Systematic analysis and comparison of peptide specificity and selectivity between their cognate receptors and noncognate decoys

Abstract

Protein–peptide interactions (PpIs) play an important role in cell signaling networks and have been exploited as new and attractive therapeutic targets. The affinity and specificity are two unity‐of‐opposite aspects of PpIs (and other biomolecular interactions); the former indicates the absolute binding strength between the peptide ligand and its cognate protein receptor in a PpI, while the latter represents the relative recognition selectivity of the peptide ligand for its cognate protein receptor in a PpI over those noncognate decoys that could be potentially encountered by the peptide in cell. Although the PpI binding affinity has been widely investigated over the past decades, the peptide recognition specificity (and selectivity) still remains largely unexplored to date. In this study, we classified PpI specificity into three types: (i) class‐I specificity: peptide selectivity for its cognate wild‐type protein receptor over the noncognate mutant decoys of this receptor, (ii) class‐II specificity: peptide selectivity for its cognate protein receptor over other noncognate decoys that are homologous with this receptor, and (iii) class‐III specificity: peptide selectivity for its cognate protein receptor over other noncognate decoys that are the cognate receptors of other peptides. We performed affinity and selectivity analysis for the three types of PpI specificity and revealed that the PpIs generally exhibit a moderate or modest specificity; peptide selectivity increases in the order: class‐I < class‐II < class‐III. All the three types of PpI specificity were observed to have no statistically significant correlation with peptide length and hydrophobicity, but the class‐I and class‐II specificities can be influenced considerably by peptide secondary structures; the high specificity is preferentially associated with ordered structure types as compared to undefined structure types. In addition, the mutation distribution (for class‐I specificity), sequence conservation (for class‐II specificity), and structural similarity (for class‐III specificity) seem also to address effects on peptide selectivity.

Molecular modeling and rational design of disulfide‐stapled self‐inhibitory peptides to target IL‐17A/IL‐17RA interaction

“…In this study, the MM/PBSA and NMA were implemented using mmpbsa and nmode modules based on MD equilibrium trajectories, respectively. The calculated binding free energy was further corrected to binding affinity by using machine learning‐based protein/peptide affinity scoring function, 34,35 which has been widely used to characterize the affinity, specificity and selectivity of peptide ligand binding to and recognized by their cognate and noncognate protein receptors 36,37 …”

Targeting peptide‐mediated interaction between the N‐protein and P‐protein of human pediatric respiratory syncytial virus by molecular design of chemically stapled helical peptides

“…The binding free energy (∆ G ) between protein and peptide was predicted using Molecular mechanics/Poisson‐Boltzmann surface area (MM/PBSA) for enthalpy contribution (∆ H ) [34] and normal mode analysis (NMA) for entropy effect (− T ∆ S ) [35], which has been widely used to characterize the affinity, specificity, and selectivity of peptide ligand binding to and recognized by their cognate and noncognate protein receptors [36, 37]. The MM was calculated via force field approach.…”

Structure‐based improvement of the binding affinity and recognition specificity of peptide competitors to target pediatric IL‐5R/IL‐5 interaction by gluing halogen bonds at their complex interface

“…Here, we further examined the effect of halogen bond on peptide specificity. 53,54 A method described previously was used to straightforwardly quantify the peptide selectivity from binding energy. 55…”