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Proteins comparison through probabilistic optimal structure local alignment.

Micale G, Pulvirenti A, Giugno R, Ferro A - Front Genet (2014)

Bottom Line: Only the distances between all pairs of residues in the structures are computed.To show the accuracy and the effectiveness of PROPOSAL we tested it on a few families of protein structures.We also compared PROPOSAL with two state-of-the-art tools for pairwise local alignment on a dataset of manually annotated motifs.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, University of Pisa Pisa, Italy.

ABSTRACT
Multiple local structure comparison helps to identify common structural motifs or conserved binding sites in 3D structures in distantly related proteins. Since there is no best way to compare structures and evaluate the alignment, a wide variety of techniques and different similarity scoring schemes have been proposed. Existing algorithms usually compute the best superposition of two structures or attempt to solve it as an optimization problem in a simpler setting (e.g., considering contact maps or distance matrices). Here, we present PROPOSAL (PROteins comparison through Probabilistic Optimal Structure local ALignment), a stochastic algorithm based on iterative sampling for multiple local alignment of protein structures. Our method can efficiently find conserved motifs across a set of protein structures. Only the distances between all pairs of residues in the structures are computed. To show the accuracy and the effectiveness of PROPOSAL we tested it on a few families of protein structures. We also compared PROPOSAL with two state-of-the-art tools for pairwise local alignment on a dataset of manually annotated motifs. PROPOSAL is available as a Java 2D standalone application or a command line program at http://ferrolab.dmi.unict.it/proposal/proposal.html.

No MeSH data available.


Related in: MedlinePlus

Average LRMSD of the alignments returned by PROPOSAL on varying (A) α and (B) IterRefine. Default values: N = 6, w = 15, α = 0.05, IterRefine = 10.
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Figure 11: Average LRMSD of the alignments returned by PROPOSAL on varying (A) α and (B) IterRefine. Default values: N = 6, w = 15, α = 0.05, IterRefine = 10.

Mentions: Figure 11 shows the influence of α and IterRefine on the global accuracy of PROPOSAL. We measured the average RMSD over all the computed alignments. In Figure 11Aalpha varies from 0.01 to 0.30 and IterRefine is set to 10, while in Figure 11BiterRefine varies from 1 to 30 and α is set to 0.05. Default values (w = 15 and N = 6) were assigned. As expected, the best performance of our method are obtained with low values of α and high values of IterRefine. However, if we also consider the influence of such parameters on running time (in particular the IterRefine parameter), the best trade-off between speed and accuracy can be achieved with 0.01 ≤ α ≤ 0.1 and IterRefine = 10.


Proteins comparison through probabilistic optimal structure local alignment.

Micale G, Pulvirenti A, Giugno R, Ferro A - Front Genet (2014)

Average LRMSD of the alignments returned by PROPOSAL on varying (A) α and (B) IterRefine. Default values: N = 6, w = 15, α = 0.05, IterRefine = 10.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4151033&req=5

Figure 11: Average LRMSD of the alignments returned by PROPOSAL on varying (A) α and (B) IterRefine. Default values: N = 6, w = 15, α = 0.05, IterRefine = 10.
Mentions: Figure 11 shows the influence of α and IterRefine on the global accuracy of PROPOSAL. We measured the average RMSD over all the computed alignments. In Figure 11Aalpha varies from 0.01 to 0.30 and IterRefine is set to 10, while in Figure 11BiterRefine varies from 1 to 30 and α is set to 0.05. Default values (w = 15 and N = 6) were assigned. As expected, the best performance of our method are obtained with low values of α and high values of IterRefine. However, if we also consider the influence of such parameters on running time (in particular the IterRefine parameter), the best trade-off between speed and accuracy can be achieved with 0.01 ≤ α ≤ 0.1 and IterRefine = 10.

Bottom Line: Only the distances between all pairs of residues in the structures are computed.To show the accuracy and the effectiveness of PROPOSAL we tested it on a few families of protein structures.We also compared PROPOSAL with two state-of-the-art tools for pairwise local alignment on a dataset of manually annotated motifs.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, University of Pisa Pisa, Italy.

ABSTRACT
Multiple local structure comparison helps to identify common structural motifs or conserved binding sites in 3D structures in distantly related proteins. Since there is no best way to compare structures and evaluate the alignment, a wide variety of techniques and different similarity scoring schemes have been proposed. Existing algorithms usually compute the best superposition of two structures or attempt to solve it as an optimization problem in a simpler setting (e.g., considering contact maps or distance matrices). Here, we present PROPOSAL (PROteins comparison through Probabilistic Optimal Structure local ALignment), a stochastic algorithm based on iterative sampling for multiple local alignment of protein structures. Our method can efficiently find conserved motifs across a set of protein structures. Only the distances between all pairs of residues in the structures are computed. To show the accuracy and the effectiveness of PROPOSAL we tested it on a few families of protein structures. We also compared PROPOSAL with two state-of-the-art tools for pairwise local alignment on a dataset of manually annotated motifs. PROPOSAL is available as a Java 2D standalone application or a command line program at http://ferrolab.dmi.unict.it/proposal/proposal.html.

No MeSH data available.


Related in: MedlinePlus