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Characteristics of Sugar Binding Sites of Enzymatic Proteins

Characteristics of Sugar Binding Sites of Enzymatic Proteins Probing the Spatial and Chemical Features Using SVM. Khuri S.*, Nassif H., Al-Ali Merheby H., and Keyrouz W. Why Hexoses?

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Characteristics of Sugar Binding Sites of Enzymatic Proteins

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  1. Characteristics of Sugar Binding Sites of Enzymatic Proteins Probing the Spatial and Chemical Features Using SVM Khuri S.*, Nassif H., Al-Ali Merheby H., and Keyrouz W.

  2. Why Hexoses? 1- key players in many different biochemical pathways, including cellular energy release, signaling pathways, carbohydrate genesis and gene expression regulation. 2- Different types of proteins bind the hexoses, resulting in structure/function modification.

  3. Why the tool? 1- Numerous proteins of unknown functions bind hexoses. 2- Many of these proteins cannot be crystallized in the bound state. 3- Being able to predict hexose binding sites might offer insight on chemical function and metabolic links between proteins. Background review on protein chemistry: 1- Aminoacid chemistry 2- Peptide Bonds 3- Primary structure of proteins 4- Protein folding ViewAnimation

  4. Substrat specificity in binding sites Two major components: 1- Spatial specificity (Key and Lock) 2- Chemical specificity (Like Dissolves Like). Dependent on the chemical features of the atoms, not on the type of the atoms.

  5. Key and Lock Enzyme Ligand Fitting

  6. A Sample Feature Table

  7. Purpose of the Study To characterize the spatial and chemical features of Sugar Binding sites in proteins. I- Data-mine the protein structure database (PDB) 1- Collect all structures that contain bound hexoses (Glucose, Mannose, and Galactose). 2- Classify these structures based on the type of the bound hexose, and on the nature of bonding. Covalently bonded sugars are not considered ligands. 3- Get rid of redundancies (perform multiple alignments) 4- Create a representative Data set. II- Learn the characterizing chemical features of the binding sites: Vector Machines Support (VMS) III-Apply the data on a prediction tool.

  8. The input to the SVM is a vector of features per binding site. All input vectors should have the same number and order of features. Since the atoms/residues contained in a binding site will vary among different proteins, a layering approach will be used. The algorithm will generate a feature vector for each layer. The features include, among others, hydrophobicity, charge and elctronegativity values of the layer. An example of Sampled features (per quarter hemisphere) in Layer X

  9. Discussion Thank You!

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