Novel phenolic inhibitors of the sarco/endoplasmic reticulum calcium ATPase: identification and characterization by quantitative structure–activity relationship modeling and virtual screening

Figures & data

Figure 1. Correlation between experimentally determined and predicted inhibitory potencies (pIC₅₀) for initial models type-1 (left), type-2 (middle), and type-3 (right). External test set compounds are shown as empty circles.

Table 1. Descriptors and regression parameters for initial QSAR model type-1.

Variable	Regression coefficient	Error regression coefficient
y-Intercept	−0.4857	0.1587
SHBint3	−0.02555	0.00575
xp6	−1.3081	0.1406
xp7	0.6846	0.1463
xp8	1.1357	0.2911
dxp5	1.2460	0.1614
n3pag23	−0.14825	0.01676
n4pag23	−0.08370	0.01715
n4pag33	0.09731	0.02737

n = 43, r²= 0.815, q²= 0.719, s = 0.265, LOF = 0.103, PRESS = 3.61, F-value = 18.68.

Table 2. Descriptors and regression parameters for initial QSAR model type-2.

Variable	Regression coefficient	Error regression coefficient
y-Intercept	4.241	1.470
PM3_HOMO	0.792	0.167
vsurf_DW12	0.027	0.009
vsurf_HB6	−0.032	0.011
vsurf_Wp7	7.373	1.152
Smallest_Pos_Hardness	8.187	2.473
RDF060v	−0.069	0.014
TRDF010p	−0.025	0.016
TRDF050m	0.052	0.007

n = 43, r²= 0.827, q²= 0.743, s = 0.255, LOF = 0.131, PRESS = not determined, F-value = 20.3.

Table 3. Descriptors and regression parameters for initial QSAR model type-3.

Variable	Regression coefficient	Error regression coefficient
y-Intercept	−4.36	0.31
MATS5e	0.801	0.204
Mor21m	−0.807	0.214
Mor30v	1.17	0.33
Gu	3.94	0.38
Dm	3.74	0.56
(RDF030m)^2	−0.00246	0.0068
(G1u2)^2	−1.46	0.22
(Du)^2	3.46	0.99

n = 43, r²= 0.863, q²= 0.808, s = 0.227, LOF = 0.103, PRESS = 2.456, F-value = 26.9.

Figure 2. Chemical structure of the 16 compounds selected for testing.

Figure 3. Representative inhibition curves obtained for a high (•, T01), medium (▪, T08), and low (♦, T03) potency inhibitor.

Table 4. Experimentally determined and screening model-predicted inhibitory potencies of compounds selected for testing.

Compound no.	IC50 (µM)	pIC50	pIC50 (type-1)	pIC50 (type-2)	pIC50 (type-3)
T01	0.94 ± 0.62	0.03	−0.01	−0.14	−1.83
T02	1.01 ± 0.50	0.00	−0.15	0.19	−1.90
T03	6.94 ± 1.63	−0.84	−0.43	−0.81	−0.94
T04	17.9 ± 4.0	−1.25	−0.51	0.38	0.23
T05	33.3 ± 2.5	−1.52	0.10	0.79	0.32
T06	38.8 ± 4.9	−1.59	0.03	0.32	0.59
T07	53.0 ± 7.0	−1.72	0.03	−1.47	−1.46
T08	112 ± 9	−2.05	−0.45	−0.08	−0.40
T09	185 ± 46	−2.27	−0.84	−0.72	0.28
T10	409 ± 52	−2.61	0.14	−0.34	0.99
T11	419 ± 107	−2.62	−0.73	−0.52	0.21
T12	Inactive	–	−1.03	−1.45	1.54
T13	Inactive	–	0.12	−0.79	−1.32
T14	Inactive	–	1.56	−0.05	−0.68
T15	Inactive	–	0.08	0.58	−1.24
T16	Inactive	–	0.23	1.07	−1.67

Figure 4. Correlation between experimentally determined and model-predicted inhibitory potencies (pIC₅₀) for the final “master” model.

Table 5. Descriptors and regression parameters for the master QSAR model.

Variable	Regression coefficient	Error regression coefficient
y-Intercept	−1.85	0.128
SHCsatu	−0.242	0.0490
SHHBd	0.225	0.0348
dxp6	−1.02	0.112
dxvp4	−0.387	0.0939
dxvp7	1.94	0.348
np7	0.0218	0.00345
n3pag33	0.142	0.0371

n = 52, r²= 0.802, q²= 0.743, s = 0.272, LOF > 0.1, PRESS = 4.14, F-value: 25.52.

Table 6. Results of PLS analysis of the master QSAR model and descriptors making significant contributions to the first four PLS components.

Descriptor	x-Weight comp-1	x-Weight comp-2	x-Weight comp-3	x-Weight comp-4
SHCsatu	−0.388	−0.370	0.275	0.240
SHHBd	0.682	−0.353	−0.096	−0.330
dxp6	−0.486	−0.313	0.208	−0.595
dxvp4	−0.136	0.336	−0.196	−0.670
dxvp7	0.007	0.707	0.188	0.000
np7	0.093	0.145	0.734	−0.022
n3pag33	0.348	−0.079	0.510	−0.174

The most influential descriptors are shown in boldface.