Differences between G-Protein-Stabilized Agonist-GPCR Complexes and their Nanobody-Stabilized Equivalents

Nanobody engineering: computational modelling and design for biomedical and therapeutic applications

Nanobodies, the smallest functional antibody fragment derived from camelid heavy-chain-only antibodies, have emerged as powerful tools for diverse biomedical applications. In this comprehensive review, we discuss the structural characteristics, functional properties, and computational approaches driving the design and optimisation of synthetic nanobodies. We explore their unique antigen-binding domains, highlighting the critical role of complementarity-determining regions in target recognition and specificity. This review further underscores the advantages of nanobodies over conventional antibodies from a biosynthesis perspective, including their small size, stability, and solubility, which make them ideal candidates for economical antigen capture in diagnostics, therapeutics, and biosensing. We discuss the recent advancements in computational methods for nanobody modelling, epitope prediction, and affinity maturation, shedding light on their intricate antigen-binding mechanisms and conformational dynamics. Finally, we examine a direct example of how computational design strategies were implemented for improving a nanobody-based immunosensor, known as a Quenchbody. Through combining experimental findings and computational insights, this review elucidates the transformative impact of nanobodies in biotechnology and biomedical research, offering a roadmap for future advancements and applications in healthcare and diagnostics.

In-silico study of the interactions between acylated glucagon like-peptide-1 analogues and the native receptor

We have performed a series of multiple molecular dynamics (MD) simulations of glucagon-like peptide-1 (GLP-1) and acylated GLP-1 analogues in complex with the endogenous receptor (GLP-1R) to obtain a molecular understanding of how fatty acid (FA) chain structure, acylation position on the peptide, and presence of a linker affect the binding. MD simulations were analysed to extract heatmaps of receptor-peptide interaction patterns and to determine the free energy of binding using the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) approach. The extracted free energies from MM-PBSA calculations are in qualitative agreement with experimentally determined potencies. Furthermore, the interaction patterns seen in the receptor-GLP-1 complex simulations resemble previously reported binding interactions validating the simulations. Analysing the receptor-GLP-1 analogue complex simulations, we found that the major differences between the systems stem from FA interactions and positioning of acylation in the peptide. Hydrophobic interactions between the FA chain and a hydrophobic patch on the extracellular domain contribute significantly to the binding affinity. Acylation on Lys26 resulted in noticeably more interactions between the FA chain and the extracellular domain hydrophobic patch than found for acylation on Lys34 and Lys38, respectively. The presence of a charged linker between the peptide and FA chain can potentially stabilise the complex by forming hydrogen bonds to arginine residues in the linker region between the extracellular domain and the transmembrane domain. A molecular understanding of the fatty acid structure and its effect on binding provides important insights into designing acylated agonists for GLP-1R.Communicated by Ramaswamy H. Sarma.

Metadynamics simulations of ligand binding to GPCRs

Recent developments in metadynamics simulation techniques for ligand binding to Class A GPCRs are described and the results obtained elucidated. The computational protocol makes good use of modern massively parallel hardware, making simulations of the binding/unbinding process routine. The simulations reveal unprecedented details of the ligand-binding pathways, including multiple binding sites in many cases. Free energies of binding are reproduced very well and the simulations allow prediction of the efficacy (agonist, antagonist etc.) of ligands.

Multiple Binding Sites Contribute to the Mechanism of Mixed Agonistic and Positive Allosteric Modulators of the Cannabinoid CB1 Receptor

The cannabinoid CB1 receptor (CB1R) is an abundant metabotropic G-protein-coupled receptor that has been difficult to address therapeutically because of CNS side effects exerted by orthosteric drug candidates. Recent efforts have focused on developing allosteric modulators that target CB1R. Compounds from the recently discovered class of mixed agonistic and positive allosteric modulators (Ago-PAMs) based on 2-phenylindoles have shown promising functional and binding properties as CB1R ligands. Here, we identify binding modes of both the CP 55,940 agonist and GAT228, a 2-phenylindole allosteric modulator, by using our metadynamics simulation protocol, and quantify their affinity and cooperativity by atomistic simulations. We demonstrate the involvement of multiple adjunct binding sites in the Ago-PAM characteristics of the 2-phenylindole modulators and explain their ability to compete with orthosteric agonists at higher concentrations. We validate these results experimentally by showing the contribution of multiple sites on the allosteric binding of ZCZ011, another homologous member of the class, together with the orthosteric agonist.