Open AccessAb initio protein structure prediction using pathway models
Author(s) -
Yuan Xin,
Shao Yu,
Bystroff Christopher
Publication year - 2003
Publication title -
comparative and functional genomics
Language(s) - English
Resource type - Journals
eISSN - 1532-6268
pISSN - 1531-6912
DOI - 10.1002/cfg.305
Subject(s) - computer science , ab initio , set (abstract data type) , function (biology) , protein structure prediction , folding (dsp implementation) , data mining , sequence (biology) , space (punctuation) , artificial intelligence , theoretical computer science , computational biology , protein structure , chemistry , biology , engineering , biochemistry , organic chemistry , evolutionary biology , electrical engineering , programming language , operating system
Ab initio prediction is the challenging attempt to predict protein structures based only on sequence information and without using templates. It is often divided into two distinct sub‐problems: (a) the scoring function that can distinguish native, or native‐like structures, from non‐native ones; and (b) the method of searching the conformational space. Currently, there is no reliable scoring function that can always drive a search to the native fold, and there is no general search method that can guarantee a significant sampling of near‐natives. Pathway models combine the scoring function and the search. In this short review, we explore some of the ways pathway models are used in folding, in published works since 2001, and present a new pathway model, HMMSTR‐CM, that uses a fragment library and a set of nucleation/propagation‐based rules. The new method was used for ab initio predictions as part of CASP5. This work was presented at the Winter School in Bioinformatics, Bologna, Italy, 10–14 February 2003. Copyright © 2003 John Wiley & Sons, Ltd.