Computational insights into ligand-induced G protein and β-arrestin signaling of the dopamine D1 receptor

Ligand-Induced Biased Activation of GPCRs: Recent Advances and New Directions from In Silico Approaches

G-protein coupled receptors (GPCRs) are the largest family of membrane proteins engaged in transducing signals from the extracellular environment into the cell. GPCR-biased signaling occurs when two different ligands, sharing the same binding site, induce distinct signaling pathways. This selective signaling offers significant potential for the design of safer and more effective drugs. Although its molecular mechanism remains elusive, big efforts are made to try to explain this mechanism using a wide range of methods. Recent advances in computational techniques and AI technology have introduced a variety of simulations and machine learning tools that facilitate the modeling of GPCR signal transmission and the analysis of ligand-induced biased signaling. In this review, we present the current state of in silico approaches to elucidate the structural mechanism of GPCR-biased signaling. This includes molecular dynamics simulations that capture the main interactions causing the bias. We also highlight the major contributions and impacts of transmembrane domains, loops, and mutations in mediating biased signaling. Moreover, we discuss the impact of machine learning models on bias prediction and diffusion-based generative AI to design biased ligands. Ultimately, this review addresses the future directions for studying the biased signaling problem through AI approaches.

Progress on the development of Class A GPCR-biased ligands

Class A G protein-coupled receptors (GPCRs) continue to garner interest for their essential roles in cell signalling and their importance as drug targets. Although numerous drugs in the clinic target these receptors, over 60% GPCRs remain unexploited. Moreover, the adverse effects triggered by the available unbiased GPCR modulators, limit their use and therapeutic value. In this context, the elucidation of biased signalling has opened up new pharmacological avenues holding promise for safer therapeutics. Functionally selective ligands favour receptor conformations facilitating the recruitment of specific effectors and the modulation of the associated pathways. This review surveys the current drug discovery landscape of GPCR-biased modulators with a focus on recent advances. Understanding the biological effects of this preferential coupling is at different stages depending on the Class A GPCR family. Therefore, with a focus on individual GPCR families, we present a compilation of the functionally selective modulators reported over the past few years. In doing so, we dissect their therapeutic relevance, molecular determinants and potential clinical applications.

Multiple recent HCAR2 structures demonstrate a highly dynamic ligand binding and G protein activation mode

A surprisingly clear picture of the allosteric mechanism connecting G protein-coupled receptor agonists with G protein binding-and back - is revealed by a puzzle of thirty novel 3D structures of the hydroxycarboxylic acid receptor 2 (HCAR2) in complex with eight different orthosteric and a single allosteric agonist. HCAR2 is a sensor of β-hydroxybutyrate, niacin and certain anti-inflammatory drugs. Surprisingly, agonists with and without on-target side effects bound very similarly and in a completely occluded orthosteric binding site. Thus, despite the many structures we are still left with a pertinent need to understand the molecular dynamics of this and similar systems.

Probing allosteric communication with combined molecular dynamics simulations and network analysis

Understanding the allosteric mechanisms within biomolecules involved in diseases is of paramount importance for drug discovery. Indeed, characterizing communication pathways and critical hotspots in signal transduction can guide a rational approach to leverage allosteric modulation for therapeutic purposes. While the atomistic signatures of allosteric processes are difficult to determine experimentally, computational methods can be a remarkable resource. Network analysis built on Molecular Dynamics simulation data is particularly suited in this respect and is gradually becoming of routine use. Herein, we collect the recent literature in the field, discussing different aspects and available options for network construction and analysis. We further highlight interesting refinements and extensions, eventually providing our perspective on this topic.

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.

Current strategic trends in drug discovery: the present as prologue

INTRODUCTION

Escalating costs and inherent uncertainties associated with drug discovery invite initiatives to improve its efficiency and de-risk campaigns for inventing better therapeutics. One such initiative involves recognizing and exploiting current approaches in therapeutics invention with molecular mechanisms of action that hold promise for designing and targeting new chemical entities as drugs.

AREAS COVERED

This perspective considers the current contextual framework around three drug-discovery approaches and evaluates their potential to help identify new targets/modalities in small-molecule molecular pharmacology: diversifying ligand-directed phenotypes for G protein-coupled receptor (GPCR) pharmacotherapeutic signaling; developing therapeutic-protein degraders and stabilizers for proximity-inducing pharmacology; and mining organelle biology for druggable therapeutic targets.

EXPERT OPINION

The contemporary drug-discovery approaches examined appear generalizable and versatile to have applications in therapeutics invention beyond those case studies discussed herein. Accordingly, they may be considered strategic trends worthy of note in advancing the field toward novel ways of addressing pharmacotherapeutically unmet medical needs.