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Identifying affinity classes of inorganic materials binding sequences via a graph-based model

Du, Nan, Knecht, Marc R., Swihart, Mark T., Tang, Zhenghua, Walsh, Tiffany R. and Zhang, Aidong 2015, Identifying affinity classes of inorganic materials binding sequences via a graph-based model, IEEE/ACM transactions on computational biology and bioinformatics, vol. 12, no. 1, pp. 193-204, doi: 10.1109/TCBB.2014.2321158.

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Title Identifying affinity classes of inorganic materials binding sequences via a graph-based model
Author(s) Du, Nan
Knecht, Marc R.
Swihart, Mark T.
Tang, Zhenghua
Walsh, Tiffany R.ORCID iD for Walsh, Tiffany R. orcid.org/0000-0002-0233-9484
Zhang, Aidong
Journal name IEEE/ACM transactions on computational biology and bioinformatics
Volume number 12
Issue number 1
Start page 193
End page 204
Total pages 12
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Place of publication N.Y., N.Y.
Publication date 2015
ISSN 1545-5963
Keyword(s) classification
Inorganic material
peptide sequences
Summary Rapid advances in bionanotechnology have recently generated growing interest in identifying peptides that bind to inorganic materials and classifying them based on their inorganic material affinities. However, there are some distinct characteristics of inorganic materials binding sequence data that limit the performance of many widely-used classification methods when applied to this problem. In this paper, we propose a novel framework to predict the affinity classes of peptide sequences with respect to an associated inorganic material. We first generate a large set of simulated peptide sequences based on an amino acid transition matrix tailored for the specific inorganic material. Then the probability of test sequences belonging to a specific affinity class is calculated by minimizing an objective function. In addition, the objective function is minimized through iterative propagation of probability estimates among sequences and sequence clusters. Results of computational experiments on two real inorganic material binding sequence data sets show that the proposed framework is highly effective for identifying the affinity classes of inorganic material binding sequences. Moreover, the experiments on the structural classification of proteins (SCOP) data set shows that the proposed framework is general and can be applied to traditional protein sequences.
Language eng
DOI 10.1109/TCBB.2014.2321158
Field of Research 030304 Physical Chemistry of Materials
060102 Bioinformatics
030603 Colloid and Surface Chemistry
Socio Economic Objective 970103 Expanding Knowledge in the Chemical Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2015, Institute of Electrical and Electronics Engineers (IEEE)
Persistent URL http://hdl.handle.net/10536/DRO/DU:30070678

Document type: Journal Article
Collection: Institute for Frontier Materials
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