18 research outputs found
Cooperative Binding of Aflatoxin B<sub>1</sub> by Cytochrome P450 3A4: A Computational Study
Aflatoxin
B<sub>1</sub> (AFB<sub>1</sub>)î—¸the most potent
natural carcinogen known to menî—¸is metabolized by cytochrome
P450 3A4 (CYP3A4), either to the genotoxic AFB<sub>1</sub> <i>exo</i>-8,9-epoxide or to the detoxified 3α-hydroxy AFB<sub>1</sub>. The activation of the procarcinogen proceeds in a highly
cooperative fashion, which differs from common allosteric regulation
in the sense that it can be attributed to simultaneous occupancy of
a single large and malleable active site by multiple ligand molecules.
Unfortunately, unlike in the case of ketoconazole, there is currently
no experimental structure available for the doubly ligated CYP3A4-AFB<sub>1</sub> complex. Therefore, we employed a sequential molecular docking
protocol to create various possible doubly ligated complexes and subsequently
performed molecular dynamics simulations and free-energy calculations
to check for their consistency with the available experimental data
on regio- and stereoselectivity of both AFB<sub>1</sub> oxidations
as well as with available kinetic data. Only the system in which the
first AFB<sub>1</sub> molecule was bound in a face-on C8–C9
epoxidation mode and the second AFB<sub>1</sub> molecule was bound
in a side-on 3α-hydroxylation modea result of an unconstrained
molecular docking protocolî—¸has successfully fulfilled all the
imposed criteria and is therefore proposed as the most likely structure
of the doubly ligated complex of CYP3A4 with AFB<sub>1</sub>. The
empirical Linear Interaction Energy method revealed that shape complementarity
through nonpolar dispersion interactions between the two bound AFB<sub>1</sub> molecules is the main source of the experimentally observed
positive homotropic cooperativity. The reported study represents a
nice example of how state-of-the-art molecular modeling techniques
can be used to study complicated macromolecular complexes, whose structures
have not yet been experimentally determined, and to validate these
against the available experimental data. The proposed structure will
facilitate future studies on the rational design of successful AFB<sub>1</sub> modulators or on human subpopulations characterized by specific
CYP3A4 polymorphisms that are especially sensitive to AFB<sub>1</sub>
Strong Nonadditivity as a Key Structure–Activity Relationship Feature: Distinguishing Structural Changes from Assay Artifacts
Nonadditivity
in protein–ligand affinity data represents
highly instructive structure–activity relationship (SAR) features
that indicate structural changes and have the potential to guide rational
drug design. At the same time, nonadditivity is a challenge for both
basic SAR analysis as well as many ligand-based data analysis techniques
such as Free-Wilson Analysis and Matched Molecular Pair analysis,
since linear substituent contribution models inherently assume additivity
and thus do not work in such cases. While structural causes for nonadditivity
have been analyzed anecdotally, no systematic approaches to interpret
and use nonadditivity prospectively have been developed yet. In this
contribution, we lay the statistical framework for systematic analysis
of nonadditivity in a SAR series. First, we develop a general metric
to quantify nonadditivity. Then, we demonstrate the non-negligible
impact of experimental uncertainty that creates apparent nonadditivity,
and we introduce techniques to handle experimental uncertainty. Finally,
we analyze public SAR data sets for strong nonadditivity and use recourse
to the original publications and available X-ray structures to find
structural explanations for the nonadditivity observed. We find that
all cases of strong nonadditivity (ΔΔp<i>K</i><sub>i</sub> and ΔΔpIC<sub>50</sub> > 2.0 log units)
with sufficient structural information to generate reasonable hypothesis
involve changes in binding mode. With the appropriate statistical
basis, nonadditivity analysis offers a variety of new attempts for
various areas in computer-aided drug design, including the validation
of scoring functions and free energy perturbation approaches, binding
pocket classification, and novel features in SAR analysis tools
Matched Peptides: Tuning Matched Molecular Pair Analysis for Biopharmaceutical Applications
Biopharmaceuticals hold great promise
for the future of drug discovery.
Nevertheless, rational drug design strategies are mainly focused on
the discovery of small synthetic molecules. Herein we present matched
peptides, an innovative analysis technique for biological data related
to peptide and protein sequences. It represents an extension of matched
molecular pair analysis toward macromolecular sequence data and allows
quantitative predictions of the effect of single amino acid substitutions
on the basis of statistical data on known transformations. We demonstrate
the application of matched peptides to a data set of major histocompatibility
complex class II peptide ligands and discuss the trends captured with
respect to classical quantitative structure–activity relationship
approaches as well as structural aspects of the investigated protein–peptide
interface. We expect our novel readily interpretable tool at the interface
of cheminformatics and bioinformatics to support the rational design
of biopharmaceuticals and give directions for further development
of the presented methodology
Localization of Millisecond Dynamics: Dihedral Entropy from Accelerated MD
Here, we demonstrate a method to
capture local dynamics on a time
scale 3 orders of magnitude beyond state-of-the-art simulation approaches.
We apply accelerated molecular dynamics simulations for conformational
sampling and extract reweighted backbone dihedral distributions. Local
dynamics are characterized by torsional probabilities, resulting in
residue-wise dihedral entropies. Our approach is successfully validated
for three different protein systems of increasing size: alanine dipeptide,
bovine pancreatic trypsin inhibitor (BPTI), and the major birch pollen
allergen Bet v 1a. We demonstrate excellent agreement of flexibility
profiles with both large-scale computer simulations and NMR experiments.
Thus, our method provides efficient access to local protein dynamics
on extended time scales of high biological relevance
Matched Molecular Pair Analysis: Significance and the Impact of Experimental Uncertainty
Matched molecular pair analysis (MMPA)
has become a major tool
for analyzing large chemistry data sets for promising chemical transformations.
However, the dependence of MMPA predictions on data constraints such
as the number of pairs involved, experimental uncertainty, source
of the experiments, and variability of the true physical effect has
not yet been described. In this contribution the statistical basics
for judging MMPA are analyzed. We illustrate the connection between
overall MMPA statistics and individual pairs with a detailed comparison
of average CHEMBL hERG MMPA results versus pairs with extreme transformation
effects. Comparing the CHEMBL results to Novartis data, we find that
significant transformation effects agree very well if the experimental
uncertainty is considered. This indicates that caution must be exercised
for predictions from insignificant MMPAs, yet highlights the robustness
of statistically validated MMPA and shows that MMPA on public databases
can yield results that are very useful for medicinal chemistry
Peptidic Macrocycles - Conformational Sampling and Thermodynamic Characterization
Macrocycles
are of considerable interest as highly specific drug
candidates, yet they challenge standard conformer generators with
their large number of rotatable bonds and conformational restrictions.
Here, we present a molecular dynamics-based routine that bypasses
current limitations in conformational sampling and extensively profiles
the free energy landscape of peptidic macrocycles in solution. We
perform accelerated molecular dynamics simulations to capture a diverse
conformational ensemble. By applying an energetic cutoff, followed
by geometric clustering, we demonstrate the striking robustness and
efficiency of the approach in identifying highly populated conformational
states of cyclic peptides. The resulting structural and thermodynamic
information is benchmarked against interproton distances from NMR
experiments and conformational states identified by X-ray crystallography.
Using three different model systems of varying size and flexibility,
we show that the method reliably reproduces experimentally determined
structural ensembles and is capable of identifying key conformational
states that include the bioactive conformation. Thus, the described
approach is a robust method to generate conformations of peptidic
macrocycles and holds promise for structure-based drug design
Rationalizing Tight Ligand Binding through Cooperative Interaction Networks
Small modifications of the molecular structure of a ligand sometimes cause strong gains in binding affinity to a protein target, rendering a weakly active chemical series suddenly attractive for further optimization. Our goal in this study is to better rationalize and predict the occurrence of such interaction hot-spots in receptor binding sites. To this end, we introduce two new concepts into the computational description of molecular recognition. First, we take a broader view of noncovalent interactions and describe protein–ligand binding with a comprehensive set of favorable and unfavorable contact types, including for example halogen bonding and orthogonal multipolar interactions. Second, we go beyond the commonly used pairwise additive treatment of atomic interactions and use a small world network approach to describe how interactions are modulated by their environment. This approach allows us to capture local cooperativity effects and considerably improves the performance of a newly derived empirical scoring function, ScorpionScore. More importantly, however, we demonstrate how an intuitive visualization of key intermolecular interactions, interaction networks, and binding hot-spots supports the identification and rationalization of tight ligand binding
Protease Inhibitors in View of Peptide Substrate Databases
Protease
substrate profiling has nowadays almost become a routine
task for experimentalists, and the knowledge on protease peptide substrates
is easily accessible via the MEROPS database. We present a shape-based
virtual screening workflow using vROCS that applies the information
about the specificity of the proteases to find new small-molecule
inhibitors. Peptide substrate sequences for three to four substrate
positions of each substrate from the MEROPS database were used to
build the training set. Two-dimensional substrate sequences were converted
to three-dimensional conformations through mutation of a template
peptide substrate. The vROCS query was built from single amino acid
queries for each substrate position considering the relative frequencies
of the amino acids. The peptide-substrate-based shape-based virtual
screening approach gives good performance for the four proteases thrombin,
factor Xa, factor VIIa, and caspase-3 with the DUD-E data set. The
results show that the method works for protease targets with different
specificity profiles as well as for targets with different active-site
mechanisms. As no structure of the target and no information on small-molecule
inhibitors are required to use our approach, the method has significant
advantages in comparison with conventional structure- and ligand-based
methods
Comparison of the different entropy estimators.
<p>The estimation process presented in this work is outperforming the compared published estimators.</p
Systematic sketch of the estimation process for the corrected cleavage entropy.
<p>Based on experimental substrate data the specificity A is calculated. Through bootstrapping a subset is created and the specificity of this subset is calculated to generate B. By performing a linear fit and extrapolating the specificity to 0 in 1/n space we estimate the specificity for infinite substrates.</p