Sistema DF

1.5.1. SIMULATIONS WITH OLIGOBENZAMIDES

Performing what appear to have been the first binding affinity predictions for Mdm2 made using all-atom, explicit solvent molecular dynamics simulations, Fuller et al.203 demonstrated the successful application of thermodynamic integration (TI) to oligobenzamide inhibitors of the p53-Mdm2 interaction. Their study focussed on six molecules with side chains linked through oxygen. This project builds on their work, testing a much greater number of compounds to determine if such simulations are of practical use with the larger chemical space now

synthetically accessible to our collaborating chemists. Included are 31 already synthesised and tested molecules where side chains are attached via the amide group nitrogen.

Fuller et al.172,203 report how an oligobenzamide appears to be stable when bound to Mdm2 in an antiparallel orientation in addition to when parallel to the alignment of the bound p53-helix. Their results are based on simulations starting from docking poses generated using Autodock where a small docking grid was used, forcing compounds to bind close to the binding site. In my work, the greater speed of Autodock Vina permitted a far greater number of compounds to be investigated and for them to be docked quickly without restriction of the searched volume to the binding site. This enabled key assumptions to be tested, such as the hypothesis that

oligobenzamides bind in the p53 binding site and the assumption that, if and when they do, they bind with each side chain positioned so as to mimic a p53 residue. Fuller’s suggestion that the specific combination of side chains could dictate whether parallel or antiparallel binding is most efficient, is also investigated.

Fuller et al. used torsion parameters intended for use with sulphur to model the intramolecular hydrogen bonding between the side chain-linking oxygen and amide group hydrogen (p69). In this project, more recently published parameters for use with oxygen were tested to see if they also yielded stable simulations.

The results of Fuller et al. suggested that the effect of multiple side chain modifications could be predicted in a single TI analysis in which all of the side chains were simultaneously

transformed203. However, predicting the relative affinity of a large number of molecules by TI is still impractical. The paradigmatic model of an oligobenzamide inhibitor in which each side chain mimics a single amino acid suggests that side chains could make independent

contributions to the binding energy which could be summed to predict the relative binding energy of a compound with any side chain combination. In this project, I investigate if this independence is observed or whether the concerted transformation of all side chains is essential if accurate affinities are to be obtained.

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1.5.2. OTHER SIMULATIONS OF THE P53-MDM2 INTERACTION

Molecular dynamics simulations have been used extensively to study the p53-Mdm2 interaction.

The p53 residues Phe19, Trp23 and Leu26 are known to contribute significantly to the binding energy of the p53-Mdm2 interaction26. Espinoza-Fonseca et al.204 studied the duration of interactions between aromatic residues of p53 and Mdm2 and concluded that interactions between Phe19 and Trp23 of p53 and Tyr51 and Tyr63 of Mdm2 played a key role in

recognition. Verma et al.205 have investigated how the polyphenols quercetin and taxifolin bind to Mdm2 and how they might displace p53 from the binding site by disrupting its interaction with the side chain of Tyr51.

Alanine scanning has been performed on p53 in silico using the MM-PBSA method to estimate the affinity of mutated peptides195. In such experiments, a simulation of the protein is performed for each residue with the residue side chain replaced by a methyl group. Applied to p53 in the human p53-Mdm2 interface, the method correctly identifies Phe19, Trp23 and Leu26 as

residues critical for binding195. Zhong and Carlson206 performed alanine scanning on Mdm2 and identified Leu54, Ile61, Met62, Tyr67, Glu72, Val93, His96 and Tyr100 as important for p53 binding.

Ding et al.207 used quantum mechanical simulations to study the key interacting residues of p53-Mdm2. They identified p53 residue Leu22 as also important; although, they concluded that overall the p53-Mdm2 interaction was driven by van der Waals forces rather than specific residue-residue interactions.

Espinoza-Fonseca et al.208 used molecular dynamics to compare the dynamics of Mdm2 with and without bound p53. Their simulations reveal that p53 stabilises the flexible p53 binding site of Mdm2 when it binds. Joseph et al.43 investigated how the Mdm2 binding site is more flexible than the Mdm4 binding site, a feature which appears to allow Nutlin-3 to bind much more strongly to the former than the latter.

Shan et al.209 used simulations, as well as NMR, to investigate whether the second of the two p53 transactivation domain subdomains interacts with Mdm2 (in addition to the first subdomain, which contains Phe19, Trp23 and Leu26). They found that it did, but did not undergo binding- induced folding like the first subdomain does.

Dastidar et al.210 have discovered that the N-terminal part of Mdm2 can influence p53 binding through its effect on the conformation of Tyr100. Dastidar et al.211 have also used simulations to study the dynamics of the first 24 residues and how these dynamics are influenced by Ser17

96 phosphorylation and mutation of Ser17 to aspartate. Because this N-terminal region can block

access to the p53-binding site it is absent from the constructs used in this project (p32).

Using Brownian dynamics212, ElSawy et al.213 have investigated the binding of p53 and a Nutlin to both Mdm2 and Mdm4 over a longer timescale than can typically be studied in a molecular dynamics simulation. Specifically, they studied the transient interactions which p53 forms with each protein prior to formation of the stable bound state. p53 appears to interact with the N and C termini of Mdm2 before it reaches its binding site.

In this project, implicit solvent methods including the MM-GBSA method (p88) were used to predict the relative affinities of oligobenzamides for Mdm2. Chen et al.214 describe their use of the MM-GBSA method to predict the relative binding energies of four small molecules (a benzodiazepinedione, a chromenotriazolopyrimidine, a Nutlin and an imidazolylmethylindole derivative) for Mdm2. The results correlated well with previously published, experimentally determined binding affinities.

The aim of this chapter has been to introduce the motivations for, background of and theory behind this project, in preparation for the subsequent results chapters. This chapter does not introduce the statistical methods employed in the project; these can be found in Appendix A (p290).