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Modeling protein loops with knowledge-based prediction of sequence-structure alignment
Author(s) -
HungPin Peng,
AnSuei Yang
Publication year - 2007
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btm456
Subject(s) - loop (graph theory) , loop modeling , computer science , template , protein structure database , set (abstract data type) , sequence (biology) , ranking (information retrieval) , protein structure prediction , data mining , structural alignment , artificial intelligence , sequence alignment , protein structure , peptide sequence , sequence database , biology , mathematics , biochemistry , genetics , combinatorics , gene , programming language
As protein structure database expands, protein loop modeling remains an important and yet challenging problem. Knowledge-based protein loop prediction methods have met with two challenges in methodology development: (1) loop boundaries in protein structures are frequently problematic in constructing length-dependent loop databases for protein loop predictions; (2) knowledge-based modeling of loops of unknown structure requires both aligning a query loop sequence to loop templates and ranking the loop sequence-template matches.

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