Structure-based combinatorial library design: methodologies and applications.

Rational design of small focused libraries that are biased toward specific therapeutic targets is currently at the forefront of combinatorial library design. Various structure-based design strategies can be implemented in focused library design when the 3D structure of the target is available through X-ray or NMR determination. This review discusses the major methods and programs specifically developed for the purpose of designing combinatorial libraries under the constraint of the binding site of a biological target, with emphasis on their advantages and disadvantages. Examples of the successful application of these methodologies are highlighted, demonstrating their performances within the practical drug discovery process.

[1]  Hans-Joachim Böhm,et al.  The computer program LUDI: A new method for the de novo design of enzyme inhibitors , 1992, J. Comput. Aided Mol. Des..

[2]  I D Kuntz,et al.  Structure-based design and combinatorial chemistry yield low nanomolar inhibitors of cathepsin D. , 1997, Chemistry & biology.

[3]  Hans-Joachim Böhm,et al.  Combinatorial docking and combinatorial chemistry: Design of potent non-peptide thrombin inhibitors , 1999, J. Comput. Aided Mol. Des..

[4]  S. Anderson,et al.  Binding of amino acid side-chains to S1 cavities of serine proteinases. , 1997, Journal of molecular biology.

[5]  Hans-Joachim Böhm,et al.  LUDI: rule-based automatic design of new substituents for enzyme inhibitor leads , 1992, J. Comput. Aided Mol. Des..

[6]  Thomas Lengauer,et al.  A fast flexible docking method using an incremental construction algorithm. , 1996, Journal of molecular biology.

[7]  M. Murcko,et al.  Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. , 1999, Journal of medicinal chemistry.

[8]  I D Kuntz,et al.  Potent, low-molecular-weight non-peptide inhibitors of malarial aspartyl protease plasmepsin II. , 1999, Journal of medicinal chemistry.

[9]  J M Blaney,et al.  A geometric approach to macromolecule-ligand interactions. , 1982, Journal of molecular biology.

[10]  Thomas Lengauer,et al.  A recursive algorithm for efficient combinatorial library docking , 2000 .

[11]  Richard A. Lewis The Design of Small- and Medium-sized Focused Combinatorial Libraries , 2002 .

[12]  A. Caflisch,et al.  Efficient electrostatic solvation model for protein‐fragment docking , 2001, Proteins.

[13]  Malin M. Young,et al.  Design, docking, and evaluation of multiple libraries against multiple targets , 2001, Proteins.

[14]  C. Zechel,et al.  Combinatorial Synthesis of Small Organic Molecules , 1996 .

[15]  H Martin,et al.  The design of phenylglycine containing benzamidine carboxamides as potent and selective inhibitors of factor Xa. , 2001, Bioorganic & medicinal chemistry letters.

[16]  D J Diller,et al.  High throughput docking for library design and library prioritization , 2001, Proteins.

[17]  W. Janzen,et al.  High-throughput screening: advances in assay technologies. , 1997, Current opinion in chemical biology.

[18]  David E. Clark,et al.  PRO_SELECT: Combining structure-based drug design and combinatorial chemistry for rapid lead discovery. 1. Technology , 1997, J. Comput. Aided Mol. Des..

[19]  C L Verlinde,et al.  Structure-based drug design: progress, results and challenges. , 1994, Structure.

[20]  I D Kuntz,et al.  CombiDOCK: Structure-based combinatorial docking and library design , 1998, Journal of computer-aided molecular design.

[21]  Dinesh V. Patel,et al.  Strategy and Tactics in Combinatorial Organic Synthesis. Applications to Drug Discovery , 1996 .

[22]  W. C. Still,et al.  Semianalytical treatment of solvation for molecular mechanics and dynamics , 1990 .

[23]  Todd J. A. Ewing,et al.  DOCK 4.0: Search strategies for automated molecular docking of flexible molecule databases , 2001, J. Comput. Aided Mol. Des..

[24]  P. Kollman,et al.  A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules , 1995 .

[25]  S H Kim,et al.  Exploiting chemical libraries, structure, and genomics in the search for kinase inhibitors. , 1998, Science.

[26]  H J Böhm,et al.  Current computational tools for de novo ligand design. , 1996, Current opinion in biotechnology.

[27]  I. Kuntz Structure-Based Strategies for Drug Design and Discovery , 1992, Science.

[28]  R. Babine,et al.  MOLECULAR RECOGNITION OF PROTEIN-LIGAND COMPLEXES : APPLICATIONS TO DRUG DESIGN , 1997 .

[29]  M Rarey,et al.  Detailed analysis of scoring functions for virtual screening. , 2001, Journal of medicinal chemistry.

[30]  K. R. Oldenburg,et al.  Chapter 30 – Current and Future Trends in High Throughput Screening for Drug Discovery , 1998 .

[31]  Todd J. A. Ewing,et al.  DREAM++: Flexible docking program for virtual combinatorial libraries , 1999, J. Comput. Aided Mol. Des..

[32]  J Otlewski,et al.  Interscaffolding additivity: binding of P1 variants of bovine pancreatic trypsin inhibitor to four serine proteases. , 1999, Journal of molecular biology.

[33]  A R Leach,et al.  Synergy between combinatorial chemistry and de novo design. , 2000, Journal of molecular graphics & modelling.

[34]  D C Spellmeyer,et al.  Discovery of nanomolar ligands for 7-transmembrane G-protein-coupled receptors from a diverse N-(substituted)glycine peptoid library. , 1994, Journal of medicinal chemistry.

[35]  I. Kuntz,et al.  Inclusion of Solvation in Ligand Binding Free Energy Calculations Using the Generalized-Born Model , 1999 .