A framework for identifying affinity classes of inorganic materials binding peptide sequences

Du, Nan, Knecht, Marc R., Prasad, Paras N., Swihart, Mark T., Walsh, Tiffany and Zhang, Aidong 2013, A framework for identifying affinity classes of inorganic materials binding peptide sequences, in ACM-BCB 2013 : Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, Association for Computing Machinery, New York, NY, pp. 545-551.

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Title A framework for identifying affinity classes of inorganic materials binding peptide sequences
Author(s) Du, Nan
Knecht, Marc R.
Prasad, Paras N.
Swihart, Mark T.
Walsh, Tiffany
Zhang, Aidong
Conference name Bioinformatics, Computational Biology and Biomedical Informatics. ACM Conference (4th : 2013 : Washington, D.C.)
Conference location Washington, D.C.
Conference dates 22-25 Sep. 2013
Title of proceedings ACM-BCB 2013 : Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Editor(s) [Unknown]
Publication date 2013
Conference series Bioinformatics, Computational Biology and Biomedical Informatics ACM Conference
Start page 545
End page 551
Total pages 7
Publisher Association for Computing Machinery
Place of publication New York, NY
Keyword(s) bionanotechnology
peptide sequences
inorganic material binding sequences
affinity classes of peptide sequences
Summary With the rapid development of bionanotechnology, there has been a growing interest recently in identifying the affinity classes of the inorganic materials binding peptide sequences. However, there are some distinct characteristics of inorganic materials binding sequence data that limit the performance of many widely-used classification methods. 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 our new amino acid transition matrix, and then the probability of test sequences belonging to a specific affinity class is calculated through solving an objective function. In addition, the objective function is solved through iterative propagation of probability estimates among sequences and sequence clusters. Experimental results on a real inorganic material binding sequence dataset show that the proposed framework is highly effective on identifying the affinity classes of inorganic material binding sequences.
ISBN 9781450324342
Language eng
Field of Research 109999 Technology not elsewhere classified
Socio Economic Objective 970110 Expanding Knowledge in Technology
HERDC Research category E1 Full written paper - refereed
HERDC collection year 2013
Persistent URL http://hdl.handle.net/10536/DRO/DU:30061655

Document type: Conference Paper
Collection: Institute for Frontier Materials
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