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The alarming antibiotic resistance spread is the main reason
that demands the continued investigation to search for new antimicrobial
agents. DNA Gyrase is a validated antibacterial target that can be used for
this purpose. This essential prokaryotic type II topoisomerase enzyme is
involved in DNA replication, transcription and recombination by introducing
negative supercoiling in DNA at the expenses of ATP hydrolysis. It consists of two
subunits Gyrase A (GyrA) which participate in DNA breakage and reunion and two
subunits Gyrase B (GyrB) that catalyze the hydrolysis of ATP. The interest in
the Gyrase B (GyrB) subunit is growing because of persistent resistance
problems of GyrA inhibitors.
In this thesis, research on DNA Gyrase B ATPase inhibition
was performed by using both structure and ligand-based modelling techniques.
First, the antibiotic crisis, the biological function of the gyrase enzyme as
well as the outlook on the theoretical background of different target and
ligand-based modelling methods are briefly described. In addition, a review of
thestate of the art of the GyrB inhibitors and the reports of modelling
techniques used so far to address this topic are presented.
Molecular dynamic simulations were used to explore the
conformational changes of the protein in bound and free state in the endeavour
of understanding the role of flexible loops. Furthermore, force field based
free energy calculation techniques, like computational alanine scanning and
energy decomposition at an atomic level were used to study the interaction of
known inhibitors with residues of the ATP binding site. The study suggested
that both techniques could provide valuable information about the possible
binding mode when studying to-be-determinedbinders.
Molecular recognition at DNA gyrase B enzymeinhibitor
binding interface was rationalized by means of molecular docking and further
refinement of the obtained poses with short Molecular Dynamicsimulations which
enabled the use of rescoring schemes based on MM-PB(GB)SA and Zapbind. Although
MM-PBSA outperformed the rest of the methods, limitations in getting accurate
results when using this methodin virtual screening to search for GyrB
inhibitors were discussed.
In an effort to overcome these limitations, ligand-based
Quantitative-Structure-Activity-Relationship models for binary classification
(active/inactive compounds) were built. Adaboost ensembles of 1-feature Linear
Discriminant Analysis models were generated during Genetic Algorithms (GA) iterations as well as Adaboost and Voting
Meta-Ensembles. In addition, Least SquaresSupport Vector Machine involving GA
for the selection of the features (GA-LS-SVM) were explored using molecular
descriptors that encoded diverse structural information. The study revealed
that despite these models lack a straightforward interpretation of the
influence of the structural encoded information in the biological activity;
they outperformed the target based approaches when it comes to correlating
structural information with experimental enzyme activity data.</>
Moreover, information from more simple regression models and
molecular docking has been used to guide the design of new analogues with the
aim to improve the potency profile. Last but not least, a wet experimental
screening was performed by means of a supercoiling assay to a set of thirteen 2-
and 3-carboxamide chromone like compounds showing a common chromone moiety with
quercetin, a proven GyrB inhibitor. Twoout of the thirteen analogues showed
weak supercoiling inhibition and some modifications were proposed based on
molecular docking bindingmode prediction to enhance potency and selectivity
toward bacterial GyrB.
Despite the limitations of the modelling techniques explored
along this work, we consider they can be combined and used carefully to address
the search for new GyrB inhibitors.
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Project
Understanding DNA GyraseB ATPase inhibition in the light of structure and ligand-based modelling techniques.
The alarming antibiotic resistance spread is the main reason
that demands the continued investigation to search for new antimicrobial
agents. DNA Gyrase is a validated antibacterial target that can be used for
this purpose. This essential prokaryotic type II topoisomerase enzyme is
involved in DNA replication, transcription and recombination by introducing
negative supercoiling in DNA at the expenses of ATP hydrolysis. It consists of two
subunits Gyrase A (GyrA) which participate in DNA breakage and reunion and two
subunits Gyrase B (GyrB) that catalyze the hydrolysis of ATP. The interest in
the Gyrase B (GyrB) subunit is growing because of persistent resistance
problems of GyrA inhibitors.
In this thesis, research on DNA Gyrase B ATPase inhibition
was performed by using both structure and ligand-based modelling techniques.
First, the antibiotic crisis, the biological function of the gyrase enzyme as
well as the outlook on the theoretical background of different target and
ligand-based modelling methods are briefly described. In addition, a review of
thestate of the art of the GyrB inhibitors and the reports of modelling
techniques used so far to address this topic are presented.
Molecular dynamic simulations were used to explore the
conformational changes of the protein in bound and free state in the endeavour
of understanding the role of flexible loops. Furthermore, force field based
free energy calculation techniques, like computational alanine scanning and
energy decomposition at an atomic level were used to study the interaction of
known inhibitors with residues of the ATP binding site. The study suggested
that both techniques could provide valuable information about the possible
binding mode when studying to-be-determinedbinders.
Molecular recognition at DNA gyrase B enzymeinhibitor
binding interface was rationalized by means of molecular docking and further
refinement of the obtained poses with short Molecular Dynamicsimulations which
enabled the use of rescoring schemes based on MM-PB(GB)SA and Zapbind. Although
MM-PBSA outperformed the rest of the methods, limitations in getting accurate
results when using this methodin virtual screening to search for GyrB
inhibitors were discussed.
In an effort to overcome these limitations, ligand-based
Quantitative-Structure-Activity-Relationship models for binary classification
(active/inactive compounds) were built. Adaboost ensembles of 1-feature Linear
Discriminant Analysis models were generated during Genetic Algorithms (GA) iterations as well as Adaboost and Voting
Meta-Ensembles. In addition, Least SquaresSupport Vector Machine involving GA
for the selection of the features (GA-LS-SVM) were explored using molecular
descriptors that encoded diverse structural information. The study revealed
that despite these models lack a straightforward interpretation of the
influence of the structural encoded information in the biological activity;
they outperformed the target based approaches when it comes to correlating
structural information with experimental enzyme activity data.</>
Moreover, information from more simple regression models and
molecular docking has been used to guide the design of new analogues with the
aim to improve the potency profile. Last but not least, a wet experimental
screening was performed by means of a supercoiling assay to a set of thirteen 2-
and 3-carboxamide chromone like compounds showing a common chromone moiety with
quercetin, a proven GyrB inhibitor. Twoout of the thirteen analogues showed
weak supercoiling inhibition and some modifications were proposed based on
molecular docking bindingmode prediction to enhance potency and selectivity
toward bacterial GyrB.
Despite the limitations of the modelling techniques explored
along this work, we consider they can be combined and used carefully to address
the search for new GyrB inhibitors.
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Date:22 Nov 2008 → 12 Dec 2012
Keywords:Antibacterial agents
Disciplines:Biomarker discovery and evaluation, Drug discovery and development, Medicinal products, Pharmaceutics, Pharmacognosy and phytochemistry, Pharmacology, Pharmacotherapy, Toxicology and toxinology, Other pharmaceutical sciences
Project type:PhD project