58 research outputs found
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<sup>–1</sup>, 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
A<sub>2</sub>. 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
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