Limits...
CRHunter: integrating multifaceted information to predict catalytic residues in enzymes

View Article: PubMed Central - PubMed

ABSTRACT

A variety of algorithms have been developed for catalytic residue prediction based on either feature- or template-based methodology. However, no studies have systematically compared these two strategies and further considered whether their combination could improve the prediction performance. Herein, we developed an integrative algorithm named CRHunter by simultaneously using the complementarity between feature- and template-based methodologies and that between structural and sequence information. Several novel structural features were generated by the Delaunay triangulation and Laplacian transformation of enzyme structures. Combining these features with traditional descriptors, we invented two support vector machine feature predictors based on both structural and sequence information. Furthermore, we established two template predictors using structure and profile alignments. Evaluated on datasets with different levels of homology, our feature predictors achieve relatively stable performance, whereas our template predictors yield poor results when the homological relationships become weak. Nevertheless, the hybrid algorithm CRHunter consistently achieves optimal performance among all our predictors. We also illustrate that our methodology can be applied to the predicted structures of enzymes. Compared with state-of-the-art methods, CRHunter yields comparable or better performance on various datasets. Finally, the application of this algorithm to structural genomics targets sheds light on solved protein structures with unknown functions.

No MeSH data available.


Generation of the microenvironment for each residue using Delaunay triangulation.In the left image, the catalytic residue of an enzyme structure (SCOP ID: d1gpma1) is shown in red, and its neighboring residues are shown in yellow or green. The right image shows the Delaunay triangulation of this active site, in which each node represents an atom. The atoms of the catalytic residue are shown in red, and atoms of neighbors that share a common facet with any atom of the catalytic residue are shown in yellow or green. Residues shown in green are finally removed because the number of common facets is less than the optimal cutoff.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC5036049&req=5

f2: Generation of the microenvironment for each residue using Delaunay triangulation.In the left image, the catalytic residue of an enzyme structure (SCOP ID: d1gpma1) is shown in red, and its neighboring residues are shown in yellow or green. The right image shows the Delaunay triangulation of this active site, in which each node represents an atom. The atoms of the catalytic residue are shown in red, and atoms of neighbors that share a common facet with any atom of the catalytic residue are shown in yellow or green. Residues shown in green are finally removed because the number of common facets is less than the optimal cutoff.

Mentions: In this study, we used the Delaunay triangulation (DT) to generate the microenvironment of each residue16. For each target protein, the qdelaunay application of the Qhull package was used to divide the three-dimensional structure into tetrahedrons such that each vertex represents an atom17. We also generated a Voronoi diagram for atoms in the target protein, which corresponds to the geometric dual of Delaunay triangulation. In this context, two residues are considered to be in contact if any pair of heavy atoms from each residue shares a common facet in the Voronoi diagram. Figure 2 shows an example of the DT-based microenvironment of a catalytic residue.


CRHunter: integrating multifaceted information to predict catalytic residues in enzymes
Generation of the microenvironment for each residue using Delaunay triangulation.In the left image, the catalytic residue of an enzyme structure (SCOP ID: d1gpma1) is shown in red, and its neighboring residues are shown in yellow or green. The right image shows the Delaunay triangulation of this active site, in which each node represents an atom. The atoms of the catalytic residue are shown in red, and atoms of neighbors that share a common facet with any atom of the catalytic residue are shown in yellow or green. Residues shown in green are finally removed because the number of common facets is less than the optimal cutoff.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5036049&req=5

f2: Generation of the microenvironment for each residue using Delaunay triangulation.In the left image, the catalytic residue of an enzyme structure (SCOP ID: d1gpma1) is shown in red, and its neighboring residues are shown in yellow or green. The right image shows the Delaunay triangulation of this active site, in which each node represents an atom. The atoms of the catalytic residue are shown in red, and atoms of neighbors that share a common facet with any atom of the catalytic residue are shown in yellow or green. Residues shown in green are finally removed because the number of common facets is less than the optimal cutoff.
Mentions: In this study, we used the Delaunay triangulation (DT) to generate the microenvironment of each residue16. For each target protein, the qdelaunay application of the Qhull package was used to divide the three-dimensional structure into tetrahedrons such that each vertex represents an atom17. We also generated a Voronoi diagram for atoms in the target protein, which corresponds to the geometric dual of Delaunay triangulation. In this context, two residues are considered to be in contact if any pair of heavy atoms from each residue shares a common facet in the Voronoi diagram. Figure 2 shows an example of the DT-based microenvironment of a catalytic residue.

View Article: PubMed Central - PubMed

ABSTRACT

A variety of algorithms have been developed for catalytic residue prediction based on either feature- or template-based methodology. However, no studies have systematically compared these two strategies and further considered whether their combination could improve the prediction performance. Herein, we developed an integrative algorithm named CRHunter by simultaneously using the complementarity between feature- and template-based methodologies and that between structural and sequence information. Several novel structural features were generated by the Delaunay triangulation and Laplacian transformation of enzyme structures. Combining these features with traditional descriptors, we invented two support vector machine feature predictors based on both structural and sequence information. Furthermore, we established two template predictors using structure and profile alignments. Evaluated on datasets with different levels of homology, our feature predictors achieve relatively stable performance, whereas our template predictors yield poor results when the homological relationships become weak. Nevertheless, the hybrid algorithm CRHunter consistently achieves optimal performance among all our predictors. We also illustrate that our methodology can be applied to the predicted structures of enzymes. Compared with state-of-the-art methods, CRHunter yields comparable or better performance on various datasets. Finally, the application of this algorithm to structural genomics targets sheds light on solved protein structures with unknown functions.

No MeSH data available.