Free Energy Perturbation Calculation of Relative Binding Free Energy between Broadly Neutralizing Antibodies and the gp120 Glycoprotein of HIV-1. Issue 7 (7th April 2017)

• Record Type: Journal Article
• Authors: Clark, Anthony J.; Gindin, Tatyana; Zhang, Baoshan; Wang, Lingle; Abel, Robert; Murret, Colleen S.; Xu, Fang; Bao, Amy; Lu, Nina J.; Zhou, Tongqing; Kwong, Peter D.; Shapiro, Lawrence; Honig, Barry; Friesner, Richard A.
• Is Part Of: Journal of molecular biology. Volume 429:Issue 7(2017)
• Page Start: 930
• Page End: 947
• Publication Date: 2017-04-07
• Subjects: bNAbs broadly neutralizing antibodies -- CDR H2 second heavy chain complementarity-determining region -- FEP free energy perturbation -- MD molecular dynamics -- GPU graphics processing unit -- RMSE RMS error -- RSC3 resurfaced stabilized core 3 -- PDB protein data bank -- PB Poisson–Boltzmann -- REST replica exchange solute tempering; computational chemistry -- binding affinity optimization -- physics-based models -- alchemical FEP -- protein structure prediction; Molecular biology -- Periodicals; Biology -- Periodicals; Biochemistry -- Periodicals; Bacteriology -- Periodicals; Molecular Biology -- Periodicals; Biochemistry -- Periodicals; Biologie moléculaire -- Périodiques; Biologie -- Périodiques; Biochimie -- Périodiques; Moleculaire biologie; Biochemistry; Biology; Molecular biology; Periodicals; 572.805
• DOI: 10.1016/j.jmb.2016.11.021
• Languages: English
• ISSNs: 0022-2836

Abstract

Abstract: Direct calculation of relative binding affinities between antibodies and antigens is a long-sought goal. However, despite substantial efforts, no generally applicable computational method has been described. Here, we describe a systematic free energy perturbation (FEP) protocol and calculate the binding affinities between the gp120 envelope glycoprotein of HIV-1 and three broadly neutralizing antibodies (bNAbs) of the VRC01 class. The protocol has been adapted from successful studies of small molecules to address the challenges associated with modeling protein–protein interactions. Specifically, we built homology models of the three antibody–gp120 complexes, extended the sampling times for large bulky residues, incorporated the modeling of glycans on the surface of gp120, and utilized continuum solvent-based loop prediction protocols to improve sampling. We present three experimental surface plasmon resonance data sets, in which antibody residues in the antibody/gp120 interface were systematically mutated to alanine. The RMS error in the large set (55 total cases) of FEP tests as compared to these experiments, 0.68 kcal/mol, is near experimental accuracy, and it compares favorably with the results obtained from a simpler, empirical methodology. The correlation coefficient for the combined data set including residues with glycan contacts, R 2 = 0.49, should be sufficient to guide the choice of residues for antibody optimization projects, assuming that this level of accuracy can be realized in prospective prediction. More generally, these results are encouraging with regard to the possibility of using an FEP approach to calculate the magnitude of protein–protein binding affinities. Graphical Abstract: Highlights: Natural bNAbs against HIV-1 are a promising starting point for therapies. FEP provides a promising technique to computationally predict antibody structure optimizations. We show that FEP applied to bNAb mutations predicts binding affinity changes. FEP can provide an efficient method to identify potency-enhancing mutations to bNAbs.