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 cluster...

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Published inProceedings of the National Academy of Sciences - PNAS Vol. 101; no. 48; pp. 16721 - 16726
Main Authors Koch, Marcus A., Wittenberg, Lars-Oliver, Basu, Sudipta, Jeyaraj, Duraiswamy A., Gourzoulidou, Eleni, Reinecke, Kerstin, Odermatt, Alex, Waldmann, Herbert, Meinwald, Jerrold
Format Journal Article
LanguageEnglish
Published United States National Academy of Sciences 30.11.2004
National Acad Sciences
Subjects
11-beta-Hydroxysteroid Dehydrogenases - antagonists & inhibitors
Acetylcholinesterase - drug effects
Amino acids
Biochemistry
Biological Factors - chemistry
cdc25 Phosphatases - antagonists & inhibitors
Chemicals
Dehydrogenases
Enzyme Inhibitors - chemistry
Enzyme Inhibitors - pharmacology
Enzymes
Information management
Inhibitory concentration 50
Libraries
Ligands
Models, Molecular
Molecular evolution
Pain
Physical Sciences
Protein Conformation
Protein folding
Protein Tyrosine Phosphatase, Non-Receptor Type 1
Proteins
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Summary: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. On the basis of domain core similarity considerations and irrespective of sequence similarity, Cdc25A phosphatase, acetylcholinesterase, and 11β-hydroxysteroid dehydrogenases type 1 and type 2 were grouped into a similarity cluster. A 147-member compound collection derived from the naturally occurring Cdc25A inhibitor dysidiolide yielded potent and selective inhibitors of the other members of the similarity cluster with a hit rate of 2-3%. Protein structure similarity clustering may provide an experimental opportunity to identify supersites in proteins.
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Author contributions: M.A.K. and H.W. designed research; M.A.K., L.-O.W., S.B., and D.A.J. performed research; M.A.K., L.-O.W., S.B., D.A.J., E.G., K.R., and A.O. contributed new reagents/analytical tools; M.A.K. analyzed data; and M.A.K. and H.W. wrote the paper.
To whom correspondence should be addressed. E-mail: herbert.waldmann@mpi-dortmund.mpg.de.
Edited by Jerrold Meinwald, Cornell University, Ithaca, NY, and approved October 1, 2004
This paper was submitted directly (Track II) to the PNAS office.
Abbreviations: PSSC, protein structure similarity clustering; PDB, Protein Data Bank; FSSP, Fold Classification Based on Structure–Structure Alignment of Proteins; CE, Combinatorial Extension; 11βHSD, 11β-hydroxysteroid dehydrogenase; AChE, acetylcholinesterase; CPK, Corey–Pauling–Koltun.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.0404719101