11 research outputs found
Ligand Binding Pathways of Clozapine and Haloperidol in the Dopamine D<sub>2</sub> and D<sub>3</sub> Receptors
The
binding of a small molecule ligand to its protein target is most often
characterized by binding affinity and is typically viewed as an on/off
switch. The more complex reality is that binding involves the ligand
passing through a series of intermediate states between the solution
phase and the fully bound pose. We have performed a set of 29 unbiased
molecular dynamics simulations to model the binding pathways of the
dopamine receptor antagonists clozapine and haloperidol binding to
the D<sub>2</sub> and D<sub>3</sub> dopamine receptors. Through these
simulations we have captured the binding pathways of clozapine and
haloperidol from the extracellular vestibule to the orthosteric binding
site and thereby, we also predict the bound pose of each ligand. These
are the first long time scale simulations of haloperidol or clozapine
binding to dopamine receptors. From these simulations, we have identified
several important stages in the binding pathway, including the involvement
of Tyr7.35 in a “handover” mechanism that transfers
the ligand between the extracellular vestibule and Asp3.32. We have
also performed interaction and cluster analyses to determine differences
in binding pathways between the D<sub>2</sub> and D<sub>3</sub> receptors
and identified metastable states that may be of use in drug design
Homology Modeling of Human Muscarinic Acetylcholine Receptors
We have developed homology models
of the acetylcholine muscarinic
receptors M<sub>1</sub>R–M<sub>5</sub>R, based on the β<sub>2</sub>-adrenergic receptor crystal as the template. This is the
first report of homology modeling of all five subtypes of acetylcholine
muscarinic receptors with binding sites optimized for ligand binding.
The models were evaluated for their ability to discriminate between
muscarinic antagonists and decoy compounds using virtual screening
using enrichment factors, area under the ROC curve (AUC), and an early
enrichment measure, LogAUC. The models produce rational binding modes
of docked ligands as well as good enrichment capacity when tested
against property-matched decoy libraries, which demonstrates their
unbiased predictive ability. To test the relative effects of homology
model template selection and the binding site optimization procedure,
we generated and evaluated a naı̈ve M<sub>2</sub>R model,
using the M<sub>3</sub>R crystal structure as a template. Our results
confirm previous findings that binding site optimization using ligand(s)
active at a particular receptor, i.e. including functional knowledge
into the model building process, has a more pronounced effect on model
quality than target–template sequence similarity. The optimized
M<sub>1</sub>R–M<sub>5</sub>R homology models are made available
as part of the Supporting Information to allow researchers to use
these structures, compare them to their own results, and thus advance
the development of better modeling approaches
Computational Models of the Intestinal Environment. 3. The Impact of Cholesterol Content and pH on Mixed Micelle Colloids
In
this study, we use molecular dynamics (MD) and experimental
techniques (nephelometry and dynamic light scattering) to investigate
the influence of cholesterol content and pH on the colloidal structures
that form in the gastrointestinal (GI) tract upon lipid digestion.
We demonstrate that the ionization state of the molecular species
is a primary driver for the self-assembly of aggregates formed by
model bile and therefore should be considered when performing <i>in silico</i> modeling of colloidal drug delivery systems. Additionally,
the incorporation of physiological concentrations of cholesterol within
the model systems does not affect size, number, shape, or dynamics
of the aggregates to a significant degree. The MD data shows a reduction
in aggregate size with increasing pH, a preference for glycodeoxycholate
(GDX) to occupy the aggregate surface, and that the mixed micellar
aggregates are oblate spheroids (disc-like). The results obtained
assist in understanding the process by which pH and cholesterol influence
self-assembly of mixed micelles within the GI tract. The MD approach
provides a platform for investigation of interactions of drugs and
formulation excipients with the endogenous contents of the GI tract
Computational Models of the Intestinal Environment. 3. The Impact of Cholesterol Content and pH on Mixed Micelle Colloids
In
this study, we use molecular dynamics (MD) and experimental
techniques (nephelometry and dynamic light scattering) to investigate
the influence of cholesterol content and pH on the colloidal structures
that form in the gastrointestinal (GI) tract upon lipid digestion.
We demonstrate that the ionization state of the molecular species
is a primary driver for the self-assembly of aggregates formed by
model bile and therefore should be considered when performing <i>in silico</i> modeling of colloidal drug delivery systems. Additionally,
the incorporation of physiological concentrations of cholesterol within
the model systems does not affect size, number, shape, or dynamics
of the aggregates to a significant degree. The MD data shows a reduction
in aggregate size with increasing pH, a preference for glycodeoxycholate
(GDX) to occupy the aggregate surface, and that the mixed micellar
aggregates are oblate spheroids (disc-like). The results obtained
assist in understanding the process by which pH and cholesterol influence
self-assembly of mixed micelles within the GI tract. The MD approach
provides a platform for investigation of interactions of drugs and
formulation excipients with the endogenous contents of the GI tract
Computational Models of the Gastrointestinal Environment. 2. Phase Behavior and Drug Solubilization Capacity of a Type I Lipid-Based Drug Formulation after Digestion
Lipid-based drug formulations can
greatly enhance the bioavailability of poorly water-soluble drugs.
Following the oral administration of formulations containing tri-
or diglycerides, the digestive processes occurring within the gastrointestinal
(GI) tract hydrolyze the glycerides to mixtures of free fatty acids
and monoglycerides that are, in turn, solubilized by bile. The behavior
of drugs within the resulting colloidal mixtures is currently not
well characterized. This work presents matched in vitro experimental
and molecular dynamics (MD) theoretical models of the GI microenvironment
containing a digested triglyceride-based (Type I) drug formulation.
Both the experimental and theoretical models consist of molecular
species representing bile (glycodeoxycholic acid), digested triglyceride
(1:2 glyceryl-1-monooleate and oleic acid), and water. We have characterized
the phase behavior of the physical system using nephelometry, dynamic
light scattering, and polarizing light microscopy and compared these
measurements to phase behavior observed in multiple MD simulations.
Using this model microenvironment, we have investigated the dissolution
of the poorly water-soluble drug danazol experimentally using LC-MS
and theoretically by MD simulation. The results show how the formulation
lipids alter the environment of the GI tract and improve the solubility
of danazol. The MD simulations successfully reproduce the experimental
results showing the utility of MD in modeling the fate of drugs after
digestion of lipid-based formulations within the intestinal lumen
Computational Models of the Gastrointestinal Environment. 1. The Effect of Digestion on the Phase Behavior of Intestinal Fluids
Improved
models of the gastrointestinal environment have great
potential to assist the complex process of drug formulation. Molecular
dynamics (MD) is a powerful method for investigating phase behavior
at a molecular level. In this study we use multiple MD simulations
to calculate phase diagrams for bile before and after digestion. In
these computational models, undigested bile is represented by mixtures
of palmitoyl-oleoylphosphatidylcholine (POPC), sodium glycodeoxycholate
(GDX), and water. Digested bile is modeled using a 1:1 mixture of
oleic acid and palmitoylphosphatidylcholine (lysophosphatidylcholine,
LPC), GDX, and water. The computational phase diagrams of undigested
and digested bile are compared, and we describe the typical intermolecular
interactions that occur between phospholipids and bile salts. The
diffusion coefficients measured from MD simulation are compared to
experimental diffusion data measured by DOSY-NMR, where we observe
good qualitative agreement. In an additional set of simulations, the
effect of different ionization states of oleic acid on micelle formation
is investigated
Digestion of Phospholipids after Secretion of Bile into the Duodenum Changes the Phase Behavior of Bile Components
Bile
components play a significant role in the absorption of dietary
fat, by solubilizing the products of fat digestion. The absorption
of poorly water-soluble drugs from the gastrointestinal tract is often
enhanced by interaction with the pathways of fat digestion and absorption.
These processes can enhance drug absorption. Thus, the phase behavior
of bile components and digested lipids is of great interest to pharmaceutical
scientists who seek to optimize drug solubilization in the gut lumen.
This can be achieved by dosing drugs after food or preferably by formulating
the drug in a lipid-based delivery system. Phase diagrams of bile
salts, lecithin, and water have been available for many years, but
here we investigate the association structures that occur in dilute
aqueous solution, in concentrations that are present in the gut lumen.
More importantly, we have compared these structures with those that
would be expected to be present in the intestine soon after secretion
of bile. Phosphatidylcholines are rapidly hydrolyzed by pancreatic
enzymes to yield equimolar mixtures of their monoacyl equivalents
and fatty acids. We constructed phase diagrams that model the association
structures formed by the products of digestion of biliary phospholipids.
The micelle–vesicle phase boundary was clearly identifiable
by dynamic light scattering and nephelometry. These data indicate
that a significantly higher molar ratio of lipid to bile salt is required
to cause a transition to lamellar phase (i.e., liposomes in dilute
solution). Mixed micelles of digested bile have a higher capacity
for solubilization of lipids and fat digestion products and can be
expected to have a different capacity to solubilize lipophilic drugs.
We suggest that mixtures of lysolecithin, fatty acid, and bile salts
are a better model of molecular associations in the gut lumen, and
such mixtures could be used to better understand the interaction of
drugs with the fat digestion and absorption pathway
Membrane Permeating Macrocycles: Design Guidelines from Machine Learning
The
ability to predict cell-permeable candidate molecules has great
potential to assist drug discovery projects. Large molecules that
lie beyond the Rule of Five (bRo5) are increasingly important as drug
candidates and tool molecules for chemical biology. However, such
large molecules usually do not cross cell membranes and cannot access
intracellular targets or be developed as orally bioavailable drugs.
Here, we describe a random forest (RF) machine learning model for
the prediction of passive membrane permeation rates developed using
a set of over 1000 bRo5 macrocyclic compounds. The model is based
on easily calculated chemical features/descriptors as independent
variables. Our random forest (RF) model substantially outperforms
a multiple linear regression model based on the same features and
achieves better performance metrics than previously reported models
using the same underlying data. These features include: (1) polar
surface area in water, (2) the octanol-water partitioning coefficient,
(3) the number of hydrogen-bond donors, (4) the sum of the topological
distances between nitrogen atoms, (5) the sum of the topological distances
between nitrogen and oxygen atoms, and (6) the multiple molecular
path count of order 2. The last three features represent molecular
flexibility, the ability of the molecule to adopt different conformations
in the aqueous and membrane interior phases, and the molecular “chameleonicity.”
Guided by the model, we propose design guidelines for membrane-permeating
macrocycles. It is anticipated that this model will be useful in guiding
the design of large, bioactive molecules for medicinal chemistry and
chemical biology applications
Membrane Permeating Macrocycles: Design Guidelines from Machine Learning
The
ability to predict cell-permeable candidate molecules has great
potential to assist drug discovery projects. Large molecules that
lie beyond the Rule of Five (bRo5) are increasingly important as drug
candidates and tool molecules for chemical biology. However, such
large molecules usually do not cross cell membranes and cannot access
intracellular targets or be developed as orally bioavailable drugs.
Here, we describe a random forest (RF) machine learning model for
the prediction of passive membrane permeation rates developed using
a set of over 1000 bRo5 macrocyclic compounds. The model is based
on easily calculated chemical features/descriptors as independent
variables. Our random forest (RF) model substantially outperforms
a multiple linear regression model based on the same features and
achieves better performance metrics than previously reported models
using the same underlying data. These features include: (1) polar
surface area in water, (2) the octanol-water partitioning coefficient,
(3) the number of hydrogen-bond donors, (4) the sum of the topological
distances between nitrogen atoms, (5) the sum of the topological distances
between nitrogen and oxygen atoms, and (6) the multiple molecular
path count of order 2. The last three features represent molecular
flexibility, the ability of the molecule to adopt different conformations
in the aqueous and membrane interior phases, and the molecular “chameleonicity.”
Guided by the model, we propose design guidelines for membrane-permeating
macrocycles. It is anticipated that this model will be useful in guiding
the design of large, bioactive molecules for medicinal chemistry and
chemical biology applications
A Potent Cyclic Peptide Targeting SPSB2 Protein as a Potential Anti-infective Agent
The
protein SPSB2 mediates proteosomal degradation of inducible
nitric oxide synthase (iNOS). Inhibitors of SPSB2–iNOS interaction
may prolong the lifetime of iNOS and thereby enhance the killing of
persistent pathogens. We have designed a cyclic peptide, Ac-c[CVDINNNC]-NH<sub>2</sub>, containing the key sequence motif mediating the SPSB2–iNOS
interaction, which binds to the iNOS binding site on SPSB2 with a <i>K</i><sub>d</sub> of 4.4 nM, as shown by SPR, [<sup>1</sup>H,<sup>15</sup>N]-HSQC, and <sup>19</sup>F NMR. An in vitro assay on macrophage
cell lysates showed complete inhibition of SPSB2–iNOS interactions
by the cyclic peptide. Furthermore, its solution structure closely
matched (backbone rmsd 1.21 Å) that of the SPSB2-bound linear
DINNN peptide. The designed peptide was resistant to degradation by
the proteases pepsin, trypsin, and chymotrypsin and stable in human
plasma. This cyclic peptide exemplifies potentially a new class of
anti-infective agents that acts on the host innate response, thereby
avoiding the development of pathogen resistance