III: Small: Graph Generative Deep Learning for Protein Structure Prediction

III：小：用于蛋白质结构预测的图生成深度学习

• 批准号: 2110926
• 负责人: Liang Zhao
• 金额: $ 49.98万
• 依托单位: Emory University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2110926&HistoricalAwards=false
• 关键词: III; Small; Graph; Generative; Deep

Decades of scientific enquiry beyond molecular biology have demonstrated just how fundamental form is to function, whether in understanding phase transitions in statistical physics, predicting the evolution and dynamics of real networks in network science, or successfully steering an articulated robot arm to a target pose. A fundamental question in all these scientific domains is how to effectively explore the space of all possible forms of a dynamic system to uncover those that satisfy non-trivial constraints imposed by function. The most visible instantiation of this question in computational structural biology is de-novo protein structure prediction (PSP). PSP takes a structure-driven view of understanding molecular mechanisms in the cell and seeks to determine one or more biologically-active/native structures of a protein from knowledge of its chemical composition. Elucidating such structures is central to inferring the biological activities of a rapidly-growing number of protein-encoding gene sequences and thus advancing our understanding of the inner workings of a cell. While PSP has a natural formulation under stochastic optimization, current efforts are approaching a saturation point. This project proposes a radically-different, complementary approach. Inspired by recent momentum in generative deep learning, the project approaches de-novo PSP under the umbrella of generative, adversarial deep learning. The approach is firmly grounded in information integration and informatics, as it proposes generative models that learn in an adversarial setting to generate native-like tertiary protein structures. The project benefits researchers in machine learning, deep learning, and information integration with interests in graph generative models, molecule generation, and protein structure prediction. The project will result in open-source codes, online teaching modules and tutorials, publicly-available data and models, workshops, software demos, and will broaden the participation in computing of under-represented students.The activities in this project chart a new algorithmic path under the umbrella of information integration and informatics to address the current impasse in structure-function related problems in molecular biology. The focus is on the de-novo protein structure prediction problem. With experimental structure determination lagging behind the rapidly-growing number of protein-encoding gene sequences by high-throughput sequencing technologies, computational approaches have a central role in molecular biology research. Great progress has been made through stochastic optimization, but current approaches are experiencing diminishing returns, partly due to fundamental challenges concerning the resource-aware exploration-exploitation control in complex search spaces and inherently inaccurate scoring functions. This project puts forth a novel approach to structure prediction under the umbrella of generative, adversarial deep learning, leveraging recent advances and opportunities in graph generative learning, adversarial learning, and deep learning. Generative models learn in an adversarial setting to generate native-like tertiary protein structures. The proposed activities span multiple disciplines and promise to make general contributions in machine learning, deep learning, explainable AI, molecular modeling, and computational biology. The work will also benefit researchers and students interested in modeling complex, dynamic systems. The investigators will disseminate the proposed research via open-source codes in C++ and Python so as to reach diverse communities of researchers and students, online teaching modules and tutorials, trained models and data. They will actively educate involved communities through workshops, tutorials, and software demonstrations. This interdisciplinary project also creates excellent opportunities to broaden the participation in computing of under-represented students of all backgrounds.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.

几十年来，分子生物学之外的科学探索已经证明了形式对功能的重要性，无论是在统计物理学中理解相变，在网络科学中预测真实的网络的演化和动力学，还是成功地将关节式机器人手臂转向目标姿势。所有这些科学领域的一个基本问题是如何有效地探索空间的所有可能的形式的动态系统，发现那些满足非平凡的约束所施加的功能。 这个问题在计算结构生物学中最明显的实例是从头蛋白质结构预测（PSP）。PSP采用结构驱动的观点来理解细胞中的分子机制，并试图从其化学组成的知识中确定蛋白质的一个或多个生物活性/天然结构。阐明这些结构对于推断快速增长的蛋白质编码基因序列的生物活性至关重要，从而促进我们对细胞内部运作的理解。虽然PSP在随机优化下有一个自然的公式，但目前的努力正在接近饱和点。该项目提出了一种完全不同的互补方法。受生成式深度学习的最新发展势头的启发，该项目在生成式对抗式深度学习的保护伞下重新处理PSP。该方法牢固地建立在信息集成和信息学的基础上，因为它提出了在对抗环境中学习以生成类似天然的三级蛋白质结构的生成模型。该项目有利于机器学习，深度学习和信息集成的研究人员，他们对图形生成模型，分子生成和蛋白质结构预测感兴趣。该项目将产生开源代码、在线教学模块和教程、公开数据和模型、研讨会、软件演示，并将扩大代表性不足的学生对计算的参与。该项目的活动在信息集成和信息学的保护伞下绘制了一条新的算法路径，以解决当前分子生物学中结构-功能相关问题的僵局。重点是从头蛋白质结构预测问题。 随着高通量测序技术对蛋白质编码基因序列数量的快速增长，实验结构的确定滞后于此，计算方法在分子生物学研究中发挥着核心作用。通过随机优化已经取得了很大的进展，但目前的方法正在经历收益递减，部分原因是复杂搜索空间中的资源感知探索-开发控制和固有的不准确评分函数的基本挑战。该项目提出了一种新的方法，在生成，对抗深度学习的保护伞下进行结构预测，利用图生成学习，对抗学习和深度学习的最新进展和机会。生成模型在对抗环境中学习，以生成类似天然的三级蛋白质结构。拟议的活动涵盖多个学科，并有望在机器学习、深度学习、可解释人工智能、分子建模和计算生物学方面做出普遍贡献。这项工作也将有利于对复杂动态系统建模感兴趣的研究人员和学生。研究人员将通过C++和Python的开源代码传播拟议的研究，以便接触到不同的研究人员和学生社区，在线教学模块和教程，经过训练的模型和数据。他们将通过研讨会、教程和软件演示积极教育相关社区。这个跨学科的项目也创造了很好的机会，以扩大参与计算的代表性不足的学生的所有background.This奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的智力价值和更广泛的影响审查标准。

项目成果

Disentangled Spatiotemporal Graph Generative Models

解缠结的时空图生成模型

• DOI: 10.1609/aaai.v36i6.20607
• 发表时间: 2022
• 期刊: Proceedings of the AAAI Conference on Artificial Intelligence
• 作者: Du, Yuanqi;Guo, Xiaojie;Cao, Hengning;Ye, Yanfang;Zhao, Liang
• 通讯作者: Zhao, Liang

Small molecule generation via disentangled representation learning

• DOI: 10.1093/bioinformatics/btac296
• 发表时间: 2022-05
• 期刊: Bioinformatics
• 影响因子: 5.8
• 作者: Yuanqi Du;Xiaojie Guo;Yinkai Wang;Amarda Shehu;Liang Zhao

Accelerated Gradient-free Neural Network Training by Multi-convex Alternating Optimization

• DOI: 10.1016/j.neucom.2022.02.039
• 发表时间: 2018-11
• 期刊: Neurocomputing
• 影响因子: 6
• 作者: Junxiang Wang;Fuxun Yu;Xiangyi Chen;Liang Zhao

A Systematic Survey on Deep Generative Models for Graph Generation

• DOI: 10.1109/tpami.2022.3214832
• 发表时间: 2023-05-01
• 期刊: IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
• 影响因子: 23.6
• 作者: Guo, Xiaojie;Zhao, Liang
• 通讯作者: Zhao, Liang

Adaptive Kernel Graph Neural Network

• DOI: 10.1609/aaai.v36i6.20664
• 发表时间: 2021-12
• 作者: Mingxuan Ju;Shifu Hou;Yujie Fan;Jianan Zhao;Liang Zhao;Yanfang Ye