Development of QSAR‐Improved Statistical Potential for the Structure‐Based Analysis of ProteinPeptide Binding Affinities

Abstract

Proteinpeptide interactions have recently been found to play an essential role in constructing intracellular signaling networks. Understanding the molecular mechanism of such interactions and identification of the interacting partners would be of great value for developing peptide therapeutics against many severe diseases such as cancer. In this study, we describe a structure-based, general-purpose strategy for fast and reliably predicting proteinpeptide binding affinities. This strategy combines unsupervised knowledge-based statistical potential derived from 505 interfacially diverse, non-redundant proteinpeptide complex structures and supervised quantitative structure-activity relationship (QSAR) modeling trained by 250 proteinpeptide interactions with known structure and affinity data. The built partial least squares (PLS) model is confirmed to have high stability and predictive power by using internal 5-fold cross-validation and rigorous Monte Carlo cross-validation (MCCV). The model is further employed to analyze two large groups of HLA- and SH3-binding peptides based upon computationally modeled structures. Satisfactorily, although the PLS model is originally trained with dissociation constants (Kd ) of proteinpeptide binding, it shows a good correlation with other two affinity qualities, i.e. SPOT signal intensities (BLU) and half maximal competitive concentrations (IC50 ). Furthermore, we perform systematic comparisons of our method with several widely used, representative affinity predictors, including molecular mechanics-based MM-PB/SA, knowledge-based DFIRE and docking score HADDOCK, on a small panel of elaborately selected proteinpeptide systems. It is demonstrated that (i) the QSAR-improved statistical potential exhibits a comparable predictive performance with but can work faster than these traditional methods, and (ii) the crystal structure-derived statistical potential also supports the modeled and solution structures of proteinpeptide complexes. We expect that this hybrid method can be exploited as a new scoring tool to facilitate, for example, peptide docking and virtual screening.

Targeting Yes‐associated Protein with Evolved Peptide Aptamers to Disrupt TGF‐β Signaling Pathway: Therapeutic Implication for Bone Tumor

“…The fitness curve during peptide evolution process is shown in Figure 4, where the fluctuation in average population scores over the last 10 generations is less 1 %, confirming that the evolution is converged after 42 iterations. By excluding those repeats in resulting evolved population a total of 147 distinct peptides were obtained, and their binding affinities ( K d values) were scored using QSAR‐improved statistical potential 13. It is evident that the distribution of predicted affinities of the 147 peptides can be well fitted using a sigmoidal curve, where most peptides were suggested to have strong binding capability toward the domain (Figure 5A).…”

“…(2) A QSAR‐improved statistical potential proposed by Han et al 13. was utilized to assess the binding strength of peptide ligands to WW2 domain.…”

Construction of a peptide with an electroactive daunomycin like a pendant arm to detect ovalbumin

“…Therefore, a peptide-1-EMCS probe with a pendant arm easily combined with OVA relative to a straight peptide-1-EGS probe. Han et al collected binding constants of the protein-peptide complexes obtained using NMR titration, SPR, fluorescence polarization, and isothermal titration calorimetry [34]. The constants of the complexes ranged from 10 3 to 10 9 M À1 .…”

Selective targeting of MAPK family kinases JNK over p38 by rationally designed peptides as potential therapeutics for neurological disorders and epilepsy

“…The JIP1 (PKRPTTLNLF) was used as an initial peptide to start the SA iteration. Based on the modeled complex structures the kinase binding affinities A JNK and A p38 (pK d values) of the peptide were calculated using a QSAR-improved protein-peptide statistical potential developed by Han et al, 22 and their difference S DA = A JNK À A p38 was used to characterize the peptide selectivity between the two kinases, where S 4 0 and o0 indicate the selectivity of JNK-over-p38 and p38-over-JNK, respectively. Previously, the JIP1 peptide was found to bind tightly at the JNK PD site (K d = 0.8 mM) 13 to inhibit the kinase activity both in vitro and in the cell.…”