The LabelHash algorithm for substructure matching

Department of Computer Science, Rice University, Houston, TX 77005, USA.
BMC Bioinformatics (Impact Factor: 2.58). 11/2010; 11(1):555. DOI: 10.1186/1471-2105-11-555
Source: DOAJ


There is an increasing number of proteins with known structure but unknown function. Determining their function would have a significant impact on understanding diseases and designing new therapeutics. However, experimental protein function determination is expensive and very time-consuming. Computational methods can facilitate function determination by identifying proteins that have high structural and chemical similarity.
We present LabelHash, a novel algorithm for matching substructural motifs to large collections of protein structures. The algorithm consists of two phases. In the first phase the proteins are preprocessed in a fashion that allows for instant lookup of partial matches to any motif. In the second phase, partial matches for a given motif are expanded to complete matches. The general applicability of the algorithm is demonstrated with three different case studies. First, we show that we can accurately identify members of the enolase superfamily with a single motif. Next, we demonstrate how LabelHash can complement SOIPPA, an algorithm for motif identification and pairwise substructure alignment. Finally, a large collection of Catalytic Site Atlas motifs is used to benchmark the performance of the algorithm. LabelHash runs very efficiently in parallel; matching a motif against all proteins in the 95% sequence identity filtered non-redundant Protein Data Bank typically takes no more than a few minutes. The LabelHash algorithm is available through a web server and as a suite of standalone programs at The output of the LabelHash algorithm can be further analyzed with Chimera through a plugin that we developed for this purpose.
LabelHash is an efficient, versatile algorithm for large-scale substructure matching. When LabelHash is running in parallel, motifs can typically be matched against the entire PDB on the order of minutes. The algorithm is able to identify functional homologs beyond the twilight zone of sequence identity and even beyond fold similarity. The three case studies presented in this paper illustrate the versatility of the algorithm.

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    • "All in all, the validation results for the ES-descriptive motif KDEEH and the CSA library showed that clearly and meticulously defined motifs that were derived from literature-supported superfamily structures are more descriptive for EC classes and hence these motifs can be considered to be of high quality. This corresponds to observations that were proposed by previous studies (Moll et al., 2010;He et al., 2013). "
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    • "Then, these latter are expanded using a variant of the match augmentation algorithm (Chen et al., 2007). In general, the matching task can be performed with a few algorithmic techniques, such as linear programming (Lancia et al., 2001; Wohlers et al., 2009), dynamic programming (Orengo and Taylor, 1996; Jung and Lee, 2000; Ye and Godzik, 2003), depth-first searching (Stark and Russell, 2003; Ausiello et al., 2005; Chen et al., 2007), graph theory (Jambon et al., 2003; Spriggs et al., 2003; Hofbauer et al., 2004; Huan et al., 2006; Weskamp et al., 2007; Najmanovich et al., 2008; Konc and Janezic, 2010), geometric hashing (Bachar et al., 1993; Wallace et al., 1997; Shatsky et al., 2006; Moll et al., 2010), Markov chains and Monte Carlo methods (Holm and Sander, 1993; Kawabata, 2003) and combinatorial optimization (Shindyalov and Bourne, 1998; Bertolazzi et al., 2010). "
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    ABSTRACT: Multiple local structure comparison helps to identify common structural motifs or conserved binding sites in 3D structures in distantly related proteins. Since there is no best way to compare structures and evaluate the alignment, a wide variety of techniques and different similarity scoring schemes have been proposed. Existing algorithms usually compute the best superposition of two structures or attempt to solve it as an optimization problem in a simpler setting (e.g., considering contact maps or distance matrices). Here, we present PROPOSAL (PROteins comparison through Probabilistic Optimal Structure local ALignment), a stochastic algorithm based on iterative sampling for multiple local alignment of protein structures. Our method can efficiently find conserved motifs across a set of protein structures. Only the distances between all pairs of residues in the structures are computed. To show the accuracy and the effectiveness of PROPOSAL we tested it on a few families of protein structures. We also compared PROPOSAL with two state-of-the-art tools for pairwise local alignment on a dataset of manually annotated motifs. PROPOSAL is available as a Java 2D standalone application or a command line program at
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    • "To permit larger differences in protein structure, a second category of point-based representations limit the comparison of protein structures to binding sites alone, enabling the rest of the structure to change. These binding site "motifs" represent catalytic sites [1,9,10,24], evolutionarily significant amino acids [2], "pseudo-centers" of protein-ligand interactions [25], and "pseudoatoms" on amino acid sidechains [26]. These representations tolerate infinite variation outside the binding site, in order to rapidly scan databases of protein structure (e.g. the PDB [27]) and identify proteins with very different evolutionary origins but similar functional sites. "
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