Strategies for the discovery of biased GPCR ligands

Bermudez, Marcel; Nguyen, Trung Ngoc; Omieczynski, Christian; Wolber, Gerhard

doi:10.1016/j.drudis.2019.02.010

Cited by 40 publications

(26 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This concept was validated by experiments and resulted in the rational design of a full agonist, which can only adopt the binding mode stabilizing active receptor states . The pathway‐specificity of the biased ligands was studied in a similar setting and resulted in a mechanistic model whose key concept resides in the conformational restriction of the extracellular loop region . In another study, the effect of fluorination of the photoswitchable azobenzene core was investigated in muscarinic agonists.…”

Section: Application Case Studiesmentioning

confidence: 99%

Next generation 3D pharmacophore modeling

Schaller

Šribar

Noonan

et al. 2020

WIREs Comput Mol Sci

Self Cite

150

119

View full text Add to dashboard Cite

3D pharmacophore models are three‐dimensional ensembles of chemically defined interactions of a ligand in its bioactive conformation. They represent an elegant way to decipher chemically encoded ligand information and have therefore become a valuable tool in drug design. In this review, we provide an overview on the basic concept of this method and summarize key studies for applying 3D pharmacophore models in virtual screening and mechanistic studies for protein functionality. Moreover, we discuss recent developments in the field. The combination of 3D pharmacophore models with molecular dynamics simulations could be a quantum leap forward since these approaches consider macromolecule–ligand interactions as dynamic and therefore show a physiologically relevant interaction pattern. Other trends include the efficient usage of 3D pharmacophore information in machine learning and artificial intelligence applications or freely accessible web servers for 3D pharmacophore modeling. The recent developments show that 3D pharmacophore modeling is a vibrant field with various applications in drug discovery and beyond. This article is categorized under: Computer and Information Science > Chemoinformatics Computer and Information Science > Computer Algorithms and Programming Molecular and Statistical Mechanics > Molecular Interactions

show abstract

Section: Application Case Studiesmentioning

confidence: 99%

Next generation 3D pharmacophore modeling

Schaller

Šribar

Noonan

et al. 2020

WIREs Comput Mol Sci

Self Cite

150

119

View full text Add to dashboard Cite

show abstract

“…Since the breakthrough of GPCR crystallization one decade ago, the understanding of the complex biology of GPCR activation and signaling has dramatically increased [ 16 , 17 , 18 , 19 ]. Substantial advances in structural biology of GPCRs were possible by means of innovative methodological and powerful computational systems [ 20 , 21 , 22 ]. Due to its therapeutic relevance, the MOR is among the few GPCRs determined in different activation states, with the first X-ray crystal structure of the murine MOR published in 2012 in complex with the irreversible morphinan antagonist β-funaltrexamine (PDB ID: 4DKL) [ 23 ], and the 3D-structure in the active conformation reported in 2015, where the receptor was co-crystallized with the morphinan agonist BU72 (PDB ID: 5C1M) [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Mechanistic Understanding of Peptide Analogues, DALDA, [Dmt1]DALDA, and KGOP01, Binding to the Mu Opioid Receptor

et al. 2020

Self Cite

View full text Add to dashboard Cite

The mu opioid receptor (MOR) is the primary target for analgesia of endogenous opioid peptides, alkaloids, synthetic small molecules with diverse scaffolds, and peptidomimetics. Peptide-based opioids are viewed as potential analgesics with reduced side effects and have received constant scientific interest over the years. This study focuses on three potent peptide and peptidomimetic MOR agonists, DALDA, [Dmt1]DALDA, and KGOP01, and the prototypical peptide MOR agonist DAMGO. We present the first molecular modeling study and structure–activity relationships aided by in vitro assays and molecular docking of the opioid peptide analogues, in order to gain insight into their mode of binding to the MOR. In vitro binding and functional assays revealed the same rank order with KGOP01 > [Dmt1]DALDA > DAMGO > DALDA for both binding and MOR activation. Using molecular docking at the MOR and three-dimensional interaction pattern analysis, we have rationalized the experimental outcomes and highlighted key amino acid residues responsible for agonist binding to the MOR. The Dmt (2′,6′-dimethyl-L-Tyr) moiety of [Dmt1]DALDA and KGOP01 was found to represent the driving force for their high potency and agonist activity at the MOR. These findings contribute to a deeper understanding of MOR function and flexible peptide ligand–MOR interactions, that are of significant relevance for the future design of opioid peptide-based analgesics.

show abstract

“…imprinted signaling profile, which typically represents the effect of physiological ligands 9,10 . Biased ligands (also referred to as functional selective ligands) can shift this signaling profile towards other pathways (Figure 1), providing a way to pharmacologically fine-tune GPCR signaling [5][6][7][8] . Figure 1: Simplified overview on GPCR signaling pathways and important effector proteins (left).…”

mentioning

confidence: 99%

“…The concept of biased signaling in GPCRs involves a ligand-dependent shift of the activated downstream pathways (right). By taking Ligand A as a reference ligand, ligand B could be described as biased towards pathway 2. In recent years, biased signaling has drawn more and more attention in the GPCR field, with many studies focusing on ligand design, assay development for bias determination and the resulting pharmacological outcome 5,11,12 . However, the structural prerequisites of biased ligands are poorly understood and only a few studies shed light on potential mechanisms for biased signaling [13][14][15][16] .…”

mentioning

confidence: 99%

BiasDB: A Comprehensive Database for Biased GPCR Ligands

Omieczynski

Nguyen

Šribar

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

G protein-coupled receptors transmit signals across membranes via interaction with intracellular binding partners. While there is an imprinted signaling profile for each receptor, biased ligands are able to shift intracellular pathways resulting in different recruitment profiles. We present the first comprehensive database of all literature-known biased ligands as a resource for medicinal chemistry and pharmacology. In addition to careful manual curation, we provide an analysis of the data. BiasDB is available at https://biasdb.drug-design.de/.

show abstract

Strategies for the discovery of biased GPCR ligands

Cited by 40 publications

References 63 publications

Next generation 3D pharmacophore modeling

Next generation 3D pharmacophore modeling

Mechanistic Understanding of Peptide Analogues, DALDA, [Dmt1]DALDA, and KGOP01, Binding to the Mu Opioid Receptor

BiasDB: A Comprehensive Database for Biased GPCR Ligands

Contact Info

Product

Resources

About