Computational insights into diverse binding modes of the allosteric modulator and their regulation on dopamine D1 receptor.

Allosteric drugs hold the promise of addressing many challenges in the current drug development of GPCRs. However, the molecular mechanism underlying their allosteric modulations remain largely elusive. The dopamine D1 receptor (DRD1), a member of Class A GPCRs, is critical for treating psychiatric disorders, and LY3154207 serves as its promising positive allosteric modulator (PAM). In the work, we utilized extensive Gaussian-accelerated molecular dynamics simulations (a total of 41µs) for the first time probe the diverse binding modes of the allosteric modulator and their regulation effects, based on the DRD1 and LY3154207 as representative. Our simulations identify four binding modes of LY3154207 (one boat mode, two metastable vertical modes and a novel cleft-anchored mode), in which the boat mode is the most stable while there three modes are similar in the stability. However, it is interesting to observed that the most stable boat mode inversely exhibits the weakest positive allosteric effect on influencing the orthosteric ligand binding and maintaining the activity of the transducer binding site. It should result from its induced weaker correlation between the allosteric site and the orthosteric site, and between the orthosteric site and the transducer binding site than the other three binding modes, as well as its weakened interaction between a crucial activation-related residue (S2025.46) and the orthosteric ligand (dopamine). Overall, the work offers atomic-level information to advance our understanding of the complex allosteric regulation on GPCRs, which is beneficial to the allosteric modulator design and development.

Exploring biased activation characteristics by molecular dynamics simulation and machine learning for the µ-opioid receptor.

Biased ligands selectively activating specific downstream signaling pathways (termed as biased activation) exhibit significant therapeutic potential. However, the conformational characteristics revealed are very limited for the biased activation, which is not conducive to biased drug development. Motivated by the issue, we combine extensive accelerated molecular dynamics simulations and an interpretable deep learning model to probe the biased activation features for two complex systems constructed by the inactive µOR and two different biased agonists (G-protein-biased agonist TRV130 and ß-arrestin-biased agonist endomorphin2). The results indicate that TRV130 binds deeper into the receptor core compared to endomorphin2, located between W2936.48 and D1142.50, and forms hydrogen bonding with D1142.50, while endomorphin2 binds above W2936.48. The G protein-biased agonist induces greater outward movements of the TM6 intracellular end, forming a typical active conformation, while the ß-arrestin-biased agonist leads to a smaller extent of outward movements of TM6. Compared with TRV130, endomorphin2 causes more pronounced inward movements of the TM7 intracellular end and more complex conformational changes of H8 and ICL1. In addition, important residues determining the two different biased activation states were further identified by using an interpretable deep learning classification model, including some common biased activation residues across Class A GPCRs like some key residues on the TM2 extracellular end, ECL2, TM5 intracellular end, TM6 intracellular end, and TM7 intracellular end, and some specific important residues of ICL3 for µOR. The observations will provide valuable information for understanding the biased activation mechanism for GPCRs.

A fast response time gas ionization chamber detector with a grid structure.

The time response characteristic of the detector is crucial in radiation imaging systems. Unfortunately, existing parallel plate ionization chamber detectors have a slow response time, which leads to blurry radiation images. To enhance imaging quality, the electrode structure of the detector must be modified to reduce the response time. This paper proposes a gas detector with a grid structure that has a fast response time. In this study, the detector electrostatic field was calculated using COMSOL, while Garfield++ was utilized to simulate the detector's output signal. To validate the accuracy of simulation results, the experimental ionization chamber was tested on the experimental platform. The results revealed that the average electric field intensity in the induced region of the grid detector was increased by at least 33%. The detector response time was reduced to 27% -38% of that of the parallel plate detector, while the sensitivity of the detector was only reduced by 10%. Therefore, incorporating a grid structure within the parallel plate detector can significantly improve the time response characteristics of the gas detector, providing an insight for future detector enhancements.