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Rigorous Free Energy Perturbation Approach to Estimating Relative Binding Affinities between Ligands with Multiple Protonation and Tautomeric States.

César de OliveiraHaoyu S YuWei ChenRobert AbelLingle Wang
Published in: Journal of chemical theory and computation (2018)
Accurate prediction of ligand binding affinities is of key importance in small molecule lead optimization and a central task in computational medicinal chemistry. Over the years, advances in both computer hardware and computational methodologies have established free energy perturbation (FEP) methods as among the most reliable and rigorous approaches to compute protein-ligand binding free energies. However, accurate description of ionization and tautomerism of ligands is still a major challenge in structure-based prediction of binding affinities. Druglike molecules are often weak acid or bases with multiple accessible protonation and tautomeric states that can contribute significantly to the binding process. To address this issue, we introduce in this work the p Ka and tautomeric state correction approach. This approach is based on free energy perturbation formalism and provides a rigorous treatment of the ionization and tautomeric equilibria of ligands in solution and in the protein complexes. A series of Kinesin Spindle Protein (KSP) and Factor Xa inhibitor molecules were used as test cases. Our results demonstrate that the p Ka and tautomeric state correction approach is able to rigorously and accurately incorporate multiple protonation and tautomeric states in the binding affinity calculations.
Keyphrases
  • binding protein
  • small molecule
  • protein protein
  • dna binding
  • density functional theory
  • amino acid
  • high resolution
  • deep learning
  • transcription factor
  • machine learning