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

除了分子生物学之外，几十年的科学探索已经证明了形式是如何发挥作用的基本形式。活细胞中的所有化学反应都涉及分子之间的碰撞和粘连，而分子的形状或结构是分子相互作用的互补性和强度的核心决定因素。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不同过程。随着实验室中的结构确定远远落后于高通量测序技术快速增长的编码蛋白质的基因序列，蛋白质结构预测问题的计算方法现在在分子生物学中发挥着核心作用。这个项目推进了算法研究，以解决目前分子生物学中与形式功能相关的问题的僵局。特别是，该项目开发了先进的优化方法来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来有效地指导对生物活性结构的探索。该项目将造福于计算和生物科学领域不同子社区的研究人员，产生开源、公开可用的软件包，并在计算科学和计算生物学的界面上为未被充分代表的学生提供极好的培训和指导机会。该项目推进了信息集成和信息学中的算法研究，以解决目前计算结构生物学中与结构功能相关的问题的僵局。主要的焦点是去新生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学活性的核心问题。这项拟议的研究将探索和获得蛋白质结构空间的综合视图的问题概括为计算满足约束条件的各种结构集成，然后利用信息集成技术来指导对结构空间中与生物活动相关的区域的探索。该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，以指导结构空间向自然结构的探索。通过这样做，这项拟议的研究探索了一个与基于物理学的治疗相补充的方向，建议用机器学习的原生性模型取代这种治疗。这项研究将有利于机器学习、随机优化和信息集成的研究人员，并以应用驱动的兴趣在分子建模、蛋白质结构预测和复杂、动态系统的建模方面发挥作用。这项研究将通过包括开放源码软件包在内的各种场所进行传播，并将为所有级别的代表不足的学生提供优化、深度学习和计算生物学方面的培训机会。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.