GOSSIP: a method for fast and accurate global alignment of protein structures

Kifer, Ilona; Nussinov, Ruth; Wolfson, Haim J.

doi:10.1093/bioinformatics/btr044

Cited by 9 publications

(11 citation statements)

References 33 publications

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“…To make the clustering procedure feasible, we classified the BBs into categories according to the number and type of secondary structure elements. On each category, an all‐against‐all structural alignment was performed using GOSSIP,17 an efficient algorithm for global structural alignment of proteins. Two fragments are defined as similar if at least 80% of the C α s of the longer of the two BBs are at a distance of at most 2.0 Å from the corresponding C α s in the second fragment.…”

Section: Methodsmentioning

confidence: 99%

Protein structure prediction using a docking‐based hierarchical folding scheme

Kifer¹,

Nussinov²,

Wolfson³

2011

Proteins

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The pathways by which proteins fold into their specific native structure is still an unsolved mystery. Currently many methods for protein structure prediction are available, most of them tackle the problem by relying on the vast amounts of data collected from known protein structures. These methods are often not concerned with the route the protein follows to reach its final fold. This work is based on the premise that proteins fold in a hierarchical manner. We present FOBIA, an automated method for predicting a protein structure. FOBIA consists of two main stages: the first finds matches between parts of the target sequence and independently-folding structural units using profile-profile comparison. The second assembles these units into a 3D structure by searching and ranking their possible orientations towards each other using a docking-based approach. We have previously reported an application of an initial version of this strategy to homology based targets. Since then we have considerably enhanced our method’s abilities to allow it to address the more difficult template-based target category. This allows us to now apply FOBIA to the Template-Based targets of CASP8 and to show that it is both very efficient and promising. Our method can provide an alternative for Template-Based structure prediction, and in particular, the docking-based ranking technique presented here can be incorporated into any profile-profile comparison based method.

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Section: Methodsmentioning

confidence: 99%

Protein structure prediction using a docking‐based hierarchical folding scheme

Kifer¹,

Nussinov²,

Wolfson³

2011

Proteins

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“…We use this benchmark to compare alignment methods by plotting ROC curves. We compare our approach against FAST [35], GOSSIP [21] and Yakuza [7]. We use the default parameters for FAST and Yakuza.…”

Section: Copsmentioning

confidence: 99%

“…[7] characterizes proteins as sequences of α angles (dihedral angle between four consecutive α carbons) and compares them by searching common fixed length patterns. [21] performs a global alignment of structural signatures.…”

Section: Introductionmentioning

confidence: 99%

LNA: Fast Protein Structural Comparison Using a Laplacian Characterization of Tertiary Structure

Bonnel¹,

Marteau²

2012

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Abstract—In the last two decades, a lot of protein 3D shapes have been discovered, characterized, and made available thanks to the Protein Data Bank (PDB), that is nevertheless growing very quickly. New scalable methods are thus urgently required to search through the PDB efficiently. This paper presents an approach entitled LNA (Laplacian Norm Alignment) that performs a structural comparison of two proteins with dynamic programming algorithms. This is achieved by characterizing each residue in the protein with scalar features. The feature values are calculated using a Laplacian operator applied on the graph corresponding to the adjacency matrix of the residues. The weighted Laplacian operator we use estimates, at various scales, local deformations of the topology where each residue is located. On some benchmarks, which are widely shared by the community, we obtain qualitatively similar results compared to other competing approaches, but with an algorithm one or two order of magnitudes faster. 180,000 protein comparisons can be done within 1 second with a single recent Graphical Processing Unit (GPU), which makes our algorithm very scalable and suitable for real-time database querying across the web.

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“…Over the last couple of years, there have been several publications that have promoted fast algorithms for database-wide comparisons [1,8,9,10] (see the related work in [11,12,13,14,15,16,17,18,19]). These algorithms are designed to efficiently discern if two proteins share a similar molecular structure.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Programming Used to Align Protein Structures with a Spectrum Is Robust

Holder

Simon

Strauser

et al. 2013

Biology

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Several efficient algorithms to conduct pairwise comparisons among large databases of protein structures have emerged in the recent literature. The central theme is the design of a measure between the Cα atoms of two protein chains, from which dynamic programming is used to compute an alignment. The efficiency and efficacy of these algorithms allows large-scale computational studies that would have been previously impractical. The computational study herein shows that the structural alignment algorithm eigen-decomposition alignment with the spectrum (EIGAs) is robust against both parametric and structural variation.

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GOSSIP: a method for fast and accurate global alignment of protein structures

Abstract: A server, as well as an executable for download, are available at http://bioinfo3d.cs.tau.ac.il/gossip/.

Cited by 9 publications

References 33 publications

Protein structure prediction using a docking‐based hierarchical folding scheme

Protein structure prediction using a docking‐based hierarchical folding scheme

LNA: Fast Protein Structural Comparison Using a Laplacian Characterization of Tertiary Structure

Dynamic Programming Used to Align Protein Structures with a Spectrum Is Robust

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