Abstract
Structural motifs encapsulate local sequence-structure-function relationships characteristic of related proteins, enabling the prediction of functional characteristics of new proteins, providing molecular-level insights into how those functions are performed, and supporting the development of variants specifically maintaining or perturbing function in concert with other properties. Numerous computational methods have been developed to search through databases of structures for instances of specified motifs. However, it remains an open problem as to how best to leverage the local geometric and chemical constraints underlying structural motifs in order to develop motif-finding algorithms that are both theoretically and practically efficient. We present a simple, general, efficient approach, called Ballast (Ball-based algorithm for structural motifs), to match given structural motifs to given structures. Ballast combines the best properties of previously developed methods, exploiting the composition and local geometry of a structural motif and its possible instances in order to effectively filter candidate matches. We show that on a wide range of motif matching problems, Ballast efficiently and effectively finds good matches, and we provide theoretical insights into why it works well. By supporting generic measures of compositional and geometric similarity, Ballast provides a powerful substrate for the development of motif matching algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Artymiuk, P.J., Poirrette, A.R., Grindley, H.M., Rice, D.W., Willett, P.: A graph-theoretic approach to the identification of three-dimensional patterns of amino acid side-chains in protein structures. J. Mol. Biol. 243, 327–344 (1994)
Arun, K.S., Huang, T.S., Blostein, S.D.: Least-squares fitting of two 3-d point sets. IEEE Trans. Pattern Anal. Mach. Intell. 9, 698–700 (1987)
Babbitt, P.C., Hasson, M.S., et al.: The enolase superfamily: A general strategy for enzyme-catalyzed abstraction of the α-protons of carboxylic acids. Biochemistry 35(51), 16489–16501 (1996)
Bandyopadhyay, D., Huan, J., et al.: Identification of family-specific residue packing motifs and their use for structure-based protein function prediction: I. Method development. J. Comput. Aided Mol. Des. 23, 773–784 (2009)
Bandyopadhyay, D., Snoeyink, J.: Almost-delaunay simplices: nearest neighbor relations for imprecise points. In: Proc. SODA, pp. 410–419 (2004)
Barker, J.A., Thornton, J.M.: An algorithm for constraint-based structural template matching: application to 3D templates with statistical analysis. Bioinformatics 19, 1644–1649 (2003)
Bernstein, F.C., Koetzle, T.F., et al.: The Protein Data Bank: a computer-based archival file for macromolecular structures. J. Mol. Biol. 112, 535–542 (1977)
Bron, C., Kerbosch, J.: Algorithm 457: finding all cliques of an undirected graph. Commun. ACM 16, 575–577 (1973)
Chen, B.Y., Fofanov, V.Y., et al.: The MASH pipeline for protein function prediction and an algorithm for the geometric refinement of 3D motifs. J. Comput. Biol. 14, 791–816 (2007)
Feige, U., Goldwasser, S., Lovász, L., Safra, S., Szegedy, M.: Interactive proofs and the hardness of approximating cliques. J. ACM 43, 268–292 (1996)
Gardiner, E.J., Artymiuk, P.J., et al.: Clique-detection algorithms for matching three-dimensional molecular structures. J. Mol. Graph. Model. 15, 245–253 (1997)
Hegyi, H., Gerstein, M.: The relationship between protein structure and function: a comprehensive survey with application to the yeast genome. J. Mol. Biol. 288, 147–164 (1999)
Karp, R.M.: Reducibility among combinatorial problems. Complexity of Computer Computations 40(4), 85–103 (1972)
Kleywegt, G.J.: Recognition of spatial motifs in protein structures. J. Mol. Biol. 285, 1887–1897 (1999)
Loewenstein, Y., Raimondo, D., et al.: Protein function annotation by homology-based inference. Genome Biol. 10, 207 (2009)
Lueker, G.S.: A data structure for orthogonal range queries. In: Proc. FOCS, pp. 28–34. IEEE Computer Society, Washington, DC (1978)
Meng, E.C., et al.: Superfamily active site templates. Proteins 55, 962–976 (2004)
Milik, M., Szalma, S., Olszewski, K.A.: Common Structural Cliques: a tool for protein structure and function analysis. Protein Eng. 16, 543–552 (2003)
Mitzenmacher, M., Upfal, E.: Probability and Computing: Randomized Algorithms and Probabilistic Analysis. Cambridge Univ. Press, New York (2005)
Moll, M., Bryant, D.H., Kavraki, L.E.: The labelhash algorithm for substructure matching. BMC Bioinformatics 11, 555 (2010)
Muthukrishnan, S., Pandurangan, G.: The bin-covering technique for thresholding random geometric graph properties. In: Proc. SODA, pp. 989–998 (2005)
Najmanovich, R., Kurbatova, N., Thornton, J.: Detection of 3D atomic similarities and their use in the discrimination of small molecule protein-binding sites. Bioinformatics 24, i105–i111 (2008)
Nussinov, R., Wolfson, H.J.: Efficient detection of three-dimensional structural motifs in biological macromolecules by computer vision techniques. PNAS 88, 10495–10499 (1991)
Pegg, S.C., Brown, S.D., et al.: Leveraging enzyme structure-function relationships for functional inference and experimental design: the structure-function linkage database. Biochemistry 45, 2545–2555 (2006)
Penrose, M.D.: Random Geometric Graphs. Oxford University Press (2003)
Porter, C.T., Bartlett, G.J., Thornton, J.M.: The Catalytic Site Atlas: a resource of catalytic sites and residues identified in enzymes using structural data. Nucleic Acids Res. 32, D129–D133 (2004)
Shulman-Peleg, A., Nussinov, R., Wolfson, H.J.: Recognition of functional sites in protein structures. J. Mol. Biol. 339, 607–633 (2004)
Ullmann, J.R.: An algorithm for subgraph isomorphism. J. ACM 23, 31–42 (1976)
Wallace, A.C., Borkakoti, N., Thornton, J.M.: TESS: a geometric hashing algorithm for deriving 3D coordinate templates for searching structural databases. Application to enzyme active sites. Protein Sci. 6, 2308–2323 (1997)
Wangikar, P.P., et al.: Functional sites in protein families uncovered via an objective and automated graph theoretic approach. J. Mol. Biol. 326, 955–978 (2003)
Willard, D.E.: Predicate-Oriented Database Search Algorithms. Outstanding Dissertations in the Computer Sciences. Garland Publishing, New York (1978)
Wolfson, H.J., Rigoutsos, I.: Geometric hashing: An overview. Computing in Science and Engineering 4, 10–21 (1997)
Xie, L., Bourne, P.E.: Detecting evolutionary relationships across existing fold space, using sequence order-independent profile-profile alignments. PNAS 105, 5441–5446 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
He, L., Vandin, F., Pandurangan, G., Bailey-Kellogg, C. (2012). Ballast: A Ball-Based Algorithm for Structural Motifs. In: Chor, B. (eds) Research in Computational Molecular Biology. RECOMB 2012. Lecture Notes in Computer Science(), vol 7262. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29627-7_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-29627-7_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29626-0
Online ISBN: 978-3-642-29627-7
eBook Packages: Computer ScienceComputer Science (R0)