Biology subjects

Stevens, R. C.

Publications and source records attributed to Stevens, R. C..

6 recordsLinked to original sources

bioRxiv2025

The 4th GPCR Dock: assessment of blind predictions for GPCR-ligand complexes in the era of AlphaFold

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 4th 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.

molecular biology↗

bioRxiv2022

Ligand-independent modulation of GIPR signaling by splice variants

Glucose-dependent insulinotropic polypeptide receptor (GIPR) is a potential drug target for metabolic disorders. It works with glucagon-like peptide-1 receptor (GLP-1R) and glucagon receptor (GCGR) in humans to maintain glucose homeostasis. Unlike the other two receptors, GIPR has at least 7 reported (EMBL-EBI, 2022; NCBI, 2022a, 2022b) splice variants (SVs) with previously undefined functions. To explore their roles in endogenous peptide mediated GIPR signaling, we investigated the outcome of co-expressing each of the four SVs in question with GIPR in terms of ligand binding, cAMP accumulation, Gs activation, {beta}-arrestin recruitment and cell surface localization. The effects of these SVs on intracellular cAMP responses modulated by receptor activity-modifying proteins (RAMPs) were also studied. It was found that while SVs of GIPR neither bound to the hormone nor affected its signal transduction per se, they differentially regulated GIPR-mediated cAMP and {beta}-arrestin responses. Specifically, SV1 and SV4 were preferable to Gs signaling, SV3 was biased towards {beta}-arrestin recruitment, whereas SV2 was inactive on both pathways. In the presence of RAMPs, only SV1 and SV4 synergized the repressive action of RAMP3 on GIP-elicited cAMP production. The results suggest a new form of signal bias that is constitutive and ligand-independent, thereby expanding our knowledge of biased signaling beyond pharmacological manipulation (i.e., ligand specific) as well as constitutive and ligand-dependent (e.g., SV1 of the growth hormone-releasing hormone receptor).

molecular biology↗

bioRxiv2021

Bayesian metamodeling of complex biological systems across varying representations

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 {beta}-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 {beta}-Cell Consortium. Significance StatementCells 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 {beta}-Cell Consortium.

cell biology↗

bioRxiv2021

Structural insights into hormone recognition by the human glucose-dependent insulinotropic polypeptide receptor

Glucose-dependent insulinotropic polypeptide (GIP) is a peptide hormone that exerts crucial metabolic functions by binding and activating its cognate receptor, GIPR. As an important therapeutic target, GIPR has been subjected to intensive structural studies without success. Here, we report the cryo-EM structure of the human GIPR in complex with GIP and a Gs heterotrimer at a global resolution of 2.9 [A]. GIP adopts a single straight helix with its N terminus dipped into the receptor transmembrane domain (TMD), while the C-terminus is closely associated with the extracellular domain and extracellular loop 1. GIPR employs conserved residues in the lower half of the TMD pocket to recognize the common segments shared by GIP homologous peptides, while uses non-conserved residues in the upper half of the TMD pocket to interact with residues specific for GIP. These results provide a structural framework of hormone recognition and GIPR activation.

molecular biology↗

bioRxiv2020

Assessment of scoring functions to rank the quality of 3D subtomogram clusters from cryo-electron tomography

Cryo-electron tomography provides the opportunity for unsupervised discovery of endogenous complexes in situ. This process usually requires particle picking, clustering and alignment of subtomograms to produce an average structure of the complex. When applied to heterogeneous samples, template-free clustering and alignment of subtomograms can potentially lead to the discovery of structures for unknown endogenous complexes. However, such methods require useful scoring functions to measure the quality of aligned subtomogram clusters, which can be compromised by contaminations from misclassified complexes and alignment errors. To our knowledge, a comprehensive survey to assess the effectiveness of scoring functions for ranking the quality of subtomogram clusters does not exist yet. Here, we provide such a study and assess a total of 15 scoring functions for evaluating the quality of the subtomogram clusters, which differ in the amount of structural misalignments and contaminations due to misclassified complexes. We assessed both experimental and simulated subtomograms as ground truth data sets. Our analysis shows that the robustness of scoring functions varies largely. Most scores are sensitive to the signal-to-noise ratio of subtomograms and often require Gaussian filtering as preprocessing for improved performance. Two scoring functions, Spectral SNR-based Fourier Shell Correlation and Pearson Correlation in the Fourier domain with missing wedge correction, show a robust ranking of subtomogram clusters even without any preprocessing and irrespective of SNR levels of subtomograms. Of these two scoring functions, Spectral SNR-based Fourier Shell Correlation was fastest to compute and is a better choice for handling large numbers of subtomograms. Our results provide a guidance for choosing a scoring function for template-free approaches to detect complexes from heterogeneous samples.

bioinformatics↗

bioRxiv2019

Universal activation mechanism of class A GPCRs

Class A G protein-coupled receptors (GPCRs) influence virtually every aspect of human physiology. GPCR activation is an allosteric process that links agonist binding to G protein recruitment, with the hallmark outward movement of transmembrane helix 6 (TM6). However, what leads to TM6 movement and the key residue-level changes of this trigger remain less well understood. Here, by analyzing over 230 high-resolution structures of class A GPCRs, we discovered a modular, universal GPCR activation pathway that unites previous findings into a common activation mechanism, directly linking the bottom of ligand-binding pocket with G protein-coupling region. We suggest that the modular nature of the universal GPCR activation pathway allowed for the decoupling of the evolution of the ligand binding site, G protein binding region and the residues important for receptor activation. Such an architecture might have facilitated GPCRs to emerge as a highly successful family of proteins for signal transduction in nature.

bioinformatics↗