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Can Relative Binding Free Energy Predict Selectivity of Reversible Covalent Inhibitors?

ABSTRACT: Reversible covalent inhibitors have many clinical advantages over noncovalent or irreversible covalent drugs. However, apart from selecting a warhead, substantial efforts in design and synthesis are needed to optimize noncovalent interactions to improve target-selective binding. Computational prediction of binding affinity for reversible covalent inhibitors presents a unique challenge since the binding process consists of multiple steps, which are not necessarily independent of each other. In this study, we lay out the relation between relative binding free energy and the overall reversible covalent binding affinity using a two-state binding model. To prove the concept, we employed free energy perturbation (FEP) coupled with ?-exchange molecular dynamics method to calculate the binding free energy of a series of ?-ketoamide analogues relative to a common warhead scaffold, in both noncovalent and covalent binding states, and for two highly homologous proteases, calpain-1 and calpain-2. We conclude that covalent binding state alone, in general, can be used to predict reversible covalent binding selectivity. However, exceptions may exist. Therefore, we also discuss the conditions under which the noncovalent binding step is no longer negligible and propose to combine the relative FEP calculations with a single QM/MM calculation of warhead to predict the binding affinity and binding kinetics. Our FEP calculations also revealed that covalent and noncovalent binding states of an inhibitor do not necessarily exhibit the same selectivity. Thus, investigating both binding states, as well as the kinetics will provide extremely useful information for optimizing reversible covalent inhibitors.

SUBMITTER: Chatterjee P

PROVIDER: S-EPMC5729052 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Can Relative Binding Free Energy Predict Selectivity of Reversible Covalent Inhibitors?

Chatterjee Payal P Botello-Smith Wesley M WM Zhang Han H Qian Li L Alsamarah Abdelaziz A Kent David D Lacroix Jerome J JJ Baudry Michel M Luo Yun Y

Journal of the American Chemical Society 20171129 49

Reversible covalent inhibitors have many clinical advantages over noncovalent or irreversible covalent drugs. However, apart from selecting a warhead, substantial efforts in design and synthesis are needed to optimize noncovalent interactions to improve target-selective binding. Computational prediction of binding affinity for reversible covalent inhibitors presents a unique challenge since the binding process consists of multiple steps, which are not necessarily independent of each other. In th ...[more]

PMID: 29124934

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Project description:Reversible covalent inhibitors have drawn increasing attention in drug design, as they are likely more potent than noncovalent inhibitors and less toxic than covalent inhibitors. Despite those advantages, the computational prediction of reversible covalent binding presents a formidable challenge because the binding process consists of multiple steps and quantum mechanics (QM) level calculation is needed to estimate the covalent binding free energy. It has been shown that the dissociation rates and the equilibrium dissociation constants vary significantly even with similar warheads, due to noncovalent interactions. We have previously used a simplistic two-state model for predicting the relative binding selectivity of reversible covalent inhibitors ( J. Am. Chem. Soc. 2017, 139 , 17945 ). Here we go beyond binding selectivity and demonstrate that it is possible to use free energy perturbation (FEP) molecular dynamics (MD) to calculate the overall reversible covalent binding using a specially designed thermodynamic cycle. We show that FEP can predict the varying binding free energies of the analogs sharing a common warhead. More importantly, our results revealed that the chemical modification away from warhead alters the binding affinity at both noncovalent and covalent binding states, and the computational prediction can be improved by considering the binding free energy of both states. Furthermore, we explored the possibility of using a more rapid computational method, site-identification by ligand competitive saturation (SILCS), to rank the same set of reversible covalent inhibitors. We found that the fragment docking to a set of precomputed fragment maps produces a reasonable ranking. In conclusion, two independent approaches provided consistent results that the covalent binding state is suitable for the initial ranking of the reversible covalent drug candidates. For lead-optimization, the FEP approach designed here can provide more rigorous and detailed information regarding how much the covalent and noncovalent binding states are contributing to the overall binding affinity, thus offering a new avenue for fine-tuning the noncovalent interactions for optimizing reversible covalent drugs.

Project description:A significant challenge and potential high-value application of computer-aided drug design is the accurate prediction of protein-ligand binding affinities. Free energy perturbation (FEP) using molecular dynamics (MD) sampling is among the most suitable approaches to achieve accurate binding free energy predictions, due to the rigorous statistical framework of the methodology, correct representation of the energetics, and thorough treatment of the important degrees of freedom in the system (including explicit waters). Recent advances in sampling methods and force fields coupled with vast increases in computational resources have made FEP a viable technology to drive hit-to-lead and lead optimization, allowing for more efficient cycles of medicinal chemistry and the possibility to explore much larger chemical spaces. However, previous FEP applications have focused on systems with high-resolution crystal structures of the target as starting points-something that is not always available in drug discovery projects. As such, the ability to apply FEP on homology models would greatly expand the domain of applicability of FEP in drug discovery. In this work we apply a particular implementation of FEP, called FEP+, on congeneric ligand series binding to four diverse targets: a kinase (Tyk2), an epigenetic bromodomain (BRD4), a transmembrane GPCR (A2A), and a protein-protein interaction interface (BCL-2 family protein MCL-1). We apply FEP+ using both crystal structures and homology models as starting points and find that the performance using homology models is generally on a par with the results when using crystal structures. The robustness of the calculations to structural variations in the input models can likely be attributed to the conformational sampling in the molecular dynamics simulations, which allows the modeled receptor to adapt to the "real" conformation for each ligand in the series. This work exemplifies the advantages of using all-atom simulation methods with full system flexibility and offers promise for the general application of FEP to homology models, although additional validation studies should be performed to further understand the limitations of the method and the scenarios where FEP will work best.

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Can Relative Binding Free Energy Predict Selectivity of Reversible Covalent Inhibitors?

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Can Relative Binding Free Energy Predict Selectivity of Reversible Covalent Inhibitors?

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