Characteristics of Sugar Binding Sites of Enzymatic ProteinsProbing the Spatial and Chemical Features Using SVM

Khuri S.*, Nassif H., Al-Ali Merheby H., and Keyrouz W.

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.

Background review on protein chemistry:

1- Aminoacid chemistry

2- Peptide Bonds

3- Primary structure of proteins

4- Protein folding

View Animation

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.

Substrat specificity in binding sites

1- Spatial specificity (Key and Lock)

Two major components:

2- Chemical specificity (Like Dissolves Like). Dependent on the chemicalfeatures of the atoms, not on the type of the atoms.

Key and Lock Enzyme Ligand Fitting

A Sample Feature Table

Purpose of the Study

I- Data-mine the protein structure database (PDB)

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)

1- Collect all structures that contain bound hexoses (Glucose, Mannose, and Galactose).

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.

To characterize the spatial and chemical features of Sugar Binding sites in proteins.

S1 0 0 0 0 0 0

S2 Hphobic null 0 0 0 0

S3 Hphilic -ve 0 0 0 0

S4 Aroma 0 0 0 0 0

S5 0 0 0 0 0 0

S6 Hphobic Hyphobic Hphilic 0 0 0

S7 HBdonor HB donor HB acceptor

0 0 0

S8 0 0 0 0 0 0

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

Discussion

Thank You!