APOBEC3B (A3B) is implicated in DNA mutations that facilitate tumor evolution. Although structures of its individual N- and C-terminal domains (NTD and CTD) have been resolved through X-ray crystallography, the full-length A3B (fl-A3B) structure remains elusive, limiting our understanding of its dynamics and mechanisms. In particular, the APOBEC3B C-terminal domain (A3Bctd) is frequently closed in models and structures. In this study, we built several new models of fl-A3B using integrative structural biology methods and selected a top model for further dynamical investigation. We compared the dynamics of the truncated (A3Bctd) to that of the fl-A3B via conventional and Gaussian accelerated molecular dynamics (MD) simulations. Subsequently, we employed weighted ensemble methods to explore the fl-A3B active site opening mechanism, finding that interactions at the NTD-CTD interface enhance the opening frequency of the fl-A3B active site. Our findings shed light on the structural dynamics and potential druggability of fl-A3B, including observations regarding both the active and allosteric sites, which may offer new avenues for therapeutic intervention in cancer.

Rhomboid proteases are ubiquitous intramembrane serine proteases that can cleave transmembrane substrates within lipid bilayers. They exhibit many and diverse functions, such as but not limited to, growth factor signaling, immune and inflammatory response, protein quality control, and parasitic invasion. Human rhomboid protease RHBDL4 has been demonstrated to play a critical role in removing misfolded proteins from the Endoplasmic Reticulum and is implicated in severe diseases such as various cancers and Alzheimer's disease. Therefore, RHBDL4 is expected to constitute an important therapeutic target for such devastating diseases. Despite its critical role in many biological processes, the enzymatic properties of RHBDL4 remain largely unknown. To enable a comprehensive characterization of RHBDL4's kinetics, catalytic parameters, substrate specificity, and binding modality we expressed and purified recombinant RHBDL4, and employed it in a Förster Resonance Energy Transfer-based cleavage assay. Until now, kinetic studies have been limited mostly to bacterial rhomboid proteases. Our in vitro platform offers a new method for studying RHBDL4's enzymatic function and substrate preferences. Furthermore, we developed and tested potential inhibitors using our assay and successfully identified peptidyl α-ketoamide inhibitors of RHBDL4 that are highly effective against recombinant RHBDL4. We utilize ensemble docking and molecular dynamics (MD) simulations to explore the binding modality of substrate-derived peptides bound to RHBDL4. Our analysis focused on key interactions and dynamic movements within RHBDL4's active site that contributed to binding stability, offering valuable insights for optimizing the non-prime side of RHBDL4 ketoamide inhibitors. In summary, our study offers fundamental insights into RHBDL4's catalytic activities and substrate preferences, laying the foundation for downstream applications such as drug inhibitor screenings and structure-function studies, which will enable the identification of lead drug compounds for RHBDL4.

The conformational molecular dynamics of cytosolic phospholipase A2 (cPLA2) when interacting and associating with a simple phospholipid bilayer (Mouchlis et al. PNAS 2015) and macrophage membranes representing an average phospholipid composition (Mouchlis et al. JACS 2018) have been previously reported. We have now carried out molecular dynamics studies focused on the endoplasmic reticulum membrane in RAW macrophages where cPLA2 resides when carrying out enzymatic hydrolysis of its membrane phospholipids. Our current work also employed classical all-atom molecular dynamics simulations, but on much larger and more heterogeneous membranes. An extensive UPLC/MS study of the lipidome of RAW macrophage's in different subcellular organelles (Andreyev, et al. JLR 2010) had inspired us to build a more relevant membrane system and larger than previously accomplished by using more than 600 K atoms and an explicit solvent model. The interaction of the C2 domain and the catalytic domain were differentiated by simulating cPLA2 in both an aqueous solution and in a membrane-bound state. Our work also examined the role of phosphatidylinositol 4,5-bisphosphate (PIP2) in the membranes during interaction of cPLA2 with the ER membrane. These findings are important in establishing how cPLA2 functions in a more relevant membrane system and our understanding of membrane-induced allostery in PLA2's (Mouchlis and Dennis, Accounts Chem Res 2022).

The G protein-coupled receptor (GPCR) µ-opioid receptor (µOR) is one of several drug targets of commercially available therapeutics for pain. Various opioid drugs like morphines have been associated to numerous substance abuse-related deaths around the world. A better alternative to avoid this undesirable side effect is by targeting allosteric sites. In addition, understanding the underlying mechanism of allosteric ligands in µOR is highly sought for better drug optimizations. Using molecular dynamics, the allosteric behavior of the µOR and G protein complex in the presence of agonist ligand BU72 and potential positive allosteric modulator (PAM) BMS-986121 was probed by observing residue-residue contacts formation and breakage. It was found that G protein residues D959, L349, and K963 participate in the interprotein contact formation between µOR and G protein. Moreover, orthosteric binding site residues D83, Y84, and H233 polar interactions were verified to be critical not only on the agonist ligand binding, but also in the allosteric communication of the protein complex. Also, the overall decrease on the number of contacts was observed after mutations, which can trigger the opening of the orthosteric binding site. Rationalization of allosteric modulation in µ-opioid receptor-G protein complex may improve drug discovery schemes and strategies for allosteric drugs including other targets in the GPCR protein families.

Ligand-based allostery has been gaining attention for its importance in protein regulation and implication in drug design. One of the interesting cases of protein allostery is the thyroid hormone receptor - retinoid x receptor (TR:RXR), which regulates the gene expression of important physiological processes, such as development and metabolism. It is regulated by the TR native ligand triiodothyronine (T3), which displays anticooperative behavior to the RXR ligand 9-cis retinoic acid (9C). In contrast to this anticooperative behavior, 9C has been shown to increase the activity of TR:RXR. Here we probed the influence of the affinity and the interactions of the TR ligand to the allostery of the TR:RXR through contact dynamics and residue networks. The TR ligand analogs were designed to have higher (G2) and lower (N1) binding energies than T3 when docked to the TR:RXR(9C) complex. The aqueous TR(N1/T3/G2):RXR(9C) complexes were subjected to 30 ns all-atom simulations using theNAMD. The program CAMERRA was used to capture the subtle perturbations of TR:RXR by mapping the residue contact dynamics. Various parts of the TR ligands; including the hydrophilic head, the iodine substituents, and the ligand tail; have been probed for their significance in ligand affinity. The results on the T3 and G2 complexes suggest that ligand affinity can be utilized as a predictor for anticooperative systems on which ligand is more likely to dissociate or remain bound. All 3 complexes also display distinct contact networks for cross-dimer signalling and ligand communication. Understanding ligand-based allostery could potentially unveil secrets of ligand-regulated protein dynamics, a foundation for the design of better and more efficient allosteric drugs.

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.

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.

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.