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Structural alignment of protein descriptors – a combinatorial model

Overview of attention for article published in BMC Bioinformatics, September 2016
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Title
Structural alignment of protein descriptors – a combinatorial model
Published in
BMC Bioinformatics, September 2016
DOI 10.1186/s12859-016-1237-9
Pubmed ID
Authors

Maciej Antczak, Marta Kasprzak, Piotr Lukasiak, Jacek Blazewicz

Abstract

Structural alignment of proteins is one of the most challenging problems in molecular biology. The tertiary structure of a protein strictly correlates with its function and computationally predicted structures are nowadays a main premise for understanding the latter. However, computationally derived 3D models often exhibit deviations from the native structure. A way to confirm a model is a comparison with other structures. The structural alignment of a pair of proteins can be defined with the use of a concept of protein descriptors. The protein descriptors are local substructures of protein molecules, which allow us to divide the original problem into a set of subproblems and, consequently, to propose a more efficient algorithmic solution. In the literature, one can find many applications of the descriptors concept that prove its usefulness for insight into protein 3D structures, but the proposed approaches are presented rather from the biological perspective than from the computational or algorithmic point of view. Efficient algorithms for identification and structural comparison of descriptors can become crucial components of methods for structural quality assessment as well as tertiary structure prediction. In this paper, we propose a new combinatorial model and new polynomial-time algorithms for the structural alignment of descriptors. The model is based on the maximum-size assignment problem, which we define here and prove that it can be solved in polynomial time. We demonstrate suitability of this approach by comparison with an exact backtracking algorithm. Besides a simplification coming from the combinatorial modeling, both on the conceptual and complexity level, we gain with this approach high quality of obtained results, in terms of 3D alignment accuracy and processing efficiency. All the proposed algorithms were developed and integrated in a computationally efficient tool descs-standalone, which allows the user to identify and structurally compare descriptors of biological molecules, such as proteins and RNAs. Both PDB (Protein Data Bank) and mmCIF (macromolecular Crystallographic Information File) formats are supported. The proposed tool is available as an open source project stored on GitHub ( https://github.com/mantczak/descs-standalone ).

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters 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 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Poland 1 4%
Czechia 1 4%
France 1 4%
Unknown 21 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 33%
Researcher 6 25%
Student > Master 4 17%
Professor 2 8%
Student > Bachelor 1 4%
Other 1 4%
Unknown 2 8%
Readers by discipline Count As %
Computer Science 6 25%
Biochemistry, Genetics and Molecular Biology 5 21%
Agricultural and Biological Sciences 4 17%
Chemistry 2 8%
Nursing and Health Professions 1 4%
Other 3 13%
Unknown 3 13%

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 19 September 2016.
All research outputs
#13,663,454
of 17,800,904 outputs
Outputs from BMC Bioinformatics
#4,998
of 6,267 outputs
Outputs of similar age
#185,790
of 276,656 outputs
Outputs of similar age from BMC Bioinformatics
#17
of 26 outputs
Altmetric has tracked 17,800,904 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,267 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one is in the 14th percentile – i.e., 14% of its peers scored the same or lower than it.
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We're also able to compare this research output to 26 others from the same source and published within six weeks on either side of this one. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.