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A hybrid method for identification of structural domains.

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).

SUBMITTER: Hua Y

PROVIDER: S-EPMC4265785 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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A hybrid method for identification of structural domains.

Hua Yongpan Y Zhu Min M Wang Yuelong Y Xie Zhaoyang Z Li Menglong M

Scientific reports 20141215

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 identif ...[more]

PMID: 25503992

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Project description:BACKGROUND: Identification of the structural domains of proteins is important for our understanding of the organizational principles and mechanisms of protein folding, and for insights into protein function and evolution. Algorithmic methods of dissecting protein of known structure into domains developed so far are based on an examination of multiple geometrical, physical and topological features. Successful as many of these approaches are, they employ a lot of heuristics, and it is not clear whether they illuminate any deep underlying principles of protein domain organization. Other well-performing domain dissection methods rely on comparative sequence analysis. These methods are applicable to sequences with known and unknown structure alike, and their success highlights a fundamental principle of protein modularity, but this does not directly improve our understanding of protein spatial structure. RESULTS: We present a novel graph-theoretical algorithm for the identification of domains in proteins with known three-dimensional structure. We represent the protein structure as an undirected, unweighted and unlabeled graph whose nodes correspond to the secondary structure elements and edges represent physical proximity of at least one pair of alpha carbon atoms from two elements. Domains are identified as constrained partitions of the graph, corresponding to sets of vertices obtained by the maximization of the cycle distributions found in the graph. When a partition is found, the algorithm is iteratively applied to each of the resulting subgraphs. The decision to accept or reject a tentative cut position is based on a specific classifier. The algorithm is applied iteratively to each of the resulting subgraphs and terminates automatically if partitions are no longer accepted. The distribution of cycles is the only type of information on which the decision about protein dissection is based. Despite the barebone simplicity of the approach, our algorithm approaches the best heuristic algorithms in accuracy. CONCLUSION: Our graph-theoretical algorithm uses only topological information present in the protein structure itself to find the domains and does not rely on any geometrical or physical information about protein molecule. Perhaps unexpectedly, these drastic constraints on resources, which result in a seemingly approximate description of protein structures and leave only a handful of parameters available for analysis, do not lead to any significant deterioration of algorithm accuracy. It appears that protein structures can be rigorously treated as topological rather than geometrical objects and that the majority of information about protein domains can be inferred from the coarse-grained measure of pairwise proximity between elements of secondary structure elements.

Dataset Information

A hybrid method for identification of structural domains.

Publications

A hybrid method for identification of structural domains.

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