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Computing free energy, binding and competition within Fragment Based Drug Discovery

Computing free energy, binding and competition within Fragment Based Drug Discovery
Computing free energy, binding and competition within Fragment Based Drug Discovery
The development of JAFS, a new computational method to study the binding geometries of small fragment molecules to protein cavities, estimate their binding affnities and analyse how they compete for a common protein binding site, all in the context of Fragment Based Drug Discovery, is presented in this thesis.

Fragment Based Drug Discovery is an approach to drug development which studies the binding of small ligands (fragments) forming high quality interactions with their target. Further optimization of these fragments into drug-like molecules, adding functionalities to increase affnity while controlling other relevant properties such as toxicity and absorption then takes place. JAFS studies the binding of fragments to their target proteins.

The JAFS method consists of the execution and analysis of Monte Carlo simulations of fragments (and waters) in the binding cavities of proteins with an added degree of freedom which accounts for the scaling of the interaction energy of the fragment (and water). Sampling of states at very low interaction energies gives a boost in fragment configurational sampling while competition between different fragments to remain at unscaled (high) interaction energies at a given binding site provides information on their relative binding affinities. JAFS is built on the JAWS formulation for water binding to protein cavities.

The performance of the JAFS method on a range of different test cases (T4 Lyzozyme, Major Urinary Protein I, Cyclin Dependent Kinase 2 and Heat Shock Protein 90) was studied. JAFS is divided in two protocols to rank fragments by affnity and locate binding geometries, respectively. The ranking of fragments by affnity to a common protein target was satisfactory (as compared to experimental data) for the simpler systems (T4 Lyzozyme and Major Urinary Protein I). However, more demanding systems proved problematic, where the ranking of nine different ligands to the binding site of Cyclin Dependent Kinase 2 provided results unrelated to experimental binding affnities.

Studying pose generation in sets of five repeats per simulation, the crystal binding geometry of every fragment studied was found in at least one of the repeats, without providing any previous information on the system (such as the presence or location of water mediated interactions or the hydration state of the cavity). Consistency between repeats was however found to be problematic and no method is currently able to select the optimal binding geometry among all the generated poses. Suggestions are given for further developments which would provide a methodology to rank poses.
Cabedo Martinez, Ana
5eaa5bcb-fef2-4c60-babe-c92d3a8b68a4
Cabedo Martinez, Ana
5eaa5bcb-fef2-4c60-babe-c92d3a8b68a4
Essex, Jonathan
1f409cfe-6ba4-42e2-a0ab-a931826314b5

Cabedo Martinez, Ana (2016) Computing free energy, binding and competition within Fragment Based Drug Discovery. University of Southampton, Faculty of Natural and Environmental Science, Doctoral Thesis, 352pp.

Record type: Thesis (Doctoral)

Abstract

The development of JAFS, a new computational method to study the binding geometries of small fragment molecules to protein cavities, estimate their binding affnities and analyse how they compete for a common protein binding site, all in the context of Fragment Based Drug Discovery, is presented in this thesis.

Fragment Based Drug Discovery is an approach to drug development which studies the binding of small ligands (fragments) forming high quality interactions with their target. Further optimization of these fragments into drug-like molecules, adding functionalities to increase affnity while controlling other relevant properties such as toxicity and absorption then takes place. JAFS studies the binding of fragments to their target proteins.

The JAFS method consists of the execution and analysis of Monte Carlo simulations of fragments (and waters) in the binding cavities of proteins with an added degree of freedom which accounts for the scaling of the interaction energy of the fragment (and water). Sampling of states at very low interaction energies gives a boost in fragment configurational sampling while competition between different fragments to remain at unscaled (high) interaction energies at a given binding site provides information on their relative binding affinities. JAFS is built on the JAWS formulation for water binding to protein cavities.

The performance of the JAFS method on a range of different test cases (T4 Lyzozyme, Major Urinary Protein I, Cyclin Dependent Kinase 2 and Heat Shock Protein 90) was studied. JAFS is divided in two protocols to rank fragments by affnity and locate binding geometries, respectively. The ranking of fragments by affnity to a common protein target was satisfactory (as compared to experimental data) for the simpler systems (T4 Lyzozyme and Major Urinary Protein I). However, more demanding systems proved problematic, where the ranking of nine different ligands to the binding site of Cyclin Dependent Kinase 2 provided results unrelated to experimental binding affnities.

Studying pose generation in sets of five repeats per simulation, the crystal binding geometry of every fragment studied was found in at least one of the repeats, without providing any previous information on the system (such as the presence or location of water mediated interactions or the hydration state of the cavity). Consistency between repeats was however found to be problematic and no method is currently able to select the optimal binding geometry among all the generated poses. Suggestions are given for further developments which would provide a methodology to rank poses.

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Ana Cabedo Martinez final thesis.pdf - Other
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Published date: May 2016
Organisations: University of Southampton, Chemistry

Identifiers

Local EPrints ID: 403850
URI: https://eprints.soton.ac.uk/id/eprint/403850
PURE UUID: 36a51a77-adc8-492d-a5c7-fa78b3d92c63
ORCID for Jonathan Essex: ORCID iD orcid.org/0000-0003-2639-2746

Catalogue record

Date deposited: 14 Dec 2016 16:13
Last modified: 06 Oct 2018 04:12

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Contributors

Author: Ana Cabedo Martinez
Thesis advisor: Jonathan Essex ORCID iD

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