III: Medium: Collaborative Research: Guiding Exploration of Protein Structure Spaces with Deep Learning

几十年来，分子生物学以外的科学研究已经证明了基本形态是如何发挥作用的。活细胞中的所有化学反应都涉及分子之间的碰撞和粘着，分子的形式或结构是分子相互作用的互补性和强度的主要决定因素。特别是，通过假设特定的结构，蛋白质能够调节维持和复制活细胞的各种过程。由于实验室的结构测定远远落后于高通量测序技术快速增长的蛋白质编码基因序列，蛋白质结构预测问题的计算方法现在在分子生物学中起着核心作用。该项目推进算法研究，以解决目前分子生物学中形式-功能相关问题的僵局。特别是开发先进的优化方法探索广阔的蛋白质结构空间，并利用深度学习框架下的信息集成技术有效指导对生物活性结构的探索。该项目将使计算科学和生物科学的不同子社区的研究人员受益，导致开源，公开可用的软件包，并为计算科学和计算生物学界面上代表性不足的学生提供优秀的培训和指导机会。该项目推进了信息集成和信息学方面的算法研究，以解决当前计算结构生物学中结构-功能相关问题的僵局。主要的焦点是de-novo蛋白质结构预测问题，这是推断快速增长的蛋白质编码基因序列的生物活性的核心问题。提出的研究将探索和获得蛋白质结构空间的全面视图的问题概括为计算满足约束的结构的多样化集合，然后利用信息集成技术指导探索与生物活性相关的结构空间区域。本研究提出了用于蛋白质结构空间综合探索的混合随机优化算法和用于更好地评估结构原生性的深度卷积神经网络，并将两者结合在一个信息集成算法框架中，引导结构空间向原生结构探索。通过这样做，拟议的研究调查了一个补充基于物理的治疗的方向，建议用机器学习的原生模型取代这些治疗。这项研究将有利于机器学习、随机优化和信息集成方面的研究人员，以及分子建模、蛋白质结构预测和复杂动态系统建模等应用驱动的兴趣。这项研究将通过各种渠道传播，包括一个开源软件包，并将在优化、深度学习和计算生物学的界面上为各级代表性不足的学生提供培训机会。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。

Decades of scientific enquiry beyond molecular biology have demonstrated just how fundamental form is to function. All chemical reactions in the living cell involve molecules bumping and sticking to one another, and molecular form or structure is a central determinant of complementarity and strength of molecular interactions. In particular, by assuming specific structures, proteins are able to regulate diverse processes that maintain and replicate the living cell. With structure determination in laboratories lagging desperately behind the rapidly-growing number of protein-encoding gene sequences by high-throughput sequencing technologies, computational approaches to the problem of protein structure prediction now have a central role in molecular biology. This project advances algorithmic research to address the current impasse in form-function related problems in molecular biology. In particular, the project develops advanced optimization methods to explore the vast protein structure space and leverages information-integration techniques under the deep learning framework to effectively guide the exploration towards biologically-active structures. This project will benefit researchers of diverse sub-communities in computational and biological sciences, result in open source, publicly-available software packages, and provide excellent training and mentoring opportunities for under-represented students at the interface of computational science and computational biology.This project advances algorithmic research in information integration and informatics to address the current impasse in structure-function related problems in computational structural biology. The main focus is on the de-novo protein structure prediction problem, which is central to inferring biological activities of a rapidly-growing number of protein-encoding gene sequences. The proposed research generalizes the problem of exploring and obtaining a comprehensive view of a protein's structure space as that of computing a diverse ensemble of constraint-satisfying structures and then leveraging information-integration techniques to guide the exploration to regions of the structure space relevant for biological activity. The research proposes hybrid stochastic optimization algorithms for comprehensive exploration of protein structure spaces, deep convolutional neural networks for better assessment of structure nativeness, and combines the two in an information-integration algorithmic framework to guide the exploration of a structure space towards native structures. By doing so, the proposed research investigates a direction complementary to physics-based treatments, proposing to supplant such treatments with machine-learned models of nativeness. The research will benefit researchers in machine learning, stochastic optimization, and information integration with application-driven interests in molecular modeling, protein structure prediction, and modeling of complex, dynamic systems. The research will be disseminated via various venues, including an open-source software package, and will provide training opportunities for under-represented students of all levels at the interface of optimization, deep learning, and computational biology.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.