Carbohydrate-binding proteins play vital roles in many vital biological processes and study of these interactions, at residue level, are useful in treating many critical diseases. Analyzing the local sequential environments of the binding and non-binding regions to predict the protein-carbohydrate binding sites is one of the challenging problems in molecular and computational biology. Prediction of such binding sites, directly from sequences, using computational methods, can be useful to fast annotate the binding sites and guide the experimental process. Because the number of carbohydrate-binding residues is significantly lower than non-carbohydrate-binding residues, most of the methods developed are biased towards over predicting the non-carbohydrate-binding residues. Here, we propose a balanced predictor, called StackCBPred, which utilizes features, extracted from evolution-driven sequence profile, called the position-specific scoring matrix (PSSM) and several predicted structural properties of amino acids to effectively train a stacking-based machine learning method for the accurate prediction of protein-carbohydrate binding sites.
| Identifer | oai:union.ndltd.org:uno.edu/oai:scholarworks.uno.edu:td-3790 |
| Date | 23 May 2019 |
| Creators | Gattani, Suraj |
| Publisher | ScholarWorks@UNO |
| Source Sets | University of New Orleans |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | University of New Orleans Theses and Dissertations |
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