RESUMEN
Chameleonicity (the capacity of a molecule to adapt its conformations to the environment) may help to identify orally bioavailable drugs in the beyond-Rule-of-5 chemical space. Computational methods to predict the chameleonic behaviour of degraders have not yet been reported and the identification of molecular chameleons still relies on experimental evidence. Therefore, there is a need to tune predictions with experimental data. Here, we employ PROTAC-1 (a passively cell-permeable degrader), for which NMR and physicochemical data prove the chameleonic behaviour, to benchmark the capacity of two conformational sampling algorithms and selection schemes. To characterize the conformational ensembles in both polar and nonpolar environments, we compute three molecular properties proven to be essential for cell permeability: conformer shape (radius of gyration), polarity (3D PSA), and the number of intramolecular hydrogen bonds. Energetic criteria were also considered. Infographics monitored the simultaneous variation of those properties in computed and NMR conformers. Overall, we provide key points for tuning conformational sampling tools to reproduce PROTAC-1 chameleonicity according to NMR evidence. This study is expected to improve the design of PROTAC drugs and the development of computational sustainable strategies to exploit the potential of new modalities in drug discovery.
RESUMEN
PROTACs® are expected to strongly impact the future of drug discovery. Therefore, in this work we firstly performed a statistical study to highlight the distribution of E3 ligases and POIs collected in PROTAC-DB, the main online database focused on degraders. Moreover, since the emerging technology of protein degradation deals with large and complex chemical structures, the second part of the paper focuses on how to set up a property-based design strategy to obtain oral degraders. For this purpose, we calculated a pool of seven previously ad hoc selected 2D descriptors for the 2258 publicly available degraders in PROTAC-DB (average values: MW= 972.9 Da, nC= 49.5, NAR= 4.5, PHI= 17.3, nHDon= 4.5, nHAcc= 17.7 and TPSA= 240 Å2) and compared them to a dataset of 50 bRo5 orally approved drugs. Then, a chemical space based on nC, PHI and TPSA was built and subregions with optimal permeability and bioavailability were identified. Bioavailable degraders (ARV-110 and ARV-471) tend to be closer to the Ro5 region, using mainly semi-rigid linkers. Permeable degraders, on the other hand, are placed in an average central region of the chemical space but chameleonicity could allow them to be located closer to the two Arvinas compounds.