TY - JOUR
T1 - Predicting the effects of amino acid replacements in peptide hormones on their binding affinities for class B GPCRs and application to the design of secretin receptor antagonists
AU - Te, Jerez A.
AU - Dong, Maoqing
AU - Miller, Laurence J.
AU - Bordner, Andrew J.
N1 - Funding Information:
Acknowledgments The authors thank Mary Lou Augustine and Delia I. Pinon for their excellent technical assistance and the Mayo Clinic Research Computing Services and the Arizona State University Advanced Computing Center for computational resources. This work was supported by the Mayo Clinic and a grant from the National Institutes of Health (DK46577).
PY - 2012/7
Y1 - 2012/7
N2 - Computational prediction of the effects of residue changes on peptide-protein binding affinities, followed by experimental testing of the top predicted binders, is an efficient strategy for the rational structure-based design of peptide inhibitors. In this study we apply this approach to the discovery of competitive antagonists for the secretin receptor, the prototypical member of class B G protein-coupled receptors (GPCRs). Proteins in this family are involved in peptide hormone-stimulated signaling and are implicated in several human diseases, making them potential therapeutic targets. We first validated our computational method by predicting changes in the binding affinities of several peptides to their cognate class B GPCRs due to alanine replacement and compared the results with previously published experimental values. Overall, the results showed a significant correlation between the predicted and experimental DDG values. Next, we identified candidate inhibitors by applying this method to a homology model of the secretin receptor bound to an N-terminal truncated secretin peptide. Predictions were made for single residue replacements to each of the other nineteen naturally occurring amino acids at peptide residues within the segment binding the receptor N-terminal domain. Amino acid replacements predicted to most enhance receptor binding were then experimentally tested by competition-binding assays. We found two residue changes that improved binding affinities by almost one log unit. Furthermore, a peptide combining both of these favorable modifications resulted in an almost two log unit improvement in binding affinity, demonstrating the approximately additive effect of these changes on binding. In order to further investigate possible physical effects of these residue changes on receptor binding affinity, molecular dynamics simulations were performed on representatives of the successful peptide analogues (namely A17I, G25R, and A17I/G25R) in bound and unbound forms. These simulations suggested that a combination of the a-helical propensity of the unbound peptide and specific interactions between the peptide and the receptor extracellular domain contribute to their higher binding affinities.
AB - Computational prediction of the effects of residue changes on peptide-protein binding affinities, followed by experimental testing of the top predicted binders, is an efficient strategy for the rational structure-based design of peptide inhibitors. In this study we apply this approach to the discovery of competitive antagonists for the secretin receptor, the prototypical member of class B G protein-coupled receptors (GPCRs). Proteins in this family are involved in peptide hormone-stimulated signaling and are implicated in several human diseases, making them potential therapeutic targets. We first validated our computational method by predicting changes in the binding affinities of several peptides to their cognate class B GPCRs due to alanine replacement and compared the results with previously published experimental values. Overall, the results showed a significant correlation between the predicted and experimental DDG values. Next, we identified candidate inhibitors by applying this method to a homology model of the secretin receptor bound to an N-terminal truncated secretin peptide. Predictions were made for single residue replacements to each of the other nineteen naturally occurring amino acids at peptide residues within the segment binding the receptor N-terminal domain. Amino acid replacements predicted to most enhance receptor binding were then experimentally tested by competition-binding assays. We found two residue changes that improved binding affinities by almost one log unit. Furthermore, a peptide combining both of these favorable modifications resulted in an almost two log unit improvement in binding affinity, demonstrating the approximately additive effect of these changes on binding. In order to further investigate possible physical effects of these residue changes on receptor binding affinity, molecular dynamics simulations were performed on representatives of the successful peptide analogues (namely A17I, G25R, and A17I/G25R) in bound and unbound forms. These simulations suggested that a combination of the a-helical propensity of the unbound peptide and specific interactions between the peptide and the receptor extracellular domain contribute to their higher binding affinities.
KW - Binding affinity predictions
KW - G protein-coupled receptors
KW - Homology model
KW - Molecular dynamics simulations
KW - Peptide modifications
KW - Receptor antagonists
KW - Structure-based design
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U2 - 10.1007/s10822-012-9574-x
DO - 10.1007/s10822-012-9574-x
M3 - Article
C2 - 22576240
AN - SCOPUS:84865143502
SN - 0920-654X
VL - 26
SP - 835
EP - 845
JO - Journal of Computer-Aided Molecular Design
JF - Journal of Computer-Aided Molecular Design
IS - 7
ER -