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Prediction of Protein-Protein Interactions Based on Molecular Interface Features and the Support Vector Machine

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Protein-protein interactions play important roles in many biological progresses. Previous studies about proteinprotein interactions were mainly based on sequence analysis. As more 3D structural information can be obtained from protein-protein complexes, structural analysis becomes feasible and useful. In this study, we used structural alignment to predict protein-binding sites and analyzed interface properties using 3D alpha shape. We have developed a method for protein-protein interaction prediction. The result indicates good performance of our method in discriminating proteinbinding structures from non-protein-binding structures. In the experiment, our method shows best Matthews correlation coefficient of 0.204.
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Keywords: Alpha shape; Atom Index; Curvature Index; False negative; Matthews correlation coefficient; Residue Index; Support Vector Machine (SVM); TM-Score; protein-protein interaction; structural alignment

Document Type: Research Article

Publication date: 2013-02-01

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  • Current Bioinformatics aims to publish all the latest and outstanding developments in bioinformatics. Each issue contains a series of timely, in-depth reviews written by leaders in the field, covering a wide range of the integration of biology with computer and information science.

    The journal focuses on reviews on advances in computational molecular/structural biology, encompassing areas such as computing in biomedicine and genomics, computational proteomics and systems biology, and metabolic pathway engineering. Developments in these fields have direct implications on key issues related to health care, medicine, genetic disorders, development of agricultural products, renewable energy, environmental protection, etc.

    Current Bioinformatics is an essential journal for all academic and industrial researchers who want expert knowledge on all major advances in bioinformatics.
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