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. 2023 Aug 22;13(36):25118-25128.
doi: 10.1039/d3ra04916g. eCollection 2023 Aug 21.

Evaluating the ability of end-point methods to predict the binding affinity tendency of protein kinase inhibitors

Martiniano Bello  1 , Cindy Bandala  2
Affiliations

Evaluating the ability of end-point methods to predict the binding affinity tendency of protein kinase inhibitors

Martiniano Bello et al. RSC Adv. .

Abstract

Because of the high economic cost of exploring the experimental impact of mutations occurring in kinase proteins, computational approaches have been employed as alternative methods for evaluating the structural and energetic aspects of kinase mutations. Among the main computational methods used to explore the affinity linked to kinase mutations are docking procedures and molecular dynamics (MD) simulations combined with end-point methods or alchemical methods. Although it is known that end-point methods are not able to reproduce experimental binding free energy (ΔG) values, it is also true that they are able to discriminate between a better or a worse ligand through the estimation of ΔG. In this contribution, we selected ten wild-type and mutant cocrystallized EGFR-inhibitor complexes containing experimental binding affinities to evaluate whether MMGBSA or MMPBSA approaches can predict the differences in affinity between the wild type and mutants forming a complex with a similar inhibitor. Our results show that a long MD simulation (the last 50 ns of a 100 ns-long MD simulation) using the MMGBSA method without considering the entropic components reproduced the experimental affinity tendency with a Pearson correlation coefficient of 0.779 and an R2 value of 0.606. On the other hand, the correlation between theoretical and experimental ΔΔG values indicates that the MMGBSA and MMPBSA methods are helpful for obtaining a good correlation using a short rather than a long simulation period.

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Conflict of interest statement

The authors declare they have no conflict of interest in terms of the content of this manuscript.

Figures

Fig. 1
Fig. 1. Design of the PK catalytic domain. The structural topology of a PK exemplified by CDK2 (PDB entry 1QMZ). The figure illustrates the N- and C-domains of CDK2, which forms a complex with ATP and the substrate at the catalytic binding domain. The activation loop is in blue, the P-loop in red, the αC-helix in green, the hinge region in magenta, the Mg2+ ion in orange, the ATP in pink, and the substrate in cyan.
Fig. 2
Fig. 2. Structural details of the investigated systems. The set of mutated EGFR–inhibitor cocrystallized complexes.
Fig. 3
Fig. 3. Effect of MD simulation time on the binding free energy using the MMGBSA and MMPBSA methods. The ΔG values determined using the MMGBSA approach, considering the first 25 ns (A) and the last 50 ns (B) of a 100 ns-long MD simulation. The ΔG values determined using the MMPBSA approach, considering the first 25 ns (C) and the last 50 ns (D) of a 100 ns-long MD simulation.
Fig. 4
Fig. 4. Effect of MD simulation time on the relative binding free energy using the MMGBSA and MMPBSA methods. The calculated ΔΔG values between the mutated and the wild-type systems determined using the MMGBSA approach, considering the first 25 ns (A) and the last 50 ns (B) of a 100 ns-long MD simulation. The calculated ΔΔG values determined using the MMPBSA approach, considering the first 25 ns (C) and the last 50 ns (D) of a 100 ns-long MD simulation.
Fig. 5
Fig. 5. Impact of MD simulation time on the binding free energy using the MMGBSA and MMPBSA methods, considering the entropic component. ΔG values determined using the MMGBSA approach, considering the first 25 ns (A) and the last 50 ns (B) of a 100 ns-long MD simulation. ΔG values determined using the MMPBSA approach, considering the first 25 ns (C) and the last 50 ns (D) of a 100 ns-long MD simulation.
Fig. 6
Fig. 6. Impact of MD simulation time on the absolute binding free energy using the MMGBSA and MMPBSA methods. Calculated versus experimental ΔΔG between the mutated and the wild-type systems determined using the MMGBSA approach, considering the first 25 ns (A) and the last 50 ns (B) of a 100 ns-long MD simulation and considering the entropic contribution. Calculated versus experimental ΔΔG between the mutated and the wild-type systems using the MMPBSA approach, considering the first 25 ns (C) and the last 50 ns (D) of a 100 ns-long MD simulation.
Fig. 7
Fig. 7. Calculated versus experimental ΔG between the mutated and the wild-type systems obtained using docking methods. Correlation obtained using MOE (A), and SwissDock (B).
Fig. 8
Fig. 8. Calculated versus experimental ΔΔG between the mutated and the wild-type systems obtained using docking methods. Correlation obtained using MOE (A), and SwissDock (B).
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