Raymond C. Stevens

The GPCR Dock competitions are a series of community-wide assessments of computational structural modeling and ligand docking for G protein-coupled receptors, a central class of drug targets in the human proteome. The assessments are designed to provide an unbiased overview of progress and to pinpoint areas in need of refinement, thus shaping and directing the development of computational modeling methodologies for GPCRs. In the footsteps of 2008, 2010, and 2013 assessments, the 4 ^th round (GPCR Dock 2021) featured five diverse and challenging prediction targets and coincided with the emergence of AlphaFold, the revolutionary Artificial Intelligence (AI) technology for protein structure prediction from amino acid sequences. This report summarizes the assessment results and challenges in the context of the convergent evolution of computational and experimental structure determination techniques for GPCRs. We demonstrate that thanks to the breakthroughs in AI-powered modeling, the accuracy of modern computational models of GPCR complexes with peptides can not only approach but also exceed that of low-resolution experimental structures. However, our results highlight the unwavering need for high-resolution GPCR structure determination, especially with small molecule chemicals, and for the concurrent application of physics-based and expert-guided modeling methods.

Actin mediates insulin secretion in pancreatic β-cells through remodeling. Hampered by limited resolution, previous studies have offered an ambiguous depiction as depolymerization and repolymerization. We report the in situ structure of actin remodeling in INS-1E β-cells during glucose-stimulated insulin secretion at nanoscale resolution. After remodeling, the actin filament network at the cell periphery exhibits three marked differences: 12% of actin filaments reorient quasi-orthogonally to the ventral membrane; the filament network mainly remains as cell-stabilizing bundles but partially reconfigures into a less compact arrangement; actin filaments anchored to the ventral membrane reorganize from a “netlike” to a “blooming” architecture. Furthermore, the density of actin filaments and microtubules around insulin secretory granules decreases, while actin filaments and microtubules become more densely packed. The actin filament network after remodeling potentially precedes the transport and release of insulin secretory granules. These findings advance our understanding of actin remodeling and its role in glucose-stimulated insulin secretion.

Glucagon-like peptide-1 receptor (GLP-1R) is a critical therapeutic target for type 2 diabetes mellitus (T2DM). The GLP-1R cellular signaling mechanism relevant to insulin secretion and blood glucose regulation has been extensively studied. Numerous drugs targeting GLP-1R have entered clinical treatment. However, novel functional molecules with reduced side effects and enhanced therapeutic efficacy are still in high demand. In this review, we summarize the basis of GLP-1R cellular signaling, and how it is involved in the treatment of T2DM. We review the functional molecules of incretin therapy in various stages of clinical trials. We also outline the current strategies and emerging techniques that are furthering the development of novel therapeutic drugs for T2DM and other metabolic diseases.

The mesoscale description of the subcellular organization informs about cellular mechanisms in disease state. However, applications of soft X-ray tomography (SXT), an important approach for characterizing organelle organization, are limited by labor-intensive manual segmentation. Here we report a pipeline for automated segmentation and systematic analysis of SXT tomograms. Our approach combines semantic and first-applied instance segmentation to produce separate organelle masks with high Dice and Recall indexes, followed by analysis of organelle localization based on the radial distribution function. We demonstrated this technique by investigating the organization of INS-1E pancreaticβ-cell organization under different treatments at multiple time points. Consistent with a previous analysis of a similar dataset, our results revealed the impact of glucose stimulation on the localization and molecular density of insulin vesicles and mitochondria. This pipeline can be extended to SXT tomograms of any cell type to shed light on the subcellular rearrangements under different drug treatments.

Since their discovery, nicotinic acetylcholine receptors (nAChRs) have been extensively studied to understand their function, as well as the consequence of alterations leading to disease states. Importantly, these receptors represent pharmacological targets to treat a number of neurological and neurodegenerative disorders. Nevertheless, their therapeutic value has been limited by the absence of high-resolution structures that allow for the design of more specific and effective drugs. This article offers a comprehensive review of five decades of research pursuing high-resolution structures of nAChRs. We provide a historical perspective, from initial structural studies to the most recent X-ray and cryogenic electron microscopy (Cryo-EM) nAChR structures. We also discuss the most relevant structural features that emerged from these studies, as well as perspectives in the field.

Type 2 diabetes is characterized by β and α cell dysfunction. We used phasor-FLIM (Fluorescence Lifetime Imaging Microscopy) to monitor oxidative phosphorylation and glycolysis in living islet cells before and after glucose stimulation. In healthy cells, glucose enhanced oxidative phosphorylation in β cells and suppressed oxidative phosphorylation in α cells. In Type 2 diabetes, glucose increased glycolysis in β cells, and only partially suppressed oxidative phosphorylation in α cells. FLIM uncovers key perturbations in glucose induced metabolism in living islet cells and provides a sensitive tool for drug discovery in diabetes.

Comprehensive modeling of a whole cell requires an integration of vast amounts of information on various aspects of the cell and its parts. To divide-and-conquer this task, we introduce Bayesian metamodeling, a general approach to modeling complex systems by integrating a collection of heterogeneous input models. Each input model can in principle be based on any type of data and can describe a different aspect of the modeled system using any mathematical representation, scale, and level of granularity. These input models are (i) converted to a standardized statistical representation relying on Probabilistic Graphical Models, (ii) coupled by modeling their mutual relations with the physical world, and (iii) finally harmonized with respect to each other. To illustrate Bayesian metamodeling, we provide a proof-of-principle metamodel of glucose-stimulated insulin secretion by human pancreatic ß-cells. The input models include a coarse-grained spatiotemporal simulation of insulin vesicle trafficking, docking, and exocytosis; a molecular network model of glucose-stimulated insulin secretion signaling; a network model of insulin metabolism; a structural model of glucagon-like peptide-1 receptor activation; a linear model of a pancreatic cell population; and ordinary differential equations for systemic postprandial insulin response. Metamodeling benefits from decentralized computing, while often producing a more accurate, precise, and complete model that contextualizes input models as well as resolves conflicting information. We anticipate Bayesian metamodeling will facilitate collaborative science by providing a framework for sharing expertise, resources, data, and models, as exemplified by the Pancreatic ß-Cell Consortium. Significance Statement Cells are the basic units of life, yet their architecture and function remain to be fully characterized. This work describes Bayesian metamodeling, a modeling approach that divides-and-conquers a large problem of modeling numerous aspects of the cell into computing a number of smaller models of different types, followed by assembling these models into a complete map of the cell. Metamodeling enables a facile collaboration of multiple research groups and communities, thus maximizing the sharing of expertise, resources, data, and models. A proof-of-principle is provided by a model of glucose-stimulated insulin secretion produced by the Pancreatic ß-Cell Consortium.

Subcellular maps of β cells reveal the coordinated reorganization of organelles during insulin secretion.