Applying Computational Scoring Functions to Assess Biomolecular Interactions in Food Science: Applications to the Estrogen Receptors

During the last decade, computational methods, which were for the most part developed to study protein-ligand interactions and especially to discover, design and develop drugs by and for medicinal chemists, have been successfully applied in a variety of food science applications [1,2]. It is now cle...
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Gespeichert in:

Autor*in:	Francesca Spyrakis [verfasserIn] Pietro Cozzini [verfasserIn] Glen Eugene Kellogg [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	Docking Scoring Functions Computational Chemistry Nuclear Receptors Estrogen Receptors Food Science Drug Discovery Endocrine Disruptors Binding Free Energy Force Field HINT

Übergeordnetes Werk:	In: Nuclear Receptor Research - KenzPub, 2015, 3(2016), Seite 20
Übergeordnetes Werk:	volume:3 ; year:2016 ; pages:20

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.11131/2016/101202

Katalog-ID:	DOAJ054110289

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callnumber-first	Q - Science
author	Francesca Spyrakis
spellingShingle	Francesca Spyrakis misc QD415-436 misc Docking misc Scoring Functions misc Computational Chemistry misc Nuclear Receptors misc Estrogen Receptors misc Food Science misc Drug Discovery misc Endocrine Disruptors misc Binding Free Energy misc Force Field misc HINT misc Science misc Q misc Biochemistry Applying Computational Scoring Functions to Assess Biomolecular Interactions in Food Science: Applications to the Estrogen Receptors
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topic_title	QD415-436 Applying Computational Scoring Functions to Assess Biomolecular Interactions in Food Science: Applications to the Estrogen Receptors Docking Scoring Functions Computational Chemistry Nuclear Receptors Estrogen Receptors Food Science Drug Discovery Endocrine Disruptors Binding Free Energy Force Field HINT
topic	misc QD415-436 misc Docking misc Scoring Functions misc Computational Chemistry misc Nuclear Receptors misc Estrogen Receptors misc Food Science misc Drug Discovery misc Endocrine Disruptors misc Binding Free Energy misc Force Field misc HINT misc Science misc Q misc Biochemistry
topic_unstemmed	misc QD415-436 misc Docking misc Scoring Functions misc Computational Chemistry misc Nuclear Receptors misc Estrogen Receptors misc Food Science misc Drug Discovery misc Endocrine Disruptors misc Binding Free Energy misc Force Field misc HINT misc Science misc Q misc Biochemistry
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title	Applying Computational Scoring Functions to Assess Biomolecular Interactions in Food Science: Applications to the Estrogen Receptors
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title_full	Applying Computational Scoring Functions to Assess Biomolecular Interactions in Food Science: Applications to the Estrogen Receptors
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title_sort	applying computational scoring functions to assess biomolecular interactions in food science: applications to the estrogen receptors
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title_auth	Applying Computational Scoring Functions to Assess Biomolecular Interactions in Food Science: Applications to the Estrogen Receptors
abstract	During the last decade, computational methods, which were for the most part developed to study protein-ligand interactions and especially to discover, design and develop drugs by and for medicinal chemists, have been successfully applied in a variety of food science applications [1,2]. It is now clear, in fact, that drugs and nutritional molecules behave in the same way when binding to a macromolecular target or receptor, and that many of the approaches used so extensively in medicinal chemistry can be easily transferred to the fields of food science. For instance, nuclear receptors are common targets for a number of drug molecules and could be, in the same way, affected by the interaction with food or food-like molecules. Thus, key computational medicinal chemistry methods like molecular dynamics can be used to decipher protein flexibility and to obtain stable models for docking and scoring in food-related studies, and virtual screening is increasingly being applied to identify molecules with potential to act as endocrine disruptors, food mycotoxins, and new nutraceuticals [3,4,5]. All of these methods and simulations are based on protein-ligand interaction phenomena, and represent the basis for any subsequent modification of the targeted receptor's or enzyme's physiological activity. We describe here the energetics of binding of biological complexes, providing a survey of the most common and successful algorithms used in evaluating these energetics, and we report case studies in which computational techniques have been applied to food science issues. In particular, we explore a handful of studies involving the estrogen receptors for which we have a long-term interest.
abstractGer	During the last decade, computational methods, which were for the most part developed to study protein-ligand interactions and especially to discover, design and develop drugs by and for medicinal chemists, have been successfully applied in a variety of food science applications [1,2]. It is now clear, in fact, that drugs and nutritional molecules behave in the same way when binding to a macromolecular target or receptor, and that many of the approaches used so extensively in medicinal chemistry can be easily transferred to the fields of food science. For instance, nuclear receptors are common targets for a number of drug molecules and could be, in the same way, affected by the interaction with food or food-like molecules. Thus, key computational medicinal chemistry methods like molecular dynamics can be used to decipher protein flexibility and to obtain stable models for docking and scoring in food-related studies, and virtual screening is increasingly being applied to identify molecules with potential to act as endocrine disruptors, food mycotoxins, and new nutraceuticals [3,4,5]. All of these methods and simulations are based on protein-ligand interaction phenomena, and represent the basis for any subsequent modification of the targeted receptor's or enzyme's physiological activity. We describe here the energetics of binding of biological complexes, providing a survey of the most common and successful algorithms used in evaluating these energetics, and we report case studies in which computational techniques have been applied to food science issues. In particular, we explore a handful of studies involving the estrogen receptors for which we have a long-term interest.
abstract_unstemmed	During the last decade, computational methods, which were for the most part developed to study protein-ligand interactions and especially to discover, design and develop drugs by and for medicinal chemists, have been successfully applied in a variety of food science applications [1,2]. It is now clear, in fact, that drugs and nutritional molecules behave in the same way when binding to a macromolecular target or receptor, and that many of the approaches used so extensively in medicinal chemistry can be easily transferred to the fields of food science. For instance, nuclear receptors are common targets for a number of drug molecules and could be, in the same way, affected by the interaction with food or food-like molecules. Thus, key computational medicinal chemistry methods like molecular dynamics can be used to decipher protein flexibility and to obtain stable models for docking and scoring in food-related studies, and virtual screening is increasingly being applied to identify molecules with potential to act as endocrine disruptors, food mycotoxins, and new nutraceuticals [3,4,5]. All of these methods and simulations are based on protein-ligand interaction phenomena, and represent the basis for any subsequent modification of the targeted receptor's or enzyme's physiological activity. We describe here the energetics of binding of biological complexes, providing a survey of the most common and successful algorithms used in evaluating these energetics, and we report case studies in which computational techniques have been applied to food science issues. In particular, we explore a handful of studies involving the estrogen receptors for which we have a long-term interest.
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title_short	Applying Computational Scoring Functions to Assess Biomolecular Interactions in Food Science: Applications to the Estrogen Receptors
url	https://doi.org/10.11131/2016/101202 https://doaj.org/article/046dd3920ef94a839a725a081360f33b http://www.kenzpub.com/journals/nurr/2016/101202/ https://doaj.org/toc/2314-5714
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Applying Computational Scoring Functions to Assess Biomolecular Interactions in Food Science: Applications to the Estrogen Receptors

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