Computational Biology - Bioinformatik

1
How many drug targets are there? In 2002, after
the sequencing of the human genome, others
arrived at 8,000 targets of pharmacological
interest, of which nearly 5,000 could be
potentially hit by traditional drug substances,
nearly 2,400 by antibodies and 800 by
protein pharmaceuticals2. And on the basis of
ligand-binding studies, 399 molecular targets
were identified belonging to 130 protein
families, and 3,000 targets for small-molecule
drugs were predicted to exist by extrapolations
from the number of currently identified such
targets in the human genome.
2
Drug Target Enzymes
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Drug Target Enzymes II
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Drug Target Enzymes III
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Drug Target Enzymes III
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Drug Target Receptors I
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Drug Target Receptors II
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Drug Target Receptors III
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Drug Target Receptors III
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Drug Target Ion channels
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Drug Target Transport proteins
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Drug Target DNA/RNA and the ribosome
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Drug Target Targets of monoclonal antibodies
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Drug Target Various physicochemical mechanisms
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Outlook
A large part of this paper is concerned with the
nature of drug targets and the need to consider
the dynamics of the drugtargets (plural
intended) interactions, as these considerations
were used to define what we would eventually
count. Many successful drugs have emerged from
the simplistic one drug, one target, one
disease approach that continues to dominate
pharmaceutical thinking, and we have generally
used this approach when counting targets here.
However, there is an increasing readiness to
challenge this paradigm. We have discussed its
constraints and limitations in light of the
emerging network view of targets. The recent
progress made in our understanding of biochemical
pathways and their interaction with drugs is
impressive. However, it may be that the more
you know, the harder it gets. It is not the
final number of targets we counted that is the
most important aspect of this Perspective
rather, we stress how considerations about what
to count can help us gauge the scope and
limitations of our understanding of molecular
reaction partners of active pharmaceutical
ingredients. Targets are highly sophisticated,
delicate regulatory pathways and feedback loops
but, at present, we are still mainly designing
drugs that can single out and, as we tellingly
say, hit certain biochemical units the simple
definable, identifiable targets as described
here. This is not as much as we might have hoped
for, but in keeping with the saying of one of
earliest medical practitioners,
Hippocrates Life is short, and art long the
crisis fleeting experience perilous, and
decision difficult. Humility remains important
in medical pharmaceutical sciences and practice.
16
Specific example protein kinases
Phosphorylation of serine, threonine, and
tyrosine residues is a primary mechanism for
regulating protein function in eukaryotic cells.
Protein kinases, the enzymes that catalyze these
reactions, regulate essentially all cellular
processes and have thus emerged as therapeutic
targets for many human diseases. Small-molecule
inhibitors of the Abelson tyrosine kinase (Abl)
and the epidermal growth factor receptor (EGFR)
have been developed into clinically useful
anticancer drugs. Selective inhibitors can also
increase our understanding of the cellular and
organismal roles of protein kinases. However,
nearly all kinase inhibitors target the adenosine
triphosphate (ATP) binding site, which is well
conserved even among distantly related kinase
domains. For this reason, rational design of
inhibitors that selectively target even a subset
of the 491 related human kinase domains continues
to be a daunting challenge.
Cohen et al. Science 308, 1318 (2005)
17
Specific example protein kinases
Structural and mutagenesis studies have revealed
key determinants of kinase inhibitor selectivity,
including a widely exploited filter in the ATP
binding site known as the gatekeeper. A
compact gatekeeper (such as threonine) allows
bulky aromatic substituents, such as those found
in the Src family kinase inhibitors, PP1 and PP2,
to enter a deep hydrophobic pocket. In contrast,
larger gatekeepers (methionine, leucine,
isoleucine, or phenylalanine) restrict access to
this pocket. A small gatekeeper provides only
partial discrimination between kinase active
sites, however, as ca. 20 of human kinases have
a threonine at this position. Gleevec, a drug
used to treat chronic myelogenous leukemia,
exploits a threonine gatekeeper in the Abl kinase
domain, yet it also potently inhibits the
distantly related tyrosine kinase, c-KIT, as well
as the platelet-derived growth factor receptor
(PDGFR).
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Selection of gatekeeper residue
19
Outlook
In this study, we have rationally designed
halomethylketone-substituted inhibitors whose
molecular recognition by protein kinases requires
the simultaneous presence of two selectivity
filters a cysteine following the glycine-rich
loop and a threonine in the gatekeeper position.
We estimate that ca. 20 of human kinases have
a solvent-exposed cysteine in the ATP pocket.
Because of the structural conservation of the
pocket, it should be possible to predict the
orientation of these cysteines. In addition,
there are many reversible kinase inhibitors whose
binding modes have been characterized by x-ray
crystallography. The integration of both types
of information should allow the design of
scaffolds that exploit selectivity filters other
than the gatekeeper, as well as the appropriate
sites for attaching electrophilic substituents.
20
Small molecule-kinase interaction map
Figure 1. Competition binding assay for measuring
the interaction between unlinked, unmodified
('free') small molecules and kinases.(a)
Schematic overview of the assay. The phage-tagged
kinase is shown in blue, 'free' test compound in
green and immobilized 'bait' ligand in red. (b)
Binding assay for p38 MAP kinase. The immobilized
ligand was biotinylated SB202190. The final
concentration of test compounds during the
binding reaction was 10 M. (c) Determination of
quantitative binding constants. Binding of tagged
p38 to immobilized SB202190 was measured as a
function of unlinked test compound concentration.
Tagged p38 kinase was quantified by real-time
quantitative PCR and the results normalized.
Fabian et al. Nature Biotech 23, 329 (2005)
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Small molecule-kinase interaction map
22
Small molecule-kinase interaction map
Each kinase represented in the assay panel is
marked with a red circle. Gene symbols for
kinases in the panel are shown in Figure 5. TK,
nonreceptor tyrosine kinases RTK, receptor
tyrosine kinases TKL, tyrosine kinase-like
kinases CK, casein kinase family PKA, protein
kinase A family CAMK, calcium/calmodulin
dependent kinases CDK, cyclin dependent kinases
MAPK, mitogen-activated protein kinases CLK,
CDK-like kinases.
23
Small molecule-kinase interaction map
Figure 3. Specificity profiles of clinical kinase
inhibitors. Circle size is proportional to
binding affinity (on a log10 scale). Binding
constants were measured at least in duplicate for
each interaction identified in the primary
screen.
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Distribution of binding constants
For each compound the pKd (-log Kd) was plotted
for all targets identified. Primary targets, as
shown in Table 1, are in blue, and off-targets in
red. Staurosporine does not have a particular
primary target or targets, and the primary
targets for BAY-43-9006 (RAF1) and LY-333531 (PKC
) were not part of the assay panel.
25
Hierarchical cluster analysis of specificity
profiles
Lighter colors correspond to tighter
interactions. (a) Twenty kinase inhibitors
profiled against a panel of 113 different
kinases.
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Small molecule-kinase interaction map
We have described a systematic small
molecule-kinase interaction map for clinical
kinase inhibitors. Integration of the information
provided here with results from cell-based or
animal studies, and ultimately with clinical
observations, should enable a more complete
understanding of the biological consequences of
inhibiting particular combinations of kinases.
Binding profiles for larger numbers of
chemically diverse compounds, combined with the
phenotypes elicited by these compounds in
biological systems, will help identify kinases
whose inhibition leads to adverse effects,
kinases that are 'safe' to inhibit and
combinations of kinases whose inhibition can have
a synergistic beneficial effect in particular
disease states. This knowledge should enable
the development of inhibitors with 'appropriate'
specificity that target multiple kinases involved
in the disease process while avoiding kinases
implicated in side effects. The ability to
rapidly screen compounds against multiple kinases
in parallel and the incorporation of specificity
profiling during initial lead discovery and
optimization should greatly facilitate and
accelerate the drug development process.
27
Small molecule-kinase interaction map
The kinase binding profiles also provide valuable
information to guide structural studies. In many
cases kinases that tightly bind the same compound
have no obvious sequence similarity (for example,
p38 and ABL(T315I) binding to BIRB-796). In
other cases, compounds can discriminate between
kinases closely related by sequence, such as
imatinib binding to LCK but not SRC. ABL and the
imatinib-resistant ABL mutants are of particular
structural interest because some compounds bind
with good affinity to all forms (e.g., ZD-6474),
whereas BIRB-796 has a strong preference for a
particular mutant. Key insights should result
from an analysis of selected co-crystal
structures of kinase-compound combinations
identified through profiling studies, and the
large, uniform data set presented here should
serve as a valuable training set for
computation-based inhibitor design. Finally,
the use of phage-tagged proteins in quantitative
biochemical assays circumvents the need for
conventional protein production and purification,
and should help reduce one of the major
bottlenecks in modern proteomics and drug
discovery research.
28
Multidrug treatments are increasingly important
in medicine and for probing biological systems.
But little is known about the system properties
of a full drug interaction network. Epistasis
among mutations provides a basis for analysis of
gene function. Similarly, interactions among
multiple drugs provide a means to understand
their mechanism of action. Aim derive a
pairwise drug interaction network.
Yeh et al. Nature Genetics 38, 489 (2006)
29
Different ways of drug interaction
Clustering of individual drugs into functional
classes solely on the basis of properties of
their mutual interaction network. Schematic
illustration of additive, synergistic and
antagonistic interactions between drugs X and Y
by measurements of bacterial growth under the
following conditions no drugs, drug X only,
drug Y only, and both drugs X and Y.
Additive no interaction Synergistic
larger-than-additive effect Antagonistic
smaller-than-additive effect
30
Classification of drug interactions
g ?, gX, gXY growth of wild-type, with drug
X, and with drugs X and Y
This scale maps synthetic lethal interactions to
? -1, additive interactions are mapped to ?
0, antagonistic buffering to ? 1, and
antagonistic suppression to ? gt 1.
31
The Prism algorithm
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Classification
(bd) A network (b) of synergistic interactions
(red lines) and antagonistic interactions (green
lines) between drugs (black circles) can be
clustered into functional classes that interact
with each other monochromatically (that is, with
purely synergistic or purely antagonistic
interactions between any two classes c). This
classification generates a system-level
perspective of the drug network (d). (e,f) Two
independent observations indicate whether a new
drug (Z) will be clustered into a particular drug
class (a, dashed oval) mixed synergistic and
antagonistic intraclass interactions of Z with a
(e, thin dotted green and red lines) and
nonconflicting interclass interactions of Z (e,
dotted thin lines) and a (e, dotted thick lines)
with all other classes. Both intra and interclass
indications are depicted in e, and the drug is
clustered (black arrow) with an existing class.
If drug Z has no such intra- or interclass
association with any existing drug class, the
drug will be clustered in a new class (f).
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Tested drugs
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Experimental classification of drug interaction
Figure 2 Experimental classification of drug
interactions into four types using
bioluminescence measurements of bacterial growth
in the presence of sublethal concentrations of
antibiotics. (a) The pairs of antibiotics
illustrate synergistic interactions.
The number of bacteria (proportional to
bioluminescence counts per second (c.p.s.) is
shown from two replicates, for control with no
drugs (f, solid black lines), each single drug
(X, Y blue and magenta lines) and the
double-drug combination (X Y, dashed black
lines). Insets normalized growth rates (W)
with error bars for f, X, Y and XY, from left to
right, respectively. Note the contrast between
the interactions of piperacillin with the 50S
ribosomal subunit drug erythromycin (a, ERY-PIP,
synergistic) and the 30S ribosomal subunit drug
tetracycline (c, TET-PIP, antagonistic).
35
Different modes of interaction
The pairs of antibiotics illustrate synergistic
(a), additive (b), antagonistic buffering (c) and
antagonistic suppression (d) interactions
36
Systematic measurements of pairwise interactions
between antibiotics
(a) Growth measurements and classification of
interaction for all pairwise combinations of
drugs X and Y. Within each panel, the bars
represent measured growth rates for, from left to
right no drugs (f), drug X only, drug Y only and
the combination of the two drugs X and Y (see
inset). Error bars represent variability in
replicate measurements.
The background color of each graph designates the
form of epistasis according to the scale in b
synergistic (red emax lt -0.5 pink -0.5 lt emax
lt -0.25), antagonistic buffering (green 0.5 lt
emin lt 1.15 light green 0.25 lt emin lt 0.5),
antagonistic suppression (blue emin gt 1.15) or
additive (white -0.25 lt emax lt 0.5 and -0.5 lt
emin lt 0.25). Cases that do not fall into any of
these categories are labeled inconclusive (gray
background).
37
Classification into interaction classes
Unsupervised classification of the antibiotic
network into monochromatically interacting
classes of drugs with similar mechanisms of
action. (a) The unclustered network of drug-drug
interactions with synergistic (red), antagonistic
buffering (green) and antagonistic suppression
(blue) links.
38
Monochromatically interacting functional classes
(b) Prism algorithm classification of drugs into
monochromatically interacting functional classes.
This unsupervised clustering shows good
agreement with known functional mechanism of the
drugs (single letter inside each node see Table
1). Bleomycin (BLM), which is believed to
affect DNA synthesis, although its mechanism is
not well understood, cannot be clustered
monochromatically with any other class. The
multifunctional drug nitrofurantoin (NIT) shows
non-monochromatic interactions.
39
System-level interactions between the drug classes
(c) Larger ellipses show higher-level
classification of DNA gyrase inhibitors (D) with
inhibitors of biosynthesis of DNA precursors (F)
and classification of the two subclasses of drugs
involved in the inhibition of protein synthesis
via the 50S ribosomal subunit (R).
40
Outlook
We provide a complete and systematic analysis of
a drug-drug interaction network. Systems analysis
of the interaction network demonstrates that
drugs can be classified according to their action
mechanism based on their interactions with other
functional drug classes. The ability to
classify drug function based solely on phenotypic
measurements and without the tools of
biochemistry or microscopy can provide a simple
and powerful method for screening new drugs with
multiple or novel mechanisms of action. Our
systems approach is general in nature and could
be applied to other biological systems. It would
be particularly useful if the approach could be
generalized to in vivo studies and to a wider
range of phenotypes despite added complexity of
host-drug interaction. Furthermore, applying
network approaches to drug interactions may help
suggest new drug combinations and highlight the
importance of gene-environment interactions,
including, in particular, the resistance and
persistence of bacteria to antibiotics and of
cancer cells to antitumor drugs.