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. 2022 Mar 22;7(13):11057-11067.
doi: 10.1021/acsomega.1c07037. eCollection 2022 Apr 5.

Protein-Protein Binding Free Energy Predictions with the MM/PBSA Approach Complemented with the Gaussian-Based Method for Entropy Estimation

Shailesh Kumar Panday  1 , Emil Alexov  1
Affiliations

Protein-Protein Binding Free Energy Predictions with the MM/PBSA Approach Complemented with the Gaussian-Based Method for Entropy Estimation

Shailesh Kumar Panday et al. ACS Omega. .

Abstract

Here, we present a Gaussian-based method for estimation of protein-protein binding entropy to augment the molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) method for computational prediction of binding free energy (ΔG). The method is termed f5-MM/PBSA/E, where "E" stands for entropy and f5 for five adjustable parameters. The enthalpy components of ΔG (molecular mechanics, polar and non-polar solvation energies) are computed from a single implicit solvent generalized Born (GB) energy minimized structure of a protein-protein complex, while the binding entropy is computed using independently GB energy minimized unbound and bound structures. It should be emphasized that the f5-MM/PBSA/E method does not use snapshots, just energy minimized structures, and is thus very fast and computationally efficient. The method is trained and benchmarked in 5-fold validation test over a data set consisting of 46 protein-protein binding cases with experimentally determined dissociation constant K d values. This data set has been used for benchmarking in recently published protein-protein binding studies that apply conventional MM/PBSA and MM/PBSA with an enhanced sampling method. The f5-MM/PBSA/E tested on the same data set achieves similar or better performance than these computationally demanding approaches, making it an excellent choice for high throughput protein-protein binding affinity prediction studies.

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Conflict of interest statement

The authors declare no competing financial interest.

Figures

Figure 1
Figure 1
Summary of the performance of the three energy models (model 1, f3-MM/PB, i.e., eq 1; model 2, f4-MM/PBSA, i.e., eq 2; model 3, f5-MM/PBSA–TΔSGE, i.e., eq 3) with varying internal dielectric constant for the GBneck2 energy minimized set of structures of the PPI-46 benchmarking data set. (a) PCC vs internal dielectric constant value; (b) RMSE vs internal dielectric constant value.
Figure 2
Figure 2
Summary of the performance of the three energy models at ϵin = 1 for the GBneck2 energy minimized set of structures of the PPI-46 benchmarking data set. Scatter plots are shown of predicted vs experimental for (left panel) model 1, (middle panel) model 2, and (right panel) model 3.
Figure 3
Figure 3
Influence of variation of the parameters Gaussian variance σ (a and b), cutoff radius for atom surrounding (c and d), and decay rate r (e and f) for Gaussian binding entropy on the PCC (a, c, and e) and RMSE (b, d, and f) of model 3 for the PPI-46 data set.
Figure 4
Figure 4
Number of residues in the protein–protein complex vs total computation time for running DelPhi and computing entropy from the Gaussian-density map data.
Figure 5
Figure 5
Schematic representation of protocols used for computing enthalpy and entropy components of protein–protein binding free energy. (a) The enthalpy energy components are computed over the energy minimized complex structure, and monomers are extracted from it. (b) The entropy estimation is done in a protocol that the complex and two monomers are energy minimized independently.
Figure 6
Figure 6
A schematic representation of the idea of Gaussian-based entropy. An ILE residue of a protein is shown in black and white ball and stick. Neighboring atoms in radius 4 Å are shown with semitransparent cyan spheres. Left panel: all possible side chain conformations of ILE in unbound protein. Right panel: only one rotamer of the same ILE is accessible due to the presence of neighboring atoms of the binding partner.
Figure 7
Figure 7
Illustration of the interpolation scheme. (a) The effect of the value of the decay rate parameter, r, on the curvature of the exponential decay curve. (b) Illustration of obtaining the number of effective conformations for an example case where the maximum conformations is 3 at a minimum mean Gaussian density of 0.5, with the number of effective conformations (≈2) corresponding to a mean Gaussian density of 0.7 shown.
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References

    1. Lu H.; Zhou Q.; He J.; Jiang Z.; Peng C.; Tong R.; Shi J. Recent advances in the development of protein–protein interactions modulators: mechanisms and clinical trials. Signal Transduct. Target. Ther. 2020, 5, 213.10.1038/s41392-020-00315-3. - DOI - PMC - PubMed
    1. White A. W.; Westwell A. D.; Brahemi G. Protein–protein interactions as targets for small-molecule therapeutics in cancer. Expert Rev. Mol. Med. 2008, 10, e810.1017/S1462399408000641. - DOI - PubMed
    1. Rosell M.; Fernández-Recio J. Hot-spot analysis for drug discovery targeting protein-protein interactions. Expert Opin. Drug Discovery 2018, 13, 327–338. 10.1080/17460441.2018.1430763. - DOI - PubMed
    1. Ryan D. P.; Matthews J. M. Protein-protein interactions in human disease. Curr. Opin. Struct. Biol. 2005, 15, 441–446. 10.1016/j.sbi.2005.06.001. - DOI - PubMed
    1. Lage K. Protein-protein interactions and genetic diseases: The interactome. Biochim. Biophys. Acta - Mol. Basis Dis. 2014, 1842, 1971–1980. 10.1016/j.bbadis.2014.05.028. - DOI - PMC - PubMed