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Combinatorial Computational Method for Drug Leads using CombiSMoG

This research introduces CombiSMoG, a method combining combinatorial and rational drug design for generating and screening drug leads. Tested on human carbonic anhydrase II, the study validates the efficacy of this approach with promising results showcased through in vivo evaluation and X-crystallography. By utilizing a vast protein-ligand complex knowledge base and a scoring function for ligand generation, CombiSMoG efficiently produces potential ligands, demonstrating high correlation with experimental data. The discussion delves into the program's effectiveness under various conditions and the impact of a known pharmacophore on its performance.

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Combinatorial Computational Method for Drug Leads using CombiSMoG

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  1. 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

  2. Outline • 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

  3. 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

  4. algorithm • Seed = benzenesulfonamide (binds zinc in active site)

  5. Top compounds

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

  7. Verification • Binding constants (Kd) of other ligands also correlates with CombiSMoG score

  8. 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

  9. 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?

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