MAMMOTH (Matching molecular models obtained from theory): An automated method for model comparison

Ortíz, Ángel R.; Strauss, Charlie E. M.; Olmea, Osvaldo

doi:10.1110/ps.0215902

Cited by 445 publications

(360 citation statements)

References 86 publications

(131 reference statements)

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“…In order to asses the validity of our method, average similarity scores on the global comparison, "all against all" proteins, were also calculated using common multiple protein comparison algorithms like sequence alignment [25], Mammoth [26] and TMalign [27]. 'All against all" comparison of the proteins was performed using reduced dimensionality representations of the proteins, having as similarity index the hydrophobicity pattern, structural similarity and side chain similarity of amino acids.…”

Section: Accepted M Manuscriptmentioning

confidence: 99%

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Classification of proteins based on similarity of two-dimensional protein maps

Albrecht

Grant

Sisu

et al. 2008

Biophysical Chemistry

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Data reduction techniques are now a vital part of numerical analysis and principal component analysis is often used to identify important molecular features from a set of descriptors. We now take a different approach and apply data reduction techniques directly to protein structure. With this we can reduce the three-dimensional structural data into two-dimensions while preserving the correct relationships. With two-dimensional representations, structural comparisons between proteins are accelerated significantly. This means that protein-protein similarity comparisons are now feasible on a large scale. We show how the approach can help to predict the function of kinase structures according to the Hanks' classification based on their structural similarity to different kinase classes.

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Section: Accepted M Manuscriptmentioning

confidence: 99%

“…Figure 11. Heat map and dendrogram classification of test set protein kinases using Mammoth-multiple [26]. Colour code: zero similarity -black, identity -white.…”

Section: Accepted M Manuscriptmentioning

confidence: 99%

Classification of proteins based on similarity of two-dimensional protein maps

Albrecht

Grant

Sisu

et al. 2008

Biophysical Chemistry

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“…structural comparison [5], but these methods entail an exhaustive search to compare the query structure against all protein structures in the database.…”

Section: Introductionmentioning

confidence: 99%

A Complex Network Approach for the Analysis of Protein Units Similarity Using Structural Alphabet

Tung¹,

Nacher²

2013

IJBBB

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Abstract-In this paper we present a network approach based on the recent developed 3D-BLAST method of rapid protein structure search. We defined new local segments that represent structural feature of proteins named units of structural alphabet (USA). Each USA is composed of two protein secondary structures, and one loop located between these two secondary structures. We performed all-against-all structural comparison of USA and recognized the USA-based similarity network. The analytical result shows that the network with a power degree distribution is called scale free. These results not only suggest the existence of organizing principles in the local protein structure but also allow us to identify potential key fragments that could be useful for future new drug development and design.Index Terms-Local structure similarity network, network biology, protein modularity. I. INTRODUCTIONIn the past few decades, genomics (DNA sequences), structural genomics (protein structures), and proteomics (protein expression and interactions) have rapidly enhanced knowledge on biological functions and systems. With structural models developed using genome-wide investigative strategies [1]-[3], the number of protein structures in the Protein Data Bank (PDB) has rapidly increased. By Dec. 25, 2012, there were already more than 87,090 known protein structures [4]. The increasing number of known protein structures with unknown/unassigned functions emphasizes the demand for effective bioinformatics methods for annotating the structural homology or evolutionary family of proteins and inferring their cellular functions.The comparison and analysis of the relationship between new protein structures with unclear functions and well-known structures seeks to bridge the protein structure-function research gap. Given a query protein structure, we may search through the database and report similar protein structures. However, unlike one-dimensional sequence comparison, structural alignment for determining similarities is much more complex and computationally expensive. Some methods can be used for efficient pair-wise Manuscript received January 15, 2013; revised March 15, 2013. This work was supported in part by the National Science Council (NSC), Taiwan, under Contract of NSC 101-2311-B-216-001 and 101-2221-E-216-041.Chi-Hua Tung is with the Department of Bioinformatics, Chung-Hua University, Hsinchu 300, Taiwan (e-mail: chihua.tung@chu.edu.tw).Jose C. Nacher is with Department of Information Science, Faculty of Science, Toho University, Miyama 2-2-1, Funabashi, Chiba 274-8510, Japan (e-mail: nacher@is.sci.toho-u.ac.jp). structural comparison [5], but these methods entail an exhaustive search to compare the query structure against all protein structures in the database.To bridge the current protein structure-function research gap and address anterior questions, many approaches have been proposed for encoding 3D local structural fragments based on Cartesian coordinates into a one-dimensional representation using several letters called the str...

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“…The length of these fragments is either predefined [13][14][15][16] or constrained by a measure for structural similarity between fragments [17,18]. The most common and efficient technique for sequence order dependant comparisons, is dynamic programming [9,13,15,17,19].…”

Section: Introductionmentioning

confidence: 99%

Progressive structure-based alignment of homologous proteins: Adopting sequence comparison strategies

2012

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Comparison of multiple protein structures has a broad range of applications in the analysis of protein structure, function and evolution. Multiple structure alignment tools (MSTAs) are necessary to obtain a simultaneous comparison of a family of related folds. In this study, we have developed a method for multiple structure comparison largely based on sequence alignment techniques. A widely used Structural Alphabet named Protein Blocks (PBs) was used to transform the information on 3D protein backbone conformation as a 1D sequence string.A progressive alignment strategy similar to CLUSTALW was adopted for multiple PB sequence alignment (mulPBA). Highly similar stretches identified by the pairwise alignments are given higher weights during the alignment. The residue equivalences from PB based alignments are used to obtain a three dimensional fit of the structures followed by an iterative refinement of the structural superposition.Systematic comparisons using benchmark datasets of MSTAs underlines that the alignment quality is better than MULTIPROT, MUSTANG and the alignments in HOMSTRAD, in more than 85% of the cases. Comparison with other rigid-body and flexible MSTAs also indicate that mulPBA alignments are superior to most of the rigid-body MSTAs and highly comparable to the flexible alignment methods.3

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MAMMOTH (Matching molecular models obtained from theory): An automated method for model comparison

Cited by 445 publications

References 86 publications

Classification of proteins based on similarity of two-dimensional protein maps

Classification of proteins based on similarity of two-dimensional protein maps

A Complex Network Approach for the Analysis of Protein Units Similarity Using Structural Alphabet

Progressive structure-based alignment of homologous proteins: Adopting sequence comparison strategies

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