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; 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.

超出分子生物学的科学探究数十年已经证明了在理解统计物理学中的相变，预测网络科学中真实网络的演变和动态方面的基本形式，还是成功将明确的机器人臂转向目标姿势。所有这些科学领域中的一个基本问题是如何有效探索动态系统的所有可能形式的空间，以揭示那些满足功能施加的非平凡约束的人。 在计算结构生物学中，该问题最明显的实例化是De-Novo蛋白结构预测（PSP）。 PSP采用了理解细胞中分子机制的结构驱动的观点，并试图从蛋白质的化学成分中确定一种或多种蛋白质的生物学活性/天然结构。阐明这种结构对于推断快速增长的蛋白质编码基因序列的生物学活性至关重要，从而促进了我们对细胞内部起作用的理解。尽管PSP在随机优化下具有自然公式，但目前的努力正在接近饱和点。该项目提出了一种截然不同的互补方法。受生成深度学习的最新动力的启发，该项目将De-Novo PSP在生成，对抗性深度学习的保护下。该方法牢固地基于信息整合和信息学，因为它提出了在对抗性环境中学习的生成模型，以生成类似天然的三级蛋白质结构。该项目在机器学习，深度学习和信息集成中受益于图形生成模型，分子产生和蛋白质结构预测的兴趣。 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.重点是北野蛋白结构预测问题。 通过实验结构的确定落后于迅速增长的蛋白质编码基因序列通过高通量测序技术，计算方法在分子生物学研究中具有核心作用。通过随机优化取得了巨大进展，但是当前的方法正在经历降低的回报，部分原因是关于复杂搜索空间中有关资源感知的探索探索控制控制的基本挑战，并且本质上不准确得分功能。该项目在生成，对抗性深度学习的保护下提出了一种新颖的结构预测方法，利用了图形生成学习，对抗性学习和深度学习的最新进步和机会。生成模型在对抗环境中学习以产生天然的三级蛋白质结构。提出的活动涵盖了多个学科，并有望在机器学习，深度学习，可解释的AI，分子建模和计算生物学方面做出一般贡献。这项工作还将使对建模复杂的动态系统有兴趣的研究人员和学生受益。研究人员将通过C ++和Python中的开源代码传播拟议的研究，以吸引研究人员和学生的各种社区，在线教学模块和教程，训练有素的模型和数据。他们将通过研讨会，教程和软件演示积极教育社区。这个跨学科的项目还创造了极好的机会，以扩大所有背景的代表性不足学生的计算。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估审查标准来通过评估来获得支持的。