SrasV1, PascalFrancis, Curation, bibRecord, 000184

Protein Structure Prediction in Structure based drug design : Protein structure prediction in medicinal chemistry

Identifieur interne : 000184 ( PascalFrancis/Curation ); précédent : 000183; suivant : 000185

Protein Structure Prediction in Structure based drug design : Protein structure prediction in medicinal chemistry

Auteurs : Mayuko Takeda-Shitaka [Japon] ; Daisuke Takaya [Japon] ; Chieko Chiba [Japon] ; Hirokazu Tanaka [Japon] ; Hideaki Umeyama [Japon]

Source :

Current medicinal chemistry [ 0929-8673 ] ; 2004.

RBID : Pascal:04-0517242

Descripteurs français

Pascal (Inist)
- Article synthèse, Relation structure activité, Prédiction, Structure, Protéine, Structure secondaire, Génomique, Interaction moléculaire, Modélisation, Modèle moléculaire, Homologie, Coronavirus, Syndrome respiratoire aigu sévère, RNA-directed RNA polymerase, Site actif.

English descriptors

KwdEn :
- Active site, Coronavirus, Genomics, Homology, Modeling, Molecular interaction, Molecular model, Prediction, Protein, RNA-directed RNA polymerase, Review, Secondary structure, Severe acute respiratory syndrome, Structure, Structure activity relation.

Abstract

The human genome and other genome sequencing projects have generated huge amounts of protein sequence information. Recently, a structural genomics project that aims to determine at-least one representative three-dimensional structure from every protein family experimentally has been started. Homology modeling will play an essential role in structure based drug design such as in silico screening; because based on these representative structures the three-dimensional structures of the remaining proteins encoded in the various genomes can be predicted by homology modeling. The results of the last Critical Assessment of Techniques for Protein Structure Prediction (CASP5) demonstrated that the quality of homology modeling prediction has improved; reaching a level of reliability that biologists can now confidently use homology modeling. With improvements in modeling software and the growing number of known protein structures, homology modeling is becoming a more and more powerful and reliable tool. The present paper discusses the features and roles of homology modeling in structure based drug design, and describes the CHIMERA and FAMS modeling systems as examples. For a sample application, homology modeling of non-structural proteins of the severe acute respiratory syndrome (SARS) coronavirus is discussed. Many biological functions involve formation of protein-protein complexes; in which the protein molecules behave dynamically in the course of binding. Therefore, an understanding of protein-protein interaction will be very important for structure based drug design. To this end, normal mode analysis is useful. The present paper discusses the prediction of protein-protein interaction using normal mode analysis and examples of applications are given.

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C01	`01`		`ENG`	@0 The human genome and other genome sequencing projects have generated huge amounts of protein sequence information. Recently, a structural genomics project that aims to determine at-least one representative three-dimensional structure from every protein family experimentally has been started. Homology modeling will play an essential role in structure based drug design such as in silico screening; because based on these representative structures the three-dimensional structures of the remaining proteins encoded in the various genomes can be predicted by homology modeling. The results of the last Critical Assessment of Techniques for Protein Structure Prediction (CASP5) demonstrated that the quality of homology modeling prediction has improved; reaching a level of reliability that biologists can now confidently use homology modeling. With improvements in modeling software and the growing number of known protein structures, homology modeling is becoming a more and more powerful and reliable tool. The present paper discusses the features and roles of homology modeling in structure based drug design, and describes the CHIMERA and FAMS modeling systems as examples. For a sample application, homology modeling of non-structural proteins of the severe acute respiratory syndrome (SARS) coronavirus is discussed. Many biological functions involve formation of protein-protein complexes; in which the protein molecules behave dynamically in the course of binding. Therefore, an understanding of protein-protein interaction will be very important for structure based drug design. To this end, normal mode analysis is useful. The present paper discusses the prediction of protein-protein interaction using normal mode analysis and examples of applications are given.
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Le document en format XML

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<front><div type="abstract" xml:lang="en">The human genome and other genome sequencing projects have generated huge amounts of protein sequence information. Recently, a structural genomics project that aims to determine at-least one representative three-dimensional structure from every protein family experimentally has been started. Homology modeling will play an essential role in structure based drug design such as in silico screening; because based on these representative structures the three-dimensional structures of the remaining proteins encoded in the various genomes can be predicted by homology modeling. The results of the last Critical Assessment of Techniques for Protein Structure Prediction (CASP5) demonstrated that the quality of homology modeling prediction has improved; reaching a level of reliability that biologists can now confidently use homology modeling. With improvements in modeling software and the growing number of known protein structures, homology modeling is becoming a more and more powerful and reliable tool. The present paper discusses the features and roles of homology modeling in structure based drug design, and describes the CHIMERA and FAMS modeling systems as examples. For a sample application, homology modeling of non-structural proteins of the severe acute respiratory syndrome (SARS) coronavirus is discussed. Many biological functions involve formation of protein-protein complexes; in which the protein molecules behave dynamically in the course of binding. Therefore, an understanding of protein-protein interaction will be very important for structure based drug design. To this end, normal mode analysis is useful. The present paper discusses the prediction of protein-protein interaction using normal mode analysis and examples of applications are given.</div>
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<fA66 i1="01"><s0>NLD</s0>
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<fC01 i1="01" l="ENG"><s0>The human genome and other genome sequencing projects have generated huge amounts of protein sequence information. Recently, a structural genomics project that aims to determine at-least one representative three-dimensional structure from every protein family experimentally has been started. Homology modeling will play an essential role in structure based drug design such as in silico screening; because based on these representative structures the three-dimensional structures of the remaining proteins encoded in the various genomes can be predicted by homology modeling. The results of the last Critical Assessment of Techniques for Protein Structure Prediction (CASP5) demonstrated that the quality of homology modeling prediction has improved; reaching a level of reliability that biologists can now confidently use homology modeling. With improvements in modeling software and the growing number of known protein structures, homology modeling is becoming a more and more powerful and reliable tool. The present paper discusses the features and roles of homology modeling in structure based drug design, and describes the CHIMERA and FAMS modeling systems as examples. For a sample application, homology modeling of non-structural proteins of the severe acute respiratory syndrome (SARS) coronavirus is discussed. Many biological functions involve formation of protein-protein complexes; in which the protein molecules behave dynamically in the course of binding. Therefore, an understanding of protein-protein interaction will be very important for structure based drug design. To this end, normal mode analysis is useful. The present paper discusses the prediction of protein-protein interaction using normal mode analysis and examples of applications are given.</s0>
</fC01>
<fC02 i1="01" i2="X"><s0>002B02W</s0>
</fC02>
<fC03 i1="01" i2="X" l="FRE"><s0>Article synthèse</s0>
<s5>01</s5>
</fC03>
<fC03 i1="01" i2="X" l="ENG"><s0>Review</s0>
<s5>01</s5>
</fC03>
<fC03 i1="01" i2="X" l="SPA"><s0>Artículo síntesis</s0>
<s5>01</s5>
</fC03>
<fC03 i1="02" i2="X" l="FRE"><s0>Relation structure activité</s0>
<s5>03</s5>
</fC03>
<fC03 i1="02" i2="X" l="ENG"><s0>Structure activity relation</s0>
<s5>03</s5>
</fC03>
<fC03 i1="02" i2="X" l="SPA"><s0>Relación estructura actividad</s0>
<s5>03</s5>
</fC03>
<fC03 i1="03" i2="X" l="FRE"><s0>Prédiction</s0>
<s5>08</s5>
</fC03>
<fC03 i1="03" i2="X" l="ENG"><s0>Prediction</s0>
<s5>08</s5>
</fC03>
<fC03 i1="03" i2="X" l="SPA"><s0>Predicción</s0>
<s5>08</s5>
</fC03>
<fC03 i1="04" i2="X" l="FRE"><s0>Structure</s0>
<s5>09</s5>
</fC03>
<fC03 i1="04" i2="X" l="ENG"><s0>Structure</s0>
<s5>09</s5>
</fC03>
<fC03 i1="04" i2="X" l="SPA"><s0>Estructura</s0>
<s5>09</s5>
</fC03>
<fC03 i1="05" i2="X" l="FRE"><s0>Protéine</s0>
<s5>10</s5>
</fC03>
<fC03 i1="05" i2="X" l="ENG"><s0>Protein</s0>
<s5>10</s5>
</fC03>
<fC03 i1="05" i2="X" l="SPA"><s0>Proteína</s0>
<s5>10</s5>
</fC03>
<fC03 i1="06" i2="X" l="FRE"><s0>Structure secondaire</s0>
<s5>11</s5>
</fC03>
<fC03 i1="06" i2="X" l="ENG"><s0>Secondary structure</s0>
<s5>11</s5>
</fC03>
<fC03 i1="06" i2="X" l="SPA"><s0>Estructura secundaria</s0>
<s5>11</s5>
</fC03>
<fC03 i1="07" i2="X" l="FRE"><s0>Génomique</s0>
<s5>13</s5>
</fC03>
<fC03 i1="07" i2="X" l="ENG"><s0>Genomics</s0>
<s5>13</s5>
</fC03>
<fC03 i1="07" i2="X" l="SPA"><s0>Genómica</s0>
<s5>13</s5>
</fC03>
<fC03 i1="08" i2="X" l="FRE"><s0>Interaction moléculaire</s0>
<s5>15</s5>
</fC03>
<fC03 i1="08" i2="X" l="ENG"><s0>Molecular interaction</s0>
<s5>15</s5>
</fC03>
<fC03 i1="08" i2="X" l="SPA"><s0>Interacción molecular</s0>
<s5>15</s5>
</fC03>
<fC03 i1="09" i2="X" l="FRE"><s0>Modélisation</s0>
<s5>16</s5>
</fC03>
<fC03 i1="09" i2="X" l="ENG"><s0>Modeling</s0>
<s5>16</s5>
</fC03>
<fC03 i1="09" i2="X" l="SPA"><s0>Modelización</s0>
<s5>16</s5>
</fC03>
<fC03 i1="10" i2="X" l="FRE"><s0>Modèle moléculaire</s0>
<s5>17</s5>
</fC03>
<fC03 i1="10" i2="X" l="ENG"><s0>Molecular model</s0>
<s5>17</s5>
</fC03>
<fC03 i1="10" i2="X" l="SPA"><s0>Modelo molecular</s0>
<s5>17</s5>
</fC03>
<fC03 i1="11" i2="X" l="FRE"><s0>Homologie</s0>
<s5>18</s5>
</fC03>
<fC03 i1="11" i2="X" l="ENG"><s0>Homology</s0>
<s5>18</s5>
</fC03>
<fC03 i1="11" i2="X" l="SPA"><s0>Homología</s0>
<s5>18</s5>
</fC03>
<fC03 i1="12" i2="X" l="FRE"><s0>Coronavirus</s0>
<s2>NW</s2>
<s5>20</s5>
</fC03>
<fC03 i1="12" i2="X" l="ENG"><s0>Coronavirus</s0>
<s2>NW</s2>
<s5>20</s5>
</fC03>
<fC03 i1="12" i2="X" l="SPA"><s0>Coronavirus</s0>
<s2>NW</s2>
<s5>20</s5>
</fC03>
<fC03 i1="13" i2="X" l="FRE"><s0>Syndrome respiratoire aigu sévère</s0>
<s2>NM</s2>
<s5>21</s5>
</fC03>
<fC03 i1="13" i2="X" l="ENG"><s0>Severe acute respiratory syndrome</s0>
<s2>NM</s2>
<s5>21</s5>
</fC03>
<fC03 i1="13" i2="X" l="SPA"><s0>Síndrome respiratorio agudo severo</s0>
<s2>NM</s2>
<s5>21</s5>
</fC03>
<fC03 i1="14" i2="X" l="FRE"><s0>RNA-directed RNA polymerase</s0>
<s2>FE</s2>
<s5>23</s5>
</fC03>
<fC03 i1="14" i2="X" l="ENG"><s0>RNA-directed RNA polymerase</s0>
<s2>FE</s2>
<s5>23</s5>
</fC03>
<fC03 i1="14" i2="X" l="SPA"><s0>RNA-directed RNA polymerase</s0>
<s2>FE</s2>
<s5>23</s5>
</fC03>
<fC03 i1="15" i2="X" l="FRE"><s0>Site actif</s0>
<s5>24</s5>
</fC03>
<fC03 i1="15" i2="X" l="ENG"><s0>Active site</s0>
<s5>24</s5>
</fC03>
<fC03 i1="15" i2="X" l="SPA"><s0>Lugar activo</s0>
<s5>24</s5>
</fC03>
<fC07 i1="01" i2="X" l="FRE"><s0>Coronaviridae</s0>
<s2>NW</s2>
</fC07>
<fC07 i1="01" i2="X" l="ENG"><s0>Coronaviridae</s0>
<s2>NW</s2>
</fC07>
<fC07 i1="01" i2="X" l="SPA"><s0>Coronaviridae</s0>
<s2>NW</s2>
</fC07>
<fC07 i1="02" i2="X" l="FRE"><s0>Nidovirales</s0>
<s2>NW</s2>
</fC07>
<fC07 i1="02" i2="X" l="ENG"><s0>Nidovirales</s0>
<s2>NW</s2>
</fC07>
<fC07 i1="02" i2="X" l="SPA"><s0>Nidovirales</s0>
<s2>NW</s2>
</fC07>
<fC07 i1="03" i2="X" l="FRE"><s0>Virus</s0>
<s2>NW</s2>
</fC07>
<fC07 i1="03" i2="X" l="ENG"><s0>Virus</s0>
<s2>NW</s2>
</fC07>
<fC07 i1="03" i2="X" l="SPA"><s0>Virus</s0>
<s2>NW</s2>
</fC07>
<fC07 i1="04" i2="X" l="FRE"><s0>Virose</s0>
<s2>NM</s2>
</fC07>
<fC07 i1="04" i2="X" l="ENG"><s0>Viral disease</s0>
<s2>NM</s2>
</fC07>
<fC07 i1="04" i2="X" l="SPA"><s0>Virosis</s0>
<s2>NM</s2>
</fC07>
<fC07 i1="05" i2="X" l="FRE"><s0>Infection</s0>
<s2>NM</s2>
</fC07>
<fC07 i1="05" i2="X" l="ENG"><s0>Infection</s0>
<s2>NM</s2>
</fC07>
<fC07 i1="05" i2="X" l="SPA"><s0>Infección</s0>
<s2>NM</s2>
</fC07>
<fC07 i1="06" i2="X" l="FRE"><s0>Nucleotidyltransferases</s0>
<s2>FE</s2>
</fC07>
<fC07 i1="06" i2="X" l="ENG"><s0>Nucleotidyltransferases</s0>
<s2>FE</s2>
</fC07>
<fC07 i1="06" i2="X" l="SPA"><s0>Nucleotidyltransferases</s0>
<s2>FE</s2>
</fC07>
<fC07 i1="07" i2="X" l="FRE"><s0>Transferases</s0>
<s2>FE</s2>
</fC07>
<fC07 i1="07" i2="X" l="ENG"><s0>Transferases</s0>
<s2>FE</s2>
</fC07>
<fC07 i1="07" i2="X" l="SPA"><s0>Transferases</s0>
<s2>FE</s2>
</fC07>
<fC07 i1="08" i2="X" l="FRE"><s0>Enzyme</s0>
<s2>FE</s2>
</fC07>
<fC07 i1="08" i2="X" l="ENG"><s0>Enzyme</s0>
<s2>FE</s2>
</fC07>
<fC07 i1="08" i2="X" l="SPA"><s0>Enzima</s0>
<s2>FE</s2>
</fC07>
<fN21><s1>292</s1>
</fN21>
<fN44 i1="01"><s1>PSI</s1>
</fN44>
<fN82><s1>PSI</s1>
</fN82>
</pA>
</standard>
</inist>
</record>

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Protein Structure Prediction in Structure based drug design : Protein structure prediction in medicinal chemistry

Protein Structure Prediction in Structure based drug design : Protein structure prediction in medicinal chemistry

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