Evolutionary construction of multiple graph alignments for the structural analysis of biomolecules

One of the main tasks of structural biology is comparing the structure of proteins. Comparisons of protein structure can determine their functional similarities. Multigraph alignment is a useful tool for identifying functional similarities based on structural analysis. This article proposes a new algorithm for aligning protein binding sites called ACOTS-MGA. This algorithm is based on the memetic scheme. It uses the ACO method to construct a set of solutions, then selects the best solution for implementing Tabu Search to improve the solution quality. Experimental results have shown that ACOTS-MGA outperforms state-of-the-art algorithms while producing alignments of better quality.KeywordsMultiple Graph Alignment, Tabu Search, Ant Colony Optimization, local search, memetic algorithm, SMMAS pheromone update rule, protein active sitesReferencesE. Todd, C. A. Orengo, and J. M. Thornton, “Evolution of function in protein superfamilies, from a structural perspective,” J. Mol. 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Huan, “An efficient ant colony optimization algorithm for Multiple Graph Alignment,” in 2013 International Conference on Computing, Management and Telecommunications (ComManTel), 2013, pp. 386–391. F. Neri, Handbook of memetic algorithms, vol. 379. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.M. Gong, Z. Peng, L. Ma, and J. Huang, “Global Biological Network Alignment by Using Efficient Memetic Algorithm,” IEEE/ACM Trans. Comput. Biol. Bioinforma., vol. 13, no. 6, pp. 1117–1129, Nov. 2016.J. M. Caldonazzo Garbelini, A. Y. Kashiwabara, and D. S. Sanches, “Sequence motif finder using memetic algorithm,” BMC Bioinformatics, vol. 19, 2018. L. Correa, B. Borguesan, C. Farfan, M. Inostroza-Ponta, and M. Dorn, “A Memetic Algorithm for 3-D Protein Structure Prediction Problem,” IEEE/ACM Trans. Comput. Biol. Bioinforma., pp. 1–1, 2016.H. Tran Ngoc, D. Do Duc, and H. 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Multiple Graph Alignment for the Structural Analysis of Protein Active Sites

IEEE/ACM Transactions on Computational Biology and Bioinformatics ◽

10.1109/tcbb.2007.358301 ◽

2007 ◽

Vol 4 (2) ◽

pp. 310-320 ◽

Cited By ~ 17

Author(s):

Nils Weskamp ◽

Eyke Hullermeier ◽

Daniel Kuhn ◽

Gerhard Klebe

Keyword(s):

Structural Analysis ◽

Active Sites ◽

Graph Alignment ◽

Protein Active Sites ◽

Multiple Graph

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Structural analysis of the surface-layer protein of spirillum serpens by high-resolution electron microscopy

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100077268 ◽

1983 ◽

Vol 41 ◽

pp. 738-739

Author(s):

W. H. Wu ◽

R. M. Glaeser

Keyword(s):

Electron Microscopy ◽

Surface Layer ◽

Structural Analysis ◽

High Resolution ◽

Low Dose ◽

High Resolution Electron Microscopy ◽

Optical Diffraction ◽

Surface Layer Protein ◽

Morphological Unit ◽

Resolution Electron

Spirillum serpens possesses a surface layer protein which exhibits a regular hexagonal packing of the morphological subunits. A morphological model of the structure of the protein has been proposed at a resolution of about 25 Å, in which the morphological unit might be described as having the appearance of a flared-out, hollow cylinder with six ÅspokesÅ at the flared end. In order to understand the detailed association of the macromolecules, it is necessary to do a high resolution structural analysis. Large, single layered arrays of the surface layer protein have been obtained for this purpose by means of extensive heating in high CaCl2, a procedure derived from that of Buckmire and Murray. Low dose, low temperature electron microscopy has been applied to the large arrays.As a first step, the samples were negatively stained with neutralized phosphotungstic acid, and the specimens were imaged at 40,000 magnification by use of a high resolution cold stage on a JE0L 100B. Low dose images were recorded with exposures of 7-9 electrons/Å2. The micrographs obtained (Fig. 1) were examined by use of optical diffraction (Fig. 2) to tell what areas were especially well ordered.

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RNA polymerase: Preparation and structural analysis of helical polymers

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010011091x ◽

1984 ◽

Vol 42 ◽

pp. 152-153

Author(s):

E. Loren Buhle ◽

Pamela Rew ◽

Ueli Aebi

Keyword(s):

Structural Analysis ◽

Rna Polymerase ◽

Molecular Level ◽

X Ray Diffraction ◽

Helical Polymers ◽

X Ray ◽

E Coli ◽

The Past ◽

Ordered Arrays ◽

Low Ionic Strength

While DNA-dependent RNA polymerase represents one of the key enzymes involved in transcription and ultimately in gene expression in procaryotic and eucaryotic cells, little progress has been made towards elucidation of its 3-D structure at the molecular level over the past few years. This is mainly because to date no 3-D crystals suitable for X-ray diffraction analysis have been obtained with this rather large (MW ~500 kd) multi-subunit (α2ββ'ζ). As an alternative, we have been trying to form ordered arrays of RNA polymerase from E. coli suitable for structural analysis in the electron microscope combined with image processing. Here we report about helical polymers induced from holoenzyme (α2ββ'ζ) at low ionic strength with 5-7 mM MnCl2 (see Fig. 1a). The presence of the ζ-subunit (MW 86 kd) is required to form these polymers, since the core enzyme (α2ββ') does fail to assemble into such structures under these conditions.

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Interpreting HVEM of muscle-cell impulse networks

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010012103x ◽

1992 ◽

Vol 50 (1) ◽

pp. 126-127

Author(s):

Paul DeCosta ◽

Kyugon Cho ◽

Stephen Shemlon ◽

Heesung Jun ◽

Stanley M. Dunn

Keyword(s):

Structural Analysis ◽

Muscle Cell ◽

Electron Micrographs ◽

Relative Size ◽

Automatic Classification ◽

Nerve Fibers ◽

Analysis Algorithm ◽

Structural Networks

Introduction: The analysis and interpretation of electron micrographs of cells and tissues, often requires the accurate extraction of structural networks, which either provide immediate 2D or 3D information, or from which the desired information can be inferred. The images of these structures contain lines and/or curves whose orientation, lengths, and intersections characterize the overall network.Some examples exist of studies that have been done in the analysis of networks of natural structures. In, Sebok and Roemer determine the complexity of nerve structures in an EM formed slide. Here the number of nodes that exist in the image describes how dense nerve fibers are in a particular region of the skin. Hildith proposes a network structural analysis algorithm for the automatic classification of chromosome spreads (type, relative size and orientation).

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