INVESTIGADORES
GARRO Adriana Deolinda
congresos y reuniones científicas
Título:
The electron density obtained from QTAIM analysis acting as a strong molecular descriptor. A molecular modeling study performed in DHFR inhibitors
Autor/es:
RODRIGO D. TOSSO; SEBASTIÁN A. ANDUJAR; ADRIANA GARRO; FERNANDO D. SUVIRE; JUAN C. GARRO; RICARDO D. ENRIZ
Reunión:
Congreso; 10th Congress of the World Association of Theoretical and Computational Chemists WATOC; 2014
Resumen:
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A
theoretical study of two new series of inhibitors on human
dihydrofolate reductase (DHFR) has been carried out using molecular
modeling techniques. First, we performed Molecular Dynamics (MD)
simulations on the different ligand-receptor complexes. In the next
step, we carried out semiempirical (PM6), ab
initio
and DFT (Density Functional Theory) calculations using a reduced
model of the active site of the enzyme[1].
We
also used the umbrella sampling technique to determine the potential
of mean force to predict the relative free binding energy of these
inhibitors [2]. We found that the relative free energy range is
between 10 and 35 kcal/mol, these values agrees with the trend of our
experimental assays.
All
correlations obtained between the binding energies and the IC50
values only allows to differentiate compounds that bind tightly to
the enzyme from those which bind very weakly, but are unable to
differentiate between compounds that have similar affinities by the
enzyme. A similar result was obtained by the study using the umbrella
sampling technique. In the last stage of this study, a QTAIM analysis
of each complex was performed, which allowed to obtain the electronic
density values ()
of each interaction between the protein and the ligand. From these
results, it is possible not only to analyze quantitatively each
interaction but also get a correlation using these
values and the IC50
data obtained in our laboratory.
This methodology led to a better correlation (R2=0.96)
than when the binding energies calculated by quantum mechanical
methods were used, allowing to predict the biological activity of
novel compounds not yet synthesized with better accuracy.