A Framework for Identifying Affinity Classes of Inorganic Materials Binding Peptide Sequences

Authors:
Nan Du;Marc R. Knecht;Paras N. Prasad;Mark T. Swihart;Tiffany Walsh;Aidong Zhang
Affiliations:
Computer Science and Engineering Department, SUNY at Buffalo, U.S.A;Department of Chemistry, University of Miami, Coral Gables, U.S.A;Department of Chemistry, SUNY at Buffalo, U.S.A;Department of Chemical and Biological Engineering, SUNY at Buffalo, U.S.A;Institute for Frontier Materials, Deakin University, Geelong, Australia;Computer Science and Engineering Department, SUNY at Buffalo, U.S.A
Venue:
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Year:
2013

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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.