G-protein-coupled receptors (GPCRs) play important roles in physiological processes and are modulated by drugs that either activate or block signaling. Rational design of the pharmacological efficacy profiles of GPCR ligands could enable the development of more efficient drugs, but is challenging even if high-resolution receptor structures are available. We performed molecular dynamics simulations of the beta(2) adrenergic receptor in active and inactive conformations to assess if binding free energy calculations can predict differences in ligand efficacy for closely related compounds. Previously identified ligands were successfully classified into groups with comparable efficacy profiles based on the calculated shift in ligand affinity upon activation. A series of ligands were then predicted and synthesized, leading to the discovery of partial agonists with nanomolar potencies and novel scaffolds. Our results demonstrate that free energy simulations enable design of ligand efficacy and the same approach can be applied to other GPCR drug targets.

G-protein-coupled receptors (GPCRs) play important roles in physiological processes and are modulated by drugs that either activate or block signaling. Rational design of the pharmacological efficacy profiles of GPCR ligands could enable the development of more efficient drugs, but is challenging even if high-resolution receptor structures are available. We performed molecular dynamics simulations of the β2 adrenergic receptor in active and inactive conformations to assess if binding free energy calculations can predict differences in ligand efficacy for closely related compounds. Previously identified ligands were successfully classified into groups with comparable efficacy profiles based on the calculated shift in ligand affinity upon activation. A series of ligands were then predicted and synthesized, leading to the discovery of partial agonists with nanomolar potencies and novel scaffolds. Our results demonstrate that free energy simulations enable design of ligand efficacy and the same approach can be applied to other GPCR drug targets.

Nanobody binding stabilizes G-protein-coupled receptors (GPCR) in a fully active state and modulates their affinity for bound ligands. However, the atomic-level basis for this allosteric regulation remains elusive. Here, we investigate the conformational changes induced by the binding of a nanobody (Nb80) on the active-like beta 2 adrenergic receptor (beta 2AR) via enhanced sampling molecular dynamics simulations. Dimensionality reduction analysis shows that Nb80 stabilizes structural features of the beta 2AR with an similar to 14 angstrom outward movement of transmembrane helix 6 and a close proximity of transmembrane (TM) helices 5 and 7, and favors the fully active-like conformation of the receptor, independent of ligand binding, in contrast to the conditions under which no intracellular binding partner is bound, in which case the receptor is only stabilized in an intermediateactive state. This activation is supported by the residues located at hotspots located on TMs 5, 6, and 7, as shown by supervised machine learning methods. Besides, ligand-specific subtle differences in the conformations assumed by intracellular loop 2 and extracellular loop 2 are captured from the trajectories of various ligand-bound receptors in the presence of Nb80. Dynamic network analysis further reveals that Nb80 binding triggers tighter and stronger local communication networks between the Nb80 and the ligand-binding sites, primarily involving residues around ICL2 and the intracellular end of TM3, TM5, TM6, as well as ECL2, ECL3, and the extracellular ends of TM6 and TM7. In particular, we identify unique allosteric signal transmission mechanisms between the Nb80-binding site and the extracellular domains in conformations modulated by a full agonist, BI167107, and a G-protein-biased partial agonist, salmeterol, involving mainly TM1 and TM2, and TM5, respectively. Altogether, our results provide insights into the effect of intracellular binding partners on the GPCR activation mechanism, which should be taken into account in structure-based drug discovery.

Ligand binding stabilizes different G protein-coupled receptor states via a complex allosteric process that is not completely understood. Here, we have derived free energy landscapes describing activation of the beta(2) adrenergic receptor bound to ligands with different efficacy profiles using enhanced sampling molecular dynamics simulations. These reveal shifts toward active-like states at the Gprotein-binding site for receptors bound to partial and full agonists, and that the ligands modulate the conformational ensemble of the receptor by tuning protein microswitches. We indeed find an excellent correlation between the conformation of the microswitches close to the ligand binding site and in the transmembrane region and experimentally reported cyclic adenosine monophosphate signaling responses. Dimensionality reduction further reveals the similarity between the unique conformational states induced by different ligands, and examining the output of classifiers highlights two distant hotspots governing agonism on transmembrane helices 5 and 7.

Proteins are large biomolecules that carry out specific functions within living organisms. Understanding how proteins function is a massive scientific challenge with a wide area of applications. In particular, by controlling protein function we may develop therapies for many diseases. To understand a protein’s function, we need to consider its full conformational ensemble, and not only a single snapshot of a structure. Allosteric signaling is a factor often driving protein conformation change, where the binding of a molecule to one site triggers a response in another part of the protein. G protein-coupled receptors (GPCRs) are transmembrane proteins that bind molecules outside the membrane, which enables coupling to a G protein in their intracellular domain. Understanding the complex allosteric process governing this mechanism could have a significant impact on the development of novel drugs.

Molecular dynamics (MD) is a computational method that can capture protein conformational change at an atomistic level. However, MD is a computationally expensive approach to simulating proteins, and is thus infeasible for many applications. Enhanced sampling techniques have emerged to reduce the computational cost of standard MD. Another challenge with MD is to extract useful information and distinguish signal from noise in an MD trajectory. Data-driven methods can streamline analysis of protein simulations and improve our understanding of biomolecular systems.

Paper 1 and 2 contain methodological developments to analyze the results of MD in a data-driven manner. We provide methods that create interpretable maps of important molecular features from protein simulations (Paper 1) and identify allosteric communication pathways in biological systems (Paper 2). As a result, more insights can be extracted from MD trajectories. Our approach is generalizable and can become useful to analyze complex simulations of various biomolecular systems. 

In Paper 3 and 4, we combine the aforementioned methodological advancements with enhanced sampling techniques to study a prototypical GPCR, the β2 adrenergic receptor. First, we make improvements to the string method with swarms of trajectories and derive the conformational change and free energy along the receptor’s activation pathway. Next, we identify key molecular microswitches directly or allosterically controlled by orthosteric ligands and show how these couple to a shift in probability of the receptor’s active state. In Paper 4, we also find that ligands induce ligand-specific states, and the molecular basis governing these states. 

These new approaches generate insights compatible with previous simulation and experimental studies at a relatively low computational cost. Our work also provides new insights into the molecular basis of allosteric communication in membrane proteins, and might become a useful tool in the design of novel GPCR drugs.

Proteiner är stora biomolekyler som utför specifika funktioner inom levande organismer. Att förstå hur proteiner fungerar är en enorm vetenskaplig utmaning med många användningsområden. I synnerhet genom att kontrollera proteiners funktion kan vi utveckla botemedel för många sjukdomar. För att förstå ett proteins funktion måste vi beakta dess fullständiga tillståndsensemble och inte bara en enda ögonblicksbild av en struktur. Alloster reglering är en faktor som kan få proteiner att ändra tillstånd. Det innebär att en molekyl binder till ett säte och därmed orsakar förändring i en annan del av proteinet. G-proteinkopplade receptorer (GPCRs) är transmembranproteiner som binder molekyler utanför membranet, vilket möjliggör koppling till ett G-protein i deras intracellulära domän. Att förstå den komplexa allostera regleringen som styr denna mekanism kan ha en betydande inverkan på utvecklingen av nya läkemedel.

Molekylsimuleringar (Molecular dynamics; MD) är en beräkningsmetod som kan användas för att studera förändringar i proteiners struktur och tillstånd på atomnivå. MD är ett kostsamt simuleringsverktyg och är därför opraktiskt i många sammanhang. Så kallade ‘enhanced sampling’-metoder har utvecklats för att minska beräkningskostnaden av standard-MD. Ytterligare en utmaning är att utvinna användbar information och skilja signal från brus i en MD-simulering. Datadrivna metoder kan effektivisera analysen av proteinsimuleringar och förbättra vår förståelse av biomolekylära system i allmänhet.

Artikel 1 och 2 beskriver utveckling av datadrivna metoder för att analysera MD-simuleringar. Metoderna kartlägger viktiga molekylära egenskaper i proteinsimuleringar (Artikel 1) och identifierar allostera kommunikationsvägar i biologiska system (Artikel 2), samt presenterar resultaten på ett lättillgängligt sätt. Därav kan MD leda till fler och bättre insikter. Vårt tillvägagångssätt är generaliserbart och kan användas för att analysera komplexa simuleringar av många biomolekylära system.

I artikel 3 och 4 kombinerar vi de tidigare nämnda metodologiska framstegen med enhanced sampling-metoder för att studera en prototypisk GPCR, β2-adrenoceptorn. I steg ett förbättrar vi den så kallade strängmetoden, vilket är en typ av enhanced sampling-teknik, och använder den för att härleda de funktionella tillstånden och den fria energi längsmed receptorns aktiveringsväg. Därefter identifierar vi viktiga molekylära interaktioner som kontrolleras av ligander och visar hur dessa kopplas till en förändring i sannolikhet för receptorns aktiva tillstånd. I artikel 4 visar vi även att enskilda ligander inducerar specifika tillstånd samt de molekylära egenskaperna hos dessa tillstånd.

Metoderna genererar resultat som överensstämmer med tidigare simulerings- och experimentella studier till en relativt låg beräkningskostnad. Vårt arbete leder även till nya insikter om den molekylära grunden för alloster kommunikation i membranproteiner, och kan bli ett användbart verktyg i utvecklingen av framtidens GPCR-läkemedel.

Agonist binding to G protein-coupled receptors (GPCRs) leads to conformational changes in the transmembrane region that activate cytosolic signaling pathways. Although high-resolution structures of different receptor states are available, atomistic details of allosteric signaling across the membrane remain elusive. We calculated free energy landscapes of beta(2) adrenergic receptor activation using atomistic molecular dynamics simulations in an optimized string of swarms framework, which shed new light on how microswitches govern the equilibrium between conformational states. Contraction of the extracellular binding site in the presence of the agonist BI-167107 is obligatorily coupled to conformational changes in a connector motif located in the core of the transmembrane region. The connector is probabilistically coupled to the conformation of the intracellular region. An active connector promotes desolvation of a buried cavity, a twist of the conserved NPxxY motif, and an interaction between two conserved tyrosines in transmembrane helices 5 and 7 (Y-Y motif), which lead to a larger population of active-like states at the G protein binding site. This coupling is augmented by protonation of the strongly conserved Asp79(2.50). The agonist binding site hence communicates with the intracellular region via a cascade of locally connected microswitches. Characterization of these can be used to understand how ligands stabilize distinct receptor states and contribute to development drugs with specific signaling properties. The developed simulation protocol can likely be transferred to other class A GPCRs.