American Journal of Biomedical and Life Sciences

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Protein solvent accessibility prediction systemss

Received: 07 December 2014    Accepted: 09 December 2014    Published: 07 August 2015
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Abstract

Background: Prediction of protein solvent accessibility, also called accessible surface area (ASA) prediction, is an important step for tertiary structure prediction directly from one-dimensional sequences. Traditionally, predicting solvent accessibility is regarded as either a two- (exposed or buried) or three-state (exposed, intermediate or buried) classification problem. However, the states of solvent accessibility are not well-defined in real protein structures. Thus, a number of methods have been developed to directly predict the ASA based on information such as amino acid composition. Results: In this study we use physicochemical properties of amino acid such as hydrophobicity for ASA prediction by considering amino acid composition. We propose a systematic method for identifying residue groups with respect to protein solvent accessibility. The hydrophobicity of amino acid are used to generate features. Finally, Adaptive neuro fuzzy inference system (anfis) is adopted to construct a ASA predictor. Experimental results demonstrate that the features produced by the proposed selection process are informative for ASA prediction. Conclusion: Experimental results based on a widely used benchmark reveal that the proposed method performs good among several of existing packages for performing ASA prediction depending on amino acid sequence only .The program and data are available from the authors upon request.

DOI 10.11648/j.ajbls.s.2015030203.14
Published in American Journal of Biomedical and Life Sciences (Volume 3, Issue 2-3, April 2015)

This article belongs to the Special Issue Spectral Imaging for Medical Diagnosis “Modern Tool for Molecular Imaging”

Page(s) 21-24
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Protein Structure, Protein Solvent Accessibility, Accessible Surface Area, Structure Prediction, Adaptive Neuro Fuzzy Inference, Hydrophobicity

References
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Author Information
  • Department of Biomedical Engineering, Faculty of Mechanical and Electrical Engineering, Damascus University, Damascus, Syria.

  • Department of Biomedical Engineering, FMEE, Damascus University and Faculty of Informatics and Communication Engineering, Arab International University, Damascus, Syria

  • Faculty of Pharmacology, Damascus University, Damascus, Syria

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  • APA Style

    Ritta Shaheen, Hani Amasha, Majd Aljamali. (2015). Protein solvent accessibility prediction systemss. American Journal of Biomedical and Life Sciences, 3(2-3), 21-24. https://doi.org/10.11648/j.ajbls.s.2015030203.14

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    ACS Style

    Ritta Shaheen; Hani Amasha; Majd Aljamali. Protein solvent accessibility prediction systemss. Am. J. Biomed. Life Sci. 2015, 3(2-3), 21-24. doi: 10.11648/j.ajbls.s.2015030203.14

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    AMA Style

    Ritta Shaheen, Hani Amasha, Majd Aljamali. Protein solvent accessibility prediction systemss. Am J Biomed Life Sci. 2015;3(2-3):21-24. doi: 10.11648/j.ajbls.s.2015030203.14

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  • @article{10.11648/j.ajbls.s.2015030203.14,
      author = {Ritta Shaheen and Hani Amasha and Majd Aljamali},
      title = {Protein solvent accessibility prediction systemss},
      journal = {American Journal of Biomedical and Life Sciences},
      volume = {3},
      number = {2-3},
      pages = {21-24},
      doi = {10.11648/j.ajbls.s.2015030203.14},
      url = {https://doi.org/10.11648/j.ajbls.s.2015030203.14},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajbls.s.2015030203.14},
      abstract = {Background: Prediction of protein solvent accessibility, also called accessible surface area (ASA) prediction, is an important step for tertiary structure prediction directly from one-dimensional sequences. Traditionally, predicting solvent accessibility is regarded as either a two- (exposed or buried) or three-state (exposed, intermediate or buried) classification problem. However, the states of solvent accessibility are not well-defined in real protein structures. Thus, a number of methods have been developed to directly predict the ASA based on information such as amino acid composition. Results: In this study we use physicochemical properties of amino acid such as hydrophobicity for ASA prediction by considering amino acid composition. We propose a systematic method for identifying residue groups with respect to protein solvent accessibility. The hydrophobicity of amino acid are used to generate features. Finally, Adaptive neuro fuzzy inference system (anfis) is adopted to construct a ASA predictor. Experimental results demonstrate that the features produced by the proposed selection process are informative for ASA prediction. Conclusion: Experimental results based on a widely used benchmark reveal that the proposed method performs good among several of existing packages for performing ASA prediction depending on amino acid sequence only .The program and data are available from the authors upon request.},
     year = {2015}
    }
    

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  • TY  - JOUR
    T1  - Protein solvent accessibility prediction systemss
    AU  - Ritta Shaheen
    AU  - Hani Amasha
    AU  - Majd Aljamali
    Y1  - 2015/08/07
    PY  - 2015
    N1  - https://doi.org/10.11648/j.ajbls.s.2015030203.14
    DO  - 10.11648/j.ajbls.s.2015030203.14
    T2  - American Journal of Biomedical and Life Sciences
    JF  - American Journal of Biomedical and Life Sciences
    JO  - American Journal of Biomedical and Life Sciences
    SP  - 21
    EP  - 24
    PB  - Science Publishing Group
    SN  - 2330-880X
    UR  - https://doi.org/10.11648/j.ajbls.s.2015030203.14
    AB  - Background: Prediction of protein solvent accessibility, also called accessible surface area (ASA) prediction, is an important step for tertiary structure prediction directly from one-dimensional sequences. Traditionally, predicting solvent accessibility is regarded as either a two- (exposed or buried) or three-state (exposed, intermediate or buried) classification problem. However, the states of solvent accessibility are not well-defined in real protein structures. Thus, a number of methods have been developed to directly predict the ASA based on information such as amino acid composition. Results: In this study we use physicochemical properties of amino acid such as hydrophobicity for ASA prediction by considering amino acid composition. We propose a systematic method for identifying residue groups with respect to protein solvent accessibility. The hydrophobicity of amino acid are used to generate features. Finally, Adaptive neuro fuzzy inference system (anfis) is adopted to construct a ASA predictor. Experimental results demonstrate that the features produced by the proposed selection process are informative for ASA prediction. Conclusion: Experimental results based on a widely used benchmark reveal that the proposed method performs good among several of existing packages for performing ASA prediction depending on amino acid sequence only .The program and data are available from the authors upon request.
    VL  - 3
    IS  - 2-3
    ER  - 

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