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Chapter • Peptidomimetics
10
. Fig. 10.14 Concept of a3D search for scaffold mimetics with the
program CAVEAT. First, the relative orientation of the biologically relevant side chains in the peptide lead structure is dened by the
Cα–Cβ bond vectors. In this example, the three amino acids Trp, Arg,
and Tyr are taken as essential. The three vectorsA, B, andC are the
10.9 Design of Peptidomimetics:
Quo Vadis?
In this chapter, the systematic approach to the design
of peptidomimetics has been described. The approaches
have proven themselves in many cases and have led to
many attractive drugs. Nevertheless, there are also difculties. The rst problem is the stepwise approach.
Apeptide is systematically modied, and the synthesized
structures serve only to identify the essential functional
groups. The synthesis of the many resultant derivatives,
that is, practically all in which an amide group was replaced by one of the structures in . Fig.10.4, is laborious. Furthermore, these compounds only serve as tools
because most modied peptides have high molecular
weights, and this can result in poor oral bioavailability.
In the past, many new nonpeptidic active substances, especially as receptor antagonists, were found in
high-throughput screening, and these could frequently
be developed into clinical candidates in arelatively short
time. These successes have pushed rational approaches
to peptidomimetic design from the forefront. Nevertheless, the design of peptidomimetics remains an important area of research in drug design. The terphenyl scaffold helix mimetics are an example of this. The peptidic
nature of many of the enzyme inhibitors presented in
Chaps.23,24,25 is still evident. Here, the peptidic sub-
strate was clearly the inspiration for the design of ami-
metic. Therefore, peptidomimetic concepts continue to
play an important role in lead optimization.
10.10 Synopsis
Peptides are open-chain polymeric molecules made
-
up of amino acids that are mutually linked by amide
bonds. Side chains branch from the main chain at
the Cα atoms and show ahigh degree of exibility.
If such apolymer contains up to 30–50 amino acids,
crucial information used to search the 3D database for rigid scaffold
structures that bear substitutable bonds in the same relative orientation. Alist of cyclic structures that represent possible templates for
peptidomimetics is the result
it is called apeptide; beyond this limit, it is called
aprotein.
Peptides are responsible for many biological func-
-
tions; their applicability as drugs is limited due to size,
polarity, and poor proteolytic stability.
Due to their multiple functions, peptides can be mim-
-
icked by smaller—similarly binding—and metabolically stable peptidomimetics.
Peptidomimetic design starts with the identication
-
of the minimal peptide sequence responsible for abiological effect, followed by successive replacement of
each amino acid in the chain with alanine to detect
the side chains responsible for activity. Finally, individual amino acids are replaced by nonproteinogenic
ones or similar chemical building blocks.
Multiple surrogates for amino acid side chains have
-
been developed and can be tested to reveal better
binding and conformationally more stable peptidomimetics. If not involved in direct binding, main-chain
amide bonds can be replaced by alarge variety of
substitutes that achieve asimilar geometry.
Peptides are exible and adopt multiple conforma-
-
tions. If aparticular fold is adopted to correctly orient interacting side chains, the peptide backbone can
be replaced by an entirely different scaffold that correctly positions the essential interacting groups.
Peptides fold upon themselves through particular
-
turn patterns. These turns stabilize arequired conformation and can be chemically replaced by rigid
structural surrogates that freeze agiven turn conformation.
Proteins communicate with one another through the
-
formation of large, mutually shared surface patches.
Small molecules designed to bind to such at surfaces
can antagonize complex formation and interfere with
protein–protein communication.
Design of small molecules to block protein–protein
-
interfaces exploits depressions on the surface that accommodate spatial patterns such as turns or helical
Bibliography and Further Reading
portions of the penetrating contact surface of the
binding partner protein.
Peptides bind to receptors mostly via side chains, and
-
the backbone provides the scaffold for their attach-
ment. Computer programs can be used to screen struc-
tural databases to retrieve alternative scaffolds that are
able to orient substituents in very similar fashion.
Bibliography and Further Reading
General Literature
A. Giannis and T. Kolter, Peptidomimetics for receptor ligands—dis-
covery, development, and medical perspectives. Angew. Chem. Int.
Ed. Engl., 32,1244–1267 (1993)
J. Gante, Peptidomimetics—tailored enzyme inhibitors, Angew. Chem.
Int. Ed. Engl., 33, 1699–1701 (1994)
J.-M. Ahn, N. A. Boyle, M. T. MacDonald and K. D. Janda Peptid-
omimetics and Peptide Backbone Modications, Mini Rev. Med.
Chem., 2, 463–473 (2002)
M. A. Marahiel, Working outside the protein-synthesis rules: in-
sights into non-ribosomal peptide synthesis. J. Pept. Sci., 15,
799–807(2009)
R. Hirschmann, Medicinal chemistry in the golden age of biology:
lessons from steroid and peptide research. Angew. Chem. Int. Ed.
Engl., 30,1278–1301 (1991)
J. J. Perez, Designing Peptidomimetics, Curr. Top. Med. Chem., 18,
566–590 (2018)
Special Literature
G. L. Olson, D. R. Bolin, M. P. Bonner etal., Concepts and Prog-
ress in the Development of Peptide Mimetics, J. Med. Chem. 36,
3039–3049 (1993)
W. Howson, Rational Design of Tachykinin Receptor Antagonists,
Drug News & Perspectives, 8, 97–103 (1995)
A. M. McLeod, K. J. Merchant, M. A. Cascieri etal., N-Acyl-L-tryp-
tophan Benzyl Esters: Potent Substance P Receptor Antagonists,
J. Med. Chem., 36, 2044–2045 (1993)
K. J. Merchant, R. T. Lewis and A. M. MacLeod, Synthesis of Homo-
chiral Ketones Derived from L-Tryptophan: Potent Substance P
Receptor Antagonists, Tetrahedron Letters, 35, 4205–4208 (1994)
T. Oltersdorf etal. An inhibitor of Bcl-2 family proteins induces regres-
sion of solid tumours, Nature, 435, 677–681 (2005)
B. Vu etal., Discovery of RG7112: A Small-Molecule MDM2 Inhib-
itor in Clinical Development, ACS Med. Chem. Lett., 4, 466–469
(2013)
G. Lauri and P. A. Bartlett, CAVEAT: A Program to Facilitate the
Design of Organic Molecules, J. Comput.-Aided Mol. Design, 8,
51–66 (1994)
G. Lelais and D. Seebach, β2-Amino Acids-Synthesis, Occurrence in
Natural Products, and Components of β-Peptides, Biopolymers,
76, 206–243 (2004)
Experimental and
Theoretical Methods
III
Aprerequisite for the 3D structure determination of aprotein by the method of
Xray crystallography is the availability of acrystal (Chap.13). The gure shows
aset of crystals of acomplex of protein kinaseA, which was used to elucidate the
structure and reaction mechanism of this class of enzymes (Chap.26). (Courtesy of
Dr. Dirk Bossenmeyer, German Cancer Research Center, Heidelberg).
Contents
Chapter 11 Combinatorics: Chemistry with Big Numbers – 153
Chapter 12 Gene Technology in Drug Research – 169
Chapter 13 Experimental Methods of Structure
Determination – 193
Chapter 14 Three-Dimensional Structure of Biomolecules – 215
Chapter 15 Molecular Modeling – 233
Chapter 16 Conformational Analysis – 247
Combinatorics: Chemistry
with Big Numbers
Contents
11.1 How Nature Produces Chemical Multiplicity – 154
11.2 Protein Biosynthesis as aTool to Build
Compound Libraries – 155
11.3 Organic Chemistry from aDierent Angle: RandomGuided Synthesis of Compound Mixtures – 155
11.4 What Is Contained in Chemical Space? – 156
11.5 Compound Libraries on Solid Support: Complete
Conversion and Easy Purication – 157
11.6 Compound Libraries on Solid Support Need
Sophisticated Synthetic Strategies – 157
11.7 Which Compound in the Solid Support Combinatorial
Library Is Biologically Active? – 158
11.8 Combinatorial Libraries with Large Diversity: AChallenge
for Synthetic Chemistry – 159
11.9 Nanomolar Ligands for G-Protein-Coupled Receptors – 160
11.10 More Potent than Captopril: AHit from aCombinatorial
Library of Substituted Pyrrolidines – 161
11.11 Parallel or Combinatorial, in Solution
or on aSolid Support? – 161
11.12 The Protein Finds Its Own Optimal Ligand: Click Chemistry
and Dynamic Combinatorial Chemistry – 163
11.13 Synopsis – 165
Bibliography and Further Reading – 166
© 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_11
Chapter • Combinatorics: Chemistry with Big Numbers
11
The search for new lead structures and the optimization of their activity prole by systematic modication
are among the most time- and cost-demanding steps
in drug research. The optimization of a small organic
molecule can serve as an example. Even if the number
of different groups per position is limited to relatively
few, several million structures are possible as exemplarily
shown in the case of the multisubstituted tetrahydroisoquinoline carboxylic acid amide 11.1 (. Fig. 11.1).
The combinatorial explosion of all imaginable substitution possibilities can no longer be realized with classical chemical techniques. The diversity increases even
more when the different stereoisomers are considered.
Their number is, thus, on the order of magnitude of all
chemical structures recorded in Chemical Abstracts (160
million compounds, of which 68million are protein and
nucleic acid sequences) or in Beilstein (in Reaxys, 118
million compounds).
In the days when compounds were tested on whole
animals or in complex in vitro pharmacological models,
biological testing was the rate-determining step. The
introduction of molecular test models, such as enzyme
or receptor binding assays, and extensive automation
of screening has fundamentally changed this situation.
The testing of many thousands of compounds per day
is technically unproblematic (Sect.7.3). To fully exploit
the capacity of these methods, the synthesis of thousands
or even tens or hundreds of thousands of different molecules is desirable. The strategy can then shift either to
automated parallel synthesis to cover alarge number of
single compounds, or to the simultaneous production of
compound mixtures using combinatorial chemistry.
11.1 How Nature Produces Chemical
Multiplicity
Nature has shown away to achieve combinatorial diversity with the nucleic acids and with proteins. A600-basepair DNA sequence codes aprotein with 200 amino acids. From the “pool” of four nucleic acids that code for
the 20proteinogenic amino acids in triplet sequences,
600
4
(anumber with 360 digits!) different DNA sequences
are possible. This translates to 20
digits!) different amino acid sequences for the resulting
protein. Short peptides with enormous structural variety
can be constructed with just the 20proteinogenic amino
acids. If instead of amino acidA, amanageable number
of modied amino acidsM is used, the number of possible analogues will increase even more (. Table11.1).
Peptides play an important role in biological systems.
They are found as protein ligands in the free form or as
simple derivatives. Peptide sequences on the surface of
proteins determine their recognition by areceptor. For
this selective recognition, Nature exploits the full combinatorial diversity of the variable sequences in the surface
regions (epitopes) of proteins. These principles of Nature
can be used to generate vast libraries of compounds with
widely varying compositions.
. Table 11.1 A total of 400dipeptides, 8000 tripeptides,
160,000 tetrapeptides, and 64million hexapeptides can be
generated from the 20proteinogenic amino acids,A. If the
palette is expanded to 100 modied, nonproteinogenic amino
acids,M, the combinatorial diversity increases dramatically
200
(anumber with 260
. Fig. 11.1 The tetrahydroisoquinoline carboxylic acid amide 11.1
is to be substituted in 10positions. The groups in these positions encompass amultiplicity of a total of 68 building blocks (R1–R10 = 5,
10, 10, 4, 5, 5, 5, 2, 2, 20groups). Twenty million compounds can be
constructed in this way. If the structural diversity that results from
the two stereocenters (*) is considered, this number increases again
by afactor of4
Compounds Number
Natural amino acids,A 20
Dipeptides, A–A 400
Tripeptides, A–A–A 8000
Tetrapeptides, A–A–A–A 160,000
Hexapeptides, A–A–A–A–
A–A
Modied amino acids,M
Modied hexapeptides,
M–M–M–M–M–M
Number of known com-
pounds
64,000,000
100 (for example)
1,000,000,000,000
> 33,000,000
. • Organic Chemistry from aDierent Angle: Random-Guided Synthesis of Compound Mixtures
11.2 Protein Biosynthesis as aTool to
Build Compound Libraries
How can the biochemical synthesis machinery be used as
avehicle to generate amultiplicity of peptide sequences?
It is possible to connect short sequences to acarrier protein so that they are exposed on the surface and can interact with the target protein in amolecular test system. The
test system is constructed in away that the binding to the
target protein is monitored with an easily registered signal, for instance, auorescence signal or acolorimetric
reaction (Sect.7.2).
To use protein biosynthesis to construct such alibrary,
the information about the randomly assembled peptides
must be added to the “genetic make-up” of aDNA molecule. This molecule encodes the sequence of the protein
on whose surface the library will be presented and, in
addition, the randomly assembled double-stranded DNA
sequences of individual members of the peptide library.
The information of the latter is inserted into the DNA at
an appropriate position. After producing alarge number
of identical copies (cloning), the resulting genes can be
expressed. This produces alarge population of proteins
that carry the randomly assembled peptide sequences in
avery specic region, usually at the beginning or end of
the polymer sequence. The resulting proteins are then
examined in amolecular test system. The distribution
of the 20proteinogenic amino acids over the variable
sequence section is not entirely homogenous. That is because some amino acids are coded with asingle triplet
sequence (codons), and others are represented with up to
six different codons (Sect.32.7, . Fig.32.16). Because of
this, biased libraries are inevitably formed.
The bacteriophage M13 is an extremely popular expression system. M13 is avirus that infects Escherichia
coli strains well. The virus carries six proteins on its coat.
Two of these coat proteins allow randomly assembled
protein sections to be added to their ends. Using this M13
system, alibrary of 20million modied 15-mer peptides
was generated. Their binding to the protein streptavidin
was tested. Atotal of 58candidates were identied as
binding partners. They all shared the sequence segment
–His–Pro–Gln–. The crystal structure of one of these oligopeptides complexed with streptavidin was successfully
determined. The peptide occupies with its His–Pro–Gln
segment the binding pocket normally populated by biotin. This demonstrates that such astrategy can be used
to nd selectively binding peptide sequences.
The biochemical approach to generating and presenting compound libraries has the overwhelming advantage
that the high-capacity protein biosynthesis is exploited.
Furthermore, the sophisticated protein and DNA synthesis techniques and analytical methods that have been
developed for such substances (Sect.11.7) can be used to
characterize screening hits. But it also has disadvantages.
The molecular diversity is limited to the 20proteinogenic
l
-amino acids, and only peptides result as lead structures.
These are often the starting point for the development of
adrug. However, we wish to move away from metabolically unstable, poorly bioavailable peptides. Therefore,
structures are sought using classical organic molecular
scaffolds. At least peptidomimetics or peptides with
metabolically stable nonproteinogenic amino acids are
desired. Unfortunately, the step away from peptides to
alternative scaffolds that retain biological activity is not
trivial (Chap.10).
11.3 Organic Chemistry from aDifferent
Angle: Random-Guided Synthesis of
Compound Mixtures
Organic preparative methods were devised as an alternative to the biological approaches to generate compound
libraries. Simple access to acompound library is gained
by starting with reactive molecular building blocks, such
as oligofunctional acid chlorides (11.2–11.4, . Fig.11.2).
These components are simultaneously reacted with numerous reagents, for example, amines or amino acids.
Amixture of many products is formed in an uncontrolled
manner. Contrary to the general academic opinion that
organic reactions should only deliver homogenous products, in this case as much product diversity as possible is
desired. The advantage of this method is that it is easy
to carry out and that automation is readily implemented.
But this synthesis strategy also has disadvantages. The
. Fig. 11.2 The oligofunctional acid chlorides of the central build-
ing blocks cubane 11.2, xanthene 11.3, and benzene 11.4 are treated with protected amino acids (AA1–AA4). A xanthene-containing
library inhibits the digestive enzyme trypsin. The active component
of the library was deconvoluted and characterized by targeted resynthesis. In the end, isomers 11.5 and 11.6 remained as the most potent
compounds. The derivative 11.5 inhibits trypsin with aK
of 9.4 μM
i
Chapter • Combinatorics: Chemistry with Big Numbers
11
coupling partners have different reactivities. As aresult,
the products are not evenly distributed. The transformation of a particular functional group on the central
building block can depend upon which components the
central molecule has already reacted with and how this
inuences the other functional groups.
The thus-generated library is then tested. If binding
to the target protein is found, the active substance in the
mixture will be characterized, atask that is not particularly simple. On the one hand, sophisticated analytical
techniques such as liquid chromatography coupled with
NMR spectroscopy and mass spectrometry can be used.
Moreover, an attempt can be made to “deconvolute” the
library. For this, a targeted resynthesis of the library is
carried out in which apartial library is prepared by using adened selection of building blocks. This smaller
library is then tested and the composition of the active
mixture is determined. This strategy must be followed
back to the level of single dened reaction products.
11.4 What Is Contained in Chemical Space?
At this point, the fundamental question must be asked:
how many organic molecules are principally possible
from which medicinal chemists can create their candidates? What does such an initially virtual chemical space
contain? Much has been speculated about this question.
Numbers between 1020 and 10
200
possible molecules have
been named. The last claim encompasses so many molecules that the entire mass of the universe would not be
enough to synthesize at least one molecule of every compound! It is through the work of Jean-Louis Reymond’s
group at the University of Berne, Switzerland, that we
now have asomewhat more solid idea about the principal
composition of chemical space. Starting with mathematical graphs describing simple hydrocarbon scaffolds, molecules with up to 17C, N, O, S, and halogen atoms have
been generated on the computer. This selection covers
aquite relevant molecular size (up to 350 Da), as 367 of
today’s approved drugs comprise ≤ 17atoms. Combinatorially, heteroatoms and unsaturated bonds were scattered over the generated molecular graphs. Various lters, considering the chemical stability of the introduced
functional groups, the strain of generated ring systems
and the formation of tautomeric forms, resulted in the
end in adatabase of 166,443,860,262 structures. Comparing this number with the known biologically active
substances (about 2.5million) that meet the criteria of
molecules with 17atoms, it seems that only asmall fraction has been synthesized so far. It is interesting to notice
that the number of entries increases exponentially with
the square of the atomic number. For 15active agents
currently on the market with 14–17atoms, several million isomers with the same empirical formula can be
identied in each case. Molecules with small ring sys-
tems or nonaromatic heterocycles are encountered with
greater extent in the systematically generated database.
Since substances with these building blocks are more
difcult to synthesize or often do not have the required
stability, they occur much less frequently in the molecules synthesized to date. Also, acyclic substances can
be discovered in the systematically generated database
much less frequently than actually approved active substances can be found with this composition. Overall, the
generated database entries show ahigher proportion of
polar compounds compared to the known active substances. It is also signicant that many more molecules
with aspatially bulkier structure appear in the systematically generated database. Candidates from medicinal
chemistry often tend to have aspatially at geometry.
At this point, there is often acall for natural products as
supposedly better candidates for drug development, since
they usually have a“higher three-dimensionality” (often
described as “escape out of the molecular atland”). It
is all the more interesting to see that the database of systematically generated molecules comprises asignicantly
higher number of stereogenic centers per molecule than
is actually realized in the collection of known active substances of comparable size.
It is worthwhile to take acloser look at asmaller database comprising molecules of a size up to 11non-H
atoms. The average molecular mass in this database is
153 ± 7 Da. Molecules of this size fall into the range of
typical fragments or “lead-like” molecules (Sect.7.9). Exclusion criteria were proposed that emphasize promising
candidates for drug development. The so-called “rule of
three” leans on the “rule of ve,” which was established
by Chris Lipinski at Pzer (Sect.19.7). If the database is
ltered with these rules, approximately half of the entries
will remain. Of these, ca.15% are acyclic compounds, and
about 43% contain one ring. It is very enlightening to see
that only about 55% of the ring systems in the virtual database have been described in Chemical Abstracts or Beil-
stein. Comparison with adata collection of already-synthesized molecules of the same size makes clear where
the chemical space has been only sketchily explored. It
seems that very large gaps still exist! Over 99.8% of the
entries in the virtual database are waiting to be synthesized. Acomparison of the physicochemical properties of
the molecules in both databases suggests that very broad
areas still remain that until now have not been explored.
If the chemical space is limited to compounds with 7, 8,
or 9atoms, it seems that the chemical space is well covered with already prepared molecules. Approximately 2/3
of the molecules with 10 or 11atoms in the virtual database are chiral. In this group particularly, there are many
candidates that meet the “lead-like” criteria. This is areal
challenge for synthetic chemists. Chiral fused carbo- and
heterocycles are difcult to make. Nevertheless, Nature
has led the way: many biologically active natural products
contain just these building blocks.
. • Compound Libraries on Solid Support Need Sophisticated Synthetic Strategies
11.5 Compound Libraries on Solid
Support: Complete Conversion and
Easy Purification
An interesting variation to classical chemistry in solution
is found in the synthesis of compound libraries on solid
supports. Organic polymers, usually cross-linked polystyrenes, are used as carriers. This material is chemically
modied so that it carries numerous reactive functional
groups of a particular sort, for example, chloromethyl,
carboxylate, or amino groups. Through these groups,
the reaction product remains covalently attached to the
insoluble polymer during the synthetic steps. Stepwise
growth of the product is accomplished by coupling with
appropriately protected building blocks (e.g., amino acids) and subsequent cleavage of these protecting groups.
Large excess of reagents causes fast and nearly complete
transformations. Unreacted starting materials can be removed by simple washing. After assembly of the target
molecule, all protecting groups are removed. At the end
of the synthesis, the product is either tested directly on
the support or it is cleaved and its biological activity is
tested in solution (Sect.11.7).
The technique can be easily automated. In the beginning of the 1960s, Robert Bruce Merrield developed
solid-phase synthesis for peptides and small proteins
(. Fig. 11.3). This earned him the 1984 Nobel Prize
in Chemistry. At the beginning of the 1980s, the idea
to use synthetic combinatorial principles for peptide
synthesis emerged for the rst time. H.Mario Geysen
devised amultipin synthesis of peptides. By using aconventional Merrield solid-phase synthesis, 96different
peptides or dened peptide mixtures were prepared in
an 8 × 12 format on polymer pins. This concept was so
revolutionary that the originally submitted manuscript
was rejected for publication in 1984. The referees were
too severely restricted by their traditional thinking. The
absolute control of stoichiometry and yield were less in
the foreground for Geysen, rather the creation of combinatorial diversity with minimal effort was more important. In this way, thousands of different peptides could be
prepared weekly. Entire libraries of compounds could be
prepared and tested. The new methods were originally
used for “epitope mapping,” that is, the structural probing of the surface of aprotein with different antibodies
(Sect.32.1). This technique allows the recognition of
areas in apolypeptide chain that are exposed to the surface of aprotein. Later it served the search for optimal
sequences of protease substrates (Sect.14.6) and for the
synthesis of biologically active peptides. In addition to
the multipin method, high-efciency methods have been
established, for instance, the teabag method. Support
beads are lled into teabags and dipped into solutions of
protected amino acids with which their peptide sequence
is to be elongated.
. Fig. 11.3 The Merrield peptide synthesis is assembled on apoly-
meric resin that is functionalized in an appropriate way. The rst
N-terminal-protected amino acid is coupled to the chloromethylene
group (Boc=tert-butoxycarbonyl protecting group). Then the amino
group is released, activated with dicyclohexylcarbodiimide (DCCI),
and coupled with asecond amino acid. The N-terminus of the resulting dipeptide can be deprotected and elongated. It can also be cleaved
from the resin under strongly acidic conditions as apeptide
11.6 Compound Libraries on Solid Support
Need Sophisticated Synthetic
Strategies
Ahighly sophisticated synthetic strategy is required to
build compound libraries. Hexapeptides are considered
as an example. In principle, all 20proteinogenic amino
acids could be used and 206 = 64million hexapeptides
prepared and individually tested—an impossible undertaking. Therefore, intelligent strategies are needed to
quickly identify biologically active sequences. As aconsequence, an attempt is made to summarize the 64million peptides in partial libraries. They contain constant
amino acids in xed positions. For example, all 400 partial libraries should be prepared for all possible hexapeptides with the form XXABXX (A, B=predened amino
acids, and Xis any mixture of proteinogenic amino ac-