Collaborative Research: FET: Small: Hierarchical Computational Framework for large scale RNA Design Pathway Discovery through Data and Experiments

RNA纳米结构的设计受到了前所未有的关注，由于在不同的科学领域，如诊断，治疗，合成生物学，生物材料和分子编程的新兴应用的数量。然而，设计和合成具有改进的稳定性、可编程几何形状和可控功能的长RNA分子是一项极具挑战性的任务。大RNA设计的困难是由于它们的长序列和碱基之间复杂的相互作用。此外，一旦设计出结构，进行实验是耗时且昂贵的。因此，开发一个具有有效设计算法和工具的RNA设计平台，对于高效、准确地进行RNA设计具有重要的意义。该项目将促进国家健康繁荣和福利，为设计和合成具有所需功能和改善稳定性的长RNA提供所需的知识。这些RNA将在药物递送和癌症治疗等应用中产生重要的潜在影响。该团队将开发新的计算方法，以更有效和更明智的方式支持下一代纳米结构的发现。它还将提高理解和知识的基本规则，表征大规模RNA序列的折叠。该项目将涉及算法开发沿着实验活动。因此，教育材料将在科学和工程项目中开发，将不同的学生聚集在一起。大多数现有的RNA设计算法专注于保守的，自然进化的3D RNA基序。这些算法采用了“块”的思想，它由核苷酸（nts）在~10nts的规模，并调查可能的结合之间的核苷酸对块内和块（块驱动的方法）。目前的方法存在预测大RNA分子折叠（200 nts）的准确度低的问题。这是一个关键问题，因为迫切需要产生更长的序列，以充分利用RNA的功能，如催化，基因调控，蛋白质在大型机器中的组织，以及它们在材料和生物医学科学中的应用。一些挑战使得设计大规模RNA结构的任务变得困难。例如，RNA化合物即使不是最小自由能构型也可以是稳定的。此外，实验已经表明，对于相同的RNA序列，可以存在不同的构型，具有不同的最小自由能水平。因此，有必要提出实验和计算方法，这些方法可以对奖励函数不可知，例如，嵌入数据驱动的信息，以确定RNA配置存在的可能性。这个多学科项目将解决RNA结构设计发展的两个主要挑战。(i)执行优化而无需明确了解奖励函数。在这个项目中，将发展由经验丰富的专家驱动的优化概念。(ii)将（i）中的方法扩展到高维情况。一个生物启发的瓷砖概念被用来创建一个计算效率高的算法框架来生成和探索瓷砖，这将使用专家驱动的瓷砖链上的滚动进行评估。随后将使用现有的RNA数据库验证所产生的算法。新的RNA构建模块将通过算法框架提出和构建，可作为设计单通道、多通道的辅助工具，一种尺寸和复杂性不断增加的单链RNA折纸结构，可能与天然RNA机器或设计的DNA纳米结构相媲美。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的评估被认为值得支持。影响审查标准。

RNA nanostructure design has received unprecedented attention due to the number of emerging applications in different scientific fields, such as diagnostics, therapeutics, synthetic biology, biological materials, and molecular programming. However, the design and synthesis of long RNA molecules with improved stability, programmable geometries, and controllable functions is an incredibly challenging task. The difficulties of large RNA design are due to their long sequences and complex interactions between bases. In addition, once a structure is designed, conducting experiments is time-consuming and expensive. It is invaluable to develop a platform with effective design algorithms and tools for RNA design with high efficiency and accuracy. This project will advance national health prosperity and welfare, providing the required knowledge for the design and synthesis of long RNAs with the desired functionalities and improved stabilities. These RNAs will have an important potential impact in applications such as drug delivery and cancer therapy. The team will develop new computational methods enabling support to the discovery of next-generation nanostructure in a more efficient and informed manner. It will also improve the understanding and knowledge of fundamental rules that characterize the folding of large-scale RNA sequences. The project will involve algorithm development along with experimental activities. As a result, educational material will be developed across science and engineering programs bringing a diverse group of students together.Most existing RNA-design algorithms focus on conserved, naturally evolved 3D RNA motifs. These algorithms employ the idea of a “block”, which consists of nucleotides (nts) at the scale of ~10nts, and investigate the possible bindings among nucleotides pairs within and between the blocks (block-driven approach). Current approaches suffer from the low accuracy for prediction of large RNA molecules folding (200 nts). This is a critical issue because there is a compelling need to generate longer sequences to fully exploit RNA functionalities such as catalysis, gene regulation, organization of proteins in large machineries, and their use in material and biomedical sciences. Several challenges make the task of designing large-scale RNA structures hard. As an example, RNA compounds can be stable even when they are not minimum free-energy configurations. Also, experiments have shown how alternative configurations can exist for the same RNA sequence, with different associated levels of minimum free energy. It is therefore necessary to come up with approaches, experimental as well as computational, that can be agnostic to a reward function, e.g., embed data-driven information to determine the likelihood of an RNA configuration to exist. This multidisciplinary project will tackle two main challenges for the development of the design of RNA structures. (i) Perform optimization without explicit knowledge of a reward function. The concept of optimization driven by empirically developed experts will be developed in this project. (ii) Scale the methods in (i) to high-dimensional cases. A bio-inspired concept of tile is employed to create a computationally efficient algorithmic framework to generate and explore tiles, which will be evaluated using expert-driven rollout over chains of tiles. The produced algorithms will be subsequently validated using existing RNA databases. New RNA building blocks will be proposed and constructed through the algorithmic framework, to be validated as an assistant tool to the design of single-stranded RNA origami structures with increasing size and complexity that could potentially rival the natural RNA machineries or designer DNA nanostructures.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.