Name
Abstract | ||
|---|---|---|
During the last decade, significant progress has been made in solving the Protein Threading Problem (PTP). However, all previous approaches to PTP only perform global sequence–structure alignment. This obvious limitation is in clear contrast with the “world of sequences”, where local sequence–sequence alignments are widely used to find functionally important regions in families of proteins. This paper presents a novel approach to PTP which allows to align a part of a protein structure onto a protein sequence in order to detect local similarities. We show experimentally that such local sequence–structure alignments improve the quality of the prediction. Our approach is based on Mixed Integer Programming (MIP) which has been shown to be very successful in this domain. We describe five MIP models for local sequence–structure alignments, compare and analyze their performances by using ILOG CPLEX 10 solver on a benchmark of proteins. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.dam.2010.05.024 | Discrete Applied Mathematics |
Keywords | Field | DocType |
Mixed Integer Programming,Combinatorial optimization,Protein threading problem,Protein structure alignment | Discrete mathematics,Mathematical optimization,Protein sequencing,Threading (protein sequence),Combinatorial optimization,Theoretical computer science,Integer programming,Solver,Mathematics,Protein structure | Journal |
Volume | Issue | ISSN |
159 | 16 | 0166-218X |
Citations | PageRank | References |
1 | 0.37 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| G. Collet | 1 | 1 | 0.37 |
| R. Andonov | 2 | 51 | 5.97 |
| N. Yanev | 3 | 32 | 2.92 |
| Jean-françois Gibrat | 4 | 126 | 6.08 |