1 / 9

Combinatorial computational method gives new picomolar ligands for a known enzyme

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. Outline.

hop-foreman
Télécharger la présentation

Combinatorial computational method gives new picomolar ligands for a known enzyme

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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?

More Related