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 |
Protein Structure, Protein Solvent Accessibility, Accessible Surface Area, Structure Prediction, Adaptive Neuro Fuzzy Inference, Hydrophobicity
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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
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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Download@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}
}
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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