Abstract
The depth of each atom/residue in a protein structure is a key attribution that has been widely used in protein structure modeling and function annotation. However, the accurate calculation of depth is time consuming. Here, we propose to use the Euclidean distance transform (EDT) to calculate the depth, which conveniently converts the protein structure to a 3D gray-scale image with each pixel labeling the minimum distance of the pixel to the surface of the molecule (i.e. the depth). We tested the proposed EDT method on a set of 261 non-redundant protein structures. The data show that the EDT method is 2.6 times faster than the widely used method by Chakravarty and Varadarajan. The depth value by EDT method is also highly accurate, which is almost identical to the depth calculated by exhaustive search (Pearson’s correlation coefficient≈1). We believe the EDT-based depth calculation program can be used as an efficient tool to assist the studies of protein fold recognition and structure-based function annotation.
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References
Ramachandran, G.N., Sasisekharan, V.: Conformation of polypeptides and proteins. Adv. Protein Chem. 23, 283–438 (1968)
Xu, D., Zhang, Y.: Generating triangulated macromolecular surfaces by Euclidean distance transform. PLoS One 4(12), e8140 (2009)
Kabsch, W., Sander, C.: Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22(12), 2577–2637 (1983)
Chakravarty, S., Varadarajan, R.: Residue depth: a novel parameter for the analysis of protein structure and stability. Structure 7(7), 723–732 (1999)
Zhou, H., Zhou, Y.: Fold recognition by combining sequence profiles derived from evolution and from depth-dependent structural alignment of fragments. Proteins 58(2), 321–328 (2005)
Liu, S., Zhang, C., Liang, S., Zhou, Y.: Fold recognition by concurrent use of solvent accessibility and residue depth. Proteins 68(3), 636–645 (2007)
Wu, S., Zhang, Y.: MUSTER: Improving protein sequence profile-profile alignments by using multiple sources of structure information. Proteins 72(2), 547–556 (2008)
Roy, A., Yang, J., Zhang, Y.: COFACTOR: an accurate comparative algorithm for structure-based protein function annotation. Nucleic Acids Res. 40(Web Server issue), W471–W477 (2012)
Sanner, M.F., Olson, A.J., Spehner, J.C.: Reduced surface: an efficient way to compute molecular surfaces. Biopolymers 38(3), 305–320 (1996)
Zhang, H., Zhang, T., Chen, K., Shen, S., Ruan, J., Kurgan, L.: Sequence based residue depth prediction using evolutionary information and predicted secondary structure. BMC Bioinformatics 9, 388 (2008)
Yuan, Z., Wang, Z.X.: Quantifying the relationship of protein burying depth and sequence. Proteins 70(2), 509–516 (2008)
Lee, B., Richards, F.M.: The interpretation of protein structures: estimation of static accessibility. J. Mol. Biol. 55(3), 379–400 (1971)
Xu, D., Li, H.: Euclidean Distance Transform of Digital Images in Arbitrary Dimensions. In: Zhuang, Y.-T., Yang, S.-Q., Rui, Y., He, Q. (eds.) PCM 2006. LNCS, vol. 4261, pp. 72–79. Springer, Heidelberg (2006)
Choi, W.P., Lam, K.M., Siu, W.C.: Extraction of the Euclidean skeleton based on a connectivity criterion. Pattern Recognition 36(3), 721–729 (2003)
Shih, F.Y., Wu, Y.T.: Three-dimensional Euclidean distance transformation and its application to shortest path planning. Pattern Recognition 37(1), 79–92 (2004)
Xu, D., Li, H.: Shape analysis of volume models by Euclidean distance transform and moment invariants. In: 10th IEEE International Conference on Computer-Aided Design and Computer Graphics, pp. 437–440 (2007)
Tan, K.P., Varadarajan, R., Madhusudhan, M.S.: DEPTH: a web server to compute depth and predict small-molecule binding cavities in proteins. Nucleic Acids Res. 39(Web Server issue), W242–W248 (2011)
Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3d surface construction algorithm. Comput. Graph. 21(4), 163–169 (1987)
Wu, S., Zhang, Y.: LOMETS: a local meta-threading-server for protein structure prediction. Nucleic Acids Res. 35(10), 3375–3382 (2007)
Kyte, J., Doolittle, R.F.: A simple method for displaying the hydropathic character of a protein. J. Mol. Biol. 157(1), 105–132 (1982)
Wang, G., Dunbrack Jr., R.L.: PISCES: a protein sequence culling server. Bioinformatics 19(12), 1589–1591 (2003)
Berman, H.M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T.N., Weissig, H., Shindyalov, I.N., Bourne, P.E.: The Protein Data Bank. Nucleic Acids Res. 28(1), 235–242 (2000)
Zhang, Y., Kolinski, A., Skolnick, J.: TOUCHSTONE II: a new approach to ab initio protein structure prediction. Biophys. J. 85(2), 1145–1164 (2003)
Roy, A., Zhang, Y.: Recognizing protein-ligand binding sites by global structural alignment and local geometry refinement. Structure 20(6), 987–997 (2012)
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Xu, D., Li, H., Zhang, Y. (2013). Fast and Accurate Calculation of Protein Depth by Euclidean Distance Transform. In: Deng, M., Jiang, R., Sun, F., Zhang, X. (eds) Research in Computational Molecular Biology. RECOMB 2013. Lecture Notes in Computer Science(), vol 7821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37195-0_30
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DOI: https://doi.org/10.1007/978-3-642-37195-0_30
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