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#GPCR

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Martin Vögele

First preprint about my work at Schrödinger:

We show how to predict ligand efficacy via absolute binding free energy perturbation on different receptor conformations — confirming and utilizing the principle that the functional response of a receptor is mainly determined by the thermodynamics of ligand binding.

chemrxiv.org/engage/chemrxiv/a

ChemRxivIs the functional response of a receptor determined by the thermodynamics of ligand binding?Although strong binding to the target protein is a prerequisite, it is not enough to be an effective drug. To produce a particular functional response, drugs need to regulate the targets’ signal transduction pathways, either blocking the proteins’ functions or modulating their activities by changing the conformational equilibrium. The routinely calculated binding free energy of a compound to its target is a good predictor of affinity but may not always predict efficacy. While the time scales for the protein conformational changes are prohibitively long to be routinely modeled via physics-based simulations, thermodynamic principles suggest that binding free energies of the ligands with different receptor conformations may infer their efficacy if the functional response of the receptor is determined by thermodynamics. However, while this hypothesis was proposed in the past, it has not been thoroughly validated and is seldom used in practice for ligand efficacy prediction. We present an actionable protocol and a comprehensive validation study to show that binding thermodynamics provides indeed a strong predictor for the efficacy of a ligand. We apply the absolute-binding free energy perturbation (ABFEP) method to ligands bound to active and inactive states of eight G protein–coupled receptors (GPCRs) and a nuclear receptor. By comparing the resulting binding free energies, we can determine with a very high accuracy whether a ligand acts as an agonist or an antagonist. We find that carefully designed restraints are often necessary to efficiently model the corresponding conformational ensembles for each state and provide a procedure for setting up these restraints. Our method achieves excellent performance in classifying ligands as agonists or antagonists across the various investigated receptors, all of which are important drug targets.
#FEP#FreeEnergy#LigandEfficacy
A répondu dans un fil de discussion
Public
Gaspar Jekely

Previously, only one #GPCR with a ligand was known in #cnidaria, the receptor for the #Clytia Maturation-Inducing-Hormone. This receptor was found by Gonzalo Quiroga Artigas and Philipp Bauknecht in our lab, in a collaboration with Evelyn Houliston.
journals.plos.org/plosbiology/
#cnidaria #neuropeptide #evolution #neuroscience

journals.plos.orgA G protein–coupled receptor mediates neuropeptide-induced oocyte maturation in the jellyfish ClytiaA study of jellyfish oocytes identifies the receptor for Maturation-Inducing Hormone, the neuropeptide hormone that triggers oocyte maturation and spawning via GαS and cyclic AMP. This receptor defines a superfamily of hormone-receptor systems involved in regulating sexual reproduction across animal species.
Public
Gaspar Jekely

In our new preprint we characterise neuropeptide signalling in the cnidarian #Nematostella.
biorxiv.org/content/10.1101/20
Daniel Thiel and Luis Yanez-Guerra screened 64 neuropeptides against 161 G-protein coupled receptors (#GPCR) and found activating peptide ligands for 31 receptors.
#cnidaria #neuropeptide #evolution #neuroscience

bioRxivLarge-scale deorphanization of Nematostella vectensis neuropeptide GPCRs supports the independent expansion of bilaterian and cnidarian peptidergic systemsNeuropeptides are ancient signaling molecules in animals but only few peptide receptors are known outside bilaterians. Cnidarians possess a large number of G protein-coupled receptors (GPCRs), the most common receptors of bilaterian neuropeptides, but most of these remain orphan with no known ligands. We searched for neuropeptides in the sea anemone Nematostella vectensis and created a library of 64 peptides derived from 33 precursors. In a large-scale pharmacological screen with these peptides and 161 N. vectensis GPCRs, we identified 31 receptors specifically activated by one of 14 peptides. Mapping GPCR and neuropeptide expression to single-cell sequencing data revealed how cnidarian tissues are extensively wired by multilayer peptidergic networks. Phylogenetic analysis identified no direct orthology to bilaterian peptidergic systems and supports the independent expansion of neuropeptide signaling in cnidarians from a few ancestral peptide-receptor pairs. ### Competing Interest Statement The authors have declared no competing interest.
Public
Martin Vögele

New #preprint about PENSA, our flexible #OpenSource software package for comprehensive and thorough investigation of biomolecular conformational ensembles:

arxiv.org/abs/2212.02714

In three real-world examples, we show how it can be used to understand an #enzyme mechanism, #DNA #forcefield parameters, and #signaling in an #opioid #receptor.

Please share widely with the #MolecularDynamics #simulation #CompChem #MolBio #StructuralBiology #GPCR communities!
Feedback and ideas are always welcome.

arXiv.orgSystematic Analysis of Biomolecular Conformational Ensembles with PENSAMolecular simulations enable the study of biomolecules and their dynamics on an atomistic scale. A common task is to compare several simulation conditions - like mutations or different ligands - to find significant differences and interrelations between them. However, the large amount of data produced for ever larger and more complex systems often renders it difficult to identify the structural features that are relevant for a particular phenomenon. We present a flexible software package named PENSA that enables a comprehensive and thorough investigation into biomolecular conformational ensembles. It provides a wide variety of featurizations and feature transformations that allow for a complete representation of biomolecules like proteins and nucleic acids, including water and ion cavities within the biomolecular structure, thus avoiding bias that would come with manual selection of features. PENSA implements various methods to systematically compare the distributions of these features across ensembles to find the significant differences between them and identify regions of interest. It also includes a novel approach to quantify the state-specific information between two regions of a biomolecule which allows, e.g., the tracing of information flow to identify signaling pathways. PENSA also comes with convenient tools for loading data and visualizing results in ways that make them quick to process and easy to interpret. PENSA is an open-source Python library maintained at https://github.com/drorlab/pensa along with an example workflow and a tutorial. Here we demonstrate its usefulness in real-world examples by showing how it helps to determine molecular mechanisms efficiently.
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