Mahdi Hijazi

Computational design of dynamic receptor—peptide signaling complexes applied to chemotaxis

Patrick Daniel Barth, Andreas Füglistaler, Mahdi Hijazi, Robert Everett Jefferson, Aurélien Laurent Jean-Charles Oggier, Ana Paola Rico Villarreal

Engineering protein biosensors that sensitively respond to specific biomolecules by triggering precise cellular responses is a major goal of diagnostics and synthetic cell biology. Previous biosensor designs have largely relied on binding structurally well-defined molecules. In contrast, approaches that couple the sensing of flexible compounds to intended cellular responses would greatly expand potential biosensor applications. Here, to address these challenges, we develop a computational strategy for designing signaling complexes between conformationally dynamic proteins and peptides. To demonstrate the power of the approach, we create ultrasensitive chemotactic receptor—peptide pairs capable of eliciting potent signaling responses and strong chemotaxis in primary human T cells. Unlike traditional approaches that engineer static binding complexes, our dynamic structure design strategy optimizes contacts with multiple binding and allosteric sites accessible through dynamic conformational ensembles to achieve strongly enhanced signaling efficacy and potency. Our study suggests that a conformationally adaptable binding interface coupled to a robust allosteric transmission region is a key evolutionary determinant of peptidergic GPCR signaling systems. The approach lays a foundation for designing peptide-sensing receptors and signaling peptide ligands for basic and therapeutic applications.

2023

Uncovering and engineering the mechanical properties of the adhesion GPCR ADGRG1 GAIN domain

Patrick Daniel Barth, Louis Georges Baptiste Dumas, Mahdi Hijazi, Matthieu François Marfoglia, Florence Pojer

Key cellular functions depend on the transduction of extracellular mechanical signals by specialized membrane receptors including adhesion G-protein coupled receptors (aGPCRs). While recently solved structures support aGPCR activation through shedding of the extracellular GAIN domain, the molecular mechanisms underpinning receptor mechanosensing remain poorly understood. When probed using single-molecule atomic force spectroscopy and molecular simulations, ADGRG1 GAIN dissociated from its tethered agonist at forces significantly higher than other reported signaling mechanoreceptors. Strong mechanical resistance was achieved through specific structural deformations and force propagation pathways under mechanical load. ADGRG1 GAIN variants computationally designed to lock the alpha and beta subdomains and rewire mechanically-induced structural deformations were found to modulate the GPS-Stachel rupture forces. Our study provides unprecedented insights into the molecular underpinnings of GAIN mechanical stability and paves the way for engineering mechanosensors, better understanding aGPCR function, and informing drug-discovery efforts targeting this important receptor class.

2023

Computational design of ultrasensitive flexible peptide:receptor signaling complexes for enhanced chemotaxis

Patrick Daniel Barth, Andreas Füglistaler, Mahdi Hijazi, Robert Everett Jefferson, Aurélien Laurent Jean-Charles Oggier, Ana Paola Rico Villarreal

Engineering biosensors that sensitively recognize specific biomolecules and trigger functional cellular responses is a holy grail of diagnostics and synthetic cell biology. Biosensor design approaches have mostly focused on binding structurally well-defined molecules. Coupling the sensing of flexible compounds to complex cellular responses would considerably expand biosensor applications, but remains challenging. We developed a computational strategy for designing highly sensitive receptor biosensors of flexible peptides. Using the method, we created ultrasensitive chemotactic GPCR:peptide pairs capable of eliciting potent chemotaxis in human primary T cells. Through mutual induced fit, our flexible structure design approach enhanced peptide contacts with binding and allosteric sites in the ligand pocket to achieve unprecedented signaling efficacy and potency. The approach paves the road for designing peptide-sensing receptors and expanding the biosensor toolbox for basic and therapeutic applications.

bioRxiv2022

Computational rewiring of allosteric pathways reprograms GPCR selective responses to ligands

Patrick Daniel Barth, Mahdi Hijazi, Dániel Kéri, Aurélien Laurent Jean-Charles Oggier

G-protein-coupled receptors (GPCRs) are the largest class of cell surface receptors and drug targets, and respond to a wide variety of chemical stimuli to activate diverse cellular functions. Understanding and predicting how ligand binding triggers a specific signaling response is critical for drug discovery and design but remains a major challenge. Here, computational design of GPCR allosteric functions is used to uncover the mechanistic relationships between agonist ligand chemistry, receptor sequence, structure, dynamics and allosteric signaling in the dopamine D2 receptor. Designed gain of function D2 variants for dopamine displayed very divergent G-protein signaling responses to other ligand agonists that strongly correlated with ligand structural similarity. Consistent with these observations, computational analysis revealed distinct topologies of allosteric signal transduction pathways for each ligand-bound D2 pair that were perturbed differently by the designs. We leveraged these findings by rewiring ligand-specific pathways and designed receptors with highly selective ligand responses. Overall, our study suggests that distinct ligand agonists can activate a given signaling effector through specific “allosteric activator” moieties that engage partially independent signal transmission networks in GPCRs. The results provide a mechanistic framework for understanding and predicting the impact of sequence polymorphism on receptor pharmacology, informing selective drug design and rationally designing receptors with highly selective ligand responses for basic and therapeutic applications.

bioRxiv2022

Deep learning approaches for conformational flexibility and switching properties in protein design

Patrick Daniel Barth, Mahdi Hijazi

Following the hugely successful application of deep learning methods to protein structure prediction, an increasing number of design methods seek to leverage generative models to design proteins with improved functionality over native proteins or novel structure and function. The inherent flexibility of proteins, from side-chain motion to larger conformational reshuffling, poses a challenge to design methods, where the ideal approach must consider both the spatial and temporal evolution of proteins in the context of their functional capacity. In this review, we highlight existing methods for protein design before discussing how methods at the forefront of deep learning-based design accommodate flexibility and where the field could evolve in the future.

2022

Structure of a D2 dopamine receptor-G-protein complex in a lipid membrane

Patrick Daniel Barth, Mahdi Hijazi

The D2 dopamine receptor (DRD2) is a therapeutic target for Parkinson's disease(1)and antipsychotic drugs(2). DRD2 is activated by the endogenous neurotransmitter dopamine and synthetic agonist drugs such as bromocriptine(3), leading to stimulation of G(i)and inhibition of adenylyl cyclase. Here we used cryo-electron microscopy to elucidate the structure of an agonist-bound activated DRD2-G(i)complex reconstituted into a phospholipid membrane. The extracellular ligand-binding site of DRD2 is remodelled in response to agonist binding, with conformational changes in extracellular loop 2, transmembrane domain 5 (TM5), TM6 and TM7, propagating to opening of the intracellular G(i)-binding site. The DRD2-G(i)structure represents, to our knowledge, the first experimental model of a G-protein-coupled receptor-G-protein complex embedded in a phospholipid bilayer, which serves as a benchmark to validate the interactions seen in previous detergent-bound structures. The structure also reveals interactions that are unique to the membrane-embedded complex, including helix 8 burial in the inner leaflet, ordered lysine and arginine side chains in the membrane interfacial regions, and lipid anchoring of the G protein in the membrane. Our model of the activated DRD2 will help to inform the design of subtype-selective DRD2 ligands for multiple human central nervous system disorders. The structure of the D2 dopamine receptor in complex with its G protein reveals how dopamine receptors are activated and, importantly, how a G-protein-coupled receptor can interact with its G protein in a phospholipid membrane.

NATURE PUBLISHING GROUP2020

Mahdi Hijazi

Computational design of dynamic receptor—peptide signaling complexes applied to chemotaxis

Uncovering and engineering the mechanical properties of the adhesion GPCR ADGRG1 GAIN domain

Computational design of ultrasensitive flexible peptide:receptor signaling complexes for enhanced chemotaxis

Computational rewiring of allosteric pathways reprograms GPCR selective responses to ligands

Deep learning approaches for conformational flexibility and switching properties in protein design

Structure of a D2 dopamine receptor-G-protein complex in a lipid membrane

Fast-forward Langevin dynamics with momentum flips

Computational design of dynamic receptor—peptide signaling complexes applied to chemotaxis

Structure of a D2 dopamine receptor-G-protein complex in a lipid membrane

Uncovering and engineering the mechanical properties of the adhesion GPCR ADGRG1 GAIN domain

Computational rewiring of allosteric pathways reprograms GPCR selective responses to ligands

Deep learning approaches for conformational flexibility and switching properties in protein design

Fast-forward Langevin dynamics with momentum flips

Computational design of ultrasensitive flexible peptide:receptor signaling complexes for enhanced chemotaxis