2 research outputs found
Prediction of Cytochrome P450 Xenobiotic Metabolism: Tethered Docking and Reactivity Derived from Ligand Molecular Orbital Analysis
Metabolism of xenobiotic and endogenous
compounds is frequently
complex, not completely elucidated, and therefore often ambiguous.
The prediction of sites of metabolism (SoM) can be particularly helpful
as a first step toward the identification of metabolites, a process
especially relevant to drug discovery. This paper describes a reactivity
approach for predicting SoM whereby reactivity is derived directly
from the ground state ligand molecular orbital analysis, calculated
using Density Functional Theory, using a novel implementation of the
average local ionization energy. Thus each potential SoM is sampled
in the context of the whole ligand, in contrast to other popular approaches
where activation energies are calculated for a predefined database
of molecular fragments and assigned to matching moieties in a query
ligand. In addition, one of the first descriptions of molecular dynamics
of cytochrome P450 (CYP) isoforms 3A4, 2D6, and 2C9 in their Compound
I state is reported, and, from the representative protein structures
obtained, an analysis and evaluation of various docking approaches
using GOLD is performed. In particular, a covalent docking approach
is described coupled with the modeling of important electrostatic
interactions between CYP and ligand using spherical constraints. Combining
the docking and reactivity results, obtained using standard functionality
from common docking and quantum chemical applications, enables a SoM
to be identified in the top 2 predictions for 75%, 80%, and 78% of
the data sets for 3A4, 2D6, and 2C9, respectively, results that are
accessible and competitive with other recently published prediction
tools
Routine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 1. Generalized Born
We present an implementation of generalized Born implicit
solvent
all-atom classical molecular dynamics (MD) within the AMBER program
package that runs entirely on CUDA enabled NVIDIA graphics processing
units (GPUs). We discuss the algorithms that are used to exploit the
processing power of the GPUs and show the performance that can be
achieved in comparison to simulations on conventional CPU clusters.
The implementation supports three different precision models in which
the contributions to the forces are calculated in single precision
floating point arithmetic but accumulated in double precision (SPDP),
or everything is computed in single precision (SPSP) or double precision
(DPDP). In addition to performance, we have focused on understanding
the implications of the different precision models on the outcome
of implicit solvent MD simulations. We show results for a range of
tests including the accuracy of single point force evaluations and
energy conservation as well as structural properties pertainining
to protein dynamics. The numerical noise due to rounding errors within
the SPSP precision model is sufficiently large to lead to an accumulation
of errors which can result in unphysical trajectories for long time
scale simulations. We recommend the use of the mixed-precision SPDP
model since the numerical results obtained are comparable with those
of the full double precision DPDP model and the reference double precision
CPU implementation but at significantly reduced computational cost.
Our implementation provides performance for GB simulations on a single
desktop that is on par with, and in some cases exceeds, that of traditional
supercomputers