Binding affinity prediction for ligands and receptors forming tautomers and ionization species: inhibition of mitogen-activated protein kinase-activated protein kinase 2 (MK2).

Abstract

Treatment of ionization and tautomerism of ligands and receptors is one of the unresolved issues in structure-based prediction of binding affinities. Our solution utilizes the thermodynamic master equation, expressing the experimentally observed association constant as the sum of products, each valid for a specific ligand-receptor species pair, consisting of the association microconstant and the fractions of the involved ligand and receptor species. The microconstants are characterized by structure-based simulations, which are run for individual species pairs. Here we incorporated the multispecies approach into the QM/MM linear response method and used it for structural correlation of published inhibition data on mitogen-activated protein kinase (MAPK)-activated protein kinase (MK2) by 66 benzothiophene and pyrrolopyridine analogues, forming up to five tautomers and seven ionization species under experimental conditions. Extensive cross-validation showed that the resulting models were stable and predictive. Inclusion of all tautomers and ionization ligand species was essential: the explained variance increased to 90% from 66% for the single-species model.

Full text

Page 1

Binding Affinity Prediction for Ligands and Receptors Forming
Tautomers and Ionization Species: Inhibition of Mitogen-Activated
Protein Kinase-Activated Protein Kinase 2 (MK2)
Senthil Natesan, Rajesh Subramaniam, Charles Bergeron, and Stefan Balaz*
Department of Pharmaceutical Sciences, Albany College of Pharmacy and Health Sciences, Vermont Campus, 261 Mountain View
Drive, Colchester, Vermont 05446, United States
*
S Supporting Information
ABSTRACT: Treatment of ionization and tautomerism of ligands and receptors is one of the unresolved issues in structure-
based prediction of binding affinities. Our solution utilizes the thermodynamic master equation, expressing the experimentally
observed association constant as the sum of products, each valid for a specific ligand−receptor species pair, consisting of the
association microconstant and the fractions of the involved ligand and receptor species. The microconstants are characterized by
structure-based simulations, which are run for individual species pairs. Here we incorporated the multispecies approach into the
QM/MM linear response method and used it for structural correlation of published inhibition data on mitogen-activated protein
kinase (MAPK)-activated protein kinase (MK2) by 66 benzothiophene and pyrrolopyridine analogues, forming up to five
tautomers and seven ionization species under experimental conditions. Extensive cross-validation showed that the resulting
models were stable and predictive. Inclusion of all tautomers and ionization ligand species was essential: the explained variance
increased to 90% from 66% for the single-species model.
■INTRODUCTION
Quantitative prediction of binding affinities of ligands
interacting with target macromolecules is one of the most
important tasks for lead optimization and other procedures in
computational medicinal chemistry. The majority of approved
drugs and drug candidates contain tautomerism-prone
heteroaromatic ring systems and heteroatom-rich substruc-
tures1as well as one or more ionizing groups.2,3Components
of the receptor binding sites, e.g., several amino acid residues,4,5
cofactors (porphyrin,6,7NAD+, biotin,3and others), and
nucleobases,8−10are also prone to ionization and tautomerism
under physiological conditions.
Structural differences of tautomer and ionization species
lead to varying interactions with the binding site and cause
the dependence of overall affinity on several factors. In
addition to pH and temperature, the influence of medium
polarity on tautomer and ionization equilibria plays a role
because the interactions with the receptors may happen in
an aqueous medium (blood/plasma, extra- and intracellular
fluids) or in a nonpolar medium such as the bilayer core of
the cell membrane.
The time scale of establishing the tautomeric equilibria
depends on the nature of broken and created bonds. Tautomers
that interchange by CH bond cleavage and formation can often
be isolated because their half-lives (t1/2) are measured in hours
thanks to high (>30 kcal/mol) interconversion energy barriers.
The CH-to-NH, −OH, and −SH tautomer conversions have
much lower energy barriers (∼20 kcal/mol), and their t1/2
values are in the range of a second.11For the NH-to-OH and
OH-to-OH tautomerism, the rates are much faster: some keto−
enol tautomers convert on the picosecond time scale.12The fast
tautomer interconversions of the last two categories facilitate
the description of the ligand binding to macromolecules
because the tautomer fractions remain constant during the
binding process and are fully characterized by the equilibrium
Received:
Published: January 26, 2012
September 13, 2011
Article
pubs.acs.org/jmc
© 2012 American Chemical Society
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constants, eliminating the need to consider the kinetics of the
tautomer interconversion process.
Treatment of tautomers presents a challenge to methods
utilizing classical force fields because of the increased
occurrence of less common structures for which the parameters
may not be readily available. Along with this nuisance, the static
charge distribution and other approximations of the classical
force fields contribute to the increasing use of combined
quantum mechanics/molecular mechanics (QM/MM) meth-
ods in structure-based binding affinity predictions.13,14The
application of hybrid QM/MM methods alleviates several
issues in the classical description of biomolecular interactions
and enables their more accurate description at a reasonable
computational expense.15We previously reported an extension
of the linear response method16−22by using the QM/MM
energies of the time-averaged structures after MD simulation.23,24
The QM/MM-LR approach was successfully applied to
the affinity prediction of inhibitor binding to Zn-dependent
matrix metalloproteinases (MMPs).12The model was capable
of distinguishing subtle differences in the binding sites of two
related MMPs and provide intricate selectivity clues25by
including the phenomena that are out of reach of classical force
fields, such as the coordination bonds made by the studied
hydroxamates with the binding-site zinc and the proton transfer
from the hydroxamate OH group to carboxyl of a neighboring
Glu side chain in the binding site.
Treatment of multiple species resulting from ionization and
tautomerism is a challenge for current force-field based
simulations in spite of the progress in the area.26Keeping in
mind the advantages the QM/MM techniques offer for the
treatment of tautomers and polarizing interactions, we wanted
to examine the ability of the QM/MM-LR approach to describe
binding of ligands involving tautomer and ionization equilibria.
Suitable chemotypes, forming several tautomers and ionization
species in aqueous media under physiological conditions, were
found among the inhibitors of mitogen-activated protein kinase
(MAPK)-activated protein kinase 2 (MK2) for the complexes
of which a number of X-ray structures were available.27−31
MK2 has two isoforms, α and β, which are produced by
alternative splicing with 400 and 370 residues, respectively.31
The enzyme consists of N-terminal proline-rich region
(residues 10−44), catalytic region with DFG motif (residues
51−325), and C-terminal autoinhibitory region (residues 328−
364). The C-terminal region includes a nuclear export signal
(NES) of hydrophobic residues 356−368 and a nuclear
localization signal (NLS) containing basic residues 373−389.
The isoform β lacks the NLS region. During the presence of
the inactive complex of p38α and MK2 in the nucleus, the
C-terminal NLS is functional and the NES is masked. Upon stress
stimulation, upstream kinases like MAPK kinase-6 activate p38,
which in turn phosphorylates MK2, unmasking the NES, and
this active complex translocates to cytoplasm. This cascade of
events leads to coexport of activated p38 from the nucleus to
cytoplasm.
MK2 is a serine/threonine protein kinase regulating, by a
post-transcriptional mechanism, biosynthesis of tumor necrosis
factor α (TNFα) that is overproduced in inflammatory diseases
such as rheumatoid arthritis and inflammatory bowel disease.
MK2 knockout mice showed reduced expression of TNFα
when stimulated with lipopolysaccharide (LPS) and were
resistant to developing disease in arthritis models.32Mice that
lack MK2 show increased stress resistance and survive LPS-
induced endotoxic shock,33thanks to significant reduction in
the production of TNFα. Unlike p38 MAP kinase inhibition
leading to several serious side effects,34MK2 knockout mice are
healthy and have a normal phenotype. The role of MK2 and the
benefits of MK2 inhibition in other inflammatory diseases such
as Alzheimer’s disease, atherosclerosis, and cancer are being
actively investigated.35
This study focuses on characterization of structural and
energetic determinants of MK2 inhibitor binding involving
multispecies equilibria using the newly developed MS-QM/
MM-LR approach. The approach is applicable to binding
predictions of speciated small molecules to speciated macro-
molecules, which are of interest in several areas of pure and
applied chemistry.
■RESULTS AND DISCUSSION
The structures, tautomer and species, and inhibitory activities
of the studied 66 MK2 inhibitors,27−29analogues of
benzothiophene (benzothienodiazepinones, except 6 and 7)
and pyrrolopyridine, are summarized in Tables 1 and 2,
respectively. Under physiological conditions, the compounds
exhibit ionization as well as prototropic and annular
tautomerism: they form up to five different skeleton tautomers,
with some tautomers creating two to four ionization species
(Schemes 1 and 2), occasionally thanks to the ionization
centers in the side moieties R (Tables 1 and 2). All potential
tautomers within the two series are formed by the shifts of
hydrogen between C and N, C and O, and N and O (Schemes
1 and 2), so the tautomer equilibria in an aqueous solution are
expected to be established practically instantaneously.
Ligand Fractions of Tautomers and Ionization Species.
Ligand fractions of tautomers and ionization species in aqueous
solution were estimated for water under the conditions of
experiments (pH 7.5 and 30 °C), using the SPARC36online
calculator. The fractions of individual tautomers were calcula-
ted from the relative abundances of all possible tautomers at
equilibrium (Schemes 1 and 2). Ionization estimates were only
performed for tautomers with at least 0.01% fraction to deter-
mine the fraction of each species. The resulting 233 tautomers
(T1−T5) and species (S1−S12) for the studied 66 compounds
were grouped and numbered as shown in Schemes 1 and 2.
The species classification is based on the ionization state
as well as on the parent tautomer. As a result, the species
with the same number may have different overall charges as
indicated in Schemes 1 and 2.
The neutral-zwitterion tautomer pairs T3, T4, and T5 are
mainly present as zwitterions, with the proton transferred from
the hydroxyl in position 1 to N in position 2 or 5 (atom X in
the Table 1 structure). Charged skeletons are produced by
external protonation, as seen in benzothiophene tautomer T2+
(Scheme 1) and pyrrolopyridine tautomer T1+, with the
protonated N in position 5 or in the pyridine ring, respectively.
Further ionizable groups are present in the side chains R1, R3,
R4, or cyclic substructures R3−R4. Combinations of the
skeleton tautomers and the side chains ionized to varying
degrees give rise to altogether 12 species.
While both series are dominated mainly by tautomers T1
and/or T2, the presence of other tautomers cannot be
neglected. The formation of T2 and T5 results in the loss of
planarity of thiophene ring forming a new chiral center, which
exhibited stronger interactions with the MK2 enzyme when set
in S configuration in all MD simulations (described below). An
overview of speciation of individual compounds is given in
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Figure 1, with all details listed in Table S4 in Supporting
Information.
The majority of the benzothiophene analogues (Table 1 and
Figure 1) do not ionize in water under experimental conditions.
For 26 of 35 compounds, species 5/tautomer 2 (S5/T2) is the
predominant species. Compounds 6 and 7 are mainly present
as S1/T1. Only compounds 8−12 and 34 are mostly available
as ionized S6/T2 and S2/T1.
All pyrrolopyridine analogues (Table 2 and Figure 1) are
present mainly as T1 in water. While neutral species S1
dominates (>70%) for compounds 42, 56, and 62 and ionized
species S3 for compounds 36, 37, 40, 41, 46, 55, 64, and 65,
most compounds share preferences for both species 1 and 3.
Compound 65 with carboxyl group substituent is always
ionized and present as both species S2 and S3. Compound 44
also exhibits preference for neutral species S4 in addition to
species S1 and S3.
Protonation States of Ionizable Protein Residues.
Protonation of ionizable residues of MK2 was determined
by pKacalculations using the PROPKA 2.0 web server37at
the experimental conditions (pH 7.5 and 30 °C). This
empirical structure-based approach includes desolvation
effects and intramolecular interactions such as H-bonds
and charge−charge interactions. The pKa values for
ionizable residues in the binding site (Figure 2 below),
Glu139 (pKa= 1.88), Asp142 (2.51), Asp207 (1.24), and
Table 1. Benzothiophene Analogues: Structures, Considered Tautomers and Species, and MK2 Inhibition IC50Values (M).28,29
considered log(1/IC50)
ligand no.XR1R2R3R4tautomersspeciesexpcalcd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
NH
NH
NH
NH
NH
S
CH2
NH
NH
NH
NH
NH
NH
NH
NH
NH
NH
NH
NH
NH
NH
NH
NH
NH
NH
NH
NH
NH
NH
NH
NH
NH
NH
NH
NH
H
CH3
H
(CH2)2CH3
CH2OH
H
H
CH2NH2
CH2NHCH2CH3
CH2NH(CH2)2CH3
CH2NHCH2Pha
CH2N(CH3)CH2Ph
CH3
CH3
CH3
CH3
CH3
CH3
CH3
CH3
CH3
CH3
CH3
CH3
CH3
CH3
CH3
CH3
CH3
CH3
CH3
CH3
CH3
CH2N-Tpe
CH2N-Mof
H
H
CH3
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
OCH3
OCH3
OCH3
OCH3
OCH3
OCH3
OCH3
OCH3
OCH3
OCH3
OCH3
OCH3
OH
OCH2(3-OCH3-Ph)
OCH2-c-C3H5
O(CH2)2OCH3
OCH2CH(CH3)2
OCH2COOCH3
OCH2-c-C6H11
O−CHCHb
O−CH2−CH2
NCH−CHCH
OC(Ph)CH
NC(Cl)-CHCH
NC(Ph)-CHCH
NC(4-Py)-CHCHc
NC(3-Py)-CHCH
NC(5-Pm)-CHCHd
NC(2-OCH3-Ph)-CHCH
NC(3-OCH3-Ph)-CHCH
NC(2-F-Ph)-CHCH
NC(2-CH3-Ph)-CHCH
NC(4-CH3-3-Py)-CHCH
OCH3
OCH3
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
H
T1,T2
T1,T2
T1,T2
T1,T2
T1,T2,T5
T1,T3
T1,T3
T1,T2
T1,T2
T1,T2
T1,T2,T5
T1,T2,T5
T1,T2
T1,T2
T1,T2
T1,T2
T1,T2
T1,T2
T1,T2
T1,T2
T1,T2
T1,T2,T4
T1,T2
T1,T2,T4
T1,T2,T4
T1,T2,T4
T1,T2,T4
T1,T2,T4
T1,T2
T1,T2,T4
T1,T2,T4
T1,T2,T4
T1,T2,T4
T1,T2
T1,T2,T5
S1,S5,S7
S1,S5,S7
S1,S5,S7
S1,S5,S7
S1,S5,S7,S11
S1,S9
S1,S9
S1,S2,S5,S6
S1,S2,S5,S6
S1,S2,S5,S6
S1,S2,S5,S6,S12
S1,S2,S5−S7,S12
S1,S2,S5,S6−S8
S1,S5,S7
S1,S5,S7
S1,S5,S7
S1,S5,S7
S1,S5,S7
S1,S5,S7
S1,S5,S7
S1,S5,S7
S1,S5,S7,S10
S1,S5,S7
S1,S5,S7,S10
S1,S5,S7,S10
S1,S5−S7,S10
S1,S5−S7,S10
S1,S5,S7,S10
S1,S5,S7
S1,S5,S7,S10
S1,S5,S7,S10
S1,S5,S7,S10
S1,S2,S5−S7,S10
S1,S2,S5,S6
S1,S2,S5,S7,S11
6.745
7.398
6.523
5.818
7.854
5.963
6.301
8.301
6.244
6.347
6.244
5.719
6.721
6.959
5.879
6.569
6.398
7.523
5.463
7.796
7.553
9.000
6.824
8.699
8.046
7.854
8.301
7.638
7.699
7.387
7.854
7.523
8.301
<4.699
<4.699
6.739
7.635
6.713
5.486
7.446
5.547
6.281
8.436
6.021
6.621
6.418
6.074
6.944
7.022
5.874
6.769
6.238
7.212
5.593
7.775
7.593
8.549
6.801
8.474
7.758
7.967
7.705
7.356
7.517
7.199
7.705
7.771
8.265
4.879
4.812
H
H
aPh: phenyl.bR3 and R4 join the phenyl ring to form cyclic derivatives in ligands 20−33.cPy: pyridyl.dPm: pyrimidinyl.eTp: tetrahydropyrrolyl.
fMo: morpholinyl.
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Lys93 (12.83), differ from the pH 7.5 of experiment by 5 or
more logarithmic units, indicating that the binding site has
just one ionization state in fraction exceeding 0.01%, with
the side chain carboxyl groups of both Glu and Asp residues
deprotonated and the side chain ε-amino group of Lys93
protonated. No tautomerization is expected for the binding
site residues.
Multispecies Binding Equilibria. Individual ligand (L)
and receptor (R) species present in the solution differ in
structures of some fragments and, consequently, the binding
event results in different structures of the complexes, which
have different association microconstants. For the receptor,
only the ionization or tautomer species of the fragments,
which either directly participate in the binding or are in a
sufficient proximity of the site to affect the binding, will
differ in the association microconstant and need to be
considered.
The mutually exclusive 1:1 binding of multiple ligand species
(total number l) to a macromolecule receptor site species (total
number s) is illustrated in eq 1 for the ith ligand species.
The microconstants Kijcharacterizing affinities of the l × s
ligand−receptor complex species are defined, for the ith ligand
species bound to the jth receptor species, as
=
K
[LR ]
[L ][R ]
i
ij
ij
j
(2)
The association microconstants are the relevant quantities for the
correlation with structure. The measured equilibrium constant K of
the ligand, however, contains the total concentration of the ligand−
receptor complexes without distinguishing between complexes
differing in interacting species. To express K as a function of Kijs, a
series of rearrangements needs to be made, as shown in eq 3: (1)
the total bound concentration, [LR], is given as the sum of con-
centration of the l × s complex species, to get the third term; (2) in
the numerator of the third term, each summand is formally
multiplied by [Li][Rj]/[Li][Rj] and, in this way, the fractions of each
ligand species, fi= [Li]/[L], and each receptor species, fj= [Rj]/[R],
are introduced, as shown in the fourth term; (3) finally, the micro-
constants Kijare incorporated using their definition in eq 2.
∑ ∑
=
i
1
∑ ∑
=
i
1
∑ ∑
=
i
1
==
==
=
==
K
f f
i j
f f K
i j ij
[LR]
[L][R]
l
[LR ]
[L][R]
[LR ]
[L ][R ]
i
l
j
s
ij
j
s
ij
j
l
j
s
1
11
(3)
The fractions fiand fjdepend on the medium and remain constant
as long as the medium is not changing. The test media and intra/
extracellular body fluids are buffered, so the key property, the pH
value, remains invariant and the fractions fiand fjcan usually be
calculated before optimization.
For ligand ionization, the expressions for the fractions fiof the
ith species can be derived from the definition of the ionization
constants. In the case of two or more ionization groups, attention
needs to be paid to the actual macroscopic pKavalues. If the pKa
values are closer than 3−4 units, the ionization tendencies of the
two ionizable groups are not clearly separated, and mixed species
may occur. Consequently, such pKavalues are no longer equal to
the ionization microconstants, and multiple species with the same
charge (e.g., neutral molecules and zwitterions as two species with
the net zero charge, such as the neutral-zwitterion tautomer pairs
Table 2. Pyrrolopyridine Analogues: Structures, Considered
Species,aand MK2 Enzyme Inhibition IC50Values (M)27
log(1/IC50)
ligand
no.R
considered
species expcalcd
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
H
Phb
4-Pyc
3-Py
3-Thd
2-Npe
2-Btf
3-Qg
2-OH-Ph
3-OH-Ph
4-OH-Ph
2-Cl-Ph
3-Cl-Ph
4-Cl-Ph
2-F-Ph
3-F-Ph
4-F-Ph
4-CF3-Ph
4-COCH3-Ph
4-OCH3-Ph
4-NH2-Ph
4-[CONH-c-C5H9]-Ph
4-[CONH-c-C6H11]-Ph
4-[CONHCH2Ph]-Ph
4-[CONH(CH2)2Ph]-Ph
4-[CONH(CH3)CH2Ph]-Ph
Cl
5-Pmh
4-CN-Ph
4-COOH-Ph
4-[CONH-c-C3H5]-Ph
aAll compounds present as tautomers T1 and T2.bPhenyl.cPyridinyl.
dThienyl.eNaphthyl (marked as 3-Np in the original publication).
fBenzothienyl.gQuinolinyl.hPyrimidinyl.
S1,S3,S5
S1,S3,S5
S1,S2,S3,S5
S1,S3,S5
S1,S3,S5
S1,S3,S5
S1,S3,S5
S1,S3,S5
S1,S2,S3−S5
S1,S2,S3−S5
S1,S2,S3−S5
S1,S3,S5
S1,S3,S5
S1,S3,S5
S1,S3,S5
S1,S3,S5
S1,S3,S5
S1,S3,S5
S1,S3,S5
S1,S3,S5
S1,S3,S5
S1,S3,S5
S1,S3,S5
S1,S3,S5
S1,S3,S5
S1,S3,S5
S1,S3,S5
S1,S3,S5
S1,S3,S5
S2,S3,S5
S1,S3,S5
6.767
7.180
7.252
7.319
7.119
7.284
7.523
8.071
6.387
7.602
7.678
6.218
7.432
7.495
6.900
7.523
7.301
7.149
7.292
7.260
7.387
8.097
7.770
8.097
7.337
7.252
6.216
7.081
7.208
7.658
7.824
6.561
7.520
7.339
7.486
7.550
6.934
7.347
7.994
6.920
7.829
7.669
6.430
7.747
7.568
7.165
7.659
7.422
7.181
7.228
7.205
7.340
8.153
7.662
7.786
7.508
7.309
6.270
7.115
7.218
7.683
7.816
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T3−T5 in Scheme 1) can be present for a given pH value of the
medium.
For the receptors, the equilibria can be affected by through-space
proximity to other groups. Specialized techniques have been
developed to estimate the ionization constants of individual
ionizable groups37−47and the equilibrium constants for tautomers.5
The prevalence of the ith bound species for a ligand can be cal-
culated using the sum of all its complexes with the receptor species:
∑
=
j
∑
=
j
∑
=
j
∑
=
j
∑ ∑
=
i
1
=
××
××
=∼
=
K
K
f f K
i j ij
K
f f K
i j ij
f f K
i j ij
[LR ]
[LR]
[L ] [R ]
[L] [R]
s
ij
s
ijii
s
s
l
j
s
11
11
1
(4)
The numerator and denominator come from eqs 2 and 3,
respectively. The third quasi-equality in eq 4 ensures that the sum
of prevalences for all l species of a ligand equals unity. The
prevalence of the receptor species for the given ligand is calculated
in a similar way; just the summations in the numerators of eq 4
would run through all l ligand species instead of the s receptor
species.
Multispecies Inhibitor Studies. In the single-species QM/
MM-LR approach, a linear combination of the QM/MM energy
term and the solvent-accessible surface area (SASA) term is
correlated with enzyme inhibition potency, which may be given as
log(1/IC50) values, where IC50is the concentration of an inhibitor
that decreases the rate of an enzyme-catalyzed reaction by 50%.
For reversible and competitive enzyme inhibition, the direct
proportionality between IC50and ligand−receptor association
constant is given by the Cheng−Prusoff equation.48For the
multispecies correlation, which uses the total association constant
K as the dependent variable, each of the species microconstants Kij
in eq 3 needs to be expressed as an exponential with the exponent
equal to the linear combination of the ligand−protein QM/MM
interaction energy term and the SASA term and eq 3 needs to be
logarithmized. Assuming that the regression coefficients α, γ, and κ
maintain the same values for each pair of ligand and receptor
species, the inclusion of multiple species does not increase the
number of the regression coefficients. The MS-QM/MM-LR
correlation equation is then
∑ ∑
=
i
1
=
=
αΔ⟨
(
⟩ +γΔ⟨
ij
⟩ +κ
ij
⎛
⎝
⎜
⎞
⎠
⎟
IC
f f
i j
log
1
log 10
l
j
s
E
50
1
SASA)/2.303
QM/MM
(5)
Scheme 1. Tautomer and Ionization Equilibria of Studied Benzothiophene Analogues (Table 1)a
aSpecies, present in at least 0.01% fraction under experimental conditions, are labeled S1−S12 and their parent tautomers are labeled as T1−T5. The
neutral forms of the T3, T4, and T5 neutral-zwitterion pairs, marked with a prime, are present in negligible fractions and are listed only as
intermediates in the formation of more abundant zwitterionic counterparts. The protonated form of T2 is shown as T2+. The charges of substituent
groups are given in brackets. The R3−R4 substituent group for S2/T1 and S6/T2 refers to the cyclic derivatives (20−33, Table 1). Species S3 and
S4 are not present in this series.
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The term ⟨ΔEQM/MM⟩ denotes the binding energy defined as the
difference between the QM/MM energies of the complex and
those of the unbound interaction partners, all calculated for the
time-averaged structures from MD simulations. The change in
SASA upon binding is defined analogously. The QM/MM term in
eq 5 replaced the van der Waals and electrostatic energy terms in
the classical LR approach, which were scaled by coefficients α and
β, respectively.16−22
The QM/MM and SASA terms of 233 tautomers and species
of 66 ligands complexed with MK2 enzyme were calculated
using a slight modification of our previously published four-tier
approach.23,24The protocol consists of (1) flexible docking
with the poses selected based on the docking score, (2) QM/
MM geometry optimization of the docked complexes, (3) MD
simulation of the geometry optimized structures, solvated with
explicit TIP3P water molecules,55and (4) for the time-averaged
structures from step 3, calculation of the single-point QM/MM
interaction energy term and the SASA term. The two terms
were correlated with the enzyme inhibition potencies using eq 5.
Computational Protocol. In step 1 (docking), the studied
inhibitors were docked into the recently published30X-ray
structure of MK2 in active conformation, bound to a 3-
aminopyrazole derivative (PDB code 3KGA30) with 2.55 Å
resolution, taken from the Protein Data Bank.49MK2 features
the β-sheet dominated N-terminal lobe and a purely α-helical
C-terminal lobe, enclosing the ATP-binding site for which the
inhibitors compete. The binding site (Figure 2) is lined by
conserved glycine-rich motif GXGXXG of residues 71−76 and
the hinge region, extending from the gate-keeper residue
Met138 and including residues 139−142. The interaction of
conserved Lys93 with the conserved Glu104 from helix C is
considered a hallmark of the active conformation of protein
kinases.50Docking was performed using the FlexiDock module
from SYBYL-X. Two available X-ray structures, one for each
series (for ligands 3329and 5027), were used to guide
prepositioning of studied compounds. In addition to ligands,
rotatable bonds of side chains of binding site residues were
allowed to move during the docking procedure. This step was
critical in selecting the optimal binding mode with the highest
FlexiDock score, especially for more flexible compounds. The
deviations between the optimal binding modes (poses) of
individual tautomers/species of the same compounds were
significant. The respective heavy-atom rmsd values varied
between 0.55 and 10.95 Å and are summarized in Table S1 in
Supporting Information. To speed up the QM/MM con-
vergence, docked complexes were briefly minimized using
Generalized Born implicit solvent method in Amber 10.
In step 2 (QM/MM optimization), geometries of minimized
protein−ligand complexes were further optimized by the
ONIOM method,51,52a hybrid QM/MM method available in
Gaussian 09.53Ligands and key binding site components
(backbone atoms of Cys140, Leu141, and Asp142; whole
residues of Thr206 and Asp207) were included in the QM
region, and the rest of the system was defined as the MM
region. The ligand and binding site residues were allowed to
move during geometry optimization, and the rest of the system
was frozen (Figure 2). No tautomer/species conversion was
observed during the geometry optimization procedure.
In step 3 (MD simulations), the entire hydrated and
neutralized QM/MM-optimized complexes were heated and
subjected to 1 ns MD simulation in Amber 10. Analysis of the
trajectories for energy terms (potential, kinetic, and total
energies), density, volume, temperature, pressure, and rmsd
values of Cαatoms revealed that that the secondary and tertiary
structure of the ligand−receptor complex remained stable
throughout the simulation and all studied ligand−receptor
complexes attained equilibrium within the 1 ns simulation
period. For several compounds, 20 ns MD simulations were
run, showing no major differences to the 1 ns trajectories.
The MD trajectories were also analyzed to track critical
intermolecular interactions, including water bridges that were
involved in ligand binding (see below). For all complexes, the
conserved Lys93 was found to be interacting with a conserved
Scheme 2. Tautomer and Ionization Equilibria of the
Studied Pyrrolopyridine Analogues (Table 2), Showing the
Species Present in at Least 0.01% Fraction under
Experimental Conditionsa
aTautomers are labeled as T1−T2 and species are labeled S1−S5.
Protonated form of T1 is shown as T1+. The charges of substituent
groups are given in brackets. Species S1, S2, and S5 are similar to those
in Scheme 1 in the approximate position of charged atoms with regard
to the CO-NH in T1. Species S6−S12 are not present in this series.
Figure 1. Species fractions of the studied benzothiophenes (1−35,
Table 1) and pyrrolopyridines (36−66, Table 2) in water under
experimental conditions: species S1/T1, S2/T1, S3/T1, S4/T1, S5/
T2, and S6/T2 (Schemes 1 and 2) are shown in black, red, blue,
yellow, gray, and green, respectively. Only major species (>10% in at
least one compound) are shown.
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Glu104 from helix C, indicating the active conformation of
protein kinases.50The time-averaged structures from the final
900 ps of MD simulations were calculated using the ptraj
program in AmberTools,54keeping only 100 water molecules
closest to the ligands and stripping off the remaining water
molecules and counterions used for neutralizing the system.
The resulting structures were briefly minimized to remove
unnatural features.
Step 4 (single-point QM/MM energy and SASA calcu-
lations) was performed for time-averaged structures resulting
from step 3. The QM region consisted of ligands, binding site
residues, and water molecule for complexes, in the cases when
water-mediated interactions were observed. The heavy-atom
rmsd values between the time-averaged structures of individual
tautomers/species varied between 1.30 and 11.73 Å and are
summarized in Table S2 in Supporting Information.
For steps 1−4, the computations for each ligand−receptor
species took on average 15−20 min, 40−60 h, 200−240 h, and
12−24 h of single-processor time, respectively. For the 233
complexes, plus free interaction partners, this study used about
∼130000 h of CPU time.
Multispecies 3D-QSAR Correlation. The MK2 enzyme
inhibition potencies, given as logarithmized 1/IC50values, were
correlated with the QM/MM interaction terms and the SASA
terms, both calculated in step 4. The nonlinear regression
model (eq 5), with s = 1 as only one ionization/tautomer state
was predicted for the binding site, was optimized using the
Solver55software.
Contributions of individual steps of the calculation protocol
to the correlation were examined, and the results are
summarized in Table 3. The use of the FlexiDock scores in
place of the QM/MM energies in eq 5 resulted in no
correlation (r2= 0.002). The QM/MM energies and SASA,
calculated from the geometry optimization step 2, gave r2=
0.202. The use of van der Waals and electrostatic energy terms
from the MD simulation in the MS-LR fashion in eq 5, as
summarized in step 3, slightly improved the correlation (r2=
0.353), but the signs of the coefficients α and β for the van der
Waals and electrostatic energy terms, respectively, were
incorrect. The full MS-QM/MM-LR treatment (eq 5) was
necessary to achieve an agreement between experimental and
calculated inhibitory activities, with the correct signs of the
optimized coefficients α and γ (r2= 0.906).
An analysis of the importance of tautomeric and ionization
species for the correlation is summarized in Table 4. The
simplest model (row 1) uses only the QM/MM and SASA
terms for tautomer 1 (no ionization species are included), in
eq 5, which thus became a standard, one-mode QM/MM-LR
Figure 2. Time-averaged structure of MK2 with bound compound 8 (Table 1) used in step 4. The protein is shown as Cα-trace with the binding site
indicated as transparent surface of the binding site residues Leu70, Gly71, Leu72, Gly73, Ile74, Ala91, Leu92, Lys93, Met138, Glu139, Cys140,
Leu141, Asp142, Asn191, Leu192, Leu193, Thr206, and Asp207. The key residues and the ligand, shown in atom-type-colored ball and stick
representation and the other binding site residues represented as red sticks are included in QM and optimized MM regions of the QM/MM
geometry optimization, respectively (step 2). The ligand and all binding site residues were included in the flexible region in FlexiDock docking (step 1)
and the QM-region in the single-point QM/MM energy calculation (step 4).
Table 3. Linear Response Correlations Using eq 5 for Individual Steps: Optimized Coefficients and Descriptive Statistical
Indicesa
step
α × 10−3(mol/kcal)b
0.0878 ± 0.115
−0.3868 ± 21.25
10.47 ± 54.29
−1.326 ± 0.591
β × 10−4(mol/kcal)c
γ × 10−3(1/Å2)d
κ
r2
RMSE
1. docking
2. QM/MM optimization
3. MD simulation
4. single-point QM/MM
aThe squared correlation coefficient (r2) and the root-mean-square error (RMSE).bScales the QM/MM energy term (eq 5) in rows 2 and 4, and
the vdW energy term in row 3.cScales the electrostatic energy term.dScales the SASA term.
7.292 ± 0.238
4.107 ± 27.44
5.417 ± 38.22
1.160 ± 0.358
0.002
0.202
0.353
0.906
0.741
0.663
0.597
0.228
−7.354 ± 46.51
−11.61 ± 86.56
−7.761 ± 2.261
5.709 ± 62.32
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equation and exhibits a comparatively weak correlation (r2=
0.662). The addition of ionization species for tautomer 1 (row 2)
lead to a better performance (r2= 0.734). The consideration of all
tautomers and no ionization species (row 3) brought further
improvement (r2= 0.839). The model including all tautomers
and all ionization species (row 4) provided the best results (r2=
0.906). The comparison of experimental and calculated inhibitory
potencies for different species compositions (Table 4), shown in
Figure 3, also confirms the need for inclusion of all tautomers and
ionization species.
The agreement between the model and experiment is very
satisfactory, especially for the complete setup (row 4). The
LOO-cross-validation and, more importantly, the rigorous MC-
LGO cross-validation confirmed that all models are stable,
exhibit no overfitting, and have adequate predictive power
because the values of the predictive indices RMSE and q2are
similar to those of the descriptive indices RMSE and r2.
Interestingly, the optimized values of the coefficients α and γ
did not vary significantly with the addition of species, which
is also a sign of extraordinary stability of the models. As
an additional model validation attempt, we performed the
correlations with swapped values of the fractions fiof individual
tautomers/species in water. The numbers of tautomers/species
differ but are equal to three for most compounds (Tables 1 and 2).
Therefore, we swapped the fivalues in the correlation eq 5 for
the first three species (the fivalues for compounds 6 and 7,
having only two species, were always exchanged). The
descriptive abilities of the correlations using eq 5 were
characterized by r2= 0.906 for the correct (1−2−3) order of
the fivalues, r2= 0.831 for the order 3−2−1 (the fivalues for
species 1 and 3 were exchanged), r2= 0.807 for the 2−1−3
order, r2= 0.629 for the 1−3−2 order, r2= 0.702 for the 2−3−1
order, and 0.673 for the 3−2−1 order. The swapping of some fi
values, although providing only limited randomization, clearly leads
to deterioration of the correlation.
For compounds 34 and 35 with semiquantitative enzyme
inhibitory potencies (Table 1), which were excluded from the
model fitting process, predictions were made using all models.
Activities of both compounds were predicted to be very close to
the observed limits using the complete model (Table 4, line 4)
although not below them (Figure 3).
Contribution of the QM/MM energy to the correlation with
inhibitory potency is greater than that of the SASA term.
Coefficient γ associated with SASA term is approximately 7
times larger than that of the QM/MM term; however, when the
overall contributions, i.e., the products of coefficients and the
variables, are considered, the values of the QM/MM
contributions are ∼10 times larger than those of the SASA
term. There is no cross-correlation between the QM/MM
energy and SASA terms (r2= −0.334).
Standard deviations of inhibitory activities (IC50) were only
published for the series 2 compounds (36−66, Table 2) and are
listed in Table S3 in Supporting Information. No information
was published about the error distribution, so a detailed
comparison of model performance with respect to experimental
errors cannot be made. However, we can use the standard
deviations as the estimates of experimental variance. On the
logarithmic scale, which was used in the correlation, the average
error interval for log(1/IC50) is 0.220 (Supporting Information
Table S3), with the average log(1/IC50) located asymmetrically
in this interval. The average error interval, which includes both
positive and negative deviations, is smaller than RMSE = 0.228
for the best prediction (step 4 in Table 3), indicating that the
complexity of the model is adequate: the model is not overly
detailed and does not describe experimental errors.
Critical MK2−Inhibitor Interactions. Important inter-
actions between MK2 and inhibitors were tracked by the analysis
of MD trajectories of 233 complexes from the production phase.
H-Bond Interactions. H-bond interactions, both at the
catalytic end (Asp207 and Lys93) and in the hinge region
(Leu141), shown in Figure 2, are typical for all compounds
exhibiting higher inhibitory potencies. The amino and carbonyl
groups in the lactam ring make H-bond interactions with
catalytic Asp/Lys pairs and methoxy oxygen (Table 1) or
pyridinyl nitrogen (Table 2) groups interact with the backbone
NH group of Leu141 in hinge region (Figure 4). The mag-
nitudes of the average bond lengths (<2 Å) and angles (>150°,
i.e., not deviating much from the ideal 180° magnitude)
Table 4. The MS-QM/MM-LR Correlations (eq 5) for Different Tautomer and Species Composition: Optimized Coefficients
and Statistical Indices
LOOc
MC-LGOd
tautomer species
α × 10−3(mol/kcal)a
−1.212 ± 1.289
−1.291 ± 1.026
−1.338 ± 0.801
−1.326 ± 0.591
γ × 10−3(1/Å2)b
−8.985 ± 8.451
−8.664 ± 7.211
−8.334 ± 5.002
−7.761 ± 2.261
κ
r2
RMSEq2
RMSEq2
RMSE
single
single
multi
multi
single
multi
single
multi
1.151 ± 0.896
1.000 ± 0.758
0.960 ± 0.507
1.160 ± 0.358
0.662
0.734
0.839
0.906
0.431
0.382
0.297
0.228
0.617
0.690
0.819
0.897
0.458
0.412
0.315
0.237
0.616
0.691
0.819
0.894
0.459
0.411
0.315
0.241
aScales the QM/MM energy term.bScales the SASA term.cLeave-one-out cross-validation: the results reported as RMSE and the squared predictive
correlation coefficient (q2).dMonte Carlo leave-group-out cross-validation: the compounds were randomly divided into 7 groups, and each group of
∼9 compounds was used once as a test set; the procedure was repeated 10 times.
Figure 3. Correlations between experimental and calculated MK2
inhibition potencies for studied compounds are shown for all models:
single-tautomer, single-species (green squares), single-tautomer, multi-
species (red triangles), multitautomer, single-species (blue diamonds),
and multitautomer, multispecies (black circles).
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indicate strong H-bond interactions that seem to be critical for
enzyme inhibition.
Water-Mediated Interactions. Water-mediated interactions
were observed in many studied ligands bound to MK2. The
protonated N atom in position X (Table 1) of the lactam ring
in tautomers T2 and/or T4 of compounds 23 and 33 makes
the water-mediated H-bond interaction with the backbone
carbonyl oxygen of Glu190. Additionally, in compound 33, a
water molecule creates an H-bonded bridge between the
protonated N atom of pyridine ring and the carboxyl group of
the Asp142 side chain in the hinge region. Water-mediated
interactions were also observed for tautomer T2 of compounds
5, 8, 9, 11, 17, and 19, as well as for tautomers T2 and T4 of
compounds 25, 26, and 29 (Table 1), in which the protonated
N atom in the X position of the lactam ring forms H-bond
interaction with the backbone carbonyl oxygen of Leu70 in the
glycine-rich loop through a water molecule. Pyrrolopyridines
(Table 2) binding as S3/T1 possess protonated pyridine ring,
which interacts either directly with Glu139 backbone carbonyl
oxygen (compounds 40, 41, 43−46, 49, 52, and 57) or with
Leu141 backbone NH group through a water-mediated
interaction (compounds 36, 38, 39, 56, 58, 59, and 60).
Hydrophobic Interactions. Hydrophobic interactions with
the hinge region (Figure 2) are formed by several substructures
in the part of the molecules that is opposite to the lactam ring.
In benzothiophene series (Table 1), the rings attached in
positions R3−R4make compounds 22−33 relatively potent
inhibitors. On the contrary, the absence of the aryl ring
attached to the pyridyl ring decreases the potency of
pyrrolopyridines 36 and 62 (Table 2). However, the structural
dependence is more complex: substituents in the o-position of
the aryl ring attached to the pyridyl ring negatively affect
potency for pyrrolopyridines 44, 47, and 49 (Table 2). The side
chain atoms of residue Leu70 (glycine-rich loop, Figure 2) and
Leu193 make hydrophobic interactions from both sides of the
binding site with many compounds having aryl ring substituents
positioned near the hinge region of the MK2 enzyme.
Prevalences of Bound Tautomers and Species. The
prevalences, as predicted by eq 4 with the Kijvalues calculated
from eq 5 with optimized coefficients (last lines of Tables 3
or 4), are summarized in Tables S4 and S5 in Supporting
Information. The majority (28 of 35) of benzothiophenes (1−5,
12−33, and 35; Table 1) preferably bind as neutral species 5/
tautomer 2 (Scheme 1). Several benzothiophenes (1−4, 12−14,
17, 18, 24, 29, 30, and 35) also exhibit some fraction of bound
S1/T1 species. Only compounds 6 and 7 bind exclusively as
S1/T1 (Scheme 1). Compounds 8−10 and 34 prefer to bind as
protonated S6/T2 thanks to the presence of amino substituent
in the lactam ring. Compound 11 shows almost equal binding
preferences for S2/T1, S5/T2, and S6/T2. Compounds 9 and 11
bind (∼10%) in S2/T1 form as well.
Pyrrolopyridine analogues (Table 2, Scheme 2) mostly bind
in neutral form as S1/T1 (42, 50, 60, and 62) or protonized as
S3/T1 (36, 41, 43, 45, 46, 49, 54−57, 64, and 65) or both
(37−40, 43, 47, 48, 51−53, 58, 59, 61, 63, and 66).
Compound 44 binds as both S3/T1 and S4/T1 species, the
latter having the phenyl ring hydroxyl group in the
deprotonated form. The predominantly bound tautomer 1
has the planar lactam ring attached to aromatic pyrrole ring. All
compounds from this series make favorable interactions with
the Asp/Lys catalytic pair, which may explain their com-
paratively narrow potency range.
The fractions of free vs bound tautomers and species for all
studied inhibitors are plotted in Figure 5. For the majority of
the compounds, the prevalent species in solution are the bound
species as well. Nevertheless, the correlation is rather loose and
there are several noticeable differences between binding
prevalences and the fractions in solution.
For example, compounds 11 and 12 (Table 1, Scheme 1) are
mainly present as protonated species S6 in solution (>60%) but
they are bound as nonionized species S5. Compound 13 binds
to a significant extent as species S1, S5, and S6, although only
species S5 is prevalent in solution. Compound 50 (Table 2,
Scheme 2) is present as both species S1 and S3 in solution
but shows a higher preference for species S1 while binding.
Figure 4. Interaction map of compound 8 (Table 1) as species 5/
tautomer 2 (Scheme 1) in the binding site of MK2. Nitrogen, oxygen,
and sulfur atoms are printed in blue, red, and yellow colors,
respectively. H-Bond interactions are drawn in red color, and the
bond lengths and bond angles (in italics) for the time-averaged
structure are shown.
Figure 5. A comparison of predicted bound species prevalences and
species fractions in the aqueous solution for studied ligands (the
numbers correspond to the compound numbers in Tables 1 and 2).
Species 1−6 (Schemes 1 and 2) are referred to by black, cyan, red,
magenta, blue, and green colors, respectively. Only major species
(at least one compound with >10%) are shown here.
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The situation is opposite for compounds 43, 56, and 57, which
prefer to bind as species S3, even though they have about equal
concentrations of species S1 and S3 in solution.
■CONCLUSIONS
The QM/MM linear response method was extended for
multiple ligand and receptor species, resulting from tautomer-
ism and ionization, using rigorous thermodynamic principles, to
fill a gap in the armory of the computational methods for
structure-based, ensemble-utilizing, and empirically calibrated
prediction of binding affinities. Ionization and tautomerism, as
widespread phenomena among the drug-like molecules, need to
be included in the methods for prediction of binding affinities
to bring the models closer to reality. Our approach is based on
performing the simulations individually for each species pair
and combining the results of these simulations in a correlation
equation that is calibrated using the experimental data.
Interestingly, inclusion of multiple species does not increase
the number of optimized coefficients, at least in the studied
case: the same regression coefficients could be used for all
species without a detrimental impact on the quality of the fit.
The developed MS-QM/MM LR method was applied to a set
of 66 MK2 inhibitors forming up to five tautomers and seven
ionization species under the experimental conditions. The
treatment of multiple ligand species significantly improved the
correlation between experimental inhibition potencies and the
QM/MM interaction energy and SASA terms for the time-
averaged structures from MD simulations. The structural and
energetic information obtained from the time-averaged
structures highlights the critical interactions necessary for
optimal enzyme inhibition. Strength of the H-bond interactions
of ligands with the backbone amide group of Leu141 in the
hinge region, and carboxyl and ε-amino groups in the side
chains of catalytic Asp207 and Lys93, respectively, significantly
affect the inhibition potency. Additional interactions with
backbone carbonyl group of Glu190 and glycine-rich loop made
by some potent inhibitors represent hints for further improve-
ments in potency. If future studies, preferably dealing with the
experimentally observed prevalences of bound species, confirm
the utility of the MS-QM/MM-LR approach, it may become a
welcome addition to the methods for lead optimization in drug
design.
■EXPERIMENTAL SECTION
Data Set. The MK2 inhibitory activities for 66 benzothiophene and
pyrrolopyridine derivatives were reported by Anderson et al.27−29The
X-ray structures of the MK2 complexes with a benzothiophene
analogue 33 (Table 1), a pyrrolopyridine analogue 50 (Table 2), and
an untested 3-aminopyrazole derivative with the PDB49codes 3FYJ,29
2P3G,27and 3KGA,30respectively, facilitated the use of structure-
based techniques. The structure 3FYJ was preferred to 3FYK29
because its ligand is larger and provides more clues for docking
larger benzothiophene analogues. The inhibitors compete with ATP
for binding to MK2. The isoeffective concentrations (IC50) were
determined in the assay, monitoring the amount of HSP-peptide
(KKKALSRQLSVAA) that was phosphorylated by MK2 after 30 min
incubation at pH 7.5 and 30 °C.27−29Experimental errors were only
given for the pyrrolopyridine series27and are listed in Table S3 in
Supporting Information.
Tautomerism and Ionization of Ligands. The enzyme
inhibitory IC50values of the studied ligands were determined at pH
7.5 and 30 °C. The relative abundances of all possible tautomers at
equilibrium were estimated under the conditions of the experiment.
Subsequently, ionization estimates were performed only for tautomer
having the fraction of at least 0.01% to determine the fraction of
individual species as a function of pH. In both cases, the SPARC web
server36was used.
Ionization and Tautomerism of Binding Site Residues. The
protonation states of ionizable protein residues were estimated by pKa
calculations using the PROPKA 2.0 web server37at the experimental
conditions (pH 7.5 and 30 °C). There was one dominant ionization
state of the binding site (Figure 2) predicted, with Glu and Asp side
chain carboxyl groups deprotonated and Lys side chain ε-amino
groups protonated. No tautomerism was expected for the binding site
residues (Figure 2).
Ligand Preparation. Ligand structures were built in Sybyl-X 1.1.56
Geometry optimization and Mulliken charge57calculations were done
in Jaguar58(Schrodinger) using the DFT/B3LYP method59with basis
set 6-31G**.60,61Charge and spin multiplicity were entered manually
for different protonation states.
Protein Preparation. The recently published X-ray structure of
MK2 in active conformation bound to a 3-aminopyrazole derivative
(PDB49code 3KGA30) with 2.55 Å resolution is of better quality than
other available structures (3FYJ,293FYK,292P3G27), as documented
by resolution (2.55 Å vs >3.5 Å) and distribution of φ−ψ dihedrals
(90% vs <70%) in the most favored regions. The 3KGA structure was
modified with Biopolymer structure preparation tool in Sybyl-X:56the
bound ligand and water molecules were removed, heavy atoms missing
from residues were added, and the N-terminal and the C-terminal
residues were capped with N-acetyl and N-methyl amide groups.
Protonation types were set for His (ε-N protonated), Glu (negatively
charged), and Lys (positively charged) residues. Hydrogen atoms were
added, and charges were loaded using the AMBER 7 FF0262force
field. The resulting structure was briefly (100 steps) minimized only
for H-atoms by the Powell conjugate gradient method in Sybyl-X.56
Flexible Protein−Ligand Docking. Docking of ligands into MK2
binding site was performed using the FlexiDock program in Sybyl-X.56
FlexiDock deploys a genetic alghorithm-based optimization in
torsional space for both the protein binding site (rotatable bonds in
side chains) and the ligand. The fitness function uses a subset of the
Tripos force field: the van der Waals, electrostatic, torsional, and
constraint energy terms. The number of rotatable bonds in the binding
site residues (see Figure 2) of the protein was up to 57 and that of
ligands ranged from 1 to 8. The number of generations used varied
from 35000 to 60000 depending upon the total number of rotatable
bonds, satisfying the rule-of-thumb recommendation of 500−1000
generations per each rotatable bond in protein and ligand plus six. The
site-point scoring feature was activated in FlexiDock by defining the H-
bond interaction partners in the protein and ligands: the backbone NH
of Leu141 in the hinge region of MK2 as H-bond donor, and, as
acceptor, the first atom, if it was O or N, in substituent R3of
benzothiophenes (Table 1) or the N atom in the pyridyl ring of
pyrrolopyridines (Table 2). The available X-ray structures for the data
set series, with the PDB49codes 3FYJ (compound 33 In Table 1
below)29and 2P3G (compound 50 in Table 2 below),27were used to
aid the prepositioning of benzothiophene and pyrrolopyridine
analogues, respectively, in the binding site prior to docking. The
protein parts of these structures were aligned with that of the 3KGA
structure by homology in Biopolymer module in Sybyl-X. The
remaining ligands from both the series were superimposed using the fit
atoms method. The best structures were chosen based on the
FlexiDock scores and visual inspection. As tautomers with nonplanar
lactam ring for most benzothiophenes (Table 1) did not always make
catalytic end H-bond interactions, the H-bond distances to catalytic
residues Asp207 and Lys93 were not considered mandatory for the
pose selection.
The difference in the docked poses (conformations) of tautomers/
species of individual compounds were reported as the heavy-atom
rmsd values (Table S1 in Supporting Information), calculated using
Chimera.63For all compounds, species 1 served as reference structure
except for compound 65, where species 2 was used for this purpose.
The same approach was used to calculate the heavy-atom rmsd values
for the time-averaged ligand geometries of tautomers/species after MD
simulation (steps 3/4), which are summarized in Table S2 in
Supporting Information.
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QM/MM Geometry Optimization. Docked protein−ligand
complexes were briefly (1000 steps) optimized to remove any bad
contacts by modified Generalized Born implicit solvent model in
Amber 10. Optimization started with steepest descent method to
quickly remove the largest strains and was switched after 100 cycles to
the conjugate gradient method with root-mean-square of 1 cal/(mol × Å)
as the convergence criterion for fine optimization close to the energy
minimum. The nonbonded cutoff was set to 16.0 Å. Input files
for ONIOM calculations51,52in Gaussian 0953were prepared in
GaussView 5.0.64Ligands and selected binding site components
(entire residues of Thr206 and Asp207, backbone atoms of Cys140,
Leu141, and Asp142) were included in the QM region, and the rest of
the system was defined as the MM region. The partitioning of the
system in ONIOM was based on published recommendations:65the
QM/MM cuts were always made between the C−C bonds and at least
three bonds away from any potential interacting atoms. In the MM
region, the remaining binding site residues (Figure 2) were allowed to
move during geometry optimization and the rest of the system was
frozen. Charges were manually entered for both QM (along with spin
multiplicity) and MM regions. The defined QM and MM regions were
treated with DFT/B3LYP functional59using 6-31G(d,p) basis set60,61
and Amber ff99SB force field,66respectively. Electronic embedding
option was activated during calculations.
MD Simulation. Molecular dynamics simulations were performed
using Amber 10 package67under isothermal/isobaric (NPT)
conditions with Amber ff99SB66and general Amber (GAFF)68force
fields for protein and ligand molecules, respectively.
Structure Preparation. The Leap program from Antechamber tools
(AmberTools 1.4)54was used to prepare the parameter/topology
(prmtop) and input coordinate (inpcrd) files using the Mulliken
charges57from the QM/MM optimization, which were kept for
protein−ligand complexes during MD simulations. For each system,
protein−ligand complexes and unbound ligands were simulated
separately. The unbound protein was simulated only once. The net
charge of the protein−ligand complexes varied from +3 to +6
depending upon the overall charge of the ligand and was neutralized
by adding Cl−ions at positions of high positive electron potential
around the complexes. The system was immersed in a truncated
octahedral box of pre-equilibrated TIP3P69water molecules55in the
way that no atoms in the protein−ligand complexes were closer than
8 Å to any of the sides of the water box. The counterions and solvent
molecules were briefly minimized for 1000 steps to remove any bad
contacts with the complexes, whereby the protein and ligands were
position-restrained using force constant of 500 kcal/(mol × Å2).
Heating, Equilibration, and Production Phases. To allow the
readjustment of solvent molecules to the potential field of the ligand−
receptor complex, the solvent equilibration step was performed in
three stages. During the first heating phase, MD simulation was carried
out for 10 ps, with constant volume periodic boundaries with an initial
temperature of 0 K, allowing to heat up to 100 K. The second heating
phase was performed for 20 ps under constant pressure periodic
boundaries with an average pressure of 1 atm. Isotropic position
scaling was used to maintain the pressure, and a relaxation time of 2 ps
was used. The system was allowed to heat up from the initial
temperature 100 K to the final temperature 300 K. The final solvent
equilibration step was performed for 50 ps under constant pressure
periodic boundary conditions as mentioned above. The Langevin
dynamics was used in all stages to control the temperature using a
collision frequency of 1.0 ps−1. During these three stages, the protein−
ligand complexes were position-restrained with a force constant of
10 kcal/(mol × Å2). The final production phase with the entire system
was carried out under isothermal/isobaric conditions for 1 ns including
100 ps equilibration phase. The SHAKE algorithm70was used to
constrain bonds involving hydrogen, allowing time step of 2 fs, for a
total of 500000 steps. The trajectory file was written for every 100
steps, resulting in 5000 frames. The cutoff was set to 10 Å in all steps.
Tracking H-Bond and Water-Mediated Interactions. MD
trajectory analysis for H-bond and water-mediated interactions for
233 complexes from the production phase was performed using the
ptraj program in AmberTools 1.4.54The default cutoff values for
distance (3.5 Å) and angle (no value) were chosen. The following
pairwise interactions were monitored: (1) the backbone NH of
Leu141 in the hinge region of MK2 as H-bond donor, and, as H-
bond acceptor, the first atom, if it was O or N, in substituent R3of
benzothiophenes (Table 1) or the N atom in the pyridyl ring of
pyrrolopyridines (Table 2), (2) Asp207 carboxyl oxygen atoms and
the NH group of the six- or seven-membered lactam ring of ligands,
and (3) the O atom of the lactam ring and the amino group of the
Lys93 side chain. To keep track of important water-mediated inter-
actions, “solvent” keywords solventneighbor (value = 6), solventdonor
(WAT O), and solventacceptor (WAT O H1 and WAT O H2) were
specified. The solventneighbor value specifies the maximum number of
possible interactions per a given donor or acceptor.
Time-Averaged Structures. Time-averaged structures were calcu-
lated from 4000 trajectory frames (final 900 ps) of the production
phase of the MD simulation using the ptraj program in AmberTools
1.4.54The hundred water molecules closest to the ligands were kept
for all complexes, and the remaining waters and ions were stripped.
The structures were briefly minimized to relieve conflicts, resulting in
structures with close-to-standard bond length and angles and the
dihedrals representing the ensemble.
Single Point QM/MM Energy Calculations. The time-averaged
structures from MD simulations were used for single point QM/MM
energy calculation in ONIOM51,52using DFT/B3LYP functional59
method with 6-31G(d,p) basis set60,61for QM region and Amber
ff99SB66force field for MM region, respectively. Electronic embedding
option was activated during calculations. The QM region consisted of
ligands, binding site residues (Figure 2), and a water molecule for
complexes, where water-mediated interactions were observed. The
binding energy term ⟨ΔEQM/MM⟩ term was calculated as the difference
between the QM/MM energies of the complex and those of the
unbound interaction partners.
SASA Term Calculation. The solvent accessible surface area
(SASA) terms for time-averaged structures of protein−ligand
complexes and unbound interaction partners were calculated using
the program Naccess,71which implements the Lee and Richards
algorithm.72The default values (probe radius 1.4 Å; z-slices 0.05 Å;
van der Waals radii) were used in the calculations. The burial of
solvent accessible surface area upon protein−ligand binding was
calculated as the difference between the SASAs of the complex and
those of unbound interaction partners.
Regression and Cross-Validation. The coefficient values in the
model (eq 5) were optimized by nonlinear regression analysis in the
Premium Solver Platform.55The stability and predictive power of the
model was tested by the Monte Carlo leave-group-out (MC-LGO)
cross-validation. In this test, compounds were randomly divided into
seven groups of about equal size and the model calibration process
involved only compounds from six groups in any given cycle leaving
one of the groups outside as the test set. The resulting model was used
to predict the activities for the omitted test set compounds. This
process was repeated 10 times, reshuffling the compounds in each
group, resulting in 70 models in total. The leave-one-out (LOO) cross-
validation was also performed, omitting and predicting one compound
at a time.
■ASSOCIATED CONTENT
*
Tables (XLS) summarizing the heavy-atom rmsd values
between the docked poses (step 1) and between the time-
averaged poses (steps 3/4) of tautomer/species for individual
compounds, published standard deviations of inhibitory
activities of pyrrolopyridines (Table 2), fractions of free
tautomers/species and prevalences of bound tautomers/species
for benzothiophenes (Table 1) and pyrrolopyridines (Table 2),
respectively. This material is available free of charge via the
Internet at http://pubs.acs.org.
S Supporting Information
Journal of Medicinal Chemistry
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■AUTHOR INFORMATION
Corresponding Author
*Phone: 802-735-2615. E-mail: stefan.balaz@acphs.edu.
Notes
The authors declare no competing financial interest.
■ACKNOWLEDGMENTS
This work was supported in part by the NIH NIGMS grant
R01 GM80508 and by the NSF TG-MCB110017 program
through Teragrid resources provided by NCSA and Pittsburgh
Supercomputing Center. We thank Dr. Mahmoud A. Ibrahim,
School of Chemistry, The University of Manchester, UK, for
providing Amber parameter file for Gaussian 09.
■ABBREVIATIONS USED
LGO, leave-group-out (cross-validation); LOO, leave-one-out
(cross-validation); LPS, lipopolysaccharide; LR, linear response
(method); MAPK, mitogen-activated protein kinase; MC,
Monte Carlo; MD, molecular dynamics; MK2, MAPK-activated
protein kinase 2; MS, multispecies (approach); NES, nuclear
export signal; NLS, nuclear localization signal; ONIOM, our
own N-layered integrated molecular orbital and molecular
mechanics; QM/MM, quantum mechanics/molecular mechan-
ics; QSAR, quantitative structure−activity relationship; rmsd,
root-mean-square deviation; RMSE, root-mean-square error;
SASA, solvent-accessible surface area; t1/2, half-life; TNFα,
tumor necrosis factor α
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