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An efficient algorithm for protein structure comparison using elastic shape analysis

Overview of attention for article published in Algorithms for Molecular Biology, September 2016
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Title
An efficient algorithm for protein structure comparison using elastic shape analysis
Published in
Algorithms for Molecular Biology, September 2016
DOI 10.1186/s13015-016-0089-1
Pubmed ID
Authors

S. Srivastava, S. Srivastava, S. B. Lal, D. C. Mishra, U. B. Angadi, K. K. Chaturvedi, S. N. Rai, A. Rai

Abstract

Protein structure comparison play important role in in silico functional prediction of a new protein. It is also used for understanding the evolutionary relationships among proteins. A variety of methods have been proposed in literature for comparing protein structures but they have their own limitations in terms of accuracy and complexity with respect to computational time and space. There is a need to improve the computational complexity in comparison/alignment of proteins through incorporation of important biological and structural properties in the existing techniques. An efficient algorithm has been developed for comparing protein structures using elastic shape analysis in which the sequence of 3D coordinates atoms of protein structures supplemented by additional auxiliary information from side-chain properties are incorporated. The protein structure is represented by a special function called square-root velocity function. Furthermore, singular value decomposition and dynamic programming have been employed for optimal rotation and optimal matching of the proteins, respectively. Also, geodesic distance has been calculated and used as the dissimilarity score between two protein structures. The performance of the developed algorithm is tested and found to be more efficient, i.e., running time reduced by 80-90 % without compromising accuracy of comparison when compared with the existing methods. Source codes for different functions have been developed in R. Also, user friendly web-based application called ProtSComp has been developed using above algorithm for comparing protein 3D structures and is accessible free. The methodology and algorithm developed in this study is taking considerably less computational time without loss of accuracy (Table 2). The proposed algorithm is considering different criteria of representing protein structures using 3D coordinates of atoms and inclusion of residue wise molecular properties as auxiliary information.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 21 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 4 19%
Student > Ph. D. Student 4 19%
Researcher 2 10%
Student > Doctoral Student 2 10%
Student > Master 2 10%
Other 5 24%
Unknown 2 10%
Readers by discipline Count As %
Computer Science 6 29%
Agricultural and Biological Sciences 6 29%
Biochemistry, Genetics and Molecular Biology 4 19%
Mathematics 1 5%
Unspecified 1 5%
Other 1 5%
Unknown 2 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 October 2016.
All research outputs
#6,424,975
of 8,467,419 outputs
Outputs from Algorithms for Molecular Biology
#103
of 161 outputs
Outputs of similar age
#176,969
of 253,772 outputs
Outputs of similar age from Algorithms for Molecular Biology
#2
of 3 outputs
Altmetric has tracked 8,467,419 research outputs across all sources so far. This one is in the 13th percentile – i.e., 13% of other outputs scored the same or lower than it.
So far Altmetric has tracked 161 research outputs from this source. They receive a mean Attention Score of 2.7. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
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