Potency Prediction of β-Secretase (BACE-1) Inhibitors Using Density Functional Methods

Abstract

Scoring potency is a main challenge for structure based drug design. Inductive effects of subtle variations in the ligand are not possible to accurately predict by classical computational chemistry methods. In this study, the problem of predicting potency of ligands with electronic variations participating in key interactions with the protein was addressed. The potency was predicted for a large set of cyclic amidine and guanidine cores extracted from β-secretase (BACE-1) inhibitors. All cores were of similar size and had equal interaction motifs but were diverse with respect to electronic substitutions. A density functional theory approach, in combination with a representation of the active site of a protein using only key residues, was shown to be predictive. This computational approach was used to guide and support drug design, within the time frame of a normal drug discovery design cycle.

Structural Biology of Cisplatin Complexes with Cellular Targets: The Adduct with Human Copper Chaperone Atox1 in Aqueous Solution

“…The significant therapeutic interest in β‐secretase 1 has also prompted many computational studies. For example, the catalytic mechanism and the optimal protonation state of the catalytic aspartates,26 the flexibility of the active site and value of ensemble docking,27 and more recently, the accurate estimation of binding energies through quantum mechanical (QM)28 and free energy perturbation (FEP) methods 29. The two molecules (2 S ,3 R )‐ 11 and ( S )‐ 26 were docked according to the approach described in the Supporting Information.…”

Multidisciplinary approaches for targeting the secretase protein family as a therapeutic route for Alzheimer's disease

“…The utilization of QM calculations in drug design efforts against the secretase protein family had primarily focused on the binding affinity and free energy of inhibitors against their target protein. QM for β‐secretase (BACE1) inhibitors targeting the catalytic residues Asp32 and Asp228, located at the center of the substrate binding cleft of the aspartyl protease was calculated by using different methods . The potency ranking of different cores with diverse electronic substitutions can only be achieved with QM calculation within systems displaying such complicated electronic variations participating in key interactions with the protein.…”

“…QM for β-secretase (BACE1) inhibitors targeting the catalytic residues Asp32 and Asp228, located at the center of the substrate binding cleft of the aspartyl protease was calculated by using different methods. 245 The potency ranking of different cores with diverse electronic substitutions can only be achieved with QM calculation within systems displaying such complicated electronic variations participating in key interactions with the protein. The S1 site of Aβ 42 , the most amyloidogenic form of the peptide, was found to possess the highest binding affinity among the most possible sites for the styrylbenzoxazole derivatives from the QM calculation, 246 which agrees with computational methods run in parallel and other experimental works.…”

Toward Simple, Predictive Understanding of Protein-Ligand Interactions: Electronic Structure Calculations on Torpedo Californica Acetylcholinesterase Join Forces with the Chemist’s Intuition

“…Where P and L stand for protein and ligand, respectively (in their complex-structure geometry). It should be pointed out that the relationship between such calculated energetics and realistic PL systems is quite unclear (as said, PL binding is a continuous, dynamic process; representing it using such "binary" means -i.e., bound complex vs. free structures -clearly ignores this fact); still, quite a few authors have employed such quantities as bits-and-pieces of information in more-general predictive theoretical/computational schemes -where additional such pieces, obtained using different techniques (e.g., classical MD trajectories), are also used [48][49][50][51][52][53] . Needless to say, such multi-method efforts require an appropriate multi-method-expertise from the researcher, and entail lots of (perhaps undetectable) sources of error and technical difficulties -as demonstrated in Figure 1.…”