Aspectos coyunturales en la información relacionada

After a strong binding peptide has been identified using one of the techniques described in Section 6.1, a peptide mimic can be designed. Designing a peptide mimic involves deconstructing the original peptide and reassembling the essential features on a new mimetic scaffold that retains the ability to interact with the enzyme target, but circumvents the problems associated with a natural peptide. By this process, the peptide is reduced to its ‘information content’, the basis for a pharmacophore model that defines the critical features and their arrangement in space. This model supports the re-assembly of the critical elements and non-peptide variants on a modified scaffold that presents the optimized pharmacophore to the receptor. The optimized peptide-hybrid may be valuable as a lead, in addition to its role as a tool for further evolution to a mimetic.

For the peptides identified as active in the current research, a study was conducted to find compounds from a known commercial library which are similar in structure and electron density to the identified peptide but without the biological liabilities of peptides. The virtual screening of the Zinc database’s ‘Drug like molecules’ against the peptide 6.25 was conducted using the shape similarity software ROCS160 and electron density comparison software EON.161

6.8.1 ROCS160

ROCS stands for Rapid Overlay of Chemical Structures and is a ligand- based docking software, marketed by OpenEye, which uses shape comparison to identify potential inhibitors from large databases of compounds. ROCS works on the idea that molecules similar in shape to active molecules are more likely to be active molecules than randomly selected molecules. ROCS considers 3D similarity and chemical functionality such as hydrogen bond acceptors and donors, positive and negative charges. Results are ranked based upon the ROCS scoring function which includes the shape and chemical similarity.

6.8.2 EON161

EON is a program designed to compare the electrostatics of a compound to a database of known compounds to find similarity matches. EON compares electrostatic potential maps of pre-aligned molecules and determines the Tanimoto measures for the comparison. The results from the top slice of the ROCS result can be directly used as the input file for the EON run.

6.8.3 Process for identifying peptide mimics

A three-dimensional library of the Zinc databases ‘Drug-Like Molecules’ collection was created from the available two-dimensional structure library using the OMEGA software.162 The Zinc ‘Drug-Like’ library contains 1,064,843 compounds. The ‘Drug-Like’ library is based upon the Lipinski rule of 5 being applied to the full zinc library database.163 This library was used for comparison to the query molecule 6.25. ROCS was used to identify the top 1000 compounds which scored highly. The results were visually inspected in VIDA164, OpenEye’s molecular modelling visualising software, to ensure there were no major variations from 6.25. Figure 68 shows an overlay of one of the library compounds with the desired query.

Figure 68 ROCS and EON screening identified compound 6.29: a) overlay of 6.29 (cyan) onto query molecule 6.25 (green). b) 2D structures of 6.25 and 6.29

The electrostatic comparison was conducted using EON which identified the top 50 compounds with the best electrostatic overlay to query molecule 6.25.

The top 50 compounds were visually inspected using SPROUT to ensure there were no surface boundary violations.

6.8.4 Compound results

The top six scoring compounds are shown in Figure 69.

Figure 69: Structures of the top six scaffold hopping results (6.29-6.34)

From all of the selected compounds it is clear to see that ROCS and EON try to mimic the peptide backbone but do not mimic the side chain containing the thiol. When looking at the electrostatic map generated in VIDA there are no interactions around the thiol. This would be an issue if the thiol was wanted to be included as part of the designed inhibitor. An inhibitor could however be designed that does not require this functionality and therefore forms a cover over the active site zincs preventing the β-lactam from reaching the active site. Further research is therefore required to find a good peptide mimetic for this peptide structure if direct binding to the zinc is required as has previously been suggested.

6.9 Conclusions

A new method coupling in silico predictions to peptide screening to identify strong binding peptides to biological targets by making custom-built peptide libraries has been developed which could be a great improvement on the current methods of peptide identification. The method has been shown to work by identifying active peptides against the MBL NDM-1.

When docking to NDM-1 it was observed that trimer peptides provide the best predicted binding scores when compared with di and tetramer peptides. The L-amino acid trimer peptides give moderate activity against the NDM-1 enzyme with the best giving an IC50 of 183 μM. This result is not as good as

was initially hoped for but does show moderate binding to the enzyme which could be further developed into a drug compound.

The all D-absolute stereochemistry peptides are in general better inhibitors of the NDM-1 enzyme however due to the tight range of the results a firm conclusion on the extent of the improved binding affinity cannot be drawn. The best L-amino acid trimer had better inhibitory activity than the all D- trimers however was still a way off the inhibitory activity of known peptide mimic captopril.

There are still a number of limitations to this technique including the zinc atoms being given no charge in the docking runs but the zinc atoms in the MBLs are known to be in the 2+ charged state. Docking is also only conducted on a single crystal structure which is deemed to be rigid. In the biological environment the enzyme would have much more movement however as previously this would greatly increase the computational time required to complete the dockings.

There is a great potential that this method could be used to identify binding peptides for a large number of different biological targets. The libraries can be made to be tailored for each individual task increasing the chance of getting a hit which can be further developed into a peptide mimetic drug. This coupled with the reduced time to identify the hits and the reduced cost of compound production and storage needing to only make the best identified compounds makes this a very attractive hit finding method.