Protein Structure Prediction in Structure based drug design : Protein structure prediction in medicinal chemistry
Identifieur interne : 000184 ( PascalFrancis/Curation ); précédent : 000183; suivant : 000185Protein 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.
Descripteurs français
- Pascal (Inist)
English descriptors
- KwdEn :
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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<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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<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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