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MORE-Q, a dataset for molecular olfactorial receptor engineering by quantum mechanics
Li Chen1, Leonardo Medrano Sandonas2, Philipp Traber3
1Institute for Materials Science and Max Bergmann Center for Biomaterials, TUD Dresden University of Technology, 01062, Dresden, Germany.
Scientific Data
|February 22, 2025
Summary
We present MORE-Q, a quantum-mechanical dataset for non-covalent molecular sensors. This resource aids in understanding body odor volatilome-receptor interactions for advanced sensing devices.
Area of Science:
- Computational Chemistry
- Materials Science
- Biophysics
Background:
- Olfactory receptors (ORs) are crucial for detecting volatile molecules.
- Understanding molecular interactions in sensor design is vital for developing next-generation devices.
- Mucin-derived ORs offer potential for novel sensing applications.
Purpose of the Study:
- To introduce the MORE-Q dataset, a comprehensive quantum-mechanical resource for molecular sensor research.
- To provide structural and electronic data for mucin-derived ORs interacting with body odor volatilome (BOV) molecules.
- To establish a benchmark for machine learning models predicting binding features in sensor systems.
Main Methods:
- Quantum-mechanical (QM) calculations were performed using GFN2-xTB with D4 dispersion correction for geometry optimization.
- High-level PBE+D3 calculations were used to determine up to 39 physicochemical properties.
- The MORE-Q dataset is structured into three subsets: BOV-receptor, BOV-receptor configurations, and BOV-receptor-graphene systems.
Main Results:
- The MORE-Q dataset includes detailed QM data for 18 mucin-derived ORs and 102 BOV molecules.
- It encompasses 23,838 BOV-receptor configurations and 1,836 BOV-receptor-graphene systems.
- Physicochemical properties, including global, local, and binding features, were accurately computed.
Conclusions:
- The MORE-Q dataset serves as a valuable benchmark for developing and validating machine learning models in molecular sensing.
- It facilitates a deeper understanding of intra- and inter-molecular interactions in olfactory receptor-based sensors.
- This resource can accelerate the development of advanced mucin-derived olfactory receptor sensing devices.
