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Folding biological sequences on a th...
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Mayne, Martin.
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Folding biological sequences on a three-dimensional triangular lattice: Algorithms and tools.
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Folding biological sequences on a three-dimensional triangular lattice: Algorithms and tools./
作者:
Mayne, Martin.
面頁冊數:
91 p.
附註:
Adviser: Minghui Jiang.
Contained By:
Masters Abstracts International46-02.
標題:
Artificial Intelligence. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1448061
ISBN:
9780549215554
Folding biological sequences on a three-dimensional triangular lattice: Algorithms and tools.
Mayne, Martin.
Folding biological sequences on a three-dimensional triangular lattice: Algorithms and tools.
- 91 p.
Adviser: Minghui Jiang.
Thesis (M.S.)--Utah State University, 2007.
One approach to predicting the folded structure of proteins involves modeling each amino acid base as a single point on a lattice and then laying out the protein so that if forms a self-avoiding walk on the lattice. The amino acids are then divided into hydrophobic (H) and polar (P) groups. An approximation of the folded structure can then be predicted by finding a folding that maximizes the number of H-H contacts. This modelis known as the hydrophobic-hydrophilic (HP) model. Although easily adapted, this approach has not been used previously for predicting the folded structure of RNA. WE introduce the Delta, tool set for the analyzing the structure of RNA on a 3D triangular lattice. The Delta tool set includes a proof-of-concept RNA folding program that is both fast and accurate in predicting the secondary structures with pseudoknots of short RNA sequences.
ISBN: 9780549215554Subjects--Topical Terms:
769149
Artificial Intelligence.
Folding biological sequences on a three-dimensional triangular lattice: Algorithms and tools.
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One approach to predicting the folded structure of proteins involves modeling each amino acid base as a single point on a lattice and then laying out the protein so that if forms a self-avoiding walk on the lattice. The amino acids are then divided into hydrophobic (H) and polar (P) groups. An approximation of the folded structure can then be predicted by finding a folding that maximizes the number of H-H contacts. This modelis known as the hydrophobic-hydrophilic (HP) model. Although easily adapted, this approach has not been used previously for predicting the folded structure of RNA. WE introduce the Delta, tool set for the analyzing the structure of RNA on a 3D triangular lattice. The Delta tool set includes a proof-of-concept RNA folding program that is both fast and accurate in predicting the secondary structures with pseudoknots of short RNA sequences.
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