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. • The Diculty in Finding Local Minima Corresponding to the Receptor-Bound State
16.5 The Difficulty in Finding Local
Minima Corresponding to the
Receptor-Bound State
As already described, the local minima in asystematic
conformational search are obtained by subjecting all
generated geometries to aforce eld optimization. Problems can arise with this approach. To illustrate this, consider another molecule, citric acid 16.2, in the binding
. Fig. 16.3 Avalue distribution for the torsion angles with clusters
at 60, 180, and 300° is derived from adatabase of small-molecule crystal structures for the C–CH2–CH2–C fragment. Most values are found
at 180°. Torsion angles between 0 and 360° are entered as the relative
frequency in percent. The maxima of the distribution are at the angles
where the potential curve of n-butane (. Fig.16.1) shows its energy
minima
satisfactorily populate the local minima of the bound
state of adenosine monophosphate. Returning to the
initial butane example (. Fig.16.1), this means that the
starting points were well distributed so that all relevant
minima were reached.
pocket of citrate synthase. Its three carboxylate groups
and the hydroxyl group form seven hydrogen bonds with
three histidine and two arginine residues of the protein
(. Fig.16.5). Considering the free citrate molecule not
bound to the protein and minimizing its geometry in the
isolated state, it assumes aconformation with internally
saturated hydrogen bonds (Sect.15.4). Of course, it is
possible to start from adifferent geometry, but in all
cases the minimization will result in conformations with
intramolecular hydrogen bonds. Such hydrogen bonds
rarely occur in the bound state of the protein. Therefore, the conformation obtained after minimization in
. Fig. 16.4 The frequency distribution of the torsion angles of the
open-chain bonds of adenosine monophosphate as found in the crystal structures of small organic molecules. The torsion angle histograms
are constructed for fragments that are representative for correspond-
ing portions of the test molecule. There are clearly preferred values for
the angles τ1–τ3, but abroad distribution of all possible angles is found
for τ4. This knowledge is used in the conformational analyses and limits the search for τ1–τ3 to the preferred value ranges
Chapter • Conformational Analysis
. Fig. 16.5 Interactions between citric acid 16.2 and the enzyme
citrate synthase. The molecule is bound by seven hydrogen bonds to
three histidine and two arginine residues
the isolated state has little relevance to the conditions in
the bound state of the protein.
In general, ligands rarely bind to proteins in aconformation that involves extensive formation of intramolecular hydrogen bonds. The polar H-bond forming groups
are usually involved in interactions with the protein.
To avoid the problem of intramolecular H-bond
formation, a minimization of the generated starting
structure can be neglected and all geometries from the
systematic search can be used for further comparison
(Chap.17). However, then avery large number of geometries would have to be examined. This would severely
limit the scope of such comparisons for computational
reasons. In addition, such results would probably describe rather distorted geometries. The force eld responsible for the formation of intramolecular H-bonds could
be neglected. But how reliable would such an articially
simplied force eld be?
16.6 An Effective Search for Relevant
Conformations by Using
aKnowledge-Based Approach
Aknowledge-based approach rst analyzes the experimentally determined conformations and generates only
those conformations for new molecules that are consistent
with the experimental knowledge base. In this way, many
geometries are never generated in the rst place. The example of adenosine monophosphate 16.1 is used again.
The approach recognizes aexible ve-membered ring
and four open-chain rotatable bonds. Energetically favorable conformations of the ring are selected from adatabase. This database contains alarge number of different
ring systems, as they can be found, for example, in the
crystal structures of organic molecules. In this case, the
approach suggests the ve most energetically favorable
ring conformations, two of which are actually found in
the protein-bound cofactors. For the open-chain part of
the molecule, the method is guided by the aforementioned
frequency distribution of the dihedral angle (. Fig.16.4).
The starting geometries are generated only in angle ranges
where these distributions show signicant frequencies. The
distribution is still rather coarse. In anal step, the generated geometries are optimized by readjusting the torsion
angles. Clashes between noncovalently bonded atoms are
avoided. At the same time, the adjusted dihedral angles
are kept as close as possible to the preferred values. Such
aso-called knowledge-based approach requires relatively
few representative conformations. They are fairly evenly
distributed in the part of conformational space relevant
for receptor-bound conformations (. Fig.16.6).
16
. Fig. 16.6 Left Eighty-one conformers from experimentally deter-
mined protein–ligand complexes are superimposed upon one another
to illustrate the areas in space that adenosine monophosphate 16.1 can
adopt in aprotein-bound state. The ribose ring is located in the center, for which two ring conformations occur. In each case, the possible
orientations of the adenine ring are shown on the right side, and the
conformations of the exible phosphate chain are shown on the left
side. Right Similar coverage of the conformational space is achieved
with a manageable number of only 14 conformations generated by
aknowledge-based approach
. • Bibliography and Further Reading
16.7 What Is the Outcome
of aConformational Search?
Many drug-like molecules are exible. They can adopt
markedly different conformations depending on their environment. Usually, the receptor-bound geometry is not
the energetically most favorable conformation found for
the isolated state, but it will be in an energetically favorable range. For conformational analysis, this means that
it is not necessarily the deepest minimum that is sought.
Rather, it should be the “relevant” minimum corresponding to the bound state. There will only be achance to
nd it, if the criteria for the search are known. There is
no difference in the difculty of nding the energetically
most favorable conformation or the one that “ts” the
binding site best. An important tool in the search for
novel lead structures is the docking of candidate molecules into the binding pocket of a given protein. Programs that follow this approach must be able to handle
the conformation problem. Avariety of methods have
been developed that allow efcient docking searches on
computer clusters, especially for molecules of drug-like
size (Sects.7.6 and20.8).
16.8 Synopsis
Drug-like molecules exhibit multiple rotatable bonds.
-
Rotations around these bonds drive the molecules
into different conformations that correspond to local
minima on the energy surface of the molecule.
The receptor-bound conformation of a drug-like
-
molecule is the starting point for any drug-design
considerations. Therefore, many methods have been
developed to perform conformational analyses. Systematic searches by incremental rotations about each
single bond torsion angle will produce ahuge amount
of geometries that need to be optimized to the local
minima on the energy surface.
The conformation of adrug-like molecule frequently
-
changes with the environment. Usually the conformation in the protein-bound state differs from that
in solution, in the gas phase, or in the small-molecule
crystal structure.
Considering torsional fragments in small molecules
-
and analyzing them across databases of crystal structures by statistical means reveals clear-cut torsional
preferences for many examples. Such knowledge can
be exploited to perform a conformational search
more efciently. Not all values around arotatable
bond have to be tested, and the search can be limited
to the ranges that are known to be preferred.
Afurther obstacle in the conformational search of
-
the protein-bound conformation of adrug-like molecule is that the molecule will interact with its environment. This environment, which is usually the protein’s
binding pocket, is often polar and will involve the
bound ligand in multiple hydrogen bonds.
Using aknowledge base on torsional preferences of
-
small organic molecules can signicantly enhance the
conformational search, particularly during docking,
in molecular comparisons, or in database searches
based on predened pharmacophores.
Bibliography and Further Reading
General Literature
J. Dale, Stereochemistry and Conformational Analysis, VCH, Wein-
heim, New York (1978)
K. Rasmussen, Potential Energy Functions in Conformational Analy-
sis, Lecture Notes in Chemistry, Vol. 37, Springer (1985)
A. Leach, Molecular modelling: principles and applications, 2nd edn.
Prentice Hall, Englewood Cliffs (2001)
Special Literature
G. Klebe, Structure Correlation and Ligand/Receptor Interactions, in:
Structure Correlation, H. B. Bürgi and J. D. Dunitz, Eds., VCH,
Weinheim, p. 543–603 (1994)
G. R. Marshall and C. B. Naylor, Use of Molecular Graphics for Struc-
tural Analysis of Small Molecules, in: Comprehensive Medicinal
Chemistry, C. Hansch, P.G. Sammes and J.B. Taylor, Eds., Vol. 4,
Pergamon Press Oxford, p. 431–458 (1990)
G. Klebe and T. Mietzner, A Fast and Efcient Method to Generate
Biologically Relevant Conformations, J. Comput.-Aided Mol. Design, 8, 583–606 (1994)
H. J. Böhm and G. Klebe, What can we learn from molecular recog-
nition in protein–ligand complexes for the design of new drugs?
Angew. Chem. Intl. Ed. Eng., 35, 2588–2614 (1996)
G. Klebe, Toward a More Efcient Handling of Conformational Flex-
ibility in Computer-Assisted Modelling of Drug Molecules, Persp.
Drug Design and Discov., 3, 85–105 (1995)
B. Stegemann, G. Klebe, Cofactor-binding sites in proteins of deviating
sequence: Comparative analysis and clustering in torsion angle,
cavity, and fold space. Proteins, 80, 626–648 (2011)
I. J. Bruno, J. C. Cole, M. Kessler, Jie Luo, W. D. S. Motherwell, L. H.
Purkis, B. R. Smith, R. Taylor, R. I. Cooper, S. E. Harris and A.
G. Orpen, Retrieval of Crystallographically-Derived Molecular
Geometry Information, J. Chem. Inf. Comput. Sci., 44, 2133–2144
(2004)
S. J. Cottrell, T. S. G. Olsson, R. Taylor, J. C. Cole and J. W. Liebes-
chuetz, Validating and Understanding Ring Conformations Using
Small Molecule Crystallographic Data, J. Chem. Inf. Model., 52,
956–962 (2012)
Mogul, assessment of molecular conformations: https://www.ccdc.cam.
ac.uk/solutions/software/mogul/ (Last accessed Nov. 18, 2024)
Quantitative Structure-
Activity Relationships
and Design Approaches
IV
Today, drug design is supported by numerous computational approaches that, like
the pieces of apuzzle, all make their contributions from the rst design hypothesis to aclinical drug candidate (announcement poster from the author’s working group on the occasion of aconference in 2005, Rauischholzhausen, Marburg,
Germany).
Contents
Chapter 17 Pharmacophore Hypotheses and
Molecular Comparisons – 257
Chapter 18 Quantitative Structure–Activity Relationships – 273
Chapter 19 From In Vitro to In Vivo: Optimization of
ADME and Toxicology Properties – 291
Chapter 20 Protein Modeling and Structure-
Based Drug Design – 309
Chapter 21 A Case Study: Structure-Based Inhibitor Design
for tRNA-Guanine Transglycosylase – 323
Pharmacophore Hypotheses
and Molecular Comparisons
Contents
17.1 The Pharmacophore Anchors aDrug Molecule
in the Binding Pocket – 258
17.2 Structural Superposition of Drug Molecules – 258
17.3 Logical Operations with Molecular Volumes – 259
17.4 The Pharmacophore is Modied
by Conformational Transitions – 260
17.5 Systematic Conformational Search and Pharmacophore
Hypothesis: The “Active Analog Approach” – 262
17.6 Molecular Recognition Properties and the
Similarity of Molecules – 263
17.7 Automated Molecular Comparisons and Superpositioning
Based on Recognition Properties – 264
17.8 Rigid Analogues Trace the Biologically
Active Conformation – 266
17.9 If Rigid Analogues are Lacking: Model Compounds
Elucidate the Active Conformation – 266
17.10 The Protein Denes the Pharmacophore: “Hot
Spot” Analysis of the Binding Pocket – 267
17.11 Searching for Pharmacophore Patterns in Databases
Generates Ideas for Novel Lead Compounds – 270
17.12 Synopsis – 271
Bibliography and Further Reading – 272
© The Author(s), under exclusive license to Springer-Verlag GmbH, DE, part of Springer Nature 2024
G. Klebe, Drug Design, https://doi.org/10.1007/978-3-662-68998-1_17
Chapter • Pharmacophore Hypotheses and Molecular Comparisons
1
17
Emil Fischer’s lock-and-key principle (Sect.4.1) demon-
strates the specic interaction of an active compound
with its receptor. In the case of the key, the spikes and
notches on its beard interact with the pins of the lock
to open it. With active substances, it is aspecic part
of the molecule that interacts with the amino acids in
the binding pocket. In drug design, similar molecules are
often compared to generate ideas for new structures. This
chapter summarizes the criteria that make such comparisons possible. These criteria can also be used to search
databases for alternative molecules that may bind to the
protein in the same way.
17.1 The Pharmacophore Anchors aDrug
Molecule in the Binding Pocket
The structure of the binding pocket determines which
functional groups are necessary for ligand binding. The
spatial orientation of these functional groups in ligands
is referred to as the pharmacophore (Sect.8.7, . Fig.8.9).
Because of its importance in drug design and model
hypothesis in medicinal chemistry, an ofcial IUPAC
denition has been established by CamilleG. Wermuth
(. Table17.1). The interacting groups that aligand must
possess in order to interact successfully with aprotein dene the pharmacophore in space and are independent of
the specic molecular scaffold to which they are attached.
Hydrogen bonding groups or hydrophobic moieties are
considered. Amore detailed examination distinguishes between positively and negatively charged groups in amolecule. When derived from aset of similarly binding ligands, this generalized description is called aligand-based
pharmacophore. On the other hand, the protein structure
can also be used as astarting point. This is done by analyzing which amino acid functional groups are located
in the binding pocket. They dene the properties with
which aligand can bind to them. In this sense, the protein
structure determines how the pharmacophore of aligand
must be shaped to successfully bind to the protein. This
description is called the protein-based pharmacophore. In
contrast to the lock-and-key image, ligands and proteins
are exible. In ligands, the functional groups of the pharmacophore must be oriented towards the corresponding
counter groups in the protein. Therefore, detailed knowledge of the conformational properties of the ligand is
essential. Only then can it be predicted whether aligand
can potentially adopt ageometry that satises the interactions with the protein. On the side of the receptor, the
geometry of the binding pocket can adapt to the shape
of the ligand, similar to how aglove ts the hand of its
wearer (induced t, Sect.4.1). In fact, binding pockets are
found in the interior or in buried grooves on the surface of
proteins, and it is there that the small but crucial conformational changes of the protein take place. An example of
the adaptability of aprotein is presented in Sect.15.8. An
attempt is made to describe these induced-t adaptations,
or the selection of binding-competent conformers of the
protein, using molecular dynamics simulations.
17.2 Structural Superposition
of Drug Molecules
For the moment, we will limit ourselves to examples
where the receptor structure is unknown. All the effects
of ligand binding on the protein are, therefore, neglected.
An example should illustrate this. The fruit of the shrub
Anamirta cocculus, the sh berry, contains the terpene
picrotoxinin 17.1, which causes convulsions. This compound acts on the chloride channel by blocking it at
anarrow, constricted site like aplug (Sect.30.7). Because
of its central stimulatory effect, it has been used in the
past as an antidote to sleeping pill overdoses. Due to its
high toxicity, it is no longer of any therapeutic importance. The structure of picrotoxinin has been determined
by crystallography (. Fig.17.1).
Synthetic modications of the cyclic core structure
have led to active and inactive derivatives (. Fig.17.2).
The three-dimensional structure of each derivative can be
constructed on the computer from the crystal structure of
the parent compound. They are superimposed upon one
another to identify their structural differences. The parts
of the molecules that are considered equivalent in ali-
. Table 17.1 Ofcial IUPAC denition of apharmacophore. (C.G. Wermuth etal. Pure Appl. Chem., 70, 1129–1143 (1998))
– Apharmacophore is the ensemble of steric and electronic features that is necessary to ensure the optimal supramolecular interactions with aspecic biological target structure and to trigger (or to block) its biological response.
– Apharmacophore does not represent areal molecule or areal association of functional groups, but apurely abstract concept that
accounts for the common molecular interaction capacities of agroup of compounds towards their target structure.
– Apharmacophore can be considered as the largest common denominator shared by aset of active molecules. This denition
discards amisuse often found in the medicinal chemistry literature, which consists of naming as pharmacophores simple chemical
functionalities such as guanidines, sulfonamides, or dihydroimidazoles (formerly imidazolines), or typical structural skeletons such as
avones, phenothiazines, prostaglandins, or steroids.
– Apharmacophore is dened by pharmacophoric descriptors, including H-bonding, hydrophobic, and electrostatic interaction sites,
dened by atoms, ring centers, and virtual points.
. • Logical Operations with Molecular Volumes
. Fig. 17.1 Picrotoxinin 17.1 is responsible for the centrally stimu-
lating effect of the extracts of sh berries. Its structure and spatial architecture were determined by X-ray structure analysis. The molecule
inhibits the chloride channel and blocks the channel so that no more
ions can pass (Sect.30.7)
gand-based pharmacophore model are overlaid for this
superposition. The superposition of all active and inactive derivatives along with the common volumes of both
classes is shown in . Fig.17.3. The difference between
the two volumes is computed. It describes those areas in
space that are occupied only by the inactive derivatives.
Since the structure of the chloride channel was determined many years later, it can be veried that the highlighted difference volume would actually extend into the
occupied structural space of the protein. In . Fig.17.3,
the lower part shows the binding site of picrotoxin in the
channel. When the superimposed structures are placed in
the channel, steric conicts for the inactive derivatives are
indeed indicated by the differential volume.
17.3 Logical Operations
with Molecular Volumes
What information can be extracted from such comparative volumes? The working hypothesis is that amolecule
can only be bound if its size does not exceed the maximum available space. What is the maximum available
space? To get an idea, the common volumes of all active
derivatives are considered and compared with the volumes of all inactive derivatives. Apossible explanation
for the lack of activity of amolecule could then be that
the area in the binding pocket that the molecule would
likely occupy is already occupied by the protein.
Volume comparisons between active and inactive derivatives provide information about the possible shape of
the receptor pocket. Such comparisons can be very useful
in drug design. Once the “forbidden” volume area has
been determined for acompound class, it can be veried
prior to synthesis whether acompound really leaves the
“forbidden” area unoccupied.
. Fig. 17.2 By starting with picrotoxinin 17.1, active and inactive derivatives were synthesized.
1
Chapter • Pharmacophore Hypotheses and Molecular Comparisons
17
. Fig. 17.3 Superposition of the spatial structure of active (orange)
and inactive (cyan) derivatives of picrotoxinin. The united volumes
around the active derivatives are shown by the red mesh. The total
volume around all inactive derivatives is shown in blue. Adifference is
formed between the two volumes. The remaining volume (green) shows
areas that are only occupied by inactive derivatives. An explanation for
the lack of activity of these derivatives can be that they try to occupy
volume areas that are already occupied by the receptor protein. This
Due to the rigidity of the molecule, it is easy to superimpose the analogues of picrotoxin on one another.
However, in the case of exible molecules, the transition
from a2D molecular representation to a3D structure
(Chaps.15 and16) cannot be expected to yield molecules
in conformations in which all the functional groups of
the pharmacophore are already placed analogously in
space. Therefore, two issues need to be addressed:
The groups that correspond to one another in differ-
-
ent molecules and dene the pharmacophore must be
determined, and
Techniques are needed that bring the molecules into
-
conformations in which the equivalent groups of the
pharmacophores are analogously oriented in space.
17.4 The Pharmacophore is Modified
by Conformational Transitions
To solve the rst problem, it is necessary to consider
the role of the functional groups of an active substance
that make contact with the receptor. They must form
hydrogen bonds and hydrophobic interactions with the
spatial clash does not occur with the active derivatives. Comparing the
geometry of the protein environment of the chloride channel (bottom
left, overview) shows that the green contoured volume indeed indicates
steric conicts with spatially occupied regions of the protein (detailed
view, bottom right). The comparison is based on acryo-EM structure
of the complex of the channel with picrotoxinin (Sect.30.7) which was
determined many years later
protein. In this context, the similarity of the functional
groups means that they can form analogous interactions
with the protein. To dene apharmacophore in space, at
least three interacting groups are required. This can be
illustrated by considering how many ngers are needed to
hold arandomly shaped object (e.g., apotato) in space.
With only two ngers, the object can still rotate about
an axis. However, with three anchor points, its position
in space is xed. Practical experience with acompound
class is often helpful in assigning pharmacophoric
groups. For example, inhibitors of angiotensin-converting enzyme (. Fig.17.4 andSect.25.5) require aterminal carboxylate group, acarbonyl group, and agroup
that coordinates to the catalytic zinc ion.
How can it be determined whether acommon orientation exists for the assumed equivalent groups in
different molecules? In acomputational method, these
groups are assigned “virtual” springs that are coupled to
one another. The spatial overlap is increased by pulling
these springs together. To avoid acompletely distorted
molecular geometry, aforce eld is simultaneously considered for each molecule (Chap.15). As an example,
the steroid 17.2 and three different inhibitors 17.3–17.5