Publications by Patrick Daniel Barth

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

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Communication across membranes controls critical cellular processes and is achieved by receptors translating extracellular signals into selective cytoplasmic responses. While receptor tertiary structures can be readily characterized, receptor associations into quaternary structures are challenging to study and their implications in signal transduction remain poorly understood. Here, we report a computational approach for predicting receptor self-associations, and designing receptor oligomers with various quaternary structures and signaling properties. Using this approach, we designed chemokine receptor CXCR4 dimers with reprogrammed binding interactions, conformations, and abilities to activate distinct intracellular signaling proteins. In agreement with our predictions, the designed CXCR4s dimerized through distinct conformations and displayed different quaternary structural changes upon activation. Consistent with the active state models, all engineered CXCR4 oligomers activated the G protein Gi, but only specific dimer structures also recruited β-arrestins. Overall, we demonstrate that quaternary structures represent an important unforeseen mechanism of receptor biased signaling and reveal the existence of a bias switch at the dimer interface of several G protein-coupled receptors including CXCR4, mu-Opioid and type-2 Vasopressin receptors that selectively control the activation of G proteins vs β-arrestin-mediated pathways. The approach should prove useful for predicting and designing receptor associations to uncover and reprogram selective cellular signaling functions.

2022

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

Computational design of highly signaling active membrane receptors through de novo solvent-mediated allosteric networks

Patrick Daniel Barth, Jian Wang

Protein catalysis and allostery require the atomic-level orchestration and motion of residues, ligand, solvent and protein effector molecules, but the ability to design protein activity through precise protein-solvent cooperative interactions has not been demonstrated. Here, we report the design of a dozen novel membrane receptors catalyzing G-protein nucleotide exchange through diverse de novo engineered allosteric pathways mediated by cooperative networks of intra-protein, protein-ligand and solvent molecule interactions. Consistent with the predictions, designed protein activities strongly correlated with the level of solvent-mediated interaction network plasticity at flexible transmembrane helical interfaces. Several designs displayed considerably enhanced thermostability and activity compared to related natural receptors. The most stable and active variant crystallized in an unforeseen signaling active conformation, in excellent agreement with the design models. The allosteric network topologies of the best designs bear limited similarity to those of natural receptors and reveal a space of allosteric interactions larger than previously inferred from natural proteins. The approach should prove useful for engineering proteins with novel complex protein catalytic and signaling activities. Competing Interest Statement The authors have declared no competing interest.

2021

Computationally designed GPCR quaternary structures bias signaling pathway activation

Patrick Daniel Barth, Robert Everett Jefferson

Communication across membranes controls critical cellular processes and is achieved by receptors translating extracellular signals into selective cytoplasmic responses. While receptor tertiary structures can now be readily characterized, receptor associations into quaternary structures are very challenging to study and their implications in signal transduction remain poorly understood. Here, we report a computational approach for predicting membrane receptor self-associations, and designing receptor oligomers with various quaternary structures and signaling properties. Using this approach, we designed chemokine receptor CXCR4 dimers with reprogrammed stabilities, conformations, and abilities to activate distinct intracellular signaling proteins. In agreement with our predictions, the designed CXCR4s dimerized through distinct conformations and displayed different quaternary structural changes upon activation. Consistent with the active state models, all engineered CXCR4 oligomers activated the G protein Gi, but only a few specific dimer structures also recruited β-arrestins. Overall, we demonstrate that quaternary structures represent an important unforeseen mechanism of receptor biased signaling and reveal the existence of a conformational switch at the dimer interface of several G protein-coupled receptors including CXCR4, mu-Opioid and type-2 Vasopressin receptors that selectively control the activation of G proteins vs β-arrestin-mediated pathways. The approach should prove useful for predicting and designing receptor associations to uncover and reprogram selective cellular signaling functions.

2021

Computationally designed GPCR quaternary structures bias signaling pathway activation

Patrick Daniel Barth, Robert Everett Jefferson

WILEY2021

Computationally-guided tuning of ligand sensitivity in a GPCR-based sensor

Patrick Daniel Barth, Dániel Kéri

Genetically-encoded fluorescent sensors for neuromodulators are increasingly used molecular tools in neuroscience. However, these protein-based biosensors are often limited by the sensitivity of the protein scaffold towards endogenous ligands. Here, we explored the possibility of applying computational design approaches for enhancing sensor sensitivity. Using the dopamine sensor dLight1 as proof of concept, we designed two variants that boost the sensor’s potency (EC50) for dopamine and norepinephrine by up to 5- and 15-fold, respectively. Interestingly, the largest effects were obtained through improved designed allosteric transmission in the transmembrane region of the sensor. Our approach should prove generally useful for enhancing sensing capabilities of a large variety of neuromodulator sensors. Competing Interest Statement T.P. is listed as inventor on a patent application on the sensor technology described in this research. All other authors declare no competing interest.

2021