Data-informed Modeling for DNA and RNA Aptamer Design

DNA 和 RNA 适体设计的数据知情建模

• 批准号: 2155095
• 负责人: Petr Sulc
• 金额: $ 33.84万
• 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: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2155095&HistoricalAwards=false
• 关键词: Data; informed; Modeling; DNA; RNA

Petr Sulc of Arizona State University is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to develop new data-driven methods to design new RNA and DNA binders to molecular targets. Molecular interactions are at the basis of function of all living organisms, and their understanding is crucial for for diagnostic and therapeutics. Dr Sulc will develop machine learning models to analyze sequences of molecules that bind to a certain target molecule of interest (such as surface of a virus). The models extract particular structural or sequence motif in the molecule that is crucial for its function, which allows to computationally design even stronger binders. Dr Sulc’s group will train and validate the methods on both naturally occurring molecules as well as results from selection experiments against different targets (including viral surface proteins) with possible applications in diagnostics, therapeutics, as well as basic understanding of molecular interactions. Dr Sulc will further develop outreach programs that include public lectures and online activities aimed at high school students and general public to broaden participation in science and develop interdisciplinary skills that combine computer modeling, simulations and biochemistry experiments. This project will develop new machine-learning methods for processing of sequence ensembles from selection experiments. The experimental selection protocols (such as SELEX) serve to obtain DNA or RNA sequences that bind to a target of interest (e.g. protein, small molecule, or cells from a particular tissue ), where in each round a subset of the random sequence library that binds strongly to the target is amplified and kept for the next round of selection. Such methods produce large numbers of sequences, most of them only weakly binding to the target of interest, with few strongly binding candidates emerging at the end of the procedure. This project will develop novel models derived from Restricted Boltzmann Machine architectures and uses them both use as classifiers as well as generators of novel binders. Additionally, the models can be used to infer sequence and structural motifs in aptamers that are the key elements for strong affinity with the molecular target, making the models also interpretable. The models will be trained on naturally occurring non-coding RNAs, as well as multiple experimentally generated ensembles and the novel generated sequences will then be verified in experiments.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.

亚利桑那州立大学的 Petr Sulc 获得化学系化学理论、模型和计算方法项目的奖项支持，开发新的数据驱动方法来设计新的 RNA 和 DNA 分子靶标结合剂。分子相互作用是所有生物体功能的基础，它们的理解对于诊断和治疗至关重要。 Sulc 博士将开发机器学习模型来分析与特定目标分子（例如病毒表面）结合的分子序列。该模型提取分子中对其功能至关重要的特定结构或序列基序，从而可以通过计算设计出更强的结合物。 Sulc 博士的团队将在天然存在的分子以及针对不同靶标（包括病毒表面蛋白）的选择实验结果上训练和验证这些方法，并可能在诊断、治疗以及对分子相互作用的基本理解方面得到应用。 Sulc 博士将进一步开发外展计划，包括针对高中生和公众的公开讲座和在线活动，以扩大科学参与并培养结合计算机建模、模拟和生物化学实验的跨学科技能。该项目将开发新的机器学习方法，用于处理选择实验中的序列集成。实验选择方案（例如 SELEX）用于获取与感兴趣的靶标（例如蛋白质、小分子或来自特定组织的细胞）结合的 DNA 或 RNA 序列，其中在每一轮中，与靶标强烈结合的随机序列库的子集被扩增并保留用于下一轮选择。此类方法产生大量序列，其中大多数仅与感兴趣的靶标弱结合，在程序结束时出现的强结合候选序列很少。该项目将开发源自受限玻尔兹曼机架构的新颖模型，并将它们用作分类器以及新型绑定器的生成器。此外，该模型可用于推断适体中的序列和结构基序，这些是与分子靶点具有强亲和力的关键要素，使模型也可解释。这些模型将在天然存在的非编码 RNA 以及多个实验生成的集合上进行训练，然后将在实验中验证新生成的序列。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影响审查标准进行评估，被认为值得支持。