Accurate Prediction of Ligand Affinities for a Proton-Dependent Oligopeptide Transporter

Cell Chem Biol. 2016 Feb 18;23(2):299-309. doi: 10.1016/j.chembiol.2015.11.015. Epub 2016 Jan 28.


Membrane transporters are critical modulators of drug pharmacokinetics, efficacy, and safety. One example is the proton-dependent oligopeptide transporter PepT1, also known as SLC15A1, which is responsible for the uptake of the ?-lactam antibiotics and various peptide-based prodrugs. In this study, we modeled the binding of various peptides to a bacterial homolog, PepT(St), and evaluated a range of computational methods for predicting the free energy of binding. Our results show that a hybrid approach (endpoint methods to classify peptides into good and poor binders and a theoretically exact method for refinement) is able to accurately predict affinities, which we validated using proteoliposome transport assays. Applying the method to a homology model of PepT1 suggests that the approach requires a high-quality structure to be accurate. Our study provides a blueprint for extending these computational methodologies to other pharmaceutically important transporter families.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Anti-Bacterial Agents / chemistry
  • Anti-Bacterial Agents / metabolism*
  • Humans
  • Ligands
  • Molecular Dynamics Simulation
  • Oligopeptides / chemistry
  • Oligopeptides / metabolism*
  • Peptide Transporter 1
  • Prodrugs / chemistry
  • Prodrugs / metabolism*
  • Symporters / chemistry
  • Symporters / metabolism*
  • Thermodynamics


  • Anti-Bacterial Agents
  • Ligands
  • Oligopeptides
  • Peptide Transporter 1
  • Prodrugs
  • SLC15A1 protein, human
  • Symporters