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  • Carbohydrate-binding protein identification by coupling structural similarity searching with binding affinity prediction

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    Author(s)
    Zhao, Huiying
    Yang, Yuedong
    von Itzstein, Mark
    Zhou, Yaoqi
    Griffith University Author(s)
    von Itzstein, Mark
    Year published
    2014
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    Abstract
    Carbohydrate-binding proteins (CBPs) are potential biomarkers and drug targets. However, the interactions between carbohydrates and proteins are challenging to study experimentally and computationally because of their low binding affinity, high flexibility, and the lack of a linear sequence in carbohydrates as exists in RNA, DNA, and proteins. Here, we describe a structure-based function-prediction technique called SPOT-Struc that identifies carbohydrate-recognizing proteins and their binding amino acid residues by structural alignment program SPalign and binding affinity scoring according to a knowledge-based statistical ...
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    Carbohydrate-binding proteins (CBPs) are potential biomarkers and drug targets. However, the interactions between carbohydrates and proteins are challenging to study experimentally and computationally because of their low binding affinity, high flexibility, and the lack of a linear sequence in carbohydrates as exists in RNA, DNA, and proteins. Here, we describe a structure-based function-prediction technique called SPOT-Struc that identifies carbohydrate-recognizing proteins and their binding amino acid residues by structural alignment program SPalign and binding affinity scoring according to a knowledge-based statistical potential based on the distance-scaled finite-ideal gas reference state (DFIRE). The leave-one-out cross-validation of the method on 113 carbohydrate-binding domains and 3442 noncarbohydrate binding proteins yields a Matthews correlation coefficient of 0.56 for SPalign alone and 0.63 for SPOT-Struc (SPalign?+?binding affinity scoring) for CBP prediction. SPOT-Struc is a technique with high positive predictive value (79% correct predictions in all positive CBP predictions) with a reasonable sensitivity (52% positive predictions in all CBPs). The sensitivity of the method was changed slightly when applied to 31 APO (unbound) structures found in the protein databank (14/31 for APO versus 15/31 for HOLO). The result of SPOT-Struc will not change significantly if highly homologous templates were used. SPOT-Struc predicted 19 out of 2076 structural genome targets as CBPs. In particular, one uncharacterized protein in Bacillus subtilis (1oq1A) was matched to galectin-9 from Mus musculus. Thus, SPOT-Struc is useful for uncovering novel carbohydrate-binding proteins. SPOT-Struc is available at http://sparks-lab.org.
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    Journal Title
    Journal of Computational Chemistry
    Volume
    35
    Issue
    30
    DOI
    https://doi.org/10.1002/jcc.23730
    Copyright Statement
    © 2014 Wiley Periodicals, Inc.. This is the accepted version of the following article: Carbohydrate-binding protein identification by coupling structural similarity searching with binding affinity prediction, Journal of Computational Chemistry, Vol. 35(30), 2014, pp. 2177-2183, which has been published in final form at dx.doi.org/10.1002/jcc.23730 .
    Subject
    Physical chemistry
    Theoretical and computational chemistry
    Nanotechnology
    Publication URI
    http://hdl.handle.net/10072/65546
    Collection
    • Journal articles

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