Electron transfer coupling is a critical factor in determining electron transfer rates. This coupling strength can be sensitive to details in molecular geometries, especially intermolecular configurations. Thus, studying charge transporting behavior with a full first-principle approach demands a large amount of computation resources in quantum chemistry (QC) calculation. To address this issue, we developed a machine learning (ML) approach to evaluate electronic coupling. A prototypical ML model for an ethylene system was built by kernel ridge regression with Coulomb matrix representation. Since the performance of the ML models highly dependent on their building strategies, we systematically investigated the generality of the ML models, the choice of features and target labels. The best ML model trained with 40 000 samples achieved a mean absolute error of 3.5 meV and greater than 98% accuracy in predicting phases. The distance and orientation dependence of electronic coupling was successfully captured. Bypassing QC calculation, the ML model saved 10−10 4 times the computation cost. With the help of ML, reliable charge transport models and mechanisms can be further developed.

Tyramine receptor (TyrR) is a biogenic amine G protein-coupled receptor (GPCR) associated with many important physiological functions in insect locomotion, reproduction, and pheromone response. Binding of specific ligands to the TyrR triggers conformational changes, relays the signal to G proteins, and initiates an appropriate cellular response. Here, we monitor the binding effect of agonist compounds, tyramine and amitraz, to a Sitophilus oryzae tyramine receptor (SoTyrR) homology model and their elicited conformational changes. All-atom molecular dynamics (MD) simulations of SoTyrR-ligand complexes have shown varying dynamic behavior, especially at the intracellular loop 3 (IL3) region. Moreover, in contrast to SoTyrR-tyramine, SoTyrR-amitraz and non-liganded SoTyrR shows greater flexibility at IL3 residues and were found to be coupled to the most dominant motion in the receptor. Our results suggest that the conformational changes induced by amitraz are different from the natural ligand tyramine, albeit being both agonists of SoTyrR. This is the first attempt to understand the biophysical implication of amitraz and tyramine binding to the intracellular domains of TyrR. Our data may provide insights into the early effects of ligand binding to the activation process of SoTyrR.

Rice weevils (Sitophilus oryzae) are pests that feed on grain products. One strategy employed in the safe pest management is the use of essential oils from plant materials as biopesticide. Monoterpene compounds, present in essential oils, are generally less acutely toxic than other conventional insecticides and are known to possess biopesticide activity against octopaminergic receptors (OAR). Tyramine receptor (TyrR) is a desired biopesticide target due to its absence in vertebrates and its role in insect's physiological and cellular response. In this study, the biochemical basis of monoterpenes and SoTyrR interactions were determined using in silico methods: ensemble docking, 3DQSAR analysis, and toxicity prediction. Ensemble docking results showed that the lead compounds has binding affinity of − 4.2 to − 6.8 kcal/mol. Four monoterpene compounds: terpinolene, carvacrol, carene, and pulegone were considered top hits based on their favorable binding affinity. Furthermore, hydrophobic interactions of monoterpenes with residues Asp114, Val404, Lys189, Leu190, Tyr196, Phe397, and Tyr401 stabilized the observed docking poses. Upon consolidation of docking and 3DQSAR results, we functionalized top hit ligands and showed significant increase in the average binding affinity of candidate compounds, ranging from − 4.7 to − 8.3 kcal/mol. A carene derivative exhibited the highest binding energy of − 8.3 kcal/ mol with a calculated K i of 0.547 μM which surpassed the known activators of OAR. The top hit modified ligands were also clear of toxicity risks as predicted by Osiris Property Explorer. This work could provide insights in the development of effective biopesticides for rice weevils that is less toxic than conventional pesticides.

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