Theoretical prediction of the binding free energy for mutants of replication protein A

Accuracy Comparison of Generalized Born Models in the Calculation of Electrostatic Binding Free Energies

The need for accurate yet efficient representation of the aqueous environment in biomolecular modeling has led to the development of a variety of generalized Born (GB) implicit solvent models. While many studies have focused on the accuracy of available GB models in predicting solvation free energies, a systematic assessment of the quality of these models in binding free energy calculations, crucial for rational drug design, has not been undertaken. Here, we evaluate the accuracies of eight common GB flavors (GB-HCT, GB-OBC, GB-neck2, GBNSR6, GBSW, GBMV1, GBMV2, and GBMV3), available in major molecular dynamics packages, in predicting the electrostatic binding free energies ( ΔΔ G_el) for a diverse set of 60 biomolecular complexes belonging to four main classes: protein-protein, protein-drug, RNA-peptide, and small complexes. The GB flavors are examined in terms of their ability to reproduce the results from the Poisson-Boltzmann (PB) model, commonly used as accuracy reference in this context. We show that the agreement with the PB of ΔΔ G_el estimates varies widely between different GB models and also across different types of biomolecular complexes, with R² correlations ranging from 0.3772 to 0.9986. A surface-based "R6" GB model recently implemented in AMBER shows the closest overall agreement with reference PB ( R² = 0.9949, RMSD = 8.75 kcal/mol). The RNA-peptide and protein-drug complex sets appear to be most challenging for all but one model, as indicated by the large deviations from the PB in ΔΔ G_el. Small neutral complexes present the least challenge for most of the GB models tested. The quantitative demonstration of the strengths and weaknesses of the GB models across the diverse complex types provided here can be used as a guide for practical computations and future development efforts.

Effect of mutation at the interface of Trp-repressor dimeric protein: a steered molecular dynamics simulation

The strength of key interfacial contacts that stabilize protein-protein interactions have been studied by computer simulation. Experimentally, changes in the interface are evaluated by generating specific mutations at one or more points of the protein structure. Here, such an evaluation is performed by means of steered molecular dynamics and use of a dimeric model of tryptophan repressor and in-silico mutants as a test case. Analysis of four particular cases shows that, in principle, it is possible to distinguish between wild-type and mutant forms by examination of the total energy and force-extension profiles. In particular, detailed atomic level structural analysis indicates that specific mutations at the interface of the dimeric model (positions 19 and 39) alter interactions that appear in the wild-type form of tryptophan repressor, reducing the energy and force required to separate both subunits.

Locating a plausible binding site for an open-channel blocker, GlyH-101, in the pore of the cystic fibrosis transmembrane conductance regulator

High-throughput screening has led to the identification of small-molecule blockers of the cystic fibrosis transmembrane conductance regulator (CFTR) chloride channel, but the structural basis of blocker binding remains to be defined. We developed molecular models of the CFTR channel on the basis of homology to the bacterial transporter Sav1866, which could permit blocker binding to be analyzed in silico. The models accurately predicted the existence of a narrow region in the pore that is a likely candidate for the binding site of an open-channel pore blocker such as N-(2-naphthalenyl)-[(3,5-dibromo-2,4-dihydroxyphenyl)methylene]glycine hydrazide (GlyH-101), which is thought to act by entering the channel from the extracellular side. As a more-stringent test of predictions of the CFTR pore model, we applied induced-fit, virtual, ligand-docking techniques to identify potential binding sites for GlyH-101 within the CFTR pore. The highest-scoring docked position was near two pore-lining residues, Phe337 and Thr338, and the rates of reactions of anionic, thiol-directed reagents with cysteines substituted at these positions were slowed in the presence of the blocker, consistent with the predicted repulsive effect of the net negative charge on GlyH-101. When a bulky phenylalanine that forms part of the predicted binding pocket (Phe342) was replaced with alanine, the apparent affinity of the blocker was increased ∼200-fold. A molecular mechanics-generalized Born/surface area analysis of GlyH-101 binding predicted that substitution of Phe342 with alanine would substantially increase blocker affinity, primarily because of decreased intramolecular strain within the blocker-protein complex. This study suggests that GlyH-101 blocks the CFTR channel by binding within the pore bottleneck.