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Unfolding the Protein Surface for Pattern Matching

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Book cover Bioinformatics Research and Applications (ISBRA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10330))

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Abstract

Protein 3-D structural data is a valuable resource in computational biology, and the comparison and interpretation of protein structural patterns have remained scientific and computational challenges. We introduce a novel representation of 3-D protein surface patches as 2-D images, obtained using dimension reduction. We utilize image registration to compare these surface patches and infer protein function and binding based on surface similarity. Our surface representation can capture various structural and physicochemical properties, including curvature, electrostatic potential, hydrophobicity, and evolutionary conservation. The results we present support the use of surface images as a new type of family-specific signatures in functional annotation and drug-binding tasks. We demonstrate the ability of our method to detect local surface similarities between proteins and to correctly identify functional classification of proteins.

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Author information

Authors and Affiliations

  1. Facebook, 1 Hacker Way, Menlo Park, CA, 94025, USA

    Heng Yang

  2. Department of the Biomedical and Health Informatics, The Children’s Hospital of Philadelphia, Philadelphia, PA, 19104, USA

    Chunyu Zhao

  3. Biomedical Engineering, Drexel University, 3120 Market St, Philadelphia, PA, 19104, USA

    Ahmet Sacan

Authors
  1. Heng Yang
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  2. Chunyu Zhao
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  3. Ahmet Sacan
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Corresponding author

Correspondence to Ahmet Sacan .

Editor information

Editors and Affiliations

  1. Georgia State University, Atlanta, Georgia, USA

    Zhipeng Cai

  2. University of Texas at Dallas, Richardson, Texas, USA

    Ovidiu Daescu

  3. Central South University, Changsha, China

    Min Li

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Yang, H., Zhao, C., Sacan, A. (2017). Unfolding the Protein Surface for Pattern Matching. In: Cai, Z., Daescu, O., Li, M. (eds) Bioinformatics Research and Applications. ISBRA 2017. Lecture Notes in Computer Science(), vol 10330. Springer, Cham. https://doi.org/10.1007/978-3-319-59575-7_8

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  • DOI: https://doi.org/10.1007/978-3-319-59575-7_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59574-0

  • Online ISBN: 978-3-319-59575-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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