BACKGROUND: Recently, it has been shown that nuclear magnetic resonance (NMR) may be used to identify ligands that bind to low molecular weight protein drug targets. Recognizing the utility of NMR as a very sensitive method for detecting binding, we have focused on developing alternative approaches that are applicable to larger molecular weight drug targets and do not require isotopic labeling. RESULTS: A new method for lead generation (SHAPES) is described that uses NMR to detect binding of a limited but diverse library of small molecules to a potential drug target. The compound scaffolds are derived from shapes most commonly found in known therapeutic agents. NMR detection of low (microM-mM) affinity binding is achieved using either differential line broadening or transferred NOE (nuclear Overhauser effect) NMR techniques. CONCLUSIONS: The SHAPES method for lead generation by NMR is useful for identifying potential lead classes of drugs early in a drug design program, and is easily integrated with other discovery tools such as virtual screening, high-throughput screening and combinatorial chemistry.
BACKGROUND: Recently, it has been shown that nuclear magnetic resonance (NMR) may be used to identify ligands that bind to low molecular weight protein drug targets. Recognizing the utility of NMR as a very sensitive method for detecting binding, we have focused on developing alternative approaches that are applicable to larger molecular weight drug targets and do not require isotopic labeling. RESULTS: A new method for lead generation (SHAPES) is described that uses NMR to detect binding of a limited but diverse library of small molecules to a potential drug target. The compound scaffolds are derived from shapes most commonly found in known therapeutic agents. NMR detection of low (microM-mM) affinity binding is achieved using either differential line broadening or transferred NOE (nuclear Overhauser effect) NMR techniques. CONCLUSIONS: The SHAPES method for lead generation by NMR is useful for identifying potential lead classes of drugs early in a drug design program, and is easily integrated with other discovery tools such as virtual screening, high-throughput screening and combinatorial chemistry.
Authors: Marcus A Koch; Ansgar Schuffenhauer; Michael Scheck; Stefan Wetzel; Marco Casaulta; Alex Odermatt; Peter Ertl; Herbert Waldmann Journal: Proc Natl Acad Sci U S A Date: 2005-11-21 Impact factor: 11.205
Authors: Tom L Blundell; Bancinyane L Sibanda; Rinaldo Wander Montalvão; Suzanne Brewerton; Vijayalakshmi Chelliah; Catherine L Worth; Nicholas J Harmer; Owen Davies; David Burke Journal: Philos Trans R Soc Lond B Biol Sci Date: 2006-03-29 Impact factor: 6.237
Authors: Maurizio Pellecchia; Ivano Bertini; David Cowburn; Claudio Dalvit; Ernest Giralt; Wolfgang Jahnke; Thomas L James; Steve W Homans; Horst Kessler; Claudio Luchinat; Bernd Meyer; Hartmut Oschkinat; Jeff Peng; Harald Schwalbe; Gregg Siegal Journal: Nat Rev Drug Discov Date: 2008-09 Impact factor: 84.694