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An Algorithm for Protein Helix Assignment Using Helix Geometry.

Cao C, Xu S, Wang L - PLoS ONE (2015)

Bottom Line: The first step searches for a series of bona fide helical curves each one best fits the coordinates of four successive backbone Cα atoms.The second step uses the best fit helical curves as input to make helix assignment.The structural uniformity should be useful for protein structure classification and prediction while the accurate assignment of a helix to a particular type underlies structure-function relationship in proteins.

View Article: PubMed Central - PubMed

Affiliation: The College of Computer Science and Technology, Jilin University, Changchun, Jilin, China.

ABSTRACT
Helices are one of the most common and were among the earliest recognized secondary structure elements in proteins. The assignment of helices in a protein underlies the analysis of its structure and function. Though the mathematical expression for a helical curve is simple, no previous assignment programs have used a genuine helical curve as a model for helix assignment. In this paper we present a two-step assignment algorithm. The first step searches for a series of bona fide helical curves each one best fits the coordinates of four successive backbone Cα atoms. The second step uses the best fit helical curves as input to make helix assignment. The application to the protein structures in the PDB (protein data bank) proves that the algorithm is able to assign accurately not only regular α-helix but also 310 and π helices as well as their left-handed versions. One salient feature of the algorithm is that the assigned helices are structurally more uniform than those by the previous programs. The structural uniformity should be useful for protein structure classification and prediction while the accurate assignment of a helix to a particular type underlies structure-function relationship in proteins.

No MeSH data available.


A histogram of a π-helix vs a protein-ligand binding site.Figure (a) shows a histogram of the π-helix assigned by our algorithm vs a protein-ligand site. The x-axis is the distance (in Å) between the ligand and a π-helix while the y-axis is the number of π-helices. Figure (b) shows an example illustrating the difference in π-helix assignment by our algorithm and dssp. Our algorithm assigns three π-helices labeled as π1, π2 and π3, all of them are in the ligand binding site but dssp fails to assign π1 though it is able to identify both π2 and π3. In this figure, α-helices are in green, 310-helices are in orange and the ligand is shown as spheres.
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pone.0129674.g007: A histogram of a π-helix vs a protein-ligand binding site.Figure (a) shows a histogram of the π-helix assigned by our algorithm vs a protein-ligand site. The x-axis is the distance (in Å) between the ligand and a π-helix while the y-axis is the number of π-helices. Figure (b) shows an example illustrating the difference in π-helix assignment by our algorithm and dssp. Our algorithm assigns three π-helices labeled as π1, π2 and π3, all of them are in the ligand binding site but dssp fails to assign π1 though it is able to identify both π2 and π3. In this figure, α-helices are in green, 310-helices are in orange and the ligand is shown as spheres.

Mentions: Our algorithm is able to assign a helix to a particular type. As detailed above for α-helices, the agreement between our algorithm and either dssp or stride is excellent. However, for both 310 and π helices the agreements are between 56.9% and 74.1%. An interesting application to structure-function relationship [26] is to see whether there exist any correlation between the location of a π-helix and a protein-ligand binding site. Out of the 25,806 protein structures in ℕ our algorithm has assigned 6,600 π-helices from 4,329 protein structures, 3,811 of them have a bound ligand. We compute the number of π-helices that is within a certain distance range of the bound ligand. The distance between a ligand and a π-helix is defined as the shortest distance between any ligand atom and any protein atom that belongs to the π-helix. As shown in Fig 7a there exists a strong correlation between the location of a π-helix and a protein-ligand binding site: 38.6 percent of all the π-helices assigned by our algorithm are less than 6.0Å away from a ligand binding site. Furthermore, as shown in Fig 7b, compared with the program dssp our algorithm is able to assign more of such π-helices. Such correlation should be helpful for the discovery of structure-function relationship in proteins.


An Algorithm for Protein Helix Assignment Using Helix Geometry.

Cao C, Xu S, Wang L - PLoS ONE (2015)

A histogram of a π-helix vs a protein-ligand binding site.Figure (a) shows a histogram of the π-helix assigned by our algorithm vs a protein-ligand site. The x-axis is the distance (in Å) between the ligand and a π-helix while the y-axis is the number of π-helices. Figure (b) shows an example illustrating the difference in π-helix assignment by our algorithm and dssp. Our algorithm assigns three π-helices labeled as π1, π2 and π3, all of them are in the ligand binding site but dssp fails to assign π1 though it is able to identify both π2 and π3. In this figure, α-helices are in green, 310-helices are in orange and the ligand is shown as spheres.
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4488512&req=5

pone.0129674.g007: A histogram of a π-helix vs a protein-ligand binding site.Figure (a) shows a histogram of the π-helix assigned by our algorithm vs a protein-ligand site. The x-axis is the distance (in Å) between the ligand and a π-helix while the y-axis is the number of π-helices. Figure (b) shows an example illustrating the difference in π-helix assignment by our algorithm and dssp. Our algorithm assigns three π-helices labeled as π1, π2 and π3, all of them are in the ligand binding site but dssp fails to assign π1 though it is able to identify both π2 and π3. In this figure, α-helices are in green, 310-helices are in orange and the ligand is shown as spheres.
Mentions: Our algorithm is able to assign a helix to a particular type. As detailed above for α-helices, the agreement between our algorithm and either dssp or stride is excellent. However, for both 310 and π helices the agreements are between 56.9% and 74.1%. An interesting application to structure-function relationship [26] is to see whether there exist any correlation between the location of a π-helix and a protein-ligand binding site. Out of the 25,806 protein structures in ℕ our algorithm has assigned 6,600 π-helices from 4,329 protein structures, 3,811 of them have a bound ligand. We compute the number of π-helices that is within a certain distance range of the bound ligand. The distance between a ligand and a π-helix is defined as the shortest distance between any ligand atom and any protein atom that belongs to the π-helix. As shown in Fig 7a there exists a strong correlation between the location of a π-helix and a protein-ligand binding site: 38.6 percent of all the π-helices assigned by our algorithm are less than 6.0Å away from a ligand binding site. Furthermore, as shown in Fig 7b, compared with the program dssp our algorithm is able to assign more of such π-helices. Such correlation should be helpful for the discovery of structure-function relationship in proteins.

Bottom Line: The first step searches for a series of bona fide helical curves each one best fits the coordinates of four successive backbone Cα atoms.The second step uses the best fit helical curves as input to make helix assignment.The structural uniformity should be useful for protein structure classification and prediction while the accurate assignment of a helix to a particular type underlies structure-function relationship in proteins.

View Article: PubMed Central - PubMed

Affiliation: The College of Computer Science and Technology, Jilin University, Changchun, Jilin, China.

ABSTRACT
Helices are one of the most common and were among the earliest recognized secondary structure elements in proteins. The assignment of helices in a protein underlies the analysis of its structure and function. Though the mathematical expression for a helical curve is simple, no previous assignment programs have used a genuine helical curve as a model for helix assignment. In this paper we present a two-step assignment algorithm. The first step searches for a series of bona fide helical curves each one best fits the coordinates of four successive backbone Cα atoms. The second step uses the best fit helical curves as input to make helix assignment. The application to the protein structures in the PDB (protein data bank) proves that the algorithm is able to assign accurately not only regular α-helix but also 310 and π helices as well as their left-handed versions. One salient feature of the algorithm is that the assigned helices are structurally more uniform than those by the previous programs. The structural uniformity should be useful for protein structure classification and prediction while the accurate assignment of a helix to a particular type underlies structure-function relationship in proteins.

No MeSH data available.