Free Energy Perturbation Calculation of Relative Binding Free Energy between Broadly Neutralizing Antibodies and the gp120 Glycoprotein of HIV-1

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, R2 = 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.

Approaching protein design with multisite λ dynamics: Accurate and scalable mutational folding free energies in T4 lysozyme

“…In contrast, due to its smaller scope, the present study could identify poorly converged mutants and focus sampling on them with VB‐REX and reoptimization of biasing potential parameters. Second, the accuracy of both methods is limited by the force field used, and it is possible that the CHARMM36 force field used here is more accurate than OPLS3, though at least one study suggests that the two force fields to have comparable accuracy . Finally, MS λ D is more computationally efficient; one study cites a factor of 20–30; see Supporting Information for further discussion.…”

“…Due to the importance of changes in protein stability upon mutation, a wide variety of techniques exist to predict ΔΔ G Folding ( S 1 → S 2 ). Techniques range from fast physics‐based, knowledge‐based, and machine learning approaches; to implicit solvent methods; to rigorous alchemical free energy calculations in explicit solvent …”

“…It is also instructive to compare MS λ D directly to other alchemical free energy methods. Most alchemical free energy studies of protein mutations have been small; however, two large scale tests of FEP for calculating folding free energies and protein–protein binding free energies recently appeared. The binding free energy study achieved excellent accuracy (0.68 kcal/mol RMSE, Pearson correlation 0.70) due to the small data range, aggressive sampling (100 ns per window in some cases), neglect of charge changing mutations, and ability to ignore the unfolded ensemble …”

“…Studies using free energy methods to estimate changes in peptide and protein free energy upon side chain mutation are much rarer . Last year, two large‐scale applications of free energy methods to protein folding and binding upon mutation appeared . Such studies could in principle be used to guide protein design, but in practice are far too slow to be of practical use in driving design.…”

SKEMPI 2.0: An updated benchmark of changes in protein-protein binding energy, kinetics and thermodynamics upon mutation

“…Other prominent sources of data are studies into hormone receptor interactions, in particular the human growth hormone receptor from the group of Jim Wells (e.g. [14]) and the prolactin receptor from the group of Michael E. Hodsdon [35], as well as studies into antigen recognition including the combined computational and experimental design study to enhance affinity of the AQC2 antibody to integrin α-1 from the group of Herman Van Vlijmen [12], the dissection of the interactions of broadly neutralising antibodies targeting HIV gp120 [11] from the group of Richard A. Friesner, and various investigations from the group of Roy A. Mariuzza (e.g. [15]).…”