New substituted aminopyrimidine derivatives as BACE1 inhibitors: in silico design, synthesis and biological assays

Abstract

We report in this work new substituted aminopyrimidine derivatives acting as inhibitors of the catalytic site of BACE1. These compounds were obtained from a molecular modeling study. The theoretical and experimental study reported here was carried out in several steps: docking analysis, Molecular Dynamics (MD) simulations, Quantum Theory Atom in Molecules (QTAIM) calculations, synthesis and bioassays and has allowed us to propose some compounds of this series as new inhibitors of the catalytic site of BACE1. The QTAIM study has allowed us to obtain an excellent correlation between the electronic densities and the experimental data of IC. Also, using combined techniques (MD simulations and QTAIM calculations) enabled us to describe in detail the molecular interactions that stabilize the different L-R complexes. In addition, our results allowed us to determine what portion of these compounds should be changed in order to increase their affinity with the BACE1. Another interesting result is that a sort of synergism was observed when the effects of these new catalytic site inhibitors were combined with Ac-Tyr5-Pro6-Tyr7-Asp8-Ile9-Pro10-Leu11-NH, which we have recently reported as a modulator of BACE1 acting on its exosite.

Exploring protein-ligand binding affinity prediction with electron density-based geometric deep learning

“…To gain additional insights into the utility of electron density for binding affinity prediction, we turned to a simpler method, foregoing ML techniques. We pursued a previously used approach [39,44] of assessing the correlation between the sum of electron density at intermolecular BCPs on the one hand, and the binding affinity on the other hand (Section S5). Focusing this analysis on the PDBbind refined set and the PDE10A dataset was motivated by the goal of using highquality structures and binding affinity measurements and ensuring comparability between individual binding affinity measurements (e.g., not comparing IC50 values to pK I or pK D values as present in the PDBbind general set).…”

“…Third, the electron density and other derived properties at intermolecular BCPs were successfully employed in previous quantitative structure-activity relationship (QSAR) studies. [29,[36][37][38][39][40][41][42][43][44] While the idea of combining QM with machine learning (ML) is not new (e.g., ML to predict QM-calculated properties, [45][46][47][48][49][50] or QM-calculated features being used as ML inputs, [51][52][53][54][55] ) this work represents, to the best or our knowledge, the first combination of BCPs with 3D-aware neural networks. Previous studies on BCP-based QSAR models mainly relied on aggregated information or scalar descriptors of structural information.…”

Exploring protein–ligand binding affinity prediction with electron density-based geometric deep learning

“…To gain additional insights into the utility of electron density for binding affinity prediction, we turned to a simpler method, foregoing ML techniques. We pursued a previously used approach 43,48 of assessing the correlation between the sum of electron density at intermolecular BCPs on the one hand, and the binding affinity on the other hand (Section S5 †). Focusing this analysis on the PDBbind rened set and the PDE10A dataset was motivated by the goal of using high-quality structures and binding affinity measurements and ensuring comparability between individual binding affinity measurements (e.g., not comparing IC50 values to pK I or pK D values as present in the PDBbind general set).…”

“…Third, the electron density and other derived properties at intermolecular BCPs were successfully employed in previous quantitative structureactivity relationship (QSAR) studies. 32,34,[41][42][43][44][45][46][47][48] While the idea of combining QM with machine learning (ML) is not new (e.g., ML to predict QM-calculated properties, [49][50][51][52][53][54] or QM-calculated features being used as ML inputs, [55][56][57][58][59] ) this work represents, to the best or our knowledge, the rst combination of BCPs with 3D-aware neural networks. Previous studies on BCP-based QSAR models mainly relied on aggregated information or scalar descriptors of structural information.…”