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231224s2013 xx |||||o 00| ||eng c |
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|a 10.1002/jcc.23286
|2 doi
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|a pubmed24n0756.xml
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|a (NLM)23620471
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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|a Nicolini, Paolo
|e verfasserin
|4 aut
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|a Toward quantitative estimates of binding affinities for protein-ligand systems involving large inhibitor compounds
|b a steered molecular dynamics simulation route
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|c 2013
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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|a Date Completed 19.09.2014
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|a Date Revised 23.02.2022
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Copyright © 2013 Wiley Periodicals, Inc.
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|a Understanding binding mechanisms between enzymes and potential inhibitors and quantifying protein-ligand affinities in terms of binding free energy is of primary importance in drug design studies. In this respect, several approaches based on molecular dynamics simulations, often combined with docking techniques, have been exploited to investigate the physicochemical properties of complexes of pharmaceutical interest. Even if the geometric properties of a modeled protein-ligand complex can be well predicted by computational methods, it is still challenging to rank with chemical accuracy a series of ligand analogues in a consistent way. In this article, we face this issue calculating relative binding free energies of a focal adhesion kinase, an important target for the development of anticancer drugs, with pyrrolopyrimidine-based ligands having different inhibitory power. To this aim, we employ steered molecular dynamics simulations combined with nonequilibrium work theorems for free energy calculations. This technique proves very powerful when a series of ligand analogues is considered, allowing one to tackle estimation of protein-ligand relative binding free energies in a reasonable time. In our cases, the calculated binding affinities are comparable with those recovered from experiments by exploiting the Michaelis-Menten mechanism with a competitive inhibitor
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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|a Antineoplastic Agents
|2 NLM
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|a Enzyme Inhibitors
|2 NLM
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|a Ligands
|2 NLM
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|a Pyrimidines
|2 NLM
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|a Pyrroles
|2 NLM
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|a pyrrolopyrimidine
|2 NLM
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|a Focal Adhesion Protein-Tyrosine Kinases
|2 NLM
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|a EC 2.7.10.2
|2 NLM
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|a Frezzato, Diego
|e verfasserin
|4 aut
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|a Gellini, Cristina
|e verfasserin
|4 aut
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|a Bizzarri, Marco
|e verfasserin
|4 aut
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|a Chelli, Riccardo
|e verfasserin
|4 aut
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|i Enthalten in
|t Journal of computational chemistry
|d 1984
|g 34(2013), 18 vom: 05. Juli, Seite 1561-76
|w (DE-627)NLM098138448
|x 1096-987X
|7 nnns
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|g volume:34
|g year:2013
|g number:18
|g day:05
|g month:07
|g pages:1561-76
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|u http://dx.doi.org/10.1002/jcc.23286
|3 Volltext
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