Combinatorial computational method gives new picomolar ligands for a known enzyme Bartosz A. Grzybowski, Alexey V. Ishchenko, Chu- Young Kim, George Topalov,

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Combinatorial computational method gives new picomolar ligands for a known enzyme Bartosz A. Grzybowski, Alexey V. Ishchenko, Chu- Young Kim, George Topalov, Robert Chapman, David W. Christianson, George M. Whitesides, and Eugene I. Shakhnovich

Objective: Design a new method for generating and screening drug leads –CombiSMoG combines facets of combinatorial and rational drug design –Tested on human carbonic anhydrase II (HCA) –Knowledge base derived from 1,000 protein-ligand complexes in PDB Verification –Two of the best compounds were synthesized and evaluated in vivo –X-crystallography showed agreement with predicted binding mode Outline

algorithm Scoring function –Locates two atoms (on ligand and protein) closer than 5 Å –Contacts classified by atom types, frequency of occurrence Ligands are generated from 100 common functional groups Starting from a seed, program grows a ligand, evaluating after each iteration –If ligand has lower score than before, piece is rejected –Can generate 50,000 ligands/day

algorithm Seed = benzenesulfonamide (binds zinc in active site)

Top compounds

Verification of results Two of top five compounds (enantiomers) were synthesized R stereoisomer most potent HCA II inhibitor known (K d = 30 pM) X-ray crystal structure showed docking similar to predicted

Verification Binding constants (K d ) of other ligands also correlates with CombiSMoG score

Conclusions Study carried out under ideal circumstances –Large knowledge base –Known pharmacophore CombiSMoG generated 100,000 ligands in 60 h Simulations essentially correlate with experimental results

Discussion Would this program be as useful in the absence of a known pharmacophore? Without ample crystallographic knowledge? What advantages come with having a specified set of building blocks?