化全基因组PPI，以广泛应用于新疗法，清洁能源和智能材料等领域。 该项目还设计了教育活动，以促进学生，教育工作者，领域科学家和公众对数据驱动的科学发现的认识，参与，培训和交流。 高度跨学科的研究和教育活动将被整合，以培养具有全球竞争力的多元化劳动力，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个问题，解释以文本，图形或图像等各种数据形式表示的蛋白质对如何在物理学下相互作用。 为了实现这一目标，该项目的研究目标涉及三个层次的PPI结构预测，这些预测具有越来越高的分辨率和挑战性：残留水平接触图，残留水平距离分布和原子水平3D结构。 由这些目标开始，新的机器学习算法将被开发，并有助于基础算法研究，包括有效的集成和学习异构数据以及灵活的表示和领域知识的合并。 基础算法研究的这种进展将扩大PPI结构预测的适用性到基因组规模，并学习不同PPI背后的物理原理，而不是“记忆”类似PPI中的模式。 此外，这种方法的进步预计将影响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外联活动，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，以提高跨学科数据科学培训的可及性，为不同的学生团体和领域科学家。 此外，还将通过与现有项目合作开展多层次的外展活动，培养不同的中学生和公众对跨学科数据科学的认识和兴趣。该奖项反映了NSF的法定使命，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。

项目成果

Cross-Modality Protein Embedding for Compound-Protein Affinity and Contact Prediction

• DOI: 10.1101/2020.11.29.403162
• 发表时间: 2020-11
• 期刊: bioRxiv
• 作者: Yuning You;Yang Shen

Cross-modality and self-supervised protein embedding for compound–protein affinity and contact prediction

用于化合物-蛋白质亲和力和接触预测的跨模态和自监督蛋白质嵌入

• DOI: 10.1093/bioinformatics/btac470
• 发表时间: 2022
• 期刊: Bioinformatics
• 影响因子: 5.8
• 作者: You, Yuning;Shen, Yang
• 通讯作者: Shen, Yang

Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative

• DOI: 10.48550/arxiv.2210.03801
• 发表时间: 2022-10
• 期刊: Advances in neural information processing systems
• 作者: Tianxin Wei;Yuning You;Tianlong Chen;Yang Shen;Jingrui He;Zhangyang Wang

Does Inter-Protein Contact Prediction Benefit from Multi-Modal Data and Auxiliary Tasks?

• DOI: 10.1101/2022.11.29.518454
• 发表时间: 2022-12
• 期刊: bioRxiv
• 作者: Arghamitra Talukder;Rujie Yin;Yuanfei Sun;Yang Shen;Yuning You

Bayesian Modeling and Uncertainty Quantification for Learning to Optimize: What, Why, and How

• 发表时间: 2022
• 作者: Yuning You;Yue Cao;Tianlong Chen;Zhangyang Wang;Yang Shen