8 research outputs found
Docking Ligands into Flexible and Solvated Macromolecules. 6. Development and Application to the Docking of HDACs and other Zinc Metalloenzymes Inhibitors
Metalloenzymes are ubiquitous proteins
which feature one or more
metal ions either directly involved in the enzymatic activity and/or
structural properties (i.e., zinc fingers). Several members of this
class take advantage of the Lewis acidic properties of zinc ions to
carry out their various catalytic transformations including isomerization
or amide cleavage. These enzymes have been validated as drug targets
for a number of diseases including cancer; however, despite their
pharmaceutical relevance and the availability of crystal structures,
structure-based drug design methods have been poorly and indirectly
parametrized for these classes of enzymes. More specifically, the
metal coordination component and proton transfers of the process of
drugs binding to metalloenzymes have been inadequately modeled by
current docking programs, if at all. In addition, several known issues,
such as coordination geometry, atomic charge variability, and a potential
proton transfer from small molecules to a neighboring basic residue,
have often been ignored. We report herein the development of specific
functions and parameters to account for zincādrug coordination
focusing on the above-listed phenomena and their impact on docking
to zinc metalloenzymes. These atom-type-dependent but atomic charge-independent
functions implemented into Fitted 3.1 enable the simulation
of drug binding to metalloenzymes, considering an acidābase
reaction with a neighboring residue when necessary with good accuracy
Development of a Computational Tool to Rival Experts in the Prediction of Sites of Metabolism of Xenobiotics by P450s
The metabolism of xenobioticsīøand more specifically
drugsīøin
the liver is a critical process controlling their half-life. Although
there exist experimental methods, which measure the metabolic stability
of xenobiotics and identify their metabolites, developing higher throughput
predictive methods is an avenue of research. It is expected that predicting
the chemical nature of the metabolites would be an asset for designing
safer drugs and/or drugs with modulated half-lives. We have developed
IMPACTS (In-silico Metabolism Prediction by Activated Cytochromes
and Transition States), a computational tool combining docking to
metabolic enzymes, transition state modeling, and rule-based substrate
reactivity prediction to predict the site of metabolism (SoM) of xenobiotics.
Its application to sets of CYP1A2, CYP2C9, CYP2D6, and CYP3A4 substrates
and comparison to expertsā predictions demonstrates its accuracy
and significance. IMPACTS identified an experimentally observed SoM
in the top 2 predicted sites for 77% of the substrates, while the
accuracy of biotransformation expertsā prediction was 65%.
Application of IMPACTS to external sets and comparison of its accuracy
to those of eleven other methods further validated the method implemented
in IMPACTS
Development of a Computational Tool to Rival Experts in the Prediction of Sites of Metabolism of Xenobiotics by P450s
The metabolism of xenobioticsīøand more specifically
drugsīøin
the liver is a critical process controlling their half-life. Although
there exist experimental methods, which measure the metabolic stability
of xenobiotics and identify their metabolites, developing higher throughput
predictive methods is an avenue of research. It is expected that predicting
the chemical nature of the metabolites would be an asset for designing
safer drugs and/or drugs with modulated half-lives. We have developed
IMPACTS (In-silico Metabolism Prediction by Activated Cytochromes
and Transition States), a computational tool combining docking to
metabolic enzymes, transition state modeling, and rule-based substrate
reactivity prediction to predict the site of metabolism (SoM) of xenobiotics.
Its application to sets of CYP1A2, CYP2C9, CYP2D6, and CYP3A4 substrates
and comparison to expertsā predictions demonstrates its accuracy
and significance. IMPACTS identified an experimentally observed SoM
in the top 2 predicted sites for 77% of the substrates, while the
accuracy of biotransformation expertsā prediction was 65%.
Application of IMPACTS to external sets and comparison of its accuracy
to those of eleven other methods further validated the method implemented
in IMPACTS
Development of a Computational Tool to Rival Experts in the Prediction of Sites of Metabolism of Xenobiotics by P450s
The metabolism of xenobioticsīøand more specifically
drugsīøin
the liver is a critical process controlling their half-life. Although
there exist experimental methods, which measure the metabolic stability
of xenobiotics and identify their metabolites, developing higher throughput
predictive methods is an avenue of research. It is expected that predicting
the chemical nature of the metabolites would be an asset for designing
safer drugs and/or drugs with modulated half-lives. We have developed
IMPACTS (In-silico Metabolism Prediction by Activated Cytochromes
and Transition States), a computational tool combining docking to
metabolic enzymes, transition state modeling, and rule-based substrate
reactivity prediction to predict the site of metabolism (SoM) of xenobiotics.
Its application to sets of CYP1A2, CYP2C9, CYP2D6, and CYP3A4 substrates
and comparison to expertsā predictions demonstrates its accuracy
and significance. IMPACTS identified an experimentally observed SoM
in the top 2 predicted sites for 77% of the substrates, while the
accuracy of biotransformation expertsā prediction was 65%.
Application of IMPACTS to external sets and comparison of its accuracy
to those of eleven other methods further validated the method implemented
in IMPACTS
Development of a Computational Tool to Rival Experts in the Prediction of Sites of Metabolism of Xenobiotics by P450s
The metabolism of xenobioticsīøand more specifically
drugsīøin
the liver is a critical process controlling their half-life. Although
there exist experimental methods, which measure the metabolic stability
of xenobiotics and identify their metabolites, developing higher throughput
predictive methods is an avenue of research. It is expected that predicting
the chemical nature of the metabolites would be an asset for designing
safer drugs and/or drugs with modulated half-lives. We have developed
IMPACTS (In-silico Metabolism Prediction by Activated Cytochromes
and Transition States), a computational tool combining docking to
metabolic enzymes, transition state modeling, and rule-based substrate
reactivity prediction to predict the site of metabolism (SoM) of xenobiotics.
Its application to sets of CYP1A2, CYP2C9, CYP2D6, and CYP3A4 substrates
and comparison to expertsā predictions demonstrates its accuracy
and significance. IMPACTS identified an experimentally observed SoM
in the top 2 predicted sites for 77% of the substrates, while the
accuracy of biotransformation expertsā prediction was 65%.
Application of IMPACTS to external sets and comparison of its accuracy
to those of eleven other methods further validated the method implemented
in IMPACTS
Docking Ligands into Flexible and Solvated Macromolecules. 7. Impact of Protein Flexibility and Water Molecules on Docking-Based Virtual Screening Accuracy
The use of predictive computational
methods in the drug discovery
process is in a state of continual growth. Over the last two decades,
an increasingly large number of docking tools have been developed
to identify hits or optimize lead molecules through in-silico screening
of chemical libraries to proteins. In recent years, the focus has
been on implementing protein flexibility and water molecules. Our
efforts led to the development of Fitted first reported in
2007 and further developed since then. In this study, we wished to
evaluate the impact of protein flexibility and occurrence of water
molecules on the accuracy of the Fitted docking program to
discriminate active compounds from inactive compounds in virtual screening
(VS) campaigns. For this purpose, a total of 171 proteins cocrystallized
with small molecules representing 40 unique enzymes and receptors
as well as sets of known ligands and decoys were selected from the
Protein Data Bank (PDB) and the Directory of Useful Decoys (DUD),
respectively. This study revealed that implementing displaceable crystallographic
or computationally placed particle water molecules and protein flexibility
can improve the enrichment in active compounds. In addition, an informed
decision based on library diversity or research objectives (hit discovery
vs lead optimization) on which implementation to use may lead to significant
improvements
Integrating Medicinal Chemistry, Organic/Combinatorial Chemistry, and Computational Chemistry for the Discovery of Selective Estrogen Receptor Modulators with Forecaster, a Novel Platform for Drug Discovery
As part of a large medicinal chemistry program, we wish to develop novel selective estrogen receptor modulators (SERMs) as potential breast cancer treatments using a combination of experimental and computational approaches. However, one of the remaining difficulties nowadays is to fully integrate computational (i.e., virtual, theoretical) and medicinal (i.e., experimental, intuitive) chemistry to take advantage of the full potential of both. For this purpose, we have developed a Web-based platform, Forecaster, and a number of programs (e.g., Prepare, React, Select) with the aim of combining computational chemistry and medicinal chemistry expertise to facilitate drug discovery and development and more specifically to integrate synthesis into computer-aided drug design. In our quest for potent SERMs, this platform was used to build virtual combinatorial libraries, filter and extract a highly diverse library from the NCI database, and dock them to the estrogen receptor (ER), with all of these steps being fully automated by computational chemists for use by medicinal chemists. As a result, virtual screening of a diverse library seeded with active compounds followed by a search for analogs yielded an enrichment factor of 129, with 98% of the seeded active compounds recovered, while the screening of a designed virtual combinatorial library including known actives yielded an area under the receiver operating characteristic (AU-ROC) of 0.78. The lead optimization proved less successful, further demonstrating the challenge to simulate structure activity relationship studies
Virtual Screening and Computational Optimization for the Discovery of Covalent Prolyl Oligopeptidase Inhibitors with Activity in Human Cells
Our docking program, Fitted, implemented in our computational
platform, Forecaster, has been modified to carry out automated virtual
screening of covalent inhibitors. With this modified version of the
program, virtual screening and further docking-based optimization
of a selected hit led to the identification of potential covalent
reversible inhibitors of prolyl oligopeptidase activity. After visual
inspection, a virtual hit molecule together with four analogues were
selected for synthesis and made in oneāfive chemical steps.
Biological evaluations on recombinant POP and FAPĪ± enzymes,
cell extracts, and living cells demonstrated high potency and selectivity
for POP over FAPĪ± and DPPIV. Three compounds even exhibited
high nanomolar inhibitory activities in intact living human cells
and acceptable metabolic stability. This small set of molecules also
demonstrated that covalent binding and/or geometrical constraints
to the ligand/protein complex may lead to an increase in bioactivity