A history of Missouri's counties, county seats, and courthouse squares (1983)

Series information taken from "1888-1984, Missouri Agricultural Experiment Station and Cooperative Extension Service publications." May not represent this version.Includes bibliographical references (pages 139-142) and index

Multiple Binding Poses in the Hydrophobic Cavity of Bee Odorant Binding Protein AmelOBP14

In the first step of olfaction, odorants are bound and solubilized by small globular odorant binding proteins (OBPs) which shuttle them to the membrane of a sensory neuron. Low ligand affinity and selectivity at this step enable the recognition of a wide range of chemicals. Honey bee <i>Apis mellifera</i>’s OBP14 (AmelOBP14) binds different plant odorants in a largely hydrophobic cavity. In long molecular dynamics simulations in the presence and absence of ligand eugenol, we observe a highly dynamic C-terminal region which forms one side of the ligand-binding cavity, and the ligand drifts away from its crystallized orientation. Hamiltonian replica exchange simulations, allowing exchanges of conformations sampled by the real ligand with those sampled by a noninteracting dummy molecule and several intermediates, suggest an alternative, quite different ligand pose which is adopted immediately and which is stable in long simulations. Thermodynamic integration yields binding free energies which are in reasonable agreement with experimental data

Modeling of Oligosaccharides within Glycoproteins from Free-Energy Landscapes

In spite of the abundance of glycoproteins in biological processes, relatively little three-dimensional structural data is available for glycan structures. Here, we study the structure and flexibility of the vast majority of mammalian oligosaccharides appearing in N- and O-glycosylated proteins using a bottom up approach. We report the conformational free-energy landscapes of all relevant glycosidic linkages as obtained from local elevation simulations and subsequent umbrella sampling. To the best of our knowledge, this represents the first complete conformational library for the construction of N- and O-glycan structures. Next, we systematically study the effect of neighboring residues, by extensively simulating all relevant trisaccharides and one tetrasaccharide. This allows for an unprecedented comparison of disaccharide linkages in large oligosaccharides. With a small number of exceptions, the conformational preferences in the larger structures are very similar as in the disaccharides. This, finally, allows us to suggest several efficient approaches to construct complete N- and O-glycans on glycoproteins, as exemplified on two relevant examples

Cytochrome P450 3A4 Inhibition by Ketoconazole: Tackling the Problem of Ligand Cooperativity Using Molecular Dynamics Simulations and Free-Energy Calculations

Cytochrome P450 3A4 (CYP3A4) metabolizes more than 50% of clinically used drugs and is often involved in adverse drug–drug interactions. It displays atypical binding and kinetic behavior toward a number of ligands characterized by a sigmoidal shape of the corresponding titration curves, which is indicative of a positive homotropic cooperativity. This requires a participation of at least two ligand molecules, whereby the binding of the first ligand molecule increases the affinity of CYP3A4 for the binding of the second ligand molecule. In the current study, a combination of molecular dynamics simulations and free-energy calculations was applied to elucidate the physicochemical origin of the observed positive homotropic cooperativity in ketoconazole binding to CYP3A4. The binding of the first ketoconazole molecule was established to increase the affinity for the binding of the second ketoconazole molecule by 5 kJ mol^–1, which explains and quantifies the experimentally observed cooperative behavior of CYP3A4. Shape complementarity through nonpolar van der Waals interactions was identified as the main driving force of this binding, which seems to be in line with the promiscuous nature of CYP3A4. Moreover, the calculated binding free energies were found to be in good agreement with the values predicted from a simple 2-ligand binding kinetic model as well as to successfully reproduce the experimental titration curve. This confirms the general applicability of rapid free-energy methods to study challenging biomolecular systems like cytochromes P450, which are characterized by a large flexibility and malleability of their active sites

Extended Thermodynamic Integration: Efficient Prediction of Lambda Derivatives at Nonsimulated Points

Thermodynamic integration (TI) is one of the most commonly used free-energy calculation methods. The derivative of the Hamiltonian with respect to lambda, ⟨∂<i>H</i>/∂λ⟩, is determined at multiple λ-points. Because a numerical integration step is necessary, high curvature regions require simulations at densely spaced λ-points. Here, the principle of extended TI is introduced, where ⟨∂<i>H</i>/∂λ⟩ values are predicted at nonsimulated λ-points. On the basis of three model systems, it is shown that extended TI requires significantly fewer λ-points than regular TI to obtain similar accuracy

Multiple Binding Poses in the Hydrophobic Cavity of Bee Odorant Binding Protein AmelOBP14

In the first step of olfaction, odorants are bound and solubilized by small globular odorant binding proteins (OBPs) which shuttle them to the membrane of a sensory neuron. Low ligand affinity and selectivity at this step enable the recognition of a wide range of chemicals. Honey bee <i>Apis mellifera</i>’s OBP14 (AmelOBP14) binds different plant odorants in a largely hydrophobic cavity. In long molecular dynamics simulations in the presence and absence of ligand eugenol, we observe a highly dynamic C-terminal region which forms one side of the ligand-binding cavity, and the ligand drifts away from its crystallized orientation. Hamiltonian replica exchange simulations, allowing exchanges of conformations sampled by the real ligand with those sampled by a noninteracting dummy molecule and several intermediates, suggest an alternative, quite different ligand pose which is adopted immediately and which is stable in long simulations. Thermodynamic integration yields binding free energies which are in reasonable agreement with experimental data

Optimization of Protein Backbone Dihedral Angles by Means of Hamiltonian Reweighting

Molecular dynamics simulations depend critically on the accuracy of the underlying force fields in properly representing biomolecules. Hence, it is crucial to validate the force-field parameter sets in this respect. In the context of the GROMOS force field, this is usually achieved by comparing simulation data to experimental observables for small molecules. In this study, we develop new amino acid backbone dihedral angle potential energy parameters based on the widely used 54A7 parameter set by matching to experimental <i>J</i> values and secondary structure propensity scales. In order to find the most appropriate backbone parameters, close to 100 000 different combinations of parameters have been screened. However, since the sheer number of combinations considered prohibits actual molecular dynamics simulations for each of them, we instead predicted the values for every combination using Hamiltonian reweighting. While the original 54A7 parameter set fails to reproduce the experimental data, we are able to provide parameters that match significantly better. However, to ensure applicability in the context of larger peptides and full proteins, further studies have to be undertaken

Protein–Ligand Binding from Distancefield Distances and Hamiltonian Replica Exchange Simulations

The calculation of protein–ligand binding free energies is an important goal in the field of computational chemistry. Applying path-sampling methods for this purpose involves calculating the associated potential of mean force (PMF) and gives insight into the binding free energy along the binding process. Without <i>a priori</i> knowledge about the binding path, sampling reversible binding can be difficult to achieve. To alleviate this problem, we introduce the distancefield (DF) as a reaction coordinate for such calculations. DF is a grid-based method in which the shortest distance between the binding site and a ligand is determined avoiding routes that pass through the protein. Combining this reaction coordinate with Hamiltonian replica exchange molecular dynamics (HREMD) allows for the reversible binding of the ligand to the protein. A comparison is made between umbrella sampling using regular distance restraints and HREMD with DF restraints to study aspirin binding to the protein phospholipase A₂. Although the free energies of binding are similar for both methods, the increased sampling with HREMD has a significant influence on the shape of the PMF. A remarkable agreement between the calculated binding free energies from the PMF and the experimental estimate is obtained

Accelerated Enveloping Distribution Sampling: Enabling Sampling of Multiple End States while Preserving Local Energy Minima

Enveloping distribution sampling (EDS) is an efficient approach to calculate multiple free-energy differences from a single molecular dynamics (MD) simulation. However, the construction of an appropriate reference-state Hamiltonian that samples all states efficiently is not straightforward. We propose a novel approach for the construction of the EDS reference-state Hamiltonian, related to a previously described procedure to smoothen energy landscapes. In contrast to previously suggested EDS approaches, our reference-state Hamiltonian preserves local energy minima of the combined end-states. Moreover, we propose an intuitive, robust and efficient parameter optimization scheme to tune EDS Hamiltonian parameters. We demonstrate the proposed method with established and novel test systems and conclude that our approach allows for the automated calculation of multiple free-energy differences from a single simulation. Accelerated EDS promises to be a robust and user-friendly method to compute free-energy differences based on solid statistical mechanics

Comparison of binding pathways of different phosphopeptides.

<p>A) Binding pathways from DF/HRE-MD simulations depicted as connected dots referring to the probability density peaks along the respective pathway (See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0180633#pone.0180633.t001" target="_blank">Table 1</a> for more information). B) Electrostatic surface potential (ESP) of 14-3-3ζ in blue, white and red for positive, neutral, and negative surface patches, respectively. The positively charged main interaction site (IS1), and secondary interaction site (IS2) are connected by a positive surface along the binding pathways. A negative surface patch (NSP) involved in the binding process is also indicated. Serine58 (S58), a phosphorylation site is located near the binding pathway. For clarity, the 14-3-3ζ C-terminal tail is not shown.</p