scholarly journals Structure-Based Phylogeny of the Metallo-β-Lactamases

2005 ◽  
Vol 49 (7) ◽  
pp. 2778-2784 ◽  
Author(s):  
Gianpiero Garau ◽  
Anne Marie Di Guilmi ◽  
Barry G. Hall
Keyword(s):  
Protein Structure ◽  
Phylogenetic Tree ◽  
Sequence Homology ◽  
Structure Alignment ◽  
Alignment Program ◽  
Homologous Proteins ◽  
Multiple Structure ◽  
Structure Level ◽  
Class B

ABSTRACTThe metallo-β-lactamases fall into two groups: Ambler class B subgroups B1 and B2 and Ambler class B subgroup B3. The two groups are so distantly related that there is no detectable sequence homology between members of the two different groups, but homology is clearly detectable at the protein structure level. The multiple structure alignment program MAPS has been used to align the structures of eight metallo-β-lactamases and five structurally homologous proteins from the metallo-β-lactamase superfamily, and that alignment has been used to construct a phylogenetic tree of the metallo-β-lactamases. The presence of genes fromEubacteria,Archaebacteria, andEukaryotaon that tree is consistent with a very ancient origin of the metallo-β-lactamase family.

scholarly journals pmTM-align: scalable pairwise and multiple structure alignment with Apache Spark and OpenMP

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Weiya Chen ◽  
Chun Yao ◽  
Yingzhong Guo ◽  
Yan Wang ◽  
Zhidong Xue
Keyword(s):  
Protein Structure ◽  
Phylogenetic Tree ◽  
Large Scale ◽  
Evolutionary Relationship ◽  
Computation Time ◽  
Data Bank ◽  
Structure Alignment ◽  
Protein Structure Alignment ◽  
Structure Comparison ◽  
Multiple Structure

Abstract Background Structure comparison can provide useful information to identify functional and evolutionary relationship between proteins. With the dramatic increase of protein structure data in the Protein Data Bank, computation time quickly becomes the bottleneck for large scale structure comparisons. To more efficiently deal with informative multiple structure alignment tasks, we propose pmTM-align, a parallel protein structure alignment approach based on mTM-align/TM-align. pmTM-align contains two stages to handle pairwise structure alignments with Spark and the phylogenetic tree-based multiple structure alignment task on a single computer with OpenMP. Results Experiments with the SABmark dataset showed that parallelization along with data structure optimization provided considerable speedup for mTM-align. The Spark-based structure alignments achieved near ideal scalability with large datasets, and the OpenMP-based construction of the phylogenetic tree accelerated the incremental alignment of multiple structures and metrics computation by a factor of about 2–5. Conclusions pmTM-align enables scalable pairwise and multiple structure alignment computing and offers more timely responses for medium to large-sized input data than existing alignment tools such as mTM-align.

Feel the Information with VisPad: A Large Area Vibrotactile Device

2004 ◽  
Vol 3 (1) ◽  
pp. 36-48 ◽  
Author(s):  
Doanna Weissgerber ◽  
Bruce Bridgeman ◽  
Alex Pang
Keyword(s):  
Protein Structure ◽  
Structure Alignment ◽  
Protein Structure Alignment ◽  
Alignment Program ◽  
Large Area ◽  
Output Device ◽  
Graphical Visualization

A new haptics design for visualizing data is constructed out of commodity massage pads and custom controllers and interfaces to a computer. It is an output device for information that can be transmitted to a user who sits on the pad. Two unique properties of the design are: (a) its large feedback area and (b) its passive nature, where unlike most current haptics devices, the user's hands are free to work on other things. To test how useful such a device is for visualizing data, we added the VisPad interface to our protein structure-alignment program (ProtAlign) and performed usability studies. The studies demonstrated that information could be perceived significantly faster utilizing our multi-modal presentation compared to vision-based graphical visualization alone.

Searching for the ideal forms of proteins

2000 ◽  
Vol 28 (2) ◽  
pp. 264-269 ◽  
Author(s):  
W. R. Taylor
Keyword(s):  
Protein Structure ◽  
Structure Alignment ◽  
Automatic Method ◽  
Structural Elements ◽  
Alignment Program ◽  
Protein Structure Analysis ◽  
The Core ◽  
Structure Database ◽  
Definition Of ◽  
The Ideal

A modification of the Structure Alignment Program (SAP), combined with a novel automatic method for the definition of structural elements, correctly identified the core folds of a variety of small β/α proteins when compared with a series of ideal architectures. This approach opens the possibility of not just determining whether one structure is like another, but given a range of ideal forms, determining what the protein is. Preliminary studies have shown it to work equally well on the all dα-class and the all-β class of protein, each of which have corresponding ideal forms. Given the speed of the algorithm, it will be possible to compare all of these against the Protein Structure Database and determine the extent to which the current ideal forms can account for the variety of protein structure. Analysis of the remainder should provide a base for the development of further forms.

scholarly journals MICAN-SQ: a sequential protein structure alignment program that is applicable to monomers and all types of oligomers

2018 ◽  
Vol 34 (19) ◽  
pp. 3324-3331 ◽  
Author(s):  
Shintaro Minami ◽  
Kengo Sawada ◽  
Motonori Ota ◽  
George Chikenji
Keyword(s):  
Protein Structure ◽  
Structure Alignment ◽  
Protein Structure Alignment ◽  
Alignment Program

Automated multiple structure alignment and detection of a common substructural motif

2001 ◽  
Vol 43 (3) ◽  
pp. 235-245 ◽  
Author(s):  
Nathaniel Leibowitz ◽  
Zipora Y. Fligelman ◽  
Ruth Nussinov ◽  
Haim J. Wolfson
Keyword(s):  
Structure Alignment ◽  
Multiple Structure

PROTEIN STRUCTURE ALIGNMENT AND FAST SIMILARITY SEARCH USING LOCAL SHAPE SIGNATURES

2004 ◽  
Vol 02 (01) ◽  
pp. 215-239 ◽  
Author(s):  
TOLGA CAN ◽  
YUAN-FANG WANG
Keyword(s):  
Protein Structure ◽  
Protein Structures ◽  
Structure Alignment ◽  
Protein Structure Alignment ◽  
Specific Information ◽  
Alignment Algorithm ◽  
Screening Process ◽  
Domain Specific ◽  
Local Sequence ◽  
Shape Signatures

We present a new method for conducting protein structure similarity searches, which improves on the efficiency of some existing techniques. Our method is grounded in the theory of differential geometry on 3D space curve matching. We generate shape signatures for proteins that are invariant, localized, robust, compact, and biologically meaningful. The invariancy of the shape signatures allows us to improve similarity searching efficiency by adopting a hierarchical coarse-to-fine strategy. We index the shape signatures using an efficient hashing-based technique. With the help of this technique we screen out unlikely candidates and perform detailed pairwise alignments only for a small number of candidates that survive the screening process. Contrary to other hashing based techniques, our technique employs domain specific information (not just geometric information) in constructing the hash key, and hence, is more tuned to the domain of biology. Furthermore, the invariancy, localization, and compactness of the shape signatures allow us to utilize a well-known local sequence alignment algorithm for aligning two protein structures. One measure of the efficacy of the proposed technique is that we were able to perform structure alignment queries 36 times faster (on the average) than a well-known method while keeping the quality of the query results at an approximately similar level.

scholarly journals Implementation of a Parallel Protein Structure Alignment Service on Cloud

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Che-Lun Hung ◽  
Yaw-Ling Lin
Keyword(s):  
Protein Structure ◽  
Programming Model ◽  
Protein Structures ◽  
Structure Alignment ◽  
Evolutionary Relationships ◽  
Protein Structure Alignment ◽  
Alignment Algorithm ◽  
Cloud Platform ◽  
Computational Performance ◽  
Refinement Algorithm

Protein structure alignment has become an important strategy by which to identify evolutionary relationships between protein sequences. Several alignment tools are currently available for online comparison of protein structures. In this paper, we propose a parallel protein structure alignment service based on the Hadoop distribution framework. This service includes a protein structure alignment algorithm, a refinement algorithm, and a MapReduce programming model. The refinement algorithm refines the result of alignment. To process vast numbers of protein structures in parallel, the alignment and refinement algorithms are implemented using MapReduce. We analyzed and compared the structure alignments produced by different methods using a dataset randomly selected from the PDB database. The experimental results verify that the proposed algorithm refines the resulting alignments more accurately than existing algorithms. Meanwhile, the computational performance of the proposed service is proportional to the number of processors used in our cloud platform.

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