Complex peptide macrocycle optimization: combining NMR restraints with conformational analysis to guide structure-based and ligand-based design

Abstract

Systematic optimization of large macrocyclic peptide ligands is a serious challenge. Here, we describe an approach for lead-optimization using the PD-1/PD-L1 system as a retrospective example of moving from initial lead compound to clinical candidate. We show how conformational restraints can be derived by exploiting NMR data to identify low-energy solution ensembles of a lead compound. Such restraints can be used to focus conformational search for analogs in order to accurately predict bound ligand poses through molecular docking and thereby estimate ligand strain and protein-ligand intermolecular binding energy. We also describe an analogous ligand-based approach that employs molecular similarity optimization to predict bound poses. Both approaches are shown to be effective for prioritizing lead-compound analogs. Surprisingly, relatively small ligand modifications, which may have minimal effects on predicted bound pose or intermolecular interactions, often lead to large changes in estimated strain that have dominating effects on overall binding energy estimates. Effective macrocyclic conformational search is crucial, whether in the context of NMR-based restraints, X-ray ligand refinement, partial torsional restraint for docking/ligand-similarity calculations or agnostic search for nominal global minima. Lead optimization for peptidic macrocycles can be made more productive using a multi-disciplinary approach that combines biophysical data with practical and efficient computational methods.

Full text

Vol.:(0123456789)
Journal of Computer-Aided Molecular Design (2023) 37:519–535
https://doi.org/10.1007/s10822-023-00524-2
ARTICLES
Complex peptide macrocycle optimization: combining NMR
restraints withconformational analysis toguide structure‑based
andligand‑based design
AjayN.Jain1 · AlexanderC.Brueckner2 · ChristineJorge2 · AnnE.Cleves1 · PurnimaKhandelwal2·
JanetCaceresCortes2 · LucianoMueller2
Accepted: 20 July 2023 / Published online: 3 August 2023
© The Author(s) 2023, corrected publication 2024
Abstract
Systematic optimization of large macrocyclic peptide ligands is a serious challenge. Here, we describe an approach for lead-
optimization using the PD-1/PD-L1 system as a retrospective example of moving from initial lead compound to clinical
candidate. We show how conformational restraints can be derived by exploiting NMR data to identify low-energy solution
ensembles of a lead compound. Such restraints can be used to focus conformational search for analogs in order to accurately
predict bound ligand poses through molecular docking and thereby estimate ligand strain and protein-ligand intermolecular
binding energy. We also describe an analogous ligand-based approach that employs molecular similarity optimization to
predict bound poses. Both approaches are shown to be effective for prioritizing lead-compound analogs. Surprisingly,
relatively small ligand modifications, which may have minimal effects on predicted bound pose or intermolecular interactions,
often lead to large changes in estimated strain that have dominating effects on overall binding energy estimates. Effective
macrocyclic conformational search is crucial, whether in the context of NMR-based restraints, X-ray ligand refinement, partial
torsional restraint for docking/ligand-similarity calculations or agnostic search for nominal global minima. Lead optimization
for peptidic macrocycles can be made more productive using a multi-disciplinary approach that combines biophysical data
with practical and efficient computational methods.
Keywords PD-L1· Macrocycle· NMR· ForceGen· Surflex-Dock· eSim· Ligand-strain
Introduction
Affinity-based selection of invitro expressed macrocyclic
peptides using modern mRNA-display technology can iden-
tify relatively potent and selective lead compounds [1]. How-
ever, systematic optimization of large macrocyclic peptide
ligands is a serious challenge. Here, we describe an approach
for optimization of such leads using the PD-1/PD-L1 system
as a retrospective example of moving from initial lead com-
pound to clinical candidate. We show how conformational
restraints can be derived by exploiting NMR data to identify
low-energy solution ensembles of a lead compound.
A PD-L1 lead compound and numerous analogs were dis-
closed in a patent filing that became public in 2016 [2] that
demonstrated both efficacious ligand-binding and blockade
of the interaction between PD-L1 and PD-1. Figure1 (left
column) shows three examples from the initial disclosure in
decreasing order of potency along with three examples from
the lead optimization effort (right column). BMT-174900
(also known as BMS-986189) is currently in human clinical
trials along with a number of other candidates targeting the
PD-1/PD-L1 interface for cancer therapies [3]. In moving
from the initial lead compound to the clinical candidate,
modifications to 6 positions in the macrocyclic peptide were
required. This was accomplished through structure-based
drug design, in an iterative process that required synthe-
sis and evaluation of thousands of compounds. The process
was guided by multiple co-crystal structures of macrocyclic
ligands with PD-L1, but the path to BMT-174900 did not
* Ajay N. Jain
ajain@jainlab.org
* Luciano Mueller
luciano.mueller@bms.com
1 Research andDevelopment, BioPharmics LLC,
SonomaCounty, CA, USA
2 Bristol-Myers Squibb Company, Princeton, NJ, USA
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520 Journal of Computer-Aided Molecular Design (2023) 37:519–535
make extensive use of the types of computational approaches
in common use on smaller non-macrocyclic molecules.
The example structures in Fig.1 exhibit high sensitiv-
ity to minor structural changes. The change from Pep-01 to
Pep-50 involves the deletion of a single methylene at posi-
tion 4, changing a proline into the corresponding azetidine
non-natural amino acid, resulting in a decrease of nearly a
log unit of pIC
50
. Similarly, the change from Phe in Pep-01
to Ala in Pep-05 at position 1 yielded a decrease of nearly
3 log units. As we shall see, these dramatic shifts in activ-
ity can be only partially explained by protein-ligand bind-
ing interactions, with changes in conformational energetics
playing a crucial role. The changes required to move from
lead-compound Pep-01 to clinical candidate BMT-174900
took place at 6 positions and included explorations of both
natural and non-natural amino acids. Systematic exploration
of just five conservative alternatives at each of those 6 posi-
tions would require over 7000 analogs, with such systematic
exploration at all 15 positions requiring over 750,000 ana-
logs. In this study, we analyze the extent to which recently
developed approaches for modeling macrocyclic ligands can
be of use in such lead optimization projects going forward.
Over the past several years, methods for computational
modeling of macrocyclic ligands have made significant pro-
gress [4–9]. In particular, natural-product based and semi-syn-
thetic macrocycles of up to roughly 21–23 total rotatable bonds
Fig. 1 Examples of macrocyclic PD-1/PD-L1 antagonists: three
examples from a patent disclosure with IC
50
values for human PD-L1/
PD-1 binding (left column), measured by homogeneous time resolved
fluorescence (HTRF) and three examples from the subsequent lead
optimization effort (right column) with IC
50
values measuring inhibi-
tion of soluble PD-1 binding to PD-L1 expressed on the surface of
HEK293 cells
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521Journal of Computer-Aided Molecular Design (2023) 37:519–535
(including both macrocyclic bonds and exocyclic bonds) have
been shown to be tractable, in terms of accuracy and speed
of conformational search when utilizing multiple computing-
cores [9]. However, larger peptidic macrocycles remain chal-
lenging, especially in cases where “ladders” of trans-annular
hydrogen bonds do not form stabilizing networks. For com-
parison, the examples shown in Fig.1 each have 60 or more
total rotatable bonds—well beyond the tractable range without
biophysical data to reduce the search space. Recently, we have
shown how distance and dihedral restraints derived from NMR
measurements can be used to elucidate low-energy solution
ensembles for peptidic macrocycles [9–11].
Figure2 illustrates how a preferred macrocycle conforma-
tion can be derived from either NMR-restrained conforma-
tional search [9] or from X-ray crystallography coupled with
careful refinement of the bound macrocycle coordinates [12,
13]. In many cases, obtaining an X-ray co-crystal structure
of sufficient quality can be insurmountable. For heavily-
selected macrocyclic structures (either through evolutionary
pressures for natural products or through screening of very
large libraries), the solution state often reflects a large degree
of pre-organization toward the bound state. From either a
well-fit conformation into X-ray density (green in Fig.2) or a
representative exemplar from a low-energy pool of conform-
ers that satisfy NMR restraints (magenta carbons), a sub-
structure can be used to define a conformational preference.
The substructure (salmon carbons) at the bottom of Fig.2
was extracted from the lowest-energy conformer of the NMR
solution ensemble shown in magenta.
Figure3 illustrates how the conformational preference can
be used to guide conformational search toward predicting the
bound state of new analogs. Adherence to that preference can
be implemented via graph matching of proposed analogs to
the molecular fragment (salmon carbons, top). The subgraph
match between a new analog and the given fragment is used
to instantiate torsional restraints to match the conformation
of the fragment. The restraints are applied through the use of
square-welled quadratic energy penalties that allow for zero
penalty within some tolerance to deviations from the pre-
ferred torsional angle. Structure generation is done with the
given restraints and conformational search is done both with
and without the restraints. For the parts of the molecule that
match the torsional restraint, relatively little conformational
variation occurs. For the unmatched parts of the molecule, a
great deal of variation may be present, subject to the consid-
erations of energetics. The restrained conformer ensemble
Fig. 2 Scheme for deriving a
macrocycle conformational
preference, either from NMR-
restrained conformational
search or from X-ray crystal-
lography
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522 Journal of Computer-Aided Molecular Design (2023) 37:519–535
is used as input to either molecular docking or molecular
similarity calculations to predict the bound pose of the analog
(bottom left and right of Fig.3). The pose optimization that
occurs during docking or similarity-based optimization
results in well-focused bound conformer ensembles, as seen
at the bottom of Fig.3. The unrestrained ensemble is used to
identify the global minimum energy.
Docking, of course, requires at least one example of a
compliant protein conformation (bottom left, Fig.3, in slate
carbons). The structure-based protocol produces an intermo-
lecular energy value in addition to a bound conformational
energy value. The bound conformational energy together
with the global minimum provide an estimate of bound
ligand strain (the parenthetical values in score definitions
at the bottom of Fig.3). For the structure-based score, the
intermolecular energy is added to the bound ligand strain,
resulting in a final estimate for the enthalpic component of
the protein-ligand binding energy. The structure-based pro-
tocol benefits from the ability to identify new interactions
with the protein for well-designed analogs.
For the purely ligand-based protocol, the analog’s
restrained conformer ensemble is aligned to an exemplar
from the NMR-based solution-ensemble of the lead com-
pound (bottom right, Fig.3, magenta carbons), making use of
the eSim methodology [14]. This similarity-based alignment
is used for bound pose prediction, providing an analogous
bound conformational energy value to that obtained in the
structure-based protocol. Note that the nominal similarity
score value may not be of use in compound ranking when
seeking significant increases in potency, which requires
Fig. 3 Scheme for exploiting
a macrocycle conformational
preference to predict a bound
pose, either using docking
(protein structure shown in slate
carbons at bottom left) or ligand
similarity (exemplar conformer
target shown in magenta car-
bons at bottom right). For the
ligand-based score, a constant
value of −24.0 kcal/mol was
added to the estimated strain
energy in order to put the scores
from the two protocols on the
same rough scale
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523Journal of Computer-Aided Molecular Design (2023) 37:519–535
deviation from the lead compound (leading to lower similar-
ity scores). In a design scenario seeking to maintain potency
while diversifying underlying chemical structure, the simi-
larity score values may be of use. But in this work, only the
poses that result from the similarity optimization process are
used instead of using the similarity score.
Surprisingly, relatively small ligand modifications, which
may have minimal effects on the predicted bound pose or
intermolecular interactions, often lead to large changes in
estimated strain that have dominating effects on overall
binding energy estimates. In this work, changes in estimated
ligand strain explain the largest fraction of variation in meas-
ured activity. The importance of the differences in the energy
estimates for bound and solution states places a premium on
effective macrocyclic conformational search. Conformational
search must be thorough and efficient, whether in the context
of NMR-based restraints, X-ray ligand refinement, partial tor-
sional restraints for docking or ligand similarity calculations
or agnostic search for nominal global minima.
In what follows, a large set of analogs of the initial lead
compound are subjected to the retrospective application
of the structure-based and ligand-based workflows just
described. While calculations that make use of a protein
structure provide more information, a purely ligand-based
workflow can be valuable due to the large effects seen from
estimates of bound ligand strain. Lead optimization for
peptidic macrocycles can be made more productive using a
multi-disciplinary approach that combines biophysical data
with practical and efficient computational methods.
Data and methods discussed in this paper are available to
other researchers (see Declarations).
Results anddiscussion
Results for applying two computational workflows for prior-
itizing analogs of lead-compound Pep-01 will be described:
(1) a structure-based method requiring a crystal structure
of PD-L1 in a compliant conformation to bind macrocycles
in this series and (2) a purely ligand-based method. Both
approaches make use of information to partially constrain
the conformational space required to be searched to make
predictions of bound poses. The information can be derived
from experimental NMR data for Pep-01 in its solution
state, a co-crystal structure of Pep-01 bound to PD-L1, or a
structure-based prediction of the bound state of Pep-01 to a
non-cognate protein conformation.
Correspondence ofPep‑01 NMR solution ensemble
toits bound state
The NMR experimental analysis of Pep-01 yielded 50
distance restraints between single proton pairs, 115 distance
restraints where one/both ends contained chemically
equivalent protons and six torsional restraints consisting of
1 omega and 5 psi angles. Very thorough conformational
search was performed using the deep ForceGen approach
[15]. Refer to the Methods and Data for additional details on
the NMR experimental aspects and conformational search
methods.
Figure4A shows the comparison between the PD-L1
bound state of Pep-01 (green carbons) [1, 2] and the closest-
matching conformer from the ensemble that came from the
NMR-restrained conformational search procedure. The par-
ticular conformation shown from the NMR-based ensemble
(magenta carbons) was 6 kcal/mol above the lowest-energy
example, and it was a very close match to the xGen re-fit
bound ligand state (0.9 ÅRMSD for all non-hydrogen atoms
and 0.4 ÅRMSD for ring backbone atoms). Figure4B shows
the set of non-redundant conformers from the lowest 5 kcal/
mol energy window. The single lowest energy conformer
was 1.4 ÅRMSD from the bound state (0.4 ÅRMSD for
ring backbone atoms).
Clearly, the solution-state of Pep-01 is pre-organized for
binding PD-L1. In particular, buried sidechains (residues
8, 1 and 10 especially) showed relatively little movement
in the solution ensemble. By contrast, solvent-exposed
residues (e.g. 13 and 5) with little protein contact exhibited
more movement. Within the backbone itself, there are five
hydrogen bonds between amide carbonyl oxygen atoms and
amide protons, with an additional one between the indole
N-H of Trp
8
and a backbone carbonyl oxygen (see Fig.4C).
Note that these H-bonds do not form a topologically
detectable beta-hairpin-like structure [9] but form a rather
unique stabilizing framework.
Figure4D shows five alternative molecular subfragments
derived from the lowest energy conformer of the NMR-
restrained solution ensemble. These are used to establish
conformational preferences for analogs by employing graph
matching. Given an analog, the subfragments are matched
in order (left to right, top to bottom), and the first match
is used to instantiate a set of torsional preferences for the
analog during conformational search (as described earlier
in the discussion of Fig.3). The fragments are ordered from
most restrained to least, with the fifth alternative allowing
matches to variants at Pro
4
that retain both Trp residues (of
which there are a few among the patent peptides).
An underappreciated, but critical, aspect of structure-
based design in the context of peptidic macrocycles is the
difficulty in fitting large molecules into X-ray density cor-
rectly. The tools available for X-ray crystallography model
refinement are better developed for protein modeling than for
ligand modeling. Very often, the modeled ligand coordinates
yield very high energy values, and modeled coordinates
often contain serious errors. This has been established in a
number of studies concerned with estimating bound ligand
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524 Journal of Computer-Aided Molecular Design (2023) 37:519–535
strain energy [15–22] and studies and perspectives involving
X-ray model accuracy [12, 13, 23–25]. Figure5A shows the
comparison between the deposited PDB ligand coordinates
for Pep-01 (gray carbons) and the re-fit coordinates using
the xGen approach [12, 13]. Overall, the ligand had been
well-modeled, but one of the chiral centers of the ligand was
incorrect (red arrow), causing a distortion to the ring-closing
thioether linkage.
Figure5B shows a much more serious set of problems
with the deposited structure of Pep-57 (orange carbons)
compared to the xGen re-fit (yellow). Note that Pep-57
differs only at position 7 from the lead compound Pep-01,
Fig. 4 Solution ensemble for Pep-01 from NMR restrained confor-
mational search: Athe best-matching conformation from the ensem-
ble (magenta carbons) and the bound state by crystallography (green
carbons), with sidechains of specific residues numbered in red; Bthe
24 lowest energy non-redundant conformers (within 5 kcal/mol of the
minimum, magenta) superimposed on the bound state (green), Cthe
single lowest energy conformer (viewed from the solvent-exposed
side) with H-bonds labeled; D five alternative molecular subfrag-
ments derived from the lowest energy conformer
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525Journal of Computer-Aided Molecular Design (2023) 37:519–535
lacking the N-methyl and replacing Gly with Ser. Three cis-
amide bond configurations are highlighted (red arrows), but
the structure contains numerous high-strain features. If we
consider the overlay of the two deposited peptide variants in
Fig.5C, we would conclude that the macrocyclic backbone
took on substantially different conformations despite only
two minor differences between the ligands (both at position
7). However, as is clear in Fig.5D, the two variants adopt
nearly identical backbone configurations when correctly fit
into the X-ray density of PDB code 6PV9 and 5O4Y.
The importance of the above comparison for the pur-
pose of predictive modeling is that the NMR solution
ensemble of Pep-01 and both Pep-01 and Pep-57 bound
crystal structures agree extremely closely with respect to
their conformations. They are nearly identical for the mac-
rocyclic backbone and for the large, common substituents
that make strong contact with PD-L1. High-quality fitting
of low-energy conformational ensembles, whether to a set
of NMR-determined restraints from a pre-organized solu-
tion ensemble or to X-ray density, is required in order to
accurately model the likely bound states of analogs.
Results from the scheme presented in Fig.3 do not vary
substantially whether making use of the lowest energy
conformer from the NMR ensemble (Fig.4C) or deriving
analogous molecular subfragments from either the 6PV9
or 5O4Y structures. Because an NMR ensemble can be
obtained regardless of having a protein target structure, in
what follows, all results reflect the conformational restraints
Fig. 5 Comparative overlays of different ligand fits to X-ray density:
Aoriginal deposited Pep-01 structure (gray) and corrected real-space
fit (green); B original deposited Pep-57 structure (orange) and cor-
rected real-space fit (yellow); Coriginal deposited Pep-01 (gray) and
Pep-57 structures (orange); D corrected xGen re-fit Pep-01 (green)
and Pep-57 structures (yellow)
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526 Journal of Computer-Aided Molecular Design (2023) 37:519–535
that were derived from the experimental NMR data on
Pep-01.
Figure6 shows the low-energy conformational ensemble
for Pep-57 superimposed onto its crystallographic pose. The
ensemble was derived using the torsional restraints from the
lowest energy conformer of Pep-01’s NMR-derived solu-
tion ensemble (recall Fig.4C). The full ensemble contained
conformers with 1.0ÅRMSD to the bound state and the
low energy pool depicted in Fig.6 contained conformers
with 1.4ÅRMSD to the bound state. Deviations from the
crystallographic pose were in the solvent-facing residues,
with very tight correspondence among residues involved in
protein binding. The NMR-derived torsional restraints pro-
vide an effective means to identify conformers close to the
bound states of Pep-01 analogs.
Relationship ofestimated binding enthalpies
andexperimentally measured binding affinities
Figure7 (top) shows the relationship between experimental
(X-axis) and structure-based-protocol predicted binding for
63 patent peptides (violet) and 9 subsequently made and
tested project compounds (green). Note that the assays were
slightly different (e.g. for Pep-01, the difference was roughly
sixfold, with poorer nominal binding for the HTRF patent
assay), but are generally comparable. The points labeled 1,
2, and 3 correspond, respectively, to BMT-174900, BMT-
153099, and BMT-139699 from Fig.1. Kendall’s Tau (
𝜏
)
was 0.25 (p < 0.001) with ties being counted as exact values,
increasing to
𝜏=0.50
with prediction value ties defined as
being within 5.0 kcal/mol of one another (p
≪
0.001). Given
two analogs whose predicted binding enthalpy values dif-
fered by 5 kcal/mol, the likelihood that they were ranked
correctly was 75%. Pearson’s correlation (r) was 0.48. Of
Fig. 6 Non-redundant low energy (within 5 kcal/mol of the mini-
mum) pool of conformers of Pep-57 (cyan) superimposed on the
crystallographic pose of Pep-57 from 5O4Y (yellow)
Fig. 7 Relationship of predicted binding enthalpy to binding free
energy (calculated from experimentally determined IC
50
values) for
the structure-based protocol (top) and ligand-based protocol (middle),
and comparison of bound ligand strain estimates between the two
protocols (bottom)
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527Journal of Computer-Aided Molecular Design (2023) 37:519–535
note, the clinical candidate (BMT-174900) and an analog
with similar activity (BMT-153099) were among the best
scoring 6 of 72 compounds. The mean pK
d
of the ten best
predicted analogs was 8.1.
Figure7 (middle) shows the relationship between experi-
mental (X-axis) and ligand-based-protocol predicted bind-
ing for 63 patent peptides and nine subsequently made and
tested project compounds, with points colored as above. Ken-
dall’s Tau (
𝜏
) was 0.25 (p
=
0.001) with ties being counted
as exact values, increasing to
𝜏=0.47
with prediction value
ties defined as being within 5.0 kcal/mol of one another (p <
0.001). Given two analogs whose predicted binding enthalpy
values differed by 5 kcal/mol, the likelihood that they were
ranked correctly was 73%. Pearson’s correlation (r) was 0.42.
Of note, the clinical candidate (BMT-174900) was not among
the best-scoring compounds in the ligand-based protocol.
The ligand-based protocol has a fundamental lack of informa-
tion regarding the new favorable interactions of BMT-174900
with PD-L1 that are evident in the structure-based protocol.
However, five highly active analogs from the lead optimiza-
tion effort were among the top 11 predictions. The mean pK
d
of the ten best predicted analogs by the purely ligand-based
protocol was 8.0.
The direct correlation between the structure-based and
ligand-based strain estimates was high (Fig.7, bottom, with
𝜏=0.55
, p
≪
0.001,
r=0.86
, and mean absolute differ-
ence being 2.2 kcal/mol). This reflects the degree to which
the ligand-based predictions of bound ligand pose matched
those from docking (discussed below). Bound ligand strain,
by itself, was the major predictive factor of experimentally
measured analog activity, which is why the purely ligand-
based approach exhibited a similar level of predictive value
to the structure-based approach.
Expectations forligand strain
It is not clear the extent to which the predictive value of
ligand strain is a general property for macrocyclic peptides
that result from the type of intensive affinity-based screening
used to identify Pep-01 [1], but it is possible to quantify
the likelihood that ligand strain can be leveraged in lead
optimization. We have recently shown that bound ligand
strain follows a size-dependent probability distribution [15].
For Pep-01, the expected bound strain is roughly 24 kcal/mol
and the expectation is that 95% of cases will fall between in
the range of 14–34 kcal/mol. From the real-space refined
coordinates of Pep-01, we obtained an estimated bound
strain of 12.7 kcal/mol—clearly very low. From re-docking
Pep-01 into its cognate protein structure, an analogous
process to that used for the analog compounds, we obtained
an even lower strain estimate: 2.3 kcal/mol. Note that
because the conformer pool for Pep-01 was derived from
the torsional preferences of its own solution-state, it is likely
that the strain estimate from docking is systematically lower
than that of the analog compounds.
Whether considering the strain estimate from
crystallography (very low for its size) or from docking
(extremely low), one should expect that many changes to the
lead compound’s structure will result in significant increases
in strain. So, maintaining low strain in the design process
is clearly indicated based on where the lead compound falls
within the expected strain distribution. Here, using either the
structure-based protocol or the ligand-based protocol, we
see that the most active analogs have extremely low strain
compared with expectations: an average of 7.6 kcal/mol for
those with activity
≥
8.0 pIC
50
units. Conversely, the least
active analogs have approximately double the strain: an
average of 14.7 kcal/mol for those with activity
≤
6.0 pIC
50
units (still quite low, but the changes were modest).
Recall Pep-05 from Fig.3, which was a Phe to Ala change
at position 1, resulting in a decrease in activity of nearly
3 log units. The change resulted in a loss of less than 0.5
kcal/mol in intermolecular binding energy compared with
Pep-01. However, the bound strain estimate increased by
roughly 8 kcal/mol for Pep-05. The predicted loss in activity
between Pep-01 and Pep-05 based on intermolecular energy
and strain is overestimated, but it correctly deprioritizes
Pep-05 as an analog for synthesis and testing. This type of
effect is likely to be general. Large, rigid substituents such as
phenylalanine create conformational constraint by excluding
possible conformational states. Changes that decrease either
the size or rigidity of such substituents are likely to reveal
different (and lower) global minima relative to the bound
conformational energy.
Pep-50 from Fig.3 is interesting for similar reasons. The
deletion of a single methylene from the Pro residue at posi-
tion 4 makes a small change to the interaction footprint,
leading to a decrease in intermolecular binding energy of
0.7 kcal/mol. The impact of the change on estimated strain
was larger: an increase of just under 5 kcal/mol. As with
Pep-05, the predicted degree of loss in activity was over-
estimated, but the important aspect is that the rankings of
the compounds were correct: Pep-01 > Pep-50 > Pep-05.
Further, while the gap between Pep-01 and Pep-05/Pep-50
was overestimated, the gap between Pep-50 and Pep-05 was
quite closely predicted (
ΔΔ
G of 3.0 kcal/mol predicted vs.
2.7 experimental).
Because of the dominant effect of estimated strain, both
the structure-based and ligand-based protocols agreed on the
ranking of these compounds.
Protein structure adds exploitable information
In the structure-based protocol, docking score is defined
as the estimated intermolecular binding enthalpy ignoring
ligand strain. By itself, the correlation between this score
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528 Journal of Computer-Aided Molecular Design (2023) 37:519–535
and experimentally determined binding affinity was
weak (
𝜏=0.12
, p
=
0.07). However, despite strain being
the largest explanatory component of analog activity,
predicted interactions between analogs and PD-L1, both
quantitatively and qualitatively, had significant value.
Figure8A shows the predicted pose of Pep-57 (a
variant of Pep-01 differing slightly at position 7) when
docked into the cognate protein conformation of Pep-01.
The predicted pose (cyan) was just 1.0 ÅRMSD from
the experimentally determined pose (yellow). Figure8B
shows the top-scoring docked poses for all 72 of the ana-
logs. The torsionally restrained search procedure yielded
compliant conformers for all compounds, which exhib-
ited largely congruent binding interactions at the protein
interface.
Figure8C shows the predicted binding mode of BMT-
174900, with two salt-bridges to the protein at positions
5 and 10 (marked with red arrows). The structure-based
protocol predicted a marked improvement (3.7 kcal/mol in
intermolecular score) progressing from Pep-01 to BMT-
174900. The strain estimates from the structure-based and
ligand-based protocols differed by less than 0.1 kcal/mol.
It was the estimate of intermolecular binding enthalpy
from the structure-based protocol that led to the much
better ranking of BMT-174900 (see the points labeled 1
in the top and middle plots of Fig.7).
Recall BMT-153099 from Fig.1, which differs from
BMT-174900 only at position 10, with a benzothiophene
rather than the substituted indole. In the pure binding
assay, the two compounds exhibited very similar activ-
ity. BMT-153099 was the only analog with a calculated
intermolecular binding score (units of pK
d
) higher
than BMT-174900 and the calculated strain estimate
was lower in both protocols. Both protocols incorrectly
ranked BMT-153099 with respect to BMT-174900,
with the structure-based protocol predicting a smaller
gap than the ligand-based protocol. The docked pose of
BMT-153099 was not significantly different than BMT-
174900, with the change in score being driven by the
𝜋
-cation interaction of the thiophene compared with the
substituted indole.
BMT-139699 differed from BMT-153099 only at
position 7, replacing the hydroxyl with a proton. Because
the hydroxyl at position 7 is completely solvated, it was
unsurprising that the estimated intermolecular binding
energy differed only slightly (with BMT-153099 being 0.3
kcal/mol lower in intermolecular energy). Note, however,
that the difference in experimentally measured activity
corresponded to just under 2 kcal/mol. Here again, the
estimated strain pointed in the correct direction, with
significantly increased strain estimates for BMT-139699:
6.7 and 3.5 kcal/mol by the structure-based and ligand-
based protocols, respectively.
Comparison ofpredictions forbound ligand poses
The structure-based protocol produced a highly accurate
docking for Pep-57 and convincing poses for the remain-
ing analogs (see Fig.8). The parallel ligand-based pro-
tocol also predicted poses for all analogs using the eSim
method [14] in order to derive an estimate for bound con-
formational energy. Figure9A shows the optimal align-
ment of BMT-174900 to Pep-01 using ligand-based pose
prediction. Gray dots show the parts of the molecular sur-
faces that are congruent, with notable differences only at
positions 5, 10, 9 and 1. Red and blue sticks show con-
gruence of hydrogen bond donors and acceptors, includ-
ing directionality. Small spheres in the red to blue color
spectrum indicate areas where the electrostatic fields of
the molecules are congruent.
Figure9B shows the comparison of the bound pose
predictions of BMT-174900 from the structure-based
protocol (tan) and the ligand-based protocol, with an RMSD
of 0.9 Å. There are slight differences in the poses at positions
5 and 10, where docking identified quantitatively significant
interactions with the protein, but where the ligand-based
approach simply saw differences between Pep-01 and
BMT-174900. Figure9C shows the cumulative histogram
of RMSD for the ligand-based pose predictions compared
to the poorest expected RMSD for each analog. The RMSD
values for the eSim predictions were derived by comparing
the similarity-predicted poses against the top-scoring pose
family from docking for each analog. The pessimistic RMSD
values were derived by considering the lowest 10 kcal/mol
conformers from each analog’s pool and identifying the
most deviant conformer compared with the top-scoring pose
family from docking.
Over 80% of the cases showed conformer matches to
docking of 1.0 ÅRMSD or less in the ligand-based pro-
tocol, with 98% being under 1.5 ÅRMSD. This was not
simply because the torsionally restrained pools contained no
conformers that deviated from the likely docked poses. The
pools contained a diversity of conformations for each analog,
typically containing examples deviating 1.5 to−2.5Åfrom
the docked configurations. The close relationship between
the strain estimates for the structure-based and ligand-based
protocols stemmed from the quantitative similarity in their
predicted bound poses for the analogs.
Note, however, that this was a structure-enabled project,
which influenced the design of analogs. In this restrospec-
tive analysis, without protein structure, the substitution
on the Trp indole at position 10 would probably not have
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529Journal of Computer-Aided Molecular Design (2023) 37:519–535
been explored in using a systematic “conservative” strat-
egy. Combinatorial exploration of such diverse sidechain
variants would yield an extremely large space of analogs
to prioritize. It is conceivable that a position-by-position
sequential optimization, essentially an iterative line search
strategy, could be used in a “blind” exploration. Such a
Fig. 8 Docking of analogs to PD-L1 (PDB Code 6PV9): Acompari-
son of predicted (cyan) and experimentally determined (yellow, PDB
Code 5O4Y) bound pose of Pep-57; Btop-scoring docked poses of
all analogs (seen from the protein interface) superimposed on the
bound pose of Pep-01 (green carbons); Cpredicted bound pose for
BMT-174900
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530 Journal of Computer-Aided Molecular Design (2023) 37:519–535
strategy assumes that the effects of positional variations will
be largely additive.
Computational cost
Large macrocycles present special challenges, particularly
regarding the computational complexity of conformational
search. The fgen_deep search approach requires roughly
ten-fold more time than the standard thorough ForceGen
search protocol for the compounds studied here. Roughly
speaking, using the thorough ForceGen search protocol
for all conformational searches, roughly 1000 compounds
per day can be run on a 100-node cluster of 36-core nodes,
with ten-fold fewer using the deep search protocol. Deeper
conformational search produced stronger correlations
between estimated enthalpies and experimentally determined
activities, but for ranking larger sets of candidate analogs,
the faster protocol would be useful for eliminating poor
candidates. Given the availability of cloud-based high-
performance computing, with schemes that trade perfect
availability against cost, the trade-offs between calculation
speed and accuracy are complex.
Conclusion
Systematic optimization of large macrocyclic peptide
ligands is a serious challenge. We have described a lead-
optimization approach using the PD-1/PD-L1 system as a
retrospective example of moving from initial screening hit
to clinical candidate, using either a structure-enabled or a
purely ligand-based approach. Armed only with data from
the NMR solution ensemble of the lead compound from
affinity-based selection, significant efficiency can be gained.
In this study, roughly 50% of analogs could be eliminated
from synthetic consideration without breaking the successful
optimization path that resulted in BMT-174900. Protein
structural information is clearly beneficial, both in terms
of the quantitative value in ranking analogs and in terms
of helping to guide the design of specific analogs. With
the additional information provided by the structure-based
protocol, roughly 80–90% of analogs could be eliminated
from synthetic consideration.
A surprising aspect of this study is the central importance
of bound ligand strain in making predictions. Essentially, the
propensity of each macrocyclic analog to adopt a bound con-
formation very similar to the lead compound was the largest
explanatory component of activity. Relatively small ligand
modifications, which may have minimal effects on predicted
bound pose or intermolecular interactions, often lead to large
changes in estimated strain that have dominating effects on
overall binding energy estimates.
Fig. 9 Pose prediction accuracy for the ligand-based protocol: Aopti-
mal eSim alignment of BMT-174900 to Pep-01; Bsuperimposition of
pose prediction from ligand similarity (cyan) and docking (tan); and
Crelationship of pose prediction accuracy for the ligand-based pro-
tocol (violet) to the poorest possible result from the low-energy pose
pool for each analog (green)
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531Journal of Computer-Aided Molecular Design (2023) 37:519–535
In terms of prospective application of the methods
described here to macrocyclic peptide lead optimization, a
critical factor is whether solution ensembles are pre-organ-
ized for binding to the target site in question. Because affin-
ity-based selection of peptides such as those studied here
is a relatively new technological approach, it is difficult to
predict how likely such pre-organization may be. If a coher-
ent conformational ensemble exists in solution, which can
be established through NMR-based biophysical characteriza-
tion, and the on-rate of association between the ligand and
protein is fast, it is reasonable to pursue the ligand-based
strategy. Given experimental activity and predicted rank-
order over a conservatively chosen set of analogs, the strat-
egy could be quickly validated or rejected.
In the case that X-ray data is available for a bound ligand
exemplar, the structure-based protocol could be assessed
similarly. In both situations, the extent to which the protein
exhibits significant flexibility on binding different analogs
is a potentially confounding factor. In the work presented
here, PD-L1 does not appear to exhibit much conformational
variability from analog to analog. However, there is quite a
significant difference between the apo form of PD-L1 and
that bound to the peptidic macrocycles studied here. Given
the added value of structural guidance, both in terms of
improvements in the computational protocol and in terms of
aiding the design process, whenever possible, the structure
of at least one protein-ligand complex should be sought.
Effective macrocyclic conformational search is critical,
whether in the context of NMR-based restraints, X-ray
ligand refinement, using partial torsional restraints for
docking or ligand similarity calculations, or agnostic search
for nominal global minima. Our expectation is that, in many
cases, lead optimization for peptidic macrocycles can be
made more productive using a multi-disciplinary approach
that combines biophysical data with practical and efficient
computational methods.
Methods anddata
Molecular data set
The macrocyclic peptides studied here included 64 from
the original patent disclosure [2], each of which had an
associated IC
50
for inhibition of HTRF-based PD-L1/PD-1
binding. Also included were 9 compounds from various
time-points during project lead-optimization, each of which
had an associated IC
50
measured for in a HEK293 cell-based
assay in which PD-L1 is recombinantly overexpressed on the
cell surface and inhibition of binding to soluble recombinant
PD-1 is measured.
Experimental NMR data forPep‑01
NMR sample preparation ofPep‑01
A 5.1 mg sample of Pep-01 was dissolved in a 0.65 mL
binary mixture of 30% perdeuterated acetonitrile + 70%
glycine buffer in 100% H
2
O (30mM glycine-d5, pH = 2.5)
and placed in a 5mm thin-wall tube (Wilmad precision
NMR tube: 541-pp-7–5).
NMR data acquisition
All NMR spectra were acquired on an AVANCE NEO
spectrometer operating at 700.14 MHz equipped with a
TCI 5mm cryoprobe and TopSpin version 4.1.3. Spectra
acquired at 15°C:
– Proton 1D with Excitation-Sculpting water peak
suppression [26], water resonance frequency and
spectrum center at 4.585 ppm.
–
1
H-
13
C HSQC with DEPT-editing [27], sw = 14.88
ppm, sw1 = 200 ppm, o1p = 4.589 (on-resonance
with water peak), o2p = 90 ppm, td = 4096, td1 =
1024, relaxation delay d1 = 2.5 s, echo/anti-echo
acquisition.
–
1
H-
15
N HMQC with Watergate water peak suppression
[28], sw = 14.88 ppm, sw1 = 40 ppm, o1p = 4.589
(on-resonance with water peak), o3p = 116 ppm, td =
4096, td1 = 256, d1 = 1.5, relaxation delay d1 = 1.5s,
STATES-TPPI acquisition.
–
1
H-
13
C HMBC [29], sw = 14.88 ppm, sw1 = 200
ppm, o1p = 4.589, o2p = 95 ppm, td = 4096, td1 =
512, echo/anti-echo acquisition, d1 = 2.0 water peak
suppression by a combination of on-resonance pre-
saturation with a saturation field of 50 Hz amplitude
plus a 2 ms soft water flip-pack pulse preceding the
last proton echo pulse (see supplemental material for
additional details), long-range coupling delay was set
to 1/2 * 8Hz. The spectrum was processed in magnitude
mode in F2 and phase-sensitive mode in F1.
–
1
H-
1
H TOCSY [30], sw = 14.88 ppm, sw1 = 14.28,
td = 4096, td1 = 400, relaxation delay d1 = 2.0 s,
mixing time = 0.075 s, water peak suppression
by CW-presaturation and Excitation Sculpting,
suppression of peak shape distortion by inclusion of
a zero-quantum filter [31], STATES-TPPI acquisition
mode.
– Double-quantum filtered-COSY [32] with Excitation
Sculpting water-peak suppression [26]—see
supplemental materials for details on pulse sequence
customization, the sw = 14.88 ppm, sw1 = 14.28, td =
4096, td1 = 2048, d1 = 2.0 s.
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532 Journal of Computer-Aided Molecular Design (2023) 37:519–535
– 2D-NOESY: sw = 14.88 ppm, sw1 = 14.28, td = 4096, td1
= 2048, d1 = 3.5 s, CW-water peak pre-saturation during
the relaxation delay and the mixing interval of 0.5s.
Spectra acquired at 283°K:
– 2D-NOESY: sw =14.88 ppm, sw1 = 14.28, td =
4096, td1 = 800, d1 = 3.5s, water peak suppression by
Excitation Sculpting, mixing times; 0.1, 0.2, 0.3, 0.4,
0.5s
–
1
H-
13
C HSQC,
1
H-
15
N HMBC,
1
H-
1
H TOCSY employing
parameters as depicted in list of experiment which were
recorded at 288 K.
– T1-measurements: Bruker inversion recovery pulse
program, inversion recovery delays: 0.001, 0.4, 1.0,
2.5, 5.0, 9.8 s, d1 = 10s, water peak suppression using
CW-presaturation of a 50 Hz rf field. Data analysis in the
TopSpin dynamics module. Processing in TopSpin T1/
T2-relaxation module
NMR‑resonance assignments
Resonance assignments were performed on the 288K dataset
using 1D-proton, DQF-COSY,
1
H-
1
H TOCSY, 2D-NOESY
(d8=500ms),
1
H-
13
C HSQC,
1
H-
13
C, HMBC, and
1
H-
15
N-HMQC using ACD/Lab workbook version 2020.2.0
(Advanced Chemistry Development Inc., Toronto, Ontario,
Canada). All
13
C chemical shifts were within theoretical
limits of the built-in chemical shift prediction module
(add citation of ACD/Labs). Resonance assignments were
mapped to the 283K dataset using ACD labs and peak lists
were exported. The peak lists were subsequently imported
into Sparky [33] where cross-peaks were manually picked
in the 200 ms mix time NOESY spectrum. Peak volumes
were generated by the sum-over ellipse method. Proton
shift assignments and NOESY peak list were exported in
XEASY-format [34]. A total of 393 peaks across both sides
of the diagonal were picked.
Torsion angle restraints were derived using the modified
Karplus equation [35]. The J
HN−H𝛼
values were extracted
from 1D spectra with minimal apodization. Amides with
J
HN−H𝛼
> 8.0 Hz, HIS5, LEU6, TRP8, SER9 and ARG13
were assigned Phi angles from −155 to −95.
NMR-peak assignments and computation of initial
3D-structures:
CYANA [36] structure calculation required the defini-
tion of unnatural amino acid types in the CYANA-library
format. CYANA library files of unnatural amino acid types
were generated by CYLIB [37]. Editing of CYLIB-generated
CYANA-library files was aided by atom number to name
conversion utility in CYANA. Separate upper bound (*.upl)
and lower bound (*.lol) files were generated to link the
sulfur dummy atom, and the residue PHS1
Ω
angle was set
to 160–200 to properly assign the disulfide geometry for the
ring closure. Cis peptide bonds were set in the CYANA *.seq
file for residues 2 and 11 based on observation of cis NOE
patterns including, H
𝛼
–H
𝛼
NOEs for residues 10–11 and
strong N-methyl to aromatic NOEs between residues 2 to 1.
Peak integral to upper distance bounds restraints were
generated by CYANA [36] using the built in “noeassign”
command. A total of 165 peaks were assigned and translated
into upper-bound distance restraints. Tabulation of short
range and long-range NOEs used in the 3D structure calcu-
lation are included in the supplemental experimental NMR
data. Initial CYANA structures produced an ensemble of
20 3D-structures with average heavy atom RMSD to mean
of 0.95 Å(± 0.22 Å). The Ramachandran plot analysis indi-
cated that 61.4% of Phi and Psi angles resided in the most
favored regions with an additional 36.8% in the additionally
allowed region.
The experimental data yielded 50 distance restraints
between single proton pairs, 115 distance restraints where
one/both ends contained chemically equivalent protons, and
6 torsional restraints consisting of 1 omega and 5 psi angles.
Structure generation and conformational search was done
using the deep ForceGen search procedure, described below.
NMR restraints withchemically equivalent protons
Given that over two-thirds of the restraints that affect confor-
mational search for Pep-01 involved chemically equivalent
protons, the precise treatment of those restraints was impor-
tant. It has been argued that the best treatment of equivalent
or nonstereoassigned protons in calculations of biomacro-
molecular structures is done by considering the so-called
r
−6
averaged distance [38, 39]. A common alternative approach
is to make use of the so-called center-averaged distance
(along with a pseudo-atom correction to the restraint dis-
tance). Here, we introduce a new alternative, which closely
approximates the
r
−6
averaged distance, but which is simpler
and more efficient to calculate. The following defines three
different distances between equivalent spin groups a and b:
(1)
r
eff =
(
1
nanb
∑
i,j
r−6
aibj
)−
1
∕6
(2)
a
cen =
1
na
∑
i
ai,bcen =
1
nb
∑
j
b
j
(3)
rcen =racenbcen
+𝛿ab
(4)
r
qmin
=min
i,j(r
aibj
)
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533Journal of Computer-Aided Molecular Design (2023) 37:519–535
where
ai
and
𝛿ab
is the pseudo-atom correction for the two
spin groups. Equation1 defines the
r
−6
average distance,
which requires calculation of
nanb
distances, a sum over
the inverse of their sixth power, and finally the normalized
inverse of the sum’s sixth root. Equations2, 3 define the
center-averaging distance, which requires two centroids,
their distance and addition of a correction term. Equation4
defines the “qmin” distance, which requires computing
the minimum over
nanb
distances (or the square root of
the minimum over
nanb
squared distances, which is more
computationally efficient). Further, the first derivative of
qmin distance depends only on the two atomic locations that
gave rise to the minimum distance.
Figure10 shows the comparison of the restraint boundary
in two dimensions for a single proton to the two chemically
equivalent protons on a phenyl group. The qmin method
closely follows the
r
−6
averaged boundary, with the center-
averaging approach (with a well-chosen pseudo-atom cor-
rection) deviating significantly. Given the simplicity of
implementation, computational efficiency, and relatively
small deviation from the
r
−6
average, the qmin approach is
appropriate in cases where a calculation requiring a restraint
on chemically equivalent protons falls in the inner-loop of a
complex optimization procedure.
Deep ForceGen search
The ForceGen conformational search method has been
previously described [8, 9]. For small, drug-like molecules,
the -pquant level of conformational elaboration is likely to
be sufficient to make accurate estimates of global minima in
the vast majority of cases, based on the roughly 98% success
rate of identifying close-to-crystallographic conformers
(
≤
1.5 Å RMSD) beginning from random starting
conformations [9]. However, particularly for large, peptidic
macrocycles, we have developed an iterative approach to
conformational search in order to better ensure adequate
sampling [13]. This iterative search has been implemented as
a command within the Tools Module of the Surflex Platform,
called fgen_deep.
Beginning from a single input conformer, the fgen_deep
procedure performs a standard ForceGen search, with the
resulting conformer pool being clustered by RMSD. If the
resulting N lowest-energy clusters contain new conformations
compared with prior rounds, search is iterated beginning
with the lowest energy conformers from each of the N new
clusters. Multiple rounds of this are carried out, each time
consolidating the full set of conformers into a non-redundant
set within a specified energetic window prior to clustering.
The process is iterated until no new low-energy clusters are
identified.
Figure11 shows the performance of the fgen_deep pro-
cedure on a benchmark of 208 macrocycles [7, 9], com-
pared with low-mode MD implemented within MOE and
MacroModel [4, 6] and with the Prime MCS approach and
pure MD simulation [7, 9]. We see that, at the 1.5 ÅRMSD
threshold, a success rate of just over 90% was achieved using
the fgen_deep approach. Note that this still falls short of the
98% seen for “normal” small molecules, but it is substan-
tially better than the alternative methods, whose success rate
ranged from 62 to 78%.
Fig. 10 Illustration of the “qmin” approach to restraint enforcement
compared with center-averaging or
r
−
6
averaging
Fig. 11 Performance of the deep ForceGen search methodology on
the Prime MCS 208 example macrocycle benchmark
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534 Journal of Computer-Aided Molecular Design (2023) 37:519–535
Computational procedures
A rough outline of the computational protocols is provided
here (see Data and Software Availability for full details).
Following is the NMR-restrained conformational search
of Pep-01:
#
NMR-restrained deep ForceGen search of pep-01
sf-tools
-molconstraint pep-01_restraints+qmin
-pquant fgen_deeppep-01.mol2 pqd-pep-01
#Alow energy non-redundant pool was derived
#using the sf-tools combine_sfdb command with
#-enthresh 5.0 and -rms 0.25
#The exemplar pep-01 conformer wastaken as
the
#single lowest energy conformer in theNMR
#solution ensemble --> gmin-nmr-pep-01.mol2
#The torsional restraint fragments were
#taken from gmin-nmr-pep-01.mol2
#--> allfrags.mol2
Following is an outline of the structure-based and
ligand-based protocols that make use of the torsional
restraint fragments derived above from the NMR solution
ensemble of Pep-01:
#Generate 3D guided by the fragment conformers
#
Then aloosely restrained deep ForceGen search
sf-tools
-torcon allfrags.mol2 fgen3d cpd.smi
cpd-fg3d
sf-tools
-torpen 0.01 -torcon allfrags.mol2
-pquant fgen_deep cpd-fg3d.mol2 pq-cpd
#D
othe docking and pose family construction
sf-dock
-lmatch lig-6PV9.mol2 -pquant
gdock_list pq-cpd.sfdb mpro-6PV9 log-cpd
sf-dock
-posehints lig-6PV9.mol2 posefam log-cpd
#D
othe eSim alignment to exemplar conf
sf-sim
-pquant esim_list pq-cpd.sfdb
gmin-nmr-pep-01.mol2logesim-cpd
#
Bound conf energy comes from the above operations
#
using the sf-tools bm_ensemble command
#
Protein-ligand interaction score comes
#
from the sf-dock opt commmand
#
ForceGen Deep search to identify global minimum
#
Begin with the top-scoring dockedconformer
sf-tools
-pquant fgen_deep
dock-cpd-opt.mol2 pqdeep-cpd-glob
#
Global mininimum energy comes from the
#the sf-tools bm_ensemble command withnorestraint
For all conformational search (NMR restrained
or agnostic), real-space ligand refinement, docking,
ligand-similarity calculations and related strain estimates,
we employed version 5.1 of the Surflex Platform (BioPhar-
mics LLC, Sonoma County, CA 95404).
Acknowledgements The authors thank Paul Scola and the PD-1/PD-L1
discovery working group. The authors are also grateful to Simon Rüdis-
ser, Peter Güntert, and Eiso AB for support in the use of the CYANA
software.
Author contributions All authors participated in the research and in
the preparation of and final review of the manuscript.
Funding The authors have no outside funding to declare.
Data availability A freely downloadable data archive containing
additional computational and experimental details is available at http://
jainl ab. org/ downl oads. The archive contains supplementary details
regarding the NMR experimental data and a summary spreadsheet
of ligand structures and activity values along with calculated global
minimum energy values, bound energy values, derived strain estimates,
and intermolecular binding energy values. The archive also contains
scripts to reproduce the major results of the paper along with SMILES-
format input structures, generated 3D ligand structures, conformational
ensembles, protein structures, and NMR restraint data. All software
employed herein is commercially available.
Declarations
Conflict of interest The authors have no competing interests as defined
by Springer, or other interests that might be perceived to influence the
results and/or discussion reported in this paper.The authors declare no
competing interests.
Ethical approval Not applicable.
Informed consent Not applicable.
Consent for publication All authors have read and understood the pub-
lishing policy, and this manuscript is submitted in accordance with
this policy.
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format,
as long as you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons licence, and indicate
if changes were made. The images or other third party material in this
article are included in the article's Creative Commons licence, unless
indicated otherwise in a credit line to the material. If material is not
included in the article's Creative Commons licence and your intended
use is not permitted by statutory regulation or exceeds the permitted
use, you will need to obtain permission directly from the copyright
holder. To view a copy of this licence, visit http:// creat iveco mmons.
org/ licen ses/ by/4. 0/.
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