Finding local structural similarities among families of unrelated protein structures: A generic non-linear alignment algorithm

Lehtonen, Jukka V.; Denessiouk, Konstantin; May, Alex C.W.; Johnson, Mark S.

doi:10.1002/(sici)1097-0134(19990215)34:3<341::aid-prot7>3.0.co;2-z

Cited by 28 publications

(16 citation statements)

References 57 publications

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“…Second priority 1 : RMSD • S2 Alignment size and RMSD based score 2 : 3 100 N − RMSD • S3 Structal (Subbiah et al, 1993) score function: (Lethonen et al, 1999) (Lu, 2000) score function no. 1:…”

Section: Scoring Of Similaritymentioning

confidence: 99%

Flexible Secondary Structure Based Protein Structure Comparison Applied to the Detection of Circular Permutation

Vesterstrøm

Taylor²

2006

Journal of Computational Biology

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We present a novel method for structural comparison of protein structures. The approach consists of two main phases: 1) an initial search phase where, starting from aligned pairs of secondary structure elements, the space of 3D transformations is searched for similarities and 2) a subsequent refinement phase where interim solutions are subjected to parallel, local, iterative dynamic programming in the areas of possible improvement. The proposed method combines dynamic programming for finding alignments but does not restrict solutions to be sequential. In addition, to deal with the problem of nonuniqueness of optimal similarities, we introduce a consensus scoring method in selecting the preferred similarity and provide a list of top-ranked solutions. The method, called FASE (flexible alignment of secondary structure elements), was tested on well-known data and various standard problems from the literature. The results show that FASE is able to find remote and weak similarities consistently using a reasonable run time. The method was tested (using the SCOP database) on its ability to discriminate interfold pairs from intrafold pairs at the level of the best existing methods. The method was then applied to the problem of finding circular permutations in proteins.

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Section: Scoring Of Similaritymentioning

confidence: 99%

Flexible Secondary Structure Based Protein Structure Comparison Applied to the Detection of Circular Permutation

Vesterstrøm

Taylor²

2006

Journal of Computational Biology

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“…Other methods, such as GENE FIT of Lehtonen et al 4 and the approach of Poirrette et al 5 use genetic algorithms to optimally superimpose proteins in identified substructure ranges. All these approaches only use descriptors for the shape of the protein.…”

Section: Introductionmentioning

confidence: 75%

An Algorithm for Finding Similarities in Protein Active Sites

Kupas¹,

Ultsch²,

Klebe³

2005

Advances in Bioinformatics and Its Applications

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A new method has been developed to find similar substructures in protein binding cavities. It is based on the idea that protein function is intimately related to the recognition and subsequent response to the binding of an endogenous ligand in a well-characterized binding pocket. It can therefore be assumed that proteins having similar binding cavities also bind similar ligands and exhibit related function. For the comparison of the binding cavities, the binding-site exposed physicochemical characteristics are described by assigning generic pseudocenters to the functional groups of the amino acids flanking a particular active site. These pseudocenters are assembled into small substructures. To find substructures with spatial similarity and appropriate chemical properties, an emergent self-organizing map is used for clustering. Two substructures which are found to be similar form the basis for an expanded comparison of the complete cavities. Preliminary results with four pairs of binding cavities show that similarities are detected correctly and motivate further studies.

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“…Thus, during the recent decade, many heuristic methods have been proposed using various techniques: Monte Carlo optimization [DALI 5., 10.], Dynamic programming [STRUCTAL ( 11 ), LOCK ( 12 )], Graph theory [VAST 13., 14., SARF2 ( 15 )], Combinatorial extension of alignment path [CE ( 16 )], Geometric hashing [MASS ( 17 )], Hidden Markov models [SCALI ( 6 )], Genetic algorithm [Ref. 18., 19.; K2 ( 7 )], Clustering-based method [FAST ( 20 )], and so on. The review can be found in the work by Signh and Brutalg (http://cmgm.stanford.edu/~brutlag/Papers/singh00.pdf).…”

Section: Introductionmentioning

confidence: 99%

FAMCS: Finding All Maximal Common Substructures in Proteins

Yao

Xiao

Tung

et al. 2005

Genomics, Proteomics &Amp; Bioinformatics

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Finding the common substructures shared by two proteins is considered as one of the central issues in computational biology because of its usefulness in understanding the structure-function relationship and application in drug and vaccine design. In this paper, we propose a novel algorithm called FAMCS (Finding All Maximal Common Substructures) for the common substructure identification problem. Our method works initially at the protein secondary structural element (SSE) level and starts with the identification of all structurally similar SSE pairs. These SSE pairs are then merged into sets using a modified Apriori algorithm, which will test the similarity of various sets of SSE pairs incrementally until all the maximal sets of SSE pairs that deemed to be similar are found. The maximal common substructures of the two proteins will be formed from these maximal sets. A refinement algorithm is also proposed to fine tune the alignment from the SSE level to the residue level. Comparison of FAMCS with other methods on various proteins shows that FAMCS can address all four requirements and infer interesting biological discoveries.

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Finding local structural similarities among families of unrelated protein structures: A generic non-linear alignment algorithm

Cited by 28 publications

References 57 publications

Flexible Secondary Structure Based Protein Structure Comparison Applied to the Detection of Circular Permutation

Flexible Secondary Structure Based Protein Structure Comparison Applied to the Detection of Circular Permutation

An Algorithm for Finding Similarities in Protein Active Sites

FAMCS: Finding All Maximal Common Substructures in Proteins

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