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

• Applicants: Professorin Dr. Janina Kneipp; Professor Dr. Stephan Seifert
• Subject Area: Analytical Chemistry
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 511107129

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.

• DFG Programme: Research Grants