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

Nan Du, Marc Knecht, Paras N. Prasad, Mark T. Swihart, Tiffany Walsh, Aidong Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013
Pages545-551
Number of pages7
DOIs
StatePublished - Nov 28 2013
Event2013 4th ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013 - Wshington, DC, United States
Duration: Sep 22 2013Sep 25 2013

Other

Other2013 4th ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013
CountryUnited States
CityWshington, DC
Period9/22/139/25/13

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Peptides
Amino Acids
Amino acids
Datasets

ASJC Scopus subject areas

  • Bioengineering
  • Biomedical Engineering
  • Health Informatics

Cite this

Du, N., Knecht, M., Prasad, P. N., Swihart, M. T., Walsh, T., & Zhang, A. (2013). A framework for identifying affinity classes of inorganic materials binding peptide sequences. In 2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013 (pp. 545-551) https://doi.org/10.1145/2506583.2506628

A framework for identifying affinity classes of inorganic materials binding peptide sequences. / Du, Nan; Knecht, Marc; Prasad, Paras N.; Swihart, Mark T.; Walsh, Tiffany; Zhang, Aidong.

2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013. 2013. p. 545-551.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Du, N, Knecht, M, Prasad, PN, Swihart, MT, Walsh, T & Zhang, A 2013, A framework for identifying affinity classes of inorganic materials binding peptide sequences. in 2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013. pp. 545-551, 2013 4th ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013, Wshington, DC, United States, 9/22/13. https://doi.org/10.1145/2506583.2506628
Du N, Knecht M, Prasad PN, Swihart MT, Walsh T, Zhang A. A framework for identifying affinity classes of inorganic materials binding peptide sequences. In 2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013. 2013. p. 545-551 https://doi.org/10.1145/2506583.2506628
Du, Nan ; Knecht, Marc ; Prasad, Paras N. ; Swihart, Mark T. ; Walsh, Tiffany ; Zhang, Aidong. / A framework for identifying affinity classes of inorganic materials binding peptide sequences. 2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013. 2013. pp. 545-551
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