Fast protein folding in the hydrophobic-hydrophilic model within three-eights of optimal
STOC '95 Proceedings of the twenty-seventh annual ACM symposium on Theory of computing
RECOMB '97 Proceedings of the first annual international conference on Computational molecular biology
Protein folding in the hydrophobic-hydrophilic (HP) is NP-complete
RECOMB '98 Proceedings of the second annual international conference on Computational molecular biology
On the complexity of protein folding (abstract)
RECOMB '98 Proceedings of the second annual international conference on Computational molecular biology
SODA '97 Proceedings of the eighth annual ACM-SIAM symposium on Discrete algorithms
Spatial codes and the hardness of string folding problems
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
On the Complexity of String Folding
ICALP '96 Proceedings of the 23rd International Colloquium on Automata, Languages and Programming
On the Complexity of String Folding
ICALP '96 Proceedings of the 23rd International Colloquium on Automata, Languages and Programming
The algorithmics of folding proteins on lattices
Discrete Applied Mathematics - Special issue: Computational molecular biology series issue IV
Protein folding in the HP model on grid lattices with diagonals
Discrete Applied Mathematics
The inverse protein folding problem on 2D and 3D lattices
Discrete Applied Mathematics
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One of the most important open problems in computational molecular biology is the prediction of the conformation of a protein based on its amino acid sequence. In this paper, we design approximation algorithms for structure prediction in the so-called HP side chain model. The major drawback of the standard HP side chain model is the bipartiteness of the cubic lattice. To eliminate this drawback, we introduce the extended cubic lattice which extends the cubic lattice by diagonals in the plane. For this lattice, we present two linear algorithms with approximation ratios of 59/70 and 37/42, respectively. The second algorithm is designed for a 'natural' subclass of proteins, which covers more than 99:5% of all sequenced proteins. This is the first time that a protein structure prediction algorithm is designed for a 'natural' subclass of all combinatorially possible sequences.