2 research outputs found
Increasing Chemical Space Coverage by Combining Empirical and Computational Fragment Screens
Most libraries for fragment-based
drug discovery are restricted
to 1,000–10,000 compounds, but over 500,000 fragments are commercially
available and potentially accessible by virtual screening. Whether
this larger set would increase chemotype coverage, and whether a computational
screen can pragmatically prioritize them, is debated. To investigate
this question, a 1281-fragment library was screened by nuclear magnetic
resonance (NMR) against AmpC β-lactamase, and hits were confirmed
by surface plasmon resonance (SPR). Nine hits with novel chemotypes
were confirmed biochemically with <i>K</i><sub>I</sub> values
from 0.2 to low mM. We also computationally docked 290,000 purchasable
fragments with chemotypes unrepresented in the empirical library,
finding 10 that had <i>K</i><sub>I</sub> values from 0.03
to low mM. Though less novel than those discovered by NMR, the docking-derived
fragments filled chemotype holes from the empirical library. Crystal
structures of nine of the fragments in complex with AmpC β-lactamase
revealed new binding sites and explained the relatively high affinity
of the docking-derived fragments. The existence of chemotype holes
is likely a general feature of fragment libraries, as calculation
suggests that to represent the fragment substructures of even known
biogenic molecules would demand a library of minimally over 32,000
fragments. Combining computational and empirical fragment screens
enables the discovery of unexpected chemotypes, here by the NMR screen,
while capturing chemotypes missing from the empirical library and
tailored to the target, with little extra cost in resources
Complementarity Between a Docking and a High-Throughput Screen in Discovering New Cruzain Inhibitors
Virtual and high-throughput screens (HTS) should have complementary strengths and weaknesses, but studies that prospectively and comprehensively compare them are rare. We undertook a parallel docking and HTS screen of 197861 compounds against cruzain, a thiol protease target for Chagas disease, looking for reversible, competitive inhibitors. On workup, 99% of the hits were eliminated as false positives, yielding 146 well-behaved, competitive ligands. These fell into five chemotypes: two were prioritized by scoring among the top 0.1% of the docking-ranked library, two were prioritized by behavior in the HTS and by clustering, and one chemotype was prioritized by both approaches. Determination of an inhibitor/cruzain crystal structure and comparison of the high-scoring docking hits to experiment illuminated the origins of docking false-negatives and false-positives. Prioritizing molecules that are both predicted by docking and are HTS-active yields well-behaved molecules, relatively unobscured by the false-positives to which both techniques are individually prone