Frank Guarnieri

Functional Microdomains in G-protein-coupled Receptors THE CONSERVED ARGININE-CAGE MOTIF IN THE GONADOTROPIN-RELEASING HORMONE RECEPTOR

An Arg present in the third transmembrane domain of all rhodopsin-like G-protein-coupled receptors is required for efficient signal transduction. Mutation of this Arg in the gonadotropin-releasing hormone receptor to Gln, His, or Lys abolished or severely impaired agonist-stimulated inositol phosphate generation, consistent with Arg having a role in receptor activation. To investigate the contribution of the surrounding structural domain in the actions of the conserved Arg, an integrated microdomain modeling and mutagenesis approach has been utilized. Two conserved residues that constrain the Arg side chain to a limited number of conformations have been identified. In the inactive wild-type receptor, the Arg side chain is proposed to form an ionic interaction with Asp3.49(138). Experimental results for the Asp3.49(138) → Asn mutant receptor show a modestly enhanced receptor efficiency, consistent with the hypothesis that weakening the Asp3.49(138)-Arg3.50(139)interaction by protonation of the Asp or by the mutation to Asn favors activation. With activation, the Asp3.49(138)-Arg3.50(139) ionic bond would break, and the unrestrained Arg would be prevented from orienting itself toward the water phase by a steric clash with Ile3.54(143). The mutation Ile3.54(143) → Ala, which eliminates this clash in simulations, causes a marked reduction in measured receptor signaling efficiency, implying that solvation of Arg3.50(139) prevents it from functioning in the activation of the receptor. These data are consistent with residues Asp3.49(138) and Ile3.54(143) forming a structural motif, which helps position Arg in its appropriate inactive and active receptor conformations.

Making DNA Add

Recent studies have demonstrated the feasibility of using DNA-based experiments to compute solutions to combinatorial problems. However, a prerequisite for designing a computer useful in a wide range of applications is the ability to perform mathematical calculations. The development of a DNA-based algorithm for addition is presented. The DNA representation of two nonnegative binary numbers is presented in a form permitting a chain of primer extension reactions to carry out the addition operation. To demonstrate the feasibility of this algorithm, a simple example was executed biochemically.

Agonist-induced Conformational Changes at the Cytoplasmic Side of Transmembrane Segment 6 in the β2 Adrenergic Receptor Mapped by Site-selective Fluorescent Labeling

The environmentally sensitive, sulfhydryl-reactive, fluorescent probeN,N′-dimethyl-N-(iodoacetyl)-N′-(7-nitrobenz-2-oxa-1,3-diazol-4-yl) ethylene-diamine (IANBD) was used as a molecular reporter of agonist-induced conformational changes in the β2adrenergic receptor, a prototype hormone-activated G protein-coupled receptor. In the background of a mutant β2 adrenergic receptor, with a minimal number of endogenous cysteine residues, new cysteines were introduced in positions 2696.31, 2706.32, 2716.33, and 2726.34 at the cytoplasmic side of transmembrane segment (TM) 6. The resulting mutant receptors were fully functional and bound both agonists and antagonist with high affinities also upon IANBD labeling. Fluorescence spectroscopy analysis of the purified and site-selectively IANBD-labeled mutants suggested that the covalently attached fluorophore was exposed to a less polar environment at all four positions upon agonist binding. Whereas evidence for only a minor change in the molecular environment was obtained for positions 2696.31 and 2706.32, the full agonist isoproterenol caused clear dose-dependent and reversible increases in fluorescence emission at positions 2716.33 and 2726.34. The data suggest that activation of G protein-coupled receptors, which are activated by “diffusible” ligands, involves a structural rearrangement corresponding to the cytoplasmic part of TM 6. The preferred conformations of the IANBD moiety attached to the inserted cysteines were predicted by employing a computational method that incorporated the complex hydrophobic/hydrophilic environment in which the cysteines reside. Based on these preferred conformations, it is suggested that the spectral changes reflect an agonist-promoted movement of the cytoplasmic part of TM 6 away from the receptor core and upwards toward the membrane bilayer.

A Self-Consistent, Microenvironment Modulated Screened Coulomb Potential Approximation to Calculate pH-Dependent Electrostatic Effects in Proteins

An improved approach is presented for calculating pH-dependent electrostatic effects in proteins using sigmoidally screened Coulomb potentials (SCP). It is hypothesized that a key determinant of seemingly aberrant behavior in pKa shifts is due to the properties of the unique microenvironment around each residue. To help demonstrate this proposal, an approach is developed to characterize the microenvironments using the local hydrophobicity/hydrophilicity around each residue of the protein. The quantitative characterization of the microenvironments shows that the protein is a complex mosaic of differing dielectric regions that provides a physical basis for modifying the dielectric screening functions: in more hydrophobic microenvironments the screening decreases whereas the converse applies to more hydrophilic regions. The approach was applied to seven proteins providing more than 100 measured pKa values and yielded a root mean square deviation of 0.5 between calculated and experimental values. The incorporation of the local hydrophobicity characteristics into the algorithm allowed the resolution of some of the more intractable problems in the calculation of pKa. Thus, the divergent shifts of the pKa of Glu-35 and Asp-66 in hen egg white lysozyme, which are both about 90% buried, was correctly predicted. Mechanistically, the divergence occurs because Glu-35 is in a hydrophobic microenvironment, while Asp-66 is in a hydrophilic microenvironment. Furthermore, because the calculation of the microenvironmental effects takes very little CPU time, the computational speed of the SCP formulation is conserved. Finally, results from different crystal structures of a given protein were compared, and it is shown that the reliability of the calculated pKa values is sufficient to allow identification of conformations that may be more relevant for the solution structure.

A critical role for a tyrosine residue in the cannabinoid receptors for ligand recognition

Previous mutation and modeling studies have identified an aromatic cluster in the transmembrane helix (TMH) 3-4-5 region as important for ligand binding at the CB(1) and CB(2) cannabinoid receptors. Through novel mixed mode Monte Carlo/Stochastic Dynamics (MC/SD) calculations, we tested the importance of aromaticity at position 5.39(275) in CB(1). MC/SD calculations were performed on wild-type (WT) CB(1) and two mutants, Y5.39(275)F and Y5.39(275)I. Results indicated that while the CB(1) Y5.39(275)F mutant is very similar to WT, the Y5.39(275)I mutant shows pronounced topology changes in the TMH 3-4-5 region. Site-directed mutagenesis studies of tyrosine 5.39 to phenylalanine (Y-->F) or isoleucine (Y-->I) in both CB(1) and CB(2) were performed to determine the functional role of this amino acid in each receptor subtype. HEK 293 cells transfected with mutant receptor cDNAs were evaluated in radioligand binding and cyclic AMP assays. The CB(1) mutant and WT receptors were also co-expressed with G-protein-coupled inwardly rectifying channels (GIRK1 and GIRK4) in Xenopus oocytes to assess functional coupling. The Y-->F mutation resulted in cannnabinoid receptors with subtle differences in WT binding and signal transduction. In contrast, the Y-->I mutations produced receptors that could not produce signal transduction or bind to multiple cannabinoid compounds. However, immunofluorescence data indicate that the Y-->I mutation was compartmentalized and expressed at a level similar to that of the WT cannabinoid receptor. These results underscore the importance of aromaticity at position CB(1) 5.39(275) and CB(2) 5.39(191) for ligand recognition in the cannabinoid receptors.

Diverse fragment clustering and water exclusion identify protein hot spots.

Simulated annealing of chemical potential located the highest affinity positions of eight organic probes and water on eight static structures of hen egg white lysozyme (HEWL) in various conformational states. In all HELW conformations, a diverse set of organic probes clustered in the known binding site (hot spot). Fragment clusters at other locations were excluded by tightly-bound waters so that only the hot-spot cluster remained in each case. The location of the hot spot was correctly predicted irrespective of the protein conformation and without accounting for protein flexibility during the simulations. Any one of the static structures could have been used to locate the hot spot. A site on a protein where a diversity of organic probes is calculated to cluster, but where water specifically does not bind, identifies a potential small-molecule binding site or protein-protein interaction hot spot.

Conformational memories and a simulated annealing program that learns: Application to LTB4

The use of computer simulations in all areas of chemistry is growing rapidly because of the powerful insights that they have provided into many interesting phenomena. As investigators continuously examine more sophisticated problems, they need increasingly more powerful tools. Hence, much effort has gone into the development of algorithms which might extend the scope and power of standard dynamic and Monte Carlo techniques. In the Monte Carlo regime, the most common area subject to improvement is the choice of a trial move. In the ordinary case, trial moves are generated uniformly at random. In the extended and hopefully improved case, trial moves are generated randomly but not uniformly. In this article we present a new and totally general method of biased sampling which is applicable to any flexible molecule. In our method, multiple simulated annealing runs are performed to reveal populated and unpopulated regions of the multidimensional conformation space. The second phase of the simulation is done at a fixed temperature with sampling only from populated regions found in the first phase. Because the simulated annealing runs quickly reveal unpopulated regions of the conformation space, the volume of conformation space that needs to be sampled in the second phase of the algorithm is reduced by many orders of magnitude. Additionally, because no energy minimization is used, these populations represent a canonical ensemble which may be used to estimate conformational free energies.

Conformational memories with variable bond angles

Conformational Memories (CM) is a simulated annealing/Monte Carlo method that explores peptide and protein dihedral conformational space completely and efficiently, independent of the original conformation. Here we extend the CM method to include the variation of a randomly chosen bond angle, in addition to the standard variation of two or three randomly chosen dihedral angles, in each Monte Carlo trial of the CM exploratory and biased phases. We test the hypothesis that the inclusion of variable bond angles in CM leads to an improved sampling of conformational space. We compare the results with variable bond angles to CM with no bond angle variation for the following systems: (1) the pentapeptide Met‐enkephalin, which is a standard test case for conformational search methods; (2) the proline ring pucker in a 17mer model peptide, (Ala)8Pro(Ala)8; and (3) the conformations of the Ser 7.39 χ1 in transmembrane helix 7 (TMH7) of the cannabinoid CB1 receptor, a 25‐residue system. In each case, analysis of the CM results shows that the inclusion of variable bond angles results in sampling of regions of conformational space that are inaccessible to CM calculations with only variable dihedral angles, and/or a shift in conformational populations from those calculated when variable bond angles are not included. The incorporation of variable bond angles leads to an improved sampling of conformational space without loss of efficiency. Our examples show that this improved sampling leads to better exploration of biologically relevant conformations that have been experimentally validated.

COMPUTER SIMULATION STUDIES OF THE FULLY SOLVATED WILD-TYPE AND MUTATED GNRH IN EXTENDED AND BETA -TURN CONFORMATIONS

The conformational preference of the gonadotropin-releasing hormone (GnRH) and its Lys-8 mutant, studied earlier with a continuum model, was revisited using an explicit solvent model and thermodynamic integration to calculate the solvents contribution to the conformation-dependence of its free energy. In addition, the Proximity Criterion was used to further analyze the effects of conformational changes.

A fragment-based approach to the SAMPL3 Challenge

The success of molecular fragment-based design depends critically on the ability to make predictions of binding poses and of affinity ranking for compounds assembled by linking fragments. The SAMPL3 Challenge provides a unique opportunity to evaluate the performance of a state-of-the-art fragment-based design methodology with respect to these requirements. In this article, we present results derived from linking fragments to predict affinity and pose in the SAMPL3 Challenge. The goal is to demonstrate how incorporating different aspects of modeling protein–ligand interactions impact the accuracy of the predictions, including protein dielectric models, charged versus neutral ligands, ΔΔGs solvation energies, and induced conformational stress. The core method is based on annealing of chemical potential in a Grand Canonical Monte Carlo (GC/MC) simulation. By imposing an initially very high chemical potential and then automatically running a sequence of simulations at successively decreasing chemical potentials, the GC/MC simulation efficiently discovers statistical distributions of bound fragment locations and orientations not found reliably without the annealing. This method accounts for configurational entropy, the role of bound water molecules, and results in a prediction of all the locations on the protein that have any affinity for the fragment. Disregarding any of these factors in affinity-rank prediction leads to significantly worse correlation with experimentally-determined free energies of binding. We relate three important conclusions from this challenge as applied to GC/MC: (1) modeling neutral ligands—regardless of the charged state in the active site—produced better affinity ranking than using charged ligands, although, in both cases, the poses were almost exactly overlaid; (2) simulating explicit water molecules in the GC/MC gave better affinity and pose predictions; and (3) applying a ΔΔGs solvation correction further improved the ranking of the neutral ligands. Using the GC/MC method under a variety of parameters in the blinded SAMPL3 Challenge provided important insights to the relevant parameters and boundaries in predicting binding affinities using simulated annealing of chemical potential calculations.