Compound library development guided by protein structure similarity clustering and natural product structure.

To identify biologically relevant and drug-like protein ligands for medicinal chemistry and chemical biology research the grouping of proteins according to evolutionary relationships and conservation of molecular recognition is an established method. We propose to employ structure similarity clustering of the ligand-sensing cores of protein domains (PSSC) in conjunction with natural product guided compound library development as a synergistic approach for the identification of biologically prevalidated ligands with high fidelity. This is supported by the concepts that (i) in nature spatial structure is more conserved than amino acid sequence, (ii) the number of fold types characteristic for all protein domains is limited, and (iii) the underlying frameworks of natural product classes with multiple biological activities provide evolutionarily selected starting points in structural space.