Limits...
Protein folding in HP model on hexagonal lattices with diagonals.

Shaw D, Shohidull Islam AS, Sohel Rahman M, Hasan M - BMC Bioinformatics (2014)

Bottom Line: Since, this is a hard problem, a number of simplified models have been proposed in literature to capture the essential properties of this problem.In this paper we introduce the hexagonal lattices with diagonals to handle the protein folding problem considering the well researched HP model.We give two approximation algorithms for protein folding on this lattice.

View Article: PubMed Central - HTML - PubMed

ABSTRACT
Three dimensional structure prediction of a protein from its amino acid sequence, known as protein folding, is one of the most studied computational problem in bioinformatics and computational biology. Since, this is a hard problem, a number of simplified models have been proposed in literature to capture the essential properties of this problem. In this paper we introduce the hexagonal lattices with diagonals to handle the protein folding problem considering the well researched HP model. We give two approximation algorithms for protein folding on this lattice. Our first algorithm is a 5/3-approximation algorithm, which is based on the strategy of partitioning the entire protein sequence into two pieces. Our next algorithm is also based on partitioning approaches and improves upon the first algorithm.

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Eight possible neighbours of the loss edge (x, y). This figure aids in understanding the proof of Lemma 0.2.
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Figure 5: Eight possible neighbours of the loss edge (x, y). This figure aids in understanding the proof of Lemma 0.2.

Mentions: Neighbourhood of an edge e = (x, y) is shown in Figure 5 for non-diagonal edges, and in Figure 6 for diagonal edges. As can be seen from the figure for a non-diagonal edge, the number of possible neighbours is 8 whereas for a diagonal one, it is 4.


Protein folding in HP model on hexagonal lattices with diagonals.

Shaw D, Shohidull Islam AS, Sohel Rahman M, Hasan M - BMC Bioinformatics (2014)

Eight possible neighbours of the loss edge (x, y). This figure aids in understanding the proof of Lemma 0.2.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4016602&req=5

Figure 5: Eight possible neighbours of the loss edge (x, y). This figure aids in understanding the proof of Lemma 0.2.
Mentions: Neighbourhood of an edge e = (x, y) is shown in Figure 5 for non-diagonal edges, and in Figure 6 for diagonal edges. As can be seen from the figure for a non-diagonal edge, the number of possible neighbours is 8 whereas for a diagonal one, it is 4.

Bottom Line: Since, this is a hard problem, a number of simplified models have been proposed in literature to capture the essential properties of this problem.In this paper we introduce the hexagonal lattices with diagonals to handle the protein folding problem considering the well researched HP model.We give two approximation algorithms for protein folding on this lattice.

View Article: PubMed Central - HTML - PubMed

ABSTRACT
Three dimensional structure prediction of a protein from its amino acid sequence, known as protein folding, is one of the most studied computational problem in bioinformatics and computational biology. Since, this is a hard problem, a number of simplified models have been proposed in literature to capture the essential properties of this problem. In this paper we introduce the hexagonal lattices with diagonals to handle the protein folding problem considering the well researched HP model. We give two approximation algorithms for protein folding on this lattice. Our first algorithm is a 5/3-approximation algorithm, which is based on the strategy of partitioning the entire protein sequence into two pieces. Our next algorithm is also based on partitioning approaches and improves upon the first algorithm.

Show MeSH