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.

亚利桑那州州立大学的Petr Sulc获得了化学系化学理论、模型和计算方法项目的一项奖励，以开发新的数据驱动方法来设计新的RNA和DNA结合剂。分子相互作用是所有生物体功能的基础，对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（如病毒表面）结合的分子序列。这些模型提取了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剂。Sulc博士的团队将对天然存在的分子以及针对不同靶点（包括病毒表面蛋白）的选择实验结果进行培训和验证，这些方法可能应用于诊断，治疗以及对分子相互作用的基本理解。Sulc博士将进一步发展外展计划，包括面向高中生和公众的公开讲座和在线活动，以扩大对科学的参与，并发展结合联合收割机计算机建模，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，用于处理来自选择实验的序列集合。实验选择方案（例如SELEX）用于获得结合感兴趣的靶标（例如蛋白质、小分子或来自特定组织的细胞）的DNA或RNA序列，其中在每轮中，扩增与靶标强烈结合的随机序列文库的子集并保留用于下一轮选择。这样的方法产生大量的序列，它们中的大多数仅与感兴趣的靶标弱结合，在程序结束时出现很少的强结合候选物。该项目将开发来自受限玻尔兹曼机架构的新模型，并将其用作分类器以及新绑定器的生成器。此外，该模型可用于推断适体中的序列和结构基序，这些序列和结构基序是与分子靶标具有强亲和力的关键元件，使得模型也可解释。该模型将在自然发生的非编码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。