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A hybrid method for identification of structural domains.

Hua Y, Zhu M, Wang Y, Xie Z, Li M - Sci Rep (2014)

Bottom Line: The automatic algorithms identify domains slowly, while protein entities with great structural complexity are on the rise.It is faster and more stable than most current algorithms and performs better.It also indicates that when proteins are presented as some Alpha-carbon atoms in 3D space, it is feasible to identify structural domains by the spatially structural properties.

View Article: PubMed Central - PubMed

Affiliation: College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, 610064 Chengdu, China.

ABSTRACT
Structural domains in proteins are the basic units to form various proteins. In the protein's evolution and functioning, domains play important roles. But the definition of domain is not yet precisely given, and the update cycle of structural domain databases is long. The automatic algorithms identify domains slowly, while protein entities with great structural complexity are on the rise. Here, we present a method which recognizes the compact and modular segments of polypeptide chains to identify structural domains, and contrast some data sets to illuminate their effect. The method combines support vector machine (SVM) with K-means algorithm. It is faster and more stable than most current algorithms and performs better. It also indicates that when proteins are presented as some Alpha-carbon atoms in 3D space, it is feasible to identify structural domains by the spatially structural properties. We have developed a web-server, which would be helpful in identification of structural domains (http://vis.sculab.org/~huayongpan/cgi-bin/domainAssignment.cgi).

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PDB 1D0G chain T domain assignment.PDB:1D0G chain T was approximately in agreement with the assignment in the Benchmark_3.
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f7: PDB 1D0G chain T domain assignment.PDB:1D0G chain T was approximately in agreement with the assignment in the Benchmark_3.

Mentions: Surprisingly there were 3 cases with 3-domain (PDB:1KSI chain A, PDB:1D0G chain T, PDB:1DCE chain A) that could not be partitioned correctly by all current methods. However, all of them were assigned correctly by the hybrid method. The hybrid method was better at partitioning the chains with 3-domain than other algorithms. For example, PDB:1KSI was a eukaryotic copper-containing amine oxidase. In Figure 6, the 3 different colored segments displayed a certain modularity in 3D space. Though the modularity in PDB:1KSI chain A was not as obvious as PDB:1CWV chain A, it was enough to correctly identify structural domains by the hybrid method. Nevertheless, there were a few flaw between 2 domain assignments in Figure 6. However, the domain assignments on PDB:1D0G chain T was approximately in agreement with the assignment in the Benchmark_3 as shown in Figure 7.


A hybrid method for identification of structural domains.

Hua Y, Zhu M, Wang Y, Xie Z, Li M - Sci Rep (2014)

PDB 1D0G chain T domain assignment.PDB:1D0G chain T was approximately in agreement with the assignment in the Benchmark_3.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4265785&req=5

f7: PDB 1D0G chain T domain assignment.PDB:1D0G chain T was approximately in agreement with the assignment in the Benchmark_3.
Mentions: Surprisingly there were 3 cases with 3-domain (PDB:1KSI chain A, PDB:1D0G chain T, PDB:1DCE chain A) that could not be partitioned correctly by all current methods. However, all of them were assigned correctly by the hybrid method. The hybrid method was better at partitioning the chains with 3-domain than other algorithms. For example, PDB:1KSI was a eukaryotic copper-containing amine oxidase. In Figure 6, the 3 different colored segments displayed a certain modularity in 3D space. Though the modularity in PDB:1KSI chain A was not as obvious as PDB:1CWV chain A, it was enough to correctly identify structural domains by the hybrid method. Nevertheless, there were a few flaw between 2 domain assignments in Figure 6. However, the domain assignments on PDB:1D0G chain T was approximately in agreement with the assignment in the Benchmark_3 as shown in Figure 7.

Bottom Line: The automatic algorithms identify domains slowly, while protein entities with great structural complexity are on the rise.It is faster and more stable than most current algorithms and performs better.It also indicates that when proteins are presented as some Alpha-carbon atoms in 3D space, it is feasible to identify structural domains by the spatially structural properties.

View Article: PubMed Central - PubMed

Affiliation: College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, 610064 Chengdu, China.

ABSTRACT
Structural domains in proteins are the basic units to form various proteins. In the protein's evolution and functioning, domains play important roles. But the definition of domain is not yet precisely given, and the update cycle of structural domain databases is long. The automatic algorithms identify domains slowly, while protein entities with great structural complexity are on the rise. Here, we present a method which recognizes the compact and modular segments of polypeptide chains to identify structural domains, and contrast some data sets to illuminate their effect. The method combines support vector machine (SVM) with K-means algorithm. It is faster and more stable than most current algorithms and performs better. It also indicates that when proteins are presented as some Alpha-carbon atoms in 3D space, it is feasible to identify structural domains by the spatially structural properties. We have developed a web-server, which would be helpful in identification of structural domains (http://vis.sculab.org/~huayongpan/cgi-bin/domainAssignment.cgi).

Show MeSH