American Journal of Biomedical and Life Sciences
Volume 3, Issue 2-3, April 2015, Pages: 21-24
Received: Dec. 7, 2014;
Accepted: Dec. 9, 2014;
Published: Aug. 7, 2015
Views 4459 Downloads 126
Ritta Shaheen, Department of Biomedical Engineering, Faculty of Mechanical and Electrical Engineering, Damascus University, Damascus, Syria.
Hani Amasha, Department of Biomedical Engineering, FMEE, Damascus University and Faculty of Informatics and Communication Engineering, Arab International University, Damascus, Syria
Majd Aljamali, Faculty of Pharmacology, Damascus University, Damascus, Syria
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.
Protein solvent accessibility prediction systemss, American Journal of Biomedical and Life Sciences. Special Issue: Spectral Imaging for Medical Diagnosis “Modern Tool for Molecular Imaging”.
Vol. 3, No. 2-3,
2015, pp. 21-24.
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