Biology subjects

Fleetwood, O.

Publications and source records attributed to Fleetwood, O..

5 recordsLinked to original sources

bioRxiv2021

Allosteric effect of nanobody binding on ligand-specific active states of the β2-Adrenergic Receptor

Nanobody binding stabilizes the active state of G-protein-coupled receptor (GPCR) and modulates its affinity for bound ligands. However, the atomic level basis for this allosteric regulation remains elusive. Here, we investigate the conformational changes induced by the binding of a nanobody (Nb80) on the active-like {beta}2 adrenergic receptor ({beta}2AR) via enhanced sampling molecular dynamics simulations. Dimensionality reduction analysis shows that Nb80 stabilizes a highly active state of the {beta}2AR with a ~14 [A] outward movement of transmembrane helix 6 and close proximity of transmembrane (TM) helices 5 and 7. This is further supported by the residues located at hotspots located on TMs 5, 6 and 7, as shown by supervised machine learning methods. Besides, ligand-specific subtle differences in the conformations assumed by intercellular loop 2 and extracellular loop 2 are captured from the trajectories of various ligand-bound receptors in the presence of Nb80. Dynamic network analysis further reveals that Nb80 binding can enhance the communication between the binding sites of Nb80 and of the ligand. We identify unique allosteric signal transmission mechanisms between the Nb80-binding site and the extracellular domains in presence of full agonist and G-protein biased partial agonist, respectively. Altogether, our results provide insights into the effect of intracellular binding partners on the GPCR activation mechanism, which could be useful for structure-based drug discovery. TOC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=153 SRC="FIGDIR/small/451885v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@16f8ebforg.highwire.dtl.DTLVardef@104a47dorg.highwire.dtl.DTLVardef@1cb1659org.highwire.dtl.DTLVardef@284699_HPS_FORMAT_FIGEXP M_FIG Graphical Table of Contents C_FIG

biophysics↗

bioRxiv2020

Identification of ligand-specific G-protein coupled receptor states and prediction of downstream efficacy via data-driven modeling

G protein-coupled receptors (GPCRs) shift between inactive states and active signaling states, to which intracellular binding partners can bind. Extracellular binding of ligands stabilizes different receptor states and modulates the intracellular response via a complex allosteric process, which is not completely understood. Despite the recent advances in structure determination and spectroscopy techniques, a comprehensive view of the ligand-protein interplay remains a challenge. We derived free energy landscapes describing activation of the {beta}2 adrenergic receptor ({beta}2AR) bound to ligands with different efficacy profiles using enhanced sampling molecular dynamics (MD) simulations. The resulting free energy landscapes reveal clear shifts towards active-like states at the G protein binding site for receptors bound to partial and full agonists compared to antagonists and inverse agonists. Not only do the ligands control the population of states, they also modulate the conformational ensemble of the receptor by tuning allosteric protein microswitches. We find an excellent correlation between the conformation of the microswitches close to the ligand binding site and in the transmembrane region and experimentally reported cAMP signaling responses, highlighting the predictive power of our approach. Using dimensionality reduction techniques, we could further assess the similarity between the unique conformational states induced by different ligands. Two distant hotspots governing agonism on transmembrane helices 5 and 7, including the conserved NPxxY motif, formed the endpoints of an allosteric pathway between the binding sites. Our results demonstrate how molecular dynamics simulations can further provide insights into the mechanism of GPCR regulation by ligands, which may contribute to the design of drugs with specific efficacy profiles.

biophysics↗

bioRxiv2020

Network analysis reveals how lipids and other cofactors influence membrane protein allostery

Many membrane proteins are modulated by external stimuli, such as small molecule binding or change in pH, transmembrane voltage or temperature. This modulation typically occurs at sites that are structurally distant from the functional site. Revealing the communication, known as allostery, between these two sites is key to understanding the mechanistic details of these proteins. Residue interaction networks of isolated proteins are commonly used to this end. Membrane proteins, however, are embedded in a lipid bilayer which may contribute to allosteric communication. The fast diffusion of lipids hinders direct use of standard residue interaction networks. Here, we present an extension which includes cofactors such as lipids and small molecules in the network. The novel framework is applied to three membrane proteins: a voltage-gated ion channel (KCNQ1), a G-protein coupled receptor (GPCR - {beta}2 adrenergic receptor) and a pH-gated ion channel (KcsA). Through systematic analysis of the obtained networks and their components, we demonstrate the importance of lipids for membrane protein allostery. Finally, we reveal how small molecules may stabilize different protein states by allosterically coupling and decoupling the protein from the membrane.

biophysics↗

bioRxiv2019

Extracting molecular insights from conformational ensembles using Machine Learning

Biomolecular simulations are intrinsically high dimensional and generate noisy datasets of ever increasing size. Extracting important features in the data is crucial for understanding the biophysical properties of molecular processes, but remains a big challenge. Machine learning (ML) provides powerful dimensionality reduction tools. However, such methods are often criticized to resemble black boxes with limited human-interpretable insight. We use methods from supervised and unsupervised ML to efficiently create interpretable maps of important features from molecular simulations. We benchmark the performance of several methods including neural networks, random forests and principal component analysis, using a toy model with properties reminiscent of macromolecular behavior. We then analyze three diverse biological processes: conformational changes within the soluble protein calmodulin, ligand binding to a G protein-coupled receptor and activation of an ion channel voltage-sensor domain, unravelling features critical for signal transduction, ligand binding and voltage sensing. This work demonstrates the usefulness of ML in understanding biomolecular states and demystifying complex simulations. STATEMENT OF SIGNIFICANCEUnderstanding how biomolecules function requires resolving the ensemble of structures they visit. Molecular dynamics simulations compute these ensembles and generate large amounts of data that can be noisy and need to be condensed for human interpretation. Machine learning methods are designed to process large amounts of data, but are often criticized for their black-box nature and have historically been modestly used in the analysis of biomolecular systems. We demonstrate how machine learning tools can provide an interpretable overview of important features in a simulation dataset. We develop a protocol to quickly perform data-driven analysis of molecular simulations. This protocol is applied to identify the molecular basis of ligand binding to a receptor and of voltage sensitivity of an ion channel.

biophysics↗

bioRxiv2019

Energy landscapes reveal agonist’s control of GPCR activation via microswitches

Agonist binding to G protein-coupled receptors (GPCRs) leads to conformational changes in the transmembrane region that activate cytosolic signaling pathways. Al-though high resolution structures of different receptor states are available, atomistic details of the allosteric signalling across the membrane remain elusive. We calculated free energy landscapes of the {beta}2 adrenergic receptors activation using atomistic molecular dynamics simulations in an optimized string of swarms framework, which sheds new light on how microswitches govern the equilibrium between conformational states. Contraction of the extracellular binding site in the presence of the agonist BI-167107 is obligatorily coupled to conformational changes in a connector motif located in the core of the transmembrane region. The connector is probabilistically coupled to the conformation of the intracellular region. An active connector promotes desolvation of a buried cavity, a twist of the conserved NPxxY motif, and an interaction between two conserved tyrosines in transmembrane helices 5 and 7 (Y-Y motif), which leads to a larger population of active-like states at the G protein binding site. This coupling is augmented by protonation of the strongly conserved Asp792.50. The agonist binding site hence communicates with the intracellular region via a cascade of locally connected microswitches. Characterization of these can be used to understand how ligands stabilize distinct receptor states and contribute to development drugs with specific signaling properties. The developed simulation protocol is likely transferable to other class A GPCRs. Graphical TOC Entry O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=82 SRC="FIGDIR/small/627026v3_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@1dfcfb5org.highwire.dtl.DTLVardef@bdaf81org.highwire.dtl.DTLVardef@14d74e6org.highwire.dtl.DTLVardef@35cea9_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗