Activation Pathways of Neurotensin Receptor 1 Elucidated Using Statistical Machine Learning

Revealing the graded activation mechanism of neurotensin receptor 1

Graded activation contributes to the precise regulation of GPCR activity, presenting new opportunities for drug design. In this work, a total of 10 μs enhanced-sampling simulations are performed to provide molecular insights into the binding dynamics differences of the neurotensin receptor 1 (NTSR1) to the full agonist SRI-9829, partial agonist RTI-3a and inverse agonist SR48692. The possible graded activation mechanism of NTSR1 is revealed by an integrated analysis utilizing the reweighted potential of mean force (PMF), deep learning (DL) and transfer entropy (TE). Specifically, the orthosteric pocket is observed to undergo expansion and contraction, with the G-protein-binding site experiencing interconversions among the inactive, intermediate and active-like states. Detailed structural comparisons capture subtle conformational differences arising from ligand binding in allosteric signaling, which can well explain the graded activation. Critical microswitches that contribute to graded activation are efficiently identified with the DL model. TE calculations enable the visualization of allosteric communication networks within the receptor, elucidating the driver-responder relationships associated with signal transduction. Fortunately, the dissociation of the full agonist from the orthosteric pocket is observed. The current findings systematically reveal the mechanism of NTSR1 graded activation, and also provide implications for structure-based drug design.

Construction of a Lung Adenocarcinoma Prognostic Model Utilizing Serine and Glycine Metabolism-Related Genes

The objective of this study was to construct a prognostic model by utilizing serine/glycine metabolism-related genes (SGMGs), thus establishing a risk score for lung adenocarcinoma (LUAD). Based on the TCGA-LUAD and SGMG data set, two subtypes with different SGMG expression levels were identified by clustering analysis. Thirteen differential expression genes were used to construct RiskScore by Cox regression. GSE72094 data set was used for validation. The survival characteristics, immune features, and potential benefits of chemotherapy drugs were analyzed for two risk groups. RiskScore was constructed based on the genes ABCC12, RIC3, CYP4B1, SFTPB, CACNA2D2, IGF2BP1, NTSR1, DKK1, CREG2, PITX3, RGS20, FETUB, and IGFBP1. Patients in the low-risk (LR) group exhibited a superior overall survival. In addition, aDCs, iDSs, mast cells, neutrophils, HLA, and type II IFN were more abundant in the LR group with higher IPS scores and lower TIDE scores. In contrast, NK cells, APC coinhibition, and MHC-I were more common in the high-risk (HR) group, which may be more sensitive to chemotherapy drugs such as cisplatin, oxaliplatin, and nilotinib. RiskScore was a promising biomarker that can be used to distinguish LUAD prognosis, immune features, and sensitivity to chemotherapy drugs.

Mechanistic Elucidation of Activation/Deactivation Signal Transduction within Neurotensin Receptor 1 Triggered by 'Driver Chemical Groups' of Modulators: A Comparative Molecular Dynamics Simulation

Small-molecule modulators of neurotensin receptor 1 (NTSR1), a class A G-protein-coupled receptor (GPCR), has emerged as promising therapeutic agent for psychiatric disorders and cancer. Interestingly, a chemical group substitution in NTSR1 modulators can launch different types of downstream regulation, highlighting the significance of deciphering the internal fine-tuning mechanism. Here, we conducted a synergistic application of a Gaussian accelerated molecular dynamics simulation, a conventional molecular dynamics simulation, and Markov state models (MSM) to investigate the underlying mechanism of 'driver chemical groups' of modulators triggering inverse signaling. The results indicated that the flexibility of the leucine moiety in NTSR1 agonists contributes to the inward displacement of TM7 through a loosely coupled allosteric pathway, while the rigidity of the adamantane moiety in NTSR1 antagonists leads to unfavorable downward transduction of agonistic signaling. Furthermore, we found that R322^6.54, Y319^6.51, F353^7.42, R148^3.32, S356^7.45, and S357^7.46 may play a key role in inducing the activation of NTSR1. Together, our findings not only highlight the ingenious signal transduction within class A GPCRs but also lay a foundation for the development of targeted drugs harboring different regulatory functions of NTSR1.

Activity Map and Transition Pathways of G Protein-Coupled Receptor Revealed by Machine Learning

Approximately, one-third of all U.S. Food and Drug Administration approved drugs target G protein-coupled receptors (GPCRs). However, more knowledge of protein structure-activity correlation is required to improve the efficacy of the drugs targeting GPCRs. In this study, we developed a machine learning model to predict the activation state and activity level of the receptors with high prediction accuracy. Furthermore, we applied this model to thousands of molecular dynamics trajectories to correlate residue-level conformational changes of a GPCR to its activity level. Finally, the most probable transition pathway between activation states of a receptor can be identified using the state-activity information. In addition, with this model, we can associate the contribution of each amino acid to the activation process. Using this method, we can design drugs that mainly target principal amino acids driving the transition between activation states of GPCRs. Our advanced method is generalizable to all GPCR classes and provides mechanistic insight into the activation mechanism in the receptors.