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AUTOMATIC LIGAND FITTING TO CRYSTALLOGRAPHIC DENSITY

AFITT

AFITT is the only software to offer a fully automatic ligand fitting process that optimizes a real-space fit to density while keeping conformational strain to a minimum. It capitalizes on a combination of core technologies that OpenEye has developed, specifically conformer generation, shape potential, high-quality small molecule structure minimization, and visualization. The key step, after finding the appropriate conformers and aligning them to density, is the implementation of a refinement that combines force field and shape potentials, via a series of adiabatic optimizations [1, 2].

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Multiple solutions are generated and ranked.

Features

  • Fast automatic ligand fitting process fits low energy ligand conformations to density, typically in less than a minute
  • Multiple chemical forcefields available for tricky ligands (MMFF94, PM3)
  • Automatic (optional) re-fitting of side chains to density using several methods
  • Search ligands to determine if it they been previously deposited and report conflicts if they don't match
  • Automatic covalent detection
  • Refinement dictionary generation for REFMAC, PHENIX, and CNX using MMFF94
  • Excellent performance even with flexible molecules (≤ 20 rotatable bonds) and poor density (resolution ≤ 3.5 Å)
  • Integrates with Coot
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AFITT provides fully automated ligand fitting process

The AFITT command-line tools provide a powerful means for integration and automation, which is critical in the application of high-throughput crystallography for drug discovery.

For more detailed information on AFITT, check out the link below:

Documentation

References

  1. Automated ligand placement and refinement with a combined force field and shape potential Wlodek, S., Skillman A.G., Nicholls A., Acta Cryst., 2006, 741-749, D62.
  2. Improved ligand geometries in crystallographic refinement using AFITT in Phenix Janowski, P.A., Moriarty, N.W., Kelley, B.P., Case, D.A., York, D.M., Adams, P.D., and Warren, G.L., Acta Cryst D, 2016, D72, 1062-1072.

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