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

合作研究：FET：小型：大规模 RNA 设计的分层计算框架通过数据和实验发现路径

• 批准号: 2007861
• 负责人: Giulia Pedrielli
• 金额: $ 27.5万
• 依托单位: Arizona State 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: 2020-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2007861&HistoricalAwards=false
• 关键词: Collaborative; Research; FET; Small; Hierarchical

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.

由于RNA纳米结构设计在不同科学领域的大量新兴应用，如诊断、治疗、合成生物学、生物材料和分子编程，因此受到了前所未有的关注。然而，设计和合成具有更高稳定性、可编程几何形状和可控制功能的长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，一旦结构设计好，进行实验既耗时又昂贵。开发一个具有高效、准确的RNA设计算法和工具的平台是非常重要的。该项目将促进国家的健康、繁荣和福利，为设计和合成具有所需功能和提高稳定性的长rna提供所需的知识。这些rna将在药物传递和癌症治疗等应用中具有重要的潜在影响。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。它还将提高对大规模RNA序列折叠的基本规则的理解和认识。该项目将涉及算法开发以及实验活动。因此，将开发跨科学和工程项目的教育材料，将不同群体的学生聚集在一起。大多数现有的RNA设计算法都集中在保守的、自然进化的3D RNA基序上。这些算法采用了一个“块”的概念，该“块”由~10nt的核苷酸（nts）组成，并研究了块内和块之间核苷酸对之间可能的结合（块驱动方法）。目前的方法存在预测大RNA分子折叠（200 nts）精度低的问题。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、大型机械中的蛋白质组织，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，具有不同相关的最小自由能水平的不同构型是如何存在的。因此，有必要提出与奖励函数无关的实验和计算方法，例如，嵌入数据驱动的信息来确定RNA结构存在的可能性。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)在不明确知道奖励函数的情况下进行优化。由经验发展的专家驱动的优化概念将在这个项目中发展。（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高效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进行评估。生成的算法随后将使用现有的RNA数据库进行验证。新的RNA构建模块将通过算法框架提出和构建，并被验证为设计单链RNA折纸结构的辅助工具，其尺寸和复杂性越来越大，可能与天然RNA机器或设计DNA纳米结构相媲美。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。

项目成果

ExpertRNA: A New Framework for RNA Secondary Structure Prediction

• DOI: 10.1287/ijoc.2022.1188
• 发表时间: 2022-04
• 期刊: INFORMS J. Comput.
• 作者: Menghan Liu;E. Poppleton;Giulia Pedrielli;P. Šulc;D. Bertsekas

Treed-Gaussian Processes with Support Vector Machines as Nodes For Nonstationary Bayesian Optimization

以支持向量机为节点的树状高斯过程用于非平稳贝叶斯优化

• DOI: 10.1109/wsc52266.2021.9715514
• 发表时间: 2021
• 期刊: 2021 Winter Simulation Conference (WSC
• 作者: Candelieri, Antonio;Pedrielli, Giulia
• 通讯作者: Pedrielli, Giulia

From Discovery to Production: Challenges and Novel Methodologies for Next Generation Biomanufacturing

• DOI: 10.1109/wsc57314.2022.10015464
• 发表时间: 2022-05
• 期刊: 2022 Winter Simulation Conference (WSC)
• 作者: Wei-yang Xie;Giulia Pedrielli