Protein side-chain placement through MAP estimation and problem-size reduction

Authors:
Eun-Jong Hong;Tomás Lozano-Pérez
Affiliations:
Computer Science and Artificial Intelligence Lab, MIT, Cambridge, MA;Computer Science and Artificial Intelligence Lab, MIT, Cambridge, MA
Venue:
WABI'06 Proceedings of the 6th international conference on Algorithms in Bioinformatics
Year:
2006

Citing 4
Cited 0

Side Chain-Positioning as an Integer Programming Problem

WABI '01 Proceedings of the First International Workshop on Algorithms in Bioinformatics
Solving and analyzing side-chain positioning problems using linear and integer programming

Bioinformatics
Residue-rotamer-reduction algorithm for the protein side-chain conformation problem

Bioinformatics
Rapid protein side-chain packing via tree decomposition

RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present an exact method for the global minimum energy conformation (GMEC) search of protein side-chains. Our method consists of a branch-and-bound (B&B) framework and a new subproblem-pruning scheme. The pruning scheme consists of upper/lower-bounding methods and problem-size reduction techniques. We explore a way of using the tree-reweighted max-product algorithm for computing lower-bounds of the GMEC energy. The problem-size reduction techniques are necessary when the size of the subproblem is too large to rely on more accurate yet expensive bounding methods. The experimental results show our pruning scheme is effective and our B&B method exactly solves protein sequence design cases that are very hard to solve with the dead-end elimination.