Enhancing Opioid Bioactivity Predictions through Integration of Ligand-Based and Structure-Based Drug Discovery Strategies with Transfer and Deep Learning Techniques

The opioid epidemic has cast a shadow over public health, necessitating immediate action to address its devastating consequences. To effectively combat this crisis, it is crucial to discover better opioid drugs with reduced addiction potential. Artificial intelligence-based and other machine learning tools, particularly deep learning models, have garnered significant attention in recent years for their potential to advance drug discovery. However, using these tools poses challenges, especially when training samples are insufficient to achieve adequate prediction performance. In this study, we investigate the effectiveness of transfer learning in building robust deep learning models to enhance ligand bioactivity prediction for each individual opioid receptor (OR) subtype. This is achieved by leveraging knowledge obtained from pretraining a model using supervised learning on a larger data set of bioactivity data combined with ligand-based and structure-based molecular descriptors related to the entire OR subfamily. Our studies hold the potential to advance opioid research by enabling the rapid identification of novel chemical probes with specific bioactivities, which can aid in the study of receptor function and contribute to the future development of improved opioid therapeutics

How Oliceridine (TRV-130) Binds and Stabilizes a μ‑Opioid Receptor Conformational State That Selectively Triggers G Protein Signaling Pathways

Substantial attention has recently been devoted to G protein-biased agonism of the μ-opioid receptor (MOR) as an ideal new mechanism for the design of analgesics devoid of serious side effects. However, designing opioids with appropriate efficacy and bias is challenging because it requires an understanding of the ligand binding process and of the allosteric modulation of the receptor. Here, we investigated these phenomena for TRV-130, a G protein-biased MOR small-molecule agonist that has been shown to exert analgesia with less respiratory depression and constipation than morphine and that is currently being evaluated in human clinical trials for acute pain management. Specifically, we carried out multimicrosecond, all-atom molecular dynamics (MD) simulations of the binding of this ligand to the activated MOR crystal structure. Analysis of >50 μs of these MD simulations provides insights into the energetically preferred binding pathway of TRV-130 and its stable pose at the orthosteric binding site of MOR. Information transfer from the TRV-130 binding pocket to the intracellular region of the receptor was also analyzed, and was compared to a similar analysis carried out on the receptor bound to the classical unbiased agonist morphine. Taken together, these studies lead to a series of testable hypotheses of ligand–receptor interactions that are expected to inform the structure-based design of improved opioid analgesics

Atomistic structural representations of representative minima of B1AR and B2AR homodimers for the different interfaces.

<p>Panel A shows a view from the cytoplasmic side of the minima Θ1 (dark shades) and Θ2 (lighter shades) for the B1AR (red/pink) and B2AR (blue/light blue) at the TM4/3 interface. The receptors are represented as helices with the loops removed for clarity, and are aligned on the left-most protomer. This panel highlights the small difference between the structures at the same separation but with slightly different angle minima. Panel B shows a view from the cytoplasmic side, of the minima extracted from the FES for the TM1/H8 interface. B1AR is shown in red and B2AR is shown in blue. The intracellular loops are omitted for clarity, and H8 and TM1 are highlighted to indicate the packing of the helices at the interface. Contacting residues are listed in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002649#pcbi.1002649.s006" target="_blank">Table S1</a> and depicted in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002649#pcbi.1002649.s004" target="_blank">Fig. S4</a>.</p

Hypothetical arrangements of B2AR dimers interacting with the Gs protein heterotrimer.

<p>The extracellular view of the B2AR-Gs protein complex (PDB ID: 3SN6) is shown with B2AR in grey cartoon representation, and the Gs heterotrimer, is shown in both cartoon and transparent surface (α is in orange, β is in cyan and γ is in pale blue). The first protomer of each of our minimum energy dimers for B2AR (Θ2 for TM4/3) is superimposed on the B2AR from the crystal structure 3SN6, and the position of the second protomer is shown in a green cartoon representation. An interface involving TM4 would favor an exclusive interaction of the dimer with the GαsAH subunit of the G protein, very close to the Gβ subunit when the interface is TM4/3 (panel A). In contrast, with TM1/H8 at the interface, the second protomer would not be involved in significant interactions with any of the G-protein subunits (in B).</p

Results of the free energy calculations of adrenergic receptor dimers.

<p>Listed are the depths of the minima in the FES, along with the calculated dimerization constant (K_D), the standard free energy change ΔG_X°, and the estimated lifetime of each simulated B1AR and B2AR dimer. Confidence intervals are given in parentheses.</p

Assessing the Relative Stability of Dimer Interfaces in G Protein-Coupled Receptors

<div><p>Considerable evidence has accumulated in recent years suggesting that G protein-coupled receptors (GPCRs) associate in the plasma membrane to form homo- and/or heteromers. Nevertheless, the stoichiometry, fraction and lifetime of such receptor complexes in living cells remain topics of intense debate. Motivated by experimental data suggesting differing stabilities for homomers of the cognate human β1- and β2-adrenergic receptors, we have carried out approximately 160 microseconds of biased molecular dynamics simulations to calculate the dimerization free energy of crystal structure-based models of these receptors, interacting at two interfaces that have often been implicated in GPCR association under physiological conditions. Specifically, results are presented for simulations of coarse-grained (MARTINI-based) and atomistic representations of each receptor, in homodimeric configurations with either transmembrane helices TM1/H8 or TM4/3 at the interface, in an explicit lipid bilayer. Our results support a definite contribution to the relative stability of GPCR dimers from both interface sequence and configuration. We conclude that β1- and β2-adrenergic receptor homodimers with TM1/H8 at the interface are more stable than those involving TM4/3, and that this might be reconciled with experimental studies by considering a model of oligomerization in which more stable TM1 homodimers diffuse through the membrane, transiently interacting with other protomers at interfaces involving other TM helices.</p> </div

Data management in clinical research

<div><p>The lipid composition of cell membranes has increasingly been recognized as playing an important role in the function of various membrane proteins, including G Protein-Coupled Receptors (GPCRs). For instance, experimental and computational evidence has pointed to lipids influencing receptor oligomerization directly, by physically interacting with the receptor, and/or indirectly, by altering the bulk properties of the membrane. While the exact role of oligomerization in the function of class A GPCRs such as the μ-opioid receptor (MOR) is still unclear, insight as to how these receptors oligomerize and the relevance of the lipid environment to this phenomenon is crucial to our understanding of receptor function. To examine the effect of lipids and different MOR conformations on receptor oligomerization we carried out extensive coarse-grained molecular dynamics simulations of crystal structures of inactive and/or activated MOR embedded in an idealized mammalian plasma membrane composed of 63 lipid types asymmetrically distributed across the two leaflets. The results of these simulations point, for the first time, to specific direct and indirect effects of the lipids, as well as the receptor conformation, on the spatio-temporal organization of MOR in the plasma membrane. While sphingomyelin-rich, high-order lipid regions near certain transmembrane (TM) helices of MOR induce an effective long-range attractive force on individual protomers, both long-range lipid order and interface formation are found to be conformation dependent, with a larger number of different interfaces formed by inactive MOR compared to active MOR.</p></div

Free energy surface (FES) as a function of protomer separation.

<p>Panel (A) shows results obtained for the TM4/3 interface of B1AR (red) and B2AR (blue) homodimers. The monomeric state was defined as being in the <i>r</i> = 4.5–4.8 nm range, and was assigned a free energy of zero. Panel (B) shows the FES as a function of the distance between the centers of mass of the protomers at the TM1 interface formed by (red) B1AR and (blue) B2AR, with the monomeric state assigned as <i>r</i> = 5.5–5.9 nm. The error bars are given for each case, and are calculated as described in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002649#pcbi.1002649.s009" target="_blank">Text S1</a>. In each panel, the inset shows a schematic representation of the CVs (<i>r</i>, <i>θ_a</i> and <i>θ_b</i>) in each of the interface arrangements.</p

An example of OR setup at different simulation times.

<p>Location of the 16 simulated receptor molecules in one of the five runs executed for the κ-OR system (run #1), at different simulation times, (specifically: 0, 2, 6, and 10 μs)</p

Persistence times and persistence-to-exchange time ratios of cholesterol molecules derived from the OR homo-dimer simulations.

<p>Specifically, persistence times of cholesterol calculated for δ-OR/δ-OR, κ-OR/κ-OR, and μ-OR/μ-OR are reported, in ns, in panels A, B, and C, respectively. Persistence-to-exchange time ratios of cholesterol around isolated δ-OR, κ-OR, and μ-OR are shown in panels D, E, and F, respectively. Approximate locations of the centers of mass of the seven transmembrane helices (TM1-TM7) are indicated with red labels.</p