Integrated machine learning and in vitro pipeline for TRPA1-active chemesthetic flavor compounds: From predictive screening to molecular mechanisms
- 1Key Laboratory of Tobacco Flavor and Fragrance Basic Research, Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou, 450001, China
- 2School of Material Science and Engineering, Zhengzhou University, Zhengzhou, 450001, China
Abstract
Chemesthesis constitutes a fundamental dimension of food sensory perception, triggered by transient receptor potential ankyrin 1 (TRPA1) ion channels that mediate responses to pungent and irritating flavor compounds. However, efficient prediction of TRPA1 activation by flavor compounds remains limited by the low throughput of traditional experimental screening. Here, we developed an integrated computational and in vitro pipeline to overcome this limitation. Using an optimized Random Forest (RF) machine learning model, we successfully predicted and screened representative flavor compounds. We empirically validated these predictions using a newly constructed TRPA1 cell line, where calcium flux assays confirmed dose-dependent channel activation by the selected candidates. Furthermore, molecular docking and molecular dynamics simulations elucidated the activation mechanisms at the atomistic level, identifying key interacting residues. This multidisciplinary framework enables efficient targeted screening of sensory receptor ligands and provides deep molecular insights into flavor-driven chemesthetic perception.
Related
- Integrated Bioprocessing and Nanoencapsulation of Brewer’s Spent Grain Phenolic Compounds: A Sustainable Valorization Strategy
- Integrated Predictive and Remote-Sensing Framework for Early Warning and Regulatory Compliance in Environmentally Sensitive Urban Zones
- Integrated metabolomic, transcriptomic and computational analyses reveal geographical variation and potential anti-osteoarthritis bioactive compounds in Arenga pinnata pith
- Antibacterial and Anti-Inflammatory Activities of Moringa oleifera: Extracts, Bioactive Compounds, and Mechanisms.
- Screening compounds of the phenylpropanoid pathway and commercial products to mitigate methane emissions using batch culture in vitro incubations
BibTeX
@article{wen2026integrated,
title = {{Integrated machine learning and in vitro pipeline for TRPA1-active chemesthetic flavor compounds: From predictive screening to molecular mechanisms}},
author = {Xiaoming Wen and Qidong Zhang and Shan Wang and Qingzhao Shi and Jingtao Wang and Wu Fan and Shaokang Guan and Guobi Chai},
journal = {Current Research in Food Science},
year = {2026},
month = oct,
doi = {10.1016/j.crfs.2026.101611},
url = {https://doi.org/10.1016/j.crfs.2026.101611},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Integrated machine learning and in vitro pipeline for TRPA1-active chemesthetic flavor compounds: From predictive screening to molecular mechanisms
AU - Xiaoming Wen
AU - Qidong Zhang
AU - Shan Wang
AU - Qingzhao Shi
AU - Jingtao Wang
AU - Wu Fan
AU - Shaokang Guan
AU - Guobi Chai
PY - 2026
DA - 2026/10/26
JO - Current Research in Food Science
DO - 10.1016/j.crfs.2026.101611
UR - https://doi.org/10.1016/j.crfs.2026.101611
AB - Chemesthesis constitutes a fundamental dimension of food sensory perception, triggered by transient receptor potential ankyrin 1 (TRPA1) ion channels that mediate responses to pungent and irritating flavor compounds. However, efficient prediction of TRPA1 activation by flavor compounds remains limited by the low throughput of traditional experimental screening. Here, we developed an integrated computational and in vitro pipeline to overcome this limitation. Using an optimized Random Forest (RF) machine learning model, we successfully predicted and screened representative flavor compounds. We empirically validated these predictions using a newly constructed TRPA1 cell line, where calcium flux assays confirmed dose-dependent channel activation by the selected candidates. Furthermore, molecular docking and molecular dynamics simulations elucidated the activation mechanisms at the atomistic level, identifying key interacting residues. This multidisciplinary framework enables efficient targeted screening of sensory receptor ligands and provides deep molecular insights into flavor-driven chemesthetic perception.
SN - 2665-9271
ER -