Journal of Computer-Aided Molecular Design

, Volume 30, Issue 7, pp 533–539 | Cite as

The importance of protonation and tautomerization in relative binding affinity prediction: a comparison of AMBER TI and Schrödinger FEP

  • Yuan HuEmail author
  • Brad Sherborne
  • Tai-Sung Lee
  • David A. Case
  • Darrin M. YorkEmail author
  • Zhuyan GuoEmail author


In drug discovery, protonation states and tautomerization are easily overlooked. Through a Merck–Rutgers collaboration, this paper re-examined the initial settings and preparations for the Thermodynamic Integration (TI) calculation in AMBER Free-Energy Workflows, demonstrating the value of careful consideration of ligand protonation and tautomer state. Finally, promising results comparing AMBER TI and Schrödinger FEP+ are shown that should encourage others to explore the value of TI in routine Structure-based Drug Design.


Protonation Tautomerization Free energy calculation Thermodynamic integration Free energy perturbation Protein–ligand binding affinity 



Thermodynamic integration


Free energy perturbation


Molecular mechanics-generalized born surface area


Molecular mechanics-Poisson Boltzmann surface area


Linear interaction energy


Maximum common substructure search


Free-energy workflows


Structure-based drug design


Molecular dynamics



We are grateful to Merck Research Laboratories (MRL) Postdoctoral Research Fellows Program for financial support provided by a fellowship (Y. H.). We thank the AMBER FEW developers Nadine Homeyer and Holger Gohlke for valuable help and discussions in building the workflows. We thank the High Performance Computing (HPC) support at Merck.

Supplementary material

10822_2016_9920_MOESM1_ESM.docx (1.9 mb)
Supplementary material 1 (DOCX 1922 kb)


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Chemistry Modeling & InformaticsMerck Research LaboratoriesKenilworthUSA
  2. 2.Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, BioMaPS Institute for Quantitative BiologyRutgers UniversityPiscatawayUSA

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