Describing conformational changes in biomolecules using molecular dynamics simulations requires defining an appropriate low-dimensional mathematical description of the system, referred to as a set of collective variables (CVs). No single CV design strategy is universally optimal; the choice should be guided by the biological question, the property of interest, the evaluation criterion, and the chosen sampling method. Here, we discuss the physical principles that should inform CV design and categorize existing approaches. We also evaluate the relationship between different types of CVs, the amount of data required to train them, and suitable enhanced sampling approaches. Finally, we outline practical guidelines for selecting CVs, helping practitioners match methodological choices to the underlying dynamical process and to the goals of their simulations.

Three proton-sensing G protein-coupled receptors (GPCRs)-GPR4, GPR65, and GPR68-respond to extra- cellular pH to regulate diverse physiology. How protons activate these receptors is poorly understood. We determined cryogenic-electron microscopy (cryo-EM) structures of each receptor to understand the spatial arrangement of proton-sensing residues. Using deep mutational scanning (DMS), we determined the functional importance of every residue in GPR68 activation by generating 9,500 mutants and measuring their effects on signaling and surface expression. Constant-pH molecular dynamics simulations provided insights into the conformational landscape and protonation patterns of key residues. This unbiased approach revealed that, unlike other proton-sensitive channels and receptors, no single site is critical for proton recognition. Instead, a network of titratable residues extends from the extracellular surface to the transmembrane region, converging on canonical motifs to activate proton-sensing GPCRs. Our approach integrating structure, simulations, and unbiased functional interrogation provides a framework for understanding GPCR signaling complexity.

From E.coli to elephants, the cells of all living organisms are surrounded by a near impenetrable wall of lipids. The windows through the walls are membrane proteins - receptors, transporters and channels that confer communication, information and metabolites through the membrane. Without opening holes in the membrane, it is necessary for these proteins to alter their shapes by cycling between conformational states to transport signals or molecules. Owing to their important role as information bottle-necks, changes in their function can lead to cancer, infectious diseases, or metabolic disorders. Hence, they are important targets for drug discovery, therapeutic research and understanding the human body.

Due to the delicate thermodynamic balance of conformational states of these proteins that are modulated by external stimuli, it is difficult to trap them in experimental setups in which their native states are captured. To add to the problematic nature of their molecular mechanisms, they are too fast to kinetically trap in a certain state long enough to observe without breaking the molecular mechanism. Fast moving mechanisms makes them a good target for molecular dynamics (MD) simulations, where the movement of all atoms in the proteins is simulated over time. Although a powerful tool, modern MD simulations are not able to access long enough timescales to accurately measure macroscopic functionally relevant information, leaving a gap between simulations and reality in which many conclusions made with atomic resolution fail to translate into macroscopic phenomena, such as receptor activity, transport efficiency, mutational stability or allosteric signalling.

This work presents novel methodology that efficiently discovers and explores functionally relevant conformational states using MD simulations. By combining evolutionary information with physics using machine learning, the methodology accelerates the sampling while retaining the details of the molecular mechanism and the thermodynamic information. Additionally, the work shows how the methodology is capable of bridging the gap in resolution between experiments and simulations through the in-silico measurement of macroscopic phenomena on a microscopic scale. Moreover, it uniquely presents a framework applied to 4 studies on different target proteins of different families in which conformational change occurs, and is able to independently relate them to different types of measurements.

Från E.coli till elefanter är levande organismers celler omslutna av en näst-intill oigenomtränglig vägg av lipider. Fönstrena genom dessa väggar är membranproteiner - receptorer, transportörer och kanaler. Därigenom färdas signaler, information och metaboliter. Av proteinerna krävs att de måste kunna utföra dessa funktioner utan att lämna stora hål i cellerna. Evolutionen har således producerat protein som kan ändra form, eller strukturellt tillstånd som svar på externa signaler. Genom deras oklanderligt viktiga position som informationsbärande flaskhalsar är det också katastrofalt när det blir fel i deras mekanismer, vilket kan ge upphov till allt ifrån cancer till sjukdomar rörande ämnesomsättning. Därför är de också av oerhört intresse för läkemedelsutveckling, utveckling av terapeutiska strategier, eller helt enkelt för att förstå dessa hörnstenar i vår komplexa anatomi.

På grund av deras väl avvägda termodynamiska balans mellan strukturella tillstånd som dessutom är reglerade av externa signaler är det svårt att experimentellt fånga dessa flyktiga tillstånd och fortfarande bevara deras naturliga struktur. Som om det inte vore nog är deras molekylära mekanismer ofta alldeles för kortvariga för att kunna prepareras och sedan observeras. Som konsekvens har istället molekylärdynamiksimulering (MD), ett verktyg som simulerar hur varje enskild atom rör sig över tid, använts för att studera dynamiken i övergångarna mellan olika tillstånd. Trots enorma framsteg i högprestandaberäkningsvetenskap är det fortfarande svårt att nå de tidsskalorna i vilka de molekylära mekanismerna blir synliga, vilket lämnar ett stort gap mellan simuleringar och verkligheten. I det gapet faller ofta viktiga aspekter såsom receptoraktivitet, transporteffektivitet, mutationsstabilitet eller allosterisk signallering, som alla är viktiga att förstå för att kunna modulera dessa mekanismer.

Detta arbete presenterar nydanande teknik som på ett effektivt sätt upptäcker och utforskar det strukturella landskapet i vilket olika proteintillstånd ligger med hjälp av MD simuleringar. Genom att kombinera evolutionär information med fysik med hjälp av maskininlärning byggs en metod som accelerar tidsskalan för utforskandet men samtidigt bevarar de viktiga molekylära detaljerna. Dessutom visar arbetet hur metoden kan användas för att överbrygga det sistnämnda gapet mellan simuleringar och verkligheten genom att i datorn mäta storheter som förekommer på makroskopisk skala i laboratorieexperiment. Slutligen visar arbetet också 4 exempel på hur metoden gör detta på system som är av intresse för läkemedelsforskning, och tar reda på nya insikter kring dessa molekylära maskiner.

Mutations in transporters can impact an individual's response to drugs and cause many diseases. Few variants in transporters have been evaluated for their functional impact. Here, we combine saturation mutagenesis and multi-phenotypic screening to dissect the impact of 11,213 missense single-amino-acid deletions, and synonymous variants across the 554 residues of OCT1, a key liver xenobiotic transporter. By quantifying in parallel expression and substrate uptake, we find that most variants exert their primary effect on protein abundance, a phenotype not commonly measured alongside function. Using our mutagenesis results combined with structure prediction and molecular dynamic simulations, we develop accurate structure-function models of the entire transport cycle, providing biophysical characterization of all known and possible human OCT1 polymorphisms. This work provides a complete functional map of OCT1 variants along with a framework for integrating functional genomics, biophysical modeling, and human genetics to predict variant effects on disease and drug efficacy.

With the advent of AI-powered structure prediction, the scientific community is inching closer to solving protein folding. An unresolved enigma, however, is to accurately, reliably, and deterministically predict alternative conformational states that are crucial for the function of, e.g., transporters, receptors, or ion channels where conformational cycling is innately coupled to protein function. Accurately discovering and exploring all conformational states of membrane proteins has been challenging due to the need to retain atomistic detail while enhancing the sampling along interesting degrees of freedom. The challenges include but are not limited to finding which degrees of freedom are relevant, how to accelerate the sampling along them, and then quantifying the populations of each micro- and macrostate. In this work, we present a methodology that finds relevant degrees of freedom by combining evolution and physics through machine learning and apply it to the conformational sampling of the beta 2 adrenergic receptor. In addition to predicting new conformations that are beyond the training set, we have computed free energy surfaces associated with the protein's conformational landscape. We then show that the methodology is able to quantitatively predict the effect of an array of ligands on the beta 2 adrenergic receptor activation through the discovery of new metastable states not present in the training set. Lastly, we also stake out the structural determinants of activation and inactivation pathway signaling through different ligands and compare them to functional experiments to validate our methodology and potentially gain further insights into the activation mechanism of the beta 2 adrenergic receptor.

In mammals, glucose transporters (GLUT) control organism-wide blood-glucose homeostasis. In human, this is accomplished by 14 different GLUT isoforms, that transport glucose and other monosaccharides with varying substrate preferences and kinetics. Nevertheless, there is little difference between the sugar-coordinating residues in the GLUT proteins and even the malarial Plasmodium falciparum transporter PfHT1, which is uniquely able to transport a wide range of different sugars. PfHT1 was captured in an intermediate 'occluded' state, revealing how the extracellular gating helix TM7b has moved to break and occlude the sugar-binding site. Sequence difference and kinetics indicated that the TM7b gating helix dynamics and interactions likely evolved to enable substrate promiscuity in PfHT1, rather than the sugar-binding site itself. It was unclear, however, if the TM7b structural transitions observed in PfHT1 would be similar in the other GLUT proteins. Here, using enhanced sampling molecular dynamics simulations, we show that the fructose transporter GLUT5 spontaneously transitions through an occluded state that closely resembles PfHT1. The coordination of D-fructose lowers the energetic barriers between the outward- and inward-facing states, and the observed binding mode for D-fructose is consistent with biochemical analysis. Rather than a substrate-binding site that achieves strict specificity by having a high affinity for the substrate, we conclude GLUT proteins have allosterically coupled sugar binding with an extracellular gate that forms the high-affinity transition-state instead. This substrate-coupling pathway presumably enables the catalysis of fast sugar flux at physiological relevant blood-glucose concentrations.