The epidermal growth factor receptor (EGFR) is a member of the receptor tyrosine 
kinase family that plays a role in multiple cellular processes. Activation of 
EGFR requires binding of a ligand on the extracellular domain to promote 
conformational changes leading to dimerization and transphosphorylation of 
intracellular kinase domains. Seven ligands are known to bind EGFR with 
affinities ranging from sub-nanomolar to near micromolar dissociation constants. 
In the case of EGFR, distinct conformational states assumed upon binding a 
ligand is thought to be a determining factor in activation of a downstream 
signaling network. Previous biochemical studies suggest the existence of both 
low affinity and high affinity EGFR ligands. While these studies have identified 
functional effects of ligand binding, high-resolution structural data are 
lacking. To gain a better understanding of the molecular basis of EGFR binding 
affinities, we docked each EGFR ligand to the putative active state 
extracellular domain dimer and 25.0 ns molecular dynamics simulations were 
performed. MM-PBSA/GBSA are efficient computational approaches to approximate 
free energies of protein-protein interactions and decompose the free energy at 
the amino acid level. We applied these methods to the last 6.0 ns of each 
ligand-receptor simulation. MM-PBSA calculations were able to successfully rank 
all seven of the EGFR ligands based on the two affinity classes: 
EGF>HB-EGF>TGF-α>BTC>EPR>EPG>AR. Results from energy decomposition identified 
several interactions that are common among binding ligands. These findings 
reveal that while several residues are conserved among the EGFR ligand family, 
no single set of residues determines the affinity class. Instead we found 
heterogeneous sets of interactions that were driven primarily by electrostatic 
and Van der Waals forces. These results not only illustrate the complexity of 
EGFR dynamics but also pave the way for structure-based design of therapeutics 
targeting EGF ligands or the receptor itself.