Structure-based characterization of the binding of peptide to the human endophilin-1 Src homology 3 domain using position-dependent noncovalent potential analysis

Abstract

Many protein-protein interactions are mediated by a peptide-recognizing domain, such as WW, PDZ, or SH3. In the present study, we describe a new method called position-dependent noncovalent potential analysis (PDNPA), which can accurately characterize the nonbonding profile between the human endophilin-1 Src homology 3 (hEndo1 SH3) domain and its peptide ligands and quantitatively predict the binding affinity of peptide to hEndo1 SH3. In this procedure, structure models of diverse peptides in complex with the hEndo1 SH3 domain are constructed by molecular dynamics simulation and a virtual mutagenesis protocol. Subsequently, three noncovalent interactions associated with each position of the peptide ligand in the complexed state are analyzed using empirical potential functions, and the resulting potential descriptors are then correlated with the experimentally measured affinity on the basis of 1997 hEndo1 SH3-binding peptides with known activities, using linear partial least squares regression (PLS) and the nonlinear support vector machine (SVM). The results suggest that: (i) the electrostatics appears to be more important than steric properties and hydrophobicity in the formation of the hEndo1 SH3-peptide complex; (ii) P(-4) of the core decapeptide ligand with the sequence pattern P(-6)P(-5)P(-4)P(-3)P(-2)P(-1)P(0)P(1)P(2)P(3) is the most important position in terms of determining both the stability and specificity of the architecture of the complex, and; (iii) nonlinear SVM appears to be more effective than linear PLS for accurately predicting the binding affinity of a peptide ligand to hEndo1 SH3, whereas PLS models are straightforward and easy to interpret as compared to those built by SVM.

BIN1 Membrane Curvature Sensing and Generation Show Autoinhibition Regulated by Downstream Ligands and PI(4,5)P2

“…SH3 domains are found in a great variety of BAR domain proteins . SH3 domains recruit downstream proteins primarily through interaction with canonical PxxP sequences. , For instance, endophilin recruits dynamin to the neck of a clathrin-coated pit to accomplish membrane scission. , This is achieved by the hydrophobic interaction between the aromatic residues in the SH3 domain and the PxxP sequence in dynamin. − …”

Proteome-Wide Inference of Human Endophilin 1-Binding Peptides

“…electrostatic, steric, and hydrophobic, between the hEndo1 SH3 and each residue type at each position of decapeptide ligand can be described quantitatively using quasichemical potential approach, with which the electrostatic, steric, and hydrophobic potentials were calculated by Coulomb's law, Lennard-Jones 6-12 equation, and empirical hydrophobic potential, respectively. The details of the quasichemical potential approach can be found in previous publications [10,[18][19][20]. These potential forms have already been successfully used to characterize the binding behavior of oligopeptides to diverse proteins, and therefore we believe that they could be applied to the hEndo1 SH3-peptide interactions as well.…”

“…Later, Hou et al employed the CoMFA and CoMSIA methods to quantitatively characterize the 3D structural properties of a large panel of hAmph1 SH3 domain-binding peptides, and resultant field descriptors of the characterization were further correlated with experimentally measured affinity values of these peptides to generate a series of linear regression models. Recently, we have described a new strategy called position-dependent noncovalent potential analysis (PDNPA) to accurately characterize the interaction energies between the hEndo1 SH3 domain and each position of peptides and quantitatively determine the binding affinity of peptides to hEndo1 SH3 [10]. Although PDNPA seems to perform fairly well in predicting the binding capability of unknown peptides to hEndo1 SH3 domain, which requires first to construct complex structure models of SH3 with peptides before implementing the prediction, largely limiting the applicability of this method in screening proteome-wide candidates.…”

Fast and reliable prediction of domain–peptide binding affinity using coarse-grained structure models

“…Subsequently, the minimized complex structures were utilized as the starting point to rapidly generate coarse-grained structure models of domains complexed with their peptide ligands listed in SI Tables S1-S4 by using the SCWRL4 (Krivov et al, 2009)-this program was demonstrated to performed well in reproducing Xray crystal structure of protein-peptide complexes (Knapp et al, 2008), and has recently been successfully applied to implement virtual mutagenesis for human endo1 SH3 domain-binding partners (Fu et al, 2012).…”