Dataset Information

The Dipole Potential Modifies the Clustering and Ligand Binding Affinity of ErbB Proteins and Their Signaling Efficiency.

ABSTRACT: Although activation of the ErbB family of receptor tyrosine kinases (ErbB1-4) is driven by oligomerization mediated by intermolecular interactions between the extracellular, the kinase and the transmembrane domains, the transmembrane domain has been largely neglected in this regard. The largest contributor to the intramembrane electric field, the dipole potential, alters the conformation of transmembrane peptides, but its effect on ErbB proteins is unknown. Here, we show by Förster resonance energy transfer (FRET) and number and brightness (N&B) experiments that the epidermal growth factor (EGF)-induced increase in the homoassociation of ErbB1 and ErbB2 and their heteroassociation are augmented by increasing the dipole potential. These effects were even more pronounced for ErbB2 harboring an activating Val???Glu mutation in the transmembrane domain (NeuT). The signaling capacity of ErbB1 and ErbB2 was also correlated with the dipole potential. Since the dipole potential decreased the affinity of EGF to ErbB1, the augmented growth factor-induced effects at an elevated dipole potential were actually induced at lower receptor occupancy. We conclude that the dipole potential plays a permissive role in the clustering of ErbB receptors and that the effects of lipid rafts on ligand binding and receptor signaling can be partially attributed to the dipole potential.

SUBMITTER: Kovacs T

PROVIDER: S-EPMC5075772 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundModels that are capable of reliably predicting binding affinities for protein-ligand complexes play an important role the field of structure-guided drug design.MethodsHere, we begin by applying the computational geometry technique of Delaunay tessellation to each set of atomic coordinates for over 1400 diverse macromolecular structures, for the purpose of deriving a four-body statistical potential that serves as a topological scoring function. Next, we identify a second, independent set of three hundred protein-ligand complexes, having both high-resolution structures and known dissociation constants. Two-thirds of these complexes are randomly selected to train a predictive model of binding affinity as follows: two tessellations are generated in each case, one for the entire complex and another strictly for the isolated protein without its bound ligand, and a topological score is computed for each tessellation with the four-body potential. Predicted protein-ligand binding affinity is then based on an empirically derived linear function of the difference between both topological scores, one that appropriately scales the value of this difference.ResultsA comparison between experimental and calculated binding affinity values over the two hundred complexes reveals a Pearson's correlation coefficient of r = 0.79 with a standard error of SE = 1.98 kcal/mol. To validate the method, we similarly generated two tessellations for each of the remaining protein-ligand complexes, computed their topological scores and the difference between the two scores for each complex, and applied the previously derived linear transformation of this topological score difference to predict binding affinities. For these one hundred complexes, we again observe a correlation of r = 0.79 (SE = 1.93 kcal/mol) between known and calculated binding affinities. Applying our model to an independent test set of high-resolution structures for three hundred diverse enzyme-inhibitor complexes, each with an experimentally known inhibition constant, also yields a correlation of r = 0.79 (SE = 2.39 kcal/mol) between experimental and calculated binding energies.ConclusionsLastly, we generate predictions with our model on a diverse test set of one hundred protein-ligand complexes previously used to benchmark 15 related methods, and our correlation of r = 0.66 between the calculated and experimental binding energies for this dataset exceeds those of the other approaches. Compared with these related prediction methods, our approach stands out based on salient features that include the reliability of our model, combined with the rapidity of the generated predictions, which are less than one second for an average sized complex.

Dataset Information

The Dipole Potential Modifies the Clustering and Ligand Binding Affinity of ErbB Proteins and Their Signaling Efficiency.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets