Prediction of Flavin Mononucleotide (FMN) Binding Sites in Proteins Using the 3D Search Motif Method and Double-Centroid Reduced Representation of Protein 3D Structures

A pharmacophore consists of the parts of the structure of the ligand that are sufficient to express the biological and pharmacological effects of the ligand. It is usually a substructure of the entire structure of the ligand. Small organic molecules called ligands or metabolites in the cell form complexes with biomolecules (usually proteins) to serve different purposes. The sites at which the ligands bind are known as ligand binding sites, which are essentially “pockets” which have complementary shapes and patterns of charge distribution with the ligands. Sometimes a pocket is induced by the ligand itself. If we study different bound conformations of ligands it is found that they share a specific 3 dimensional pattern that is more or less common and is responsible for its binding and which is complimentary in 3 dimensional geometry and charge distribution pattern with its cognate binding site in the protein. This work studies the three dimensional structure of the consensus ligand binding site for the ligand FMN. A training set for the ligand binding sites was made and a 3D consensus binding site motif was determined for FMN. The FMN system was studied and its binding sites in its respective regulator proteins. The ability to identify ligand binding site by scanning the 3D binding site consensus motif in protein 3D structures is an important step in drug target discovery. Once a pharmacophore template is found it can also be used to design other potential molecules that can bind to it and thus serve as novel drugs. Tables: Table 1: Training Set........................... .................................................................................................................20 Table 2: Predominant amino acids interaction..............................................................................................25 Table 3 Algorithm tested with the nodes as C2, C6, O2P and O3P................................................................38 Table 4: Algorithm tested with the nodes as C2, C6, O2 andC5a (error margin 1.8 Å)...............................41 Table 5: Algorithm tested with the nodes as C2, C6, O2 andC5a (error margin 1.4 Å)...............................44 Table 6: Algorithm tested with the nodes as C2, C6, O2 andC5a (error margin 1.0Å)...............................48