Data-informed Modeling for DNA and RNA Aptamer Design

亚利桑那州立大学的peter Sulc获得了化学系化学理论、模型和计算方法项目的奖励，以开发新的数据驱动方法来设计新的RNA和DNA结合物以达到分子目标。分子相互作用是所有生物功能的基础，对分子相互作用的理解对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。该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，这使得计算设计更强的结合剂成为可能。Sulc博士的团队将训练和验证这些方法，包括自然产生的分子，以及针对不同目标（包括病毒表面蛋白）的选择实验结果，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，包括针对高中生和普通公众的公开讲座和在线活动，以扩大对科学的参与，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，用于处理来自选择实验的序列集成。实验选择方案（例如SELEX）用于获得与感兴趣的目标（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，与目标结合强烈的随机序列文库的一个子集被扩增并保留以供下一轮选择。这些方法产生大量的序列，其中大多数仅与感兴趣的目标弱结合，在过程结束时出现少数强结合的候选序列。该项目将开发源自受限玻尔兹曼机器架构的新模型，并将它们用作分类器和新粘合剂的生成器。此外，该模型还可用于推断适配体中的序列和结构基序，这些基序是与分子靶标具有强亲和力的关键要素，使该模型具有可解释性。这些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。

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.