Date of Award
Spring 5-2019
Degree Type
Thesis
Degree Name
M.S.
Degree Program
Computer Science
Department
Computer Science
Major Professor
Dr. Md Tamjidul Hoque
Second Advisor
Dr. Christopher M. Summa
Third Advisor
Dr. Shaikh M. Arifuzzaman
Abstract
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.
Recommended Citation
Gattani, Suraj, "StackCBpred: A Stacking based Prediction of Protein-Carbohydrate Binding Sites from Sequence" (2019). University of New Orleans Theses and Dissertations. 2605.
https://scholarworks.uno.edu/td/2605
Included in
Biological Engineering Commons, Computer Engineering Commons, Medicine and Health Sciences Commons
Rights
The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.