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Protein solvent accessibility prediction systemss

Received: 7 December 2014     Accepted: 9 December 2014     Published: 7 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.

Published in American Journal of Biomedical and Life Sciences (Volume 3, Issue 2-3)

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

DOI 10.11648/j.ajbls.s.2015030203.14
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), 2015. Published by Science Publishing Group

Keywords

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

References
[1] DW, Mount. Bioinformatics: sequenceand genome analysis. s.l. : Cold Spring Harbor,N.Y., 2004. Vol. 2nd edition.
[2] Chan HS, Dill KA. Origins of Structure in Globular-Proteins. s.l. : Proc Natl Acad Sci, 1990.
[3] Raih MF, Ahmad S, Zheng R, Mohamed R. Solvent accessibility in native and isolated domain environments: general features and implications to interface predictability. Biophys Chem. 2005.
[4] Holbrook SR, Muskal SM, Kim SH. Predicting Surface Exposure of Amino-Acids fromProtein-Sequence. Protein Eng. 1990.
[5] Rost B, Sander C. Conservation and Prediction of Solvent Accessibility in Protein Families. Proteins. 1994.
[6] Pascarella S, De PersioR, Bossa F, Argos P. Easy method to predict solvent accessibility frommultiple protein sequence. Proteins. 1998.
[7] Cuff JA, Barton GJ. Application of multiplesequence alignment profiles to improve protein secondary structure prediction. Proteins. 2000.
[8] Fariselli P, Casadio R. RCNPRED: prediction of the residue co-ordination numbers in proteins. Bioinformatics. 2001.
[9] Li X, Pan XM. New method for accurate prediction of solvent. Proteins. 2001.
[10] Ahmad S, Gromiha MM. NETASA: neural network based prediction of solvent accessibility. Bioinformatics. 2002.
[11] Pollastri G, Baldi P, Fariselli P, Casadio R. Prediction of coordination number and relative solvent accessibility in proteins. Proteins. 2002.
[12] Thompson MJ, Goldstein RA:. Predicting solvent accessibility:Higher accuracy using Bayesian statistics and optimized resdue substitution classes. Proteins. 1996.
[13] Mucchielli-Giorgi MH, Hazout S, Tuffery P:. PredAcc: prediction of solvent accessibility. Bioinformatics. 1999.
[14] Richardson CJ, Barlow DJ. The bottom line for prediction of residue solvent accessibility. Protein Eng. 1999.
[15] O, Carugo. Predicting residue solvent accessibility from protein sequence by considering the sequence environment. Protein Eng. 2000.
[16] Naderi-Manesh H, Sadeghi M, Arab S, Movahedi AAM. Prediction of protein surface accessibilitywith information theory. Proteins. 2001.
[17] Yuan Z, Burrage K, Mattick JS. Prediction of protein solvent accessibility using support vector machines. Proteins. 2002.
[18] Kim H, Park H. Prediction of protein relative solvent accessibility with support vector machines and long-range interaction 3D local descriptor. Proteins. 2004.
[19] Nguyen MN, Rajapakse JC. Prediction of proteinrelative solvent accessibility with a two-stage SVM approach. Proteins. 2005.
[20] Gianese G, Bossa F, Pascarella S. Improvement in prediction of solvent accessibility by probability profiles. Protein Eng. 2003.
[21] Altschul SF, Madden TL, Schaffer AA, Zhang JH, Zhang Z, Miller W,Lipman DJ. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997.
[22] Ahmad S, Gromiha MM, Sarai A. Real value prediction of solvent accessibility from amino acid sequence. Proteins. 2003.
[23] Yuan Z, Huang BX. Prediction of protein accessible surface areas by support vector regression. Proteins. 2004.
[24] Wang JY, Lee HM, Ahmad S. Prediction and evolutionary information analysis of protein solvent accessibility using multiple linear regression. protein solvent accessibility using multiple. 2005.
[25] Garg A, Kaur H, Raghava GPS. Real value prediction of solvent accessibility in proteins using multiple sequence alignment and secondary structure. Proteins. 2005.
[26] Nguyen MN, Rajapakse JC. Two-stage support vector regression approach for predicting accessible surface areas of amino acids. Proteins. 2006.
[27] Predicting the protein disordered region using modified position specific scoring matrix. Shimizu K, Hirose S, Noguchi T, Muraoka Y. Yokohama Pacifico, Japan : s.n., December 16–18 2004. 15th International Conference on Genome Informatics.
[28] Su CT, Chen CY, Ou YY. Protein disorder prediction by condensed PSSM considering propensity for order or disorder. BMC Bioinformatics. 2006.
[29] Adamczak R, Porollo A, Meller J. Accurate prediction of solvent accessibility using neural networks-based regression. Proteins. 2004.
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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://article.sciencepublishinggroup.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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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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