Utilization of random forest approaches to obtain information on biomolecule structure and interaction from SERS experiments

利用随机森林方法从 SERS 实验中获取生物分子结构和相互作用的信息

• 批准号: 511107129
• 负责人: Professorin Dr. Janina Kneipp
• 金额: --
• 依托单位: Arbeitsgruppe Optische Nanospektroskopie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/511107129?language=en
• 关键词: Utilization; random; forest; approaches; obtain

Surface-enhanced Raman scattering (SERS) in the absence of labels, tags or reporters is a powerful method to obtain comprehensive and diverse information about the composition and structure of biomolecular samples. Because of the nature of SERS that probes the varying interaction of the molecules with a nanostructured metal substrate and the proximity of specific functional groups, it is difficult to exploit the highly complex data that are generated. Specific challenges are posed by biological samples that are characterized by many different biomolecules and changing conditions, influencing the obtained SERS spectra. This is why efficient and unbiased approaches for the utilization of SERS data are needed. In this project, random forest (RF) based methods, which have been shown to be very robust when applied to real SERS data, will be adapted and used for the analysis of model data obtained under well-defined experimental conditions. The complexity of the systems that are analyzed here will be increased step-wise from individual molecular components such as building blocks of lipid membranes or a drug molecule, over the combination of two components, to the complex environment of an endolysosomal vesicle inside cultured cells. The RF analysis will be established in such a way that it can serve three different purposes: (i) the selection of important spectral features for direct structural interpretation, (ii) the identification of co-occurring spectral features to analyze the interaction of different molecules, and (iii) the integration of a priori knowledge from previous experiments, including those conducted with respective other models in this project. The development of such a multi-purpose framework will rely on the different degrees of complexity of the SERS experiments as well as on a set of experimental conditions that are defined and modified in a systematic way. As a further aspect, simulated data that are specifically generated to imitate the impact of a particular experimental condition will be compared with actual experiments in an iterative fashion. The successful completion of this project will mark an important step towards utilizing SERS for the structural characterization of well-defined biophysical models as well as of molecules in complex biological systems.

表面增强拉曼散射（Sers）技术是一种在无标记物、标签或报告物存在的情况下获得生物分子样品组成和结构信息的有效方法。由于Sers探测分子与纳米结构金属基底的不同相互作用以及特定官能团的接近性的性质，很难利用所产生的高度复杂的数据。生物样品的特征在于许多不同的生物分子和变化的条件，从而影响所获得的Sers光谱。这就是为什么需要有效和公正的方法来利用Sers数据。在这个项目中，随机森林（RF）为基础的方法，这已被证明是非常强大的应用于真实的Sers数据时，将被改编和用于分析模型数据下得到明确的实验条件。这里分析的系统的复杂性将逐步增加，从单个分子组分，如脂质膜或药物分子的结构单元，到两种组分的组合，再到培养细胞内的内溶酶体囊泡的复杂环境。RF分析将以这样一种方式建立，它可以服务于三个不同的目的：（i）选择重要的光谱特征用于直接结构解释，（ii）识别共同出现的光谱特征以分析不同分子的相互作用，以及（iii）整合先前实验的先验知识，包括本项目中使用相应其他模型进行的实验。这种多用途框架的开发将依赖于Sers实验的不同复杂程度以及以系统方式定义和修改的一组实验条件。作为另一方面，将以迭代方式将专门生成以模拟特定实验条件的影响的模拟数据与实际实验进行比较。该项目的成功完成将标志着利用Sers对定义明确的生物物理模型以及复杂生物系统中的分子进行结构表征的重要一步。