Identification of Novel BACE-1 Inhibitors through an Advanced Structure-Based Virtual Screening Protocol Puneet Kacker,a Angela De Simone,a Matteo Masetti,b Giovanni Bottegoni,a Vincenza Andrisano,b Andrea Cavalli a,b

a D3-Department of Drug Discovery and Development, IIT - Istituto Italiano di Tecnologia, Via Morego n.30 16163, Genova – IT; b Dipartimento di Scienze Farmaceutiche, Università di Bologna, via Belmeloro n.6 40127, Bologna – IT

Email: puneet.kacker@iit.it

The influence of different ligand chemotypes on the protonation state of the catalytic dyad has been systematically assessed in an exploratory DFT and MD studies Several dyad protonation states and protein conformations were simultaneously employed in a VLS protocol As a result, 13 compounds were prioritized for testing; 10 compounds turned out to be mildly active BACE-1 inhibitors One compound (ARN1348; IC50 = 63.2 μM) was selected for a preliminary hit optimization program

References Kacker et al., Combining dyad protonation and active site plasticity in BACE-1 structure-based drug design. J. Chem. Inf. Model. 2012, doi: 10.1021/ci200366z Vassar et al., The β-secretase enzyme BACE in health and Alzheimer's disease: regulation, cell biology, function, and therapeutic potential. J. Neurosci. 2009, 29, 12787-12794 F. Mancini et al., β-secretase as a target for Alzheimer's disease drug discovery: an overview of in vitro methods for characterization of inhibitors. Anal. Bioanal. Chem., 2011, 400, 1979-1996

Protonation States DFT-assigned

Protonation State(s) DFT+MD-assigned

Protonation State(s) Monoprotonated Di-deprotonated

PDBid 32i 32o 228i 228o ddp

1W51 0.17 3.02 2.15 0.00 15.34 32i, 228o 32i

1YM2 0.00 9.13 5.59 1.62 90.71 32i, 228o 32i

2QU3 0.00 6.30 3.87 1.02 6.02 32i, 228o 228i, ddp

2FDP 3.06 5.50 0.00 9.19 8.81 228i 228i

2IS0 5.36 1.47 0.00 9.69 14.07 32o, 228i 228i

2QZL 1.48 1.33 0.00 9.18 13.84 32i, 32o, 228i 32i, 228i

2WF4 1.57 9.25 0.00 11.06 17.82 32i, 228i 32i, 228i

BACE-1 is considered a challenging target for two main reasons:

(1) the protonation state of the catalytic dyad varies depending on

the nature of the interacting molecule, and (2) the protein binding

site is extremely flexible. Here, we propose a virtual ligand

screening (VLS) protocol that addresses these two issues

simultaneously.

VLS was performed on 70,885 compounds obtained combining several protease-focused libraries H-bond interactions with the dyad were employed as a post- processing filter 13 compounds were prioritized to be tested in a FRET assay

13 congenerics (10 active, 3 inactive) Best activity:

ARN1348 (IC50 = 63.2 μM)

1. Whole Library

2. Dyad interacting pose

3. Removal of known inhibitors 4. High scoring molecules

5. Cluster analysis/Visual inspection

6. Prioritized for biochemical assay (classes)

70,885

47,170

46,791

370

34

1 class

HIT

Total Docking Runs Performed

708,850

A 10 members conformational ensemble was compiled selecting one or more conformers from each cluster Protonation states were assigned in two different ways: (1) QM and MD results, and (2) retrospective Self- and Cross-docking studies Two benchmarks—Craig’s dataset and HEA dataset—were employed to test the performance of this ensemble in retrospective VLS studies

In both cases, the conformational ensemble method statistically outperformed the single conformer approach.

A set of 47 BACE-1 high quality crystal complexes was selected from the Protein Data Bank and grouped into 7 clusters according to the ligand moiety interacting directly with the catalytic dyad

QM calculations on a representative structure from each cluster were performed by applying locally dense basis sets, namely 6-31++G** (purple), 6-31G* (orange) and 3-21G (green) To assess the reliability of QM protocol, all the QM results displaying energy differences less than 2.0 kcal/mol were further investigated by means of MD simulations

QM optimized geometry Basis sets partitioning scheme

Table: Relative QM energies (kcal/mol) and assigned protonation states

1. Hydroxyethylamine

2. Hydroxyethylene

3. Acylguanidine 5. Hydroxy and Primary amine

4. Primary amine 6. Reduced amide

7. Di-hydroxyethylamine

DFT-MD-based selection Docking-based selection (DOCK set)

Cluster PDBid Protonation

state

BEDROC (Craig

dataset)

BEDROC (HEA

decoys)

Protonation state

BEDROC (Craig

dataset)

BEDROC (HEA

decoys) 1 2IQG 32i 0.67 0.91 32i 0.67 0.91 1 2P83 32i 0.63 0.83 32i 0.63 0.83 1 2WF3 32i 0.62 0.92 32i 0.62 0.92 2 1YM2 32i 0.44 0.65 228o 0.47 0.47 3 2ZDZ 228i 0.17 0.12 ddp 0.19 0.20

ddp 0.19 0.20 4 2FDP 228i 0.52 0.86 ddp 0.68 0.75 4 2IRZ 228i 0.59 0.51 ddp 0.68 0.59 5 2IS0 228i 0.62 0.59 228i 0.62 0.59 6 2QZL 32i 0.57 0.83 32i 0.57 0.83

228i 0.56 0.58 7 2WF4 32i 0.58 0.97

228i 0.54 0.96 228i 0.54 0.96 Combined 0.73 0.85 Combined 0.78 0.87