Download Structural biology and drug discovery for protein–protein

TIPS-957; No. of Pages 8
Opinion
Structural biology and drug discovery
for protein–protein interactions
Harry Jubb, Alicia P. Higueruelo, Anja Winter and Tom L. Blundell
Department of Biochemistry, University of Cambridge, Cambridge CB1 2GA, UK
Although targeting protein–protein interfaces of regulatory multiprotein complexes has become a significant
focus in drug discovery, it continues to pose major
challenges. Most interfaces would be classed as ‘undruggable’ by conventional analyses, as they tend to be large,
flat and featureless. Over the past decade, encouragement has come from the discovery of hotspots that
contribute much of the free energy of interaction, and
this has led to the development of tethering methods
that target small molecules to these sites, often inducing
adaptive changes. Equally important has been the recognition that many protein–protein interactions involve
a continuous epitope of one partner and a well-defined
groove or series of specific small pockets. These observations have stimulated the development of stapled ahelical peptides and other proteomimetic approaches.
They have also led to the realisation that fragments
might gain low-affinity ‘footholds’ on some protein–
protein interfaces, and that these fragments might be
elaborated to useful modulators of the interactions.
Outwith the druggable genome
The concept of the ‘druggable genome’ [1] has concentrated
thinking in target-based drug discovery in two very different ways. On the one hand, it has raised awareness that
current methods and libraries work well only with restricted groups of targets. These are often enzymes, ion channels
or receptors, with well-defined and preformed concave
binding sites. This has encouraged a focus on targeting
large protein superfamilies involved in cell regulation that
possess these ‘druggable’ characteristics, for example Gprotein-coupled receptors (GPCRs), protein kinases and
proteases. Furthermore, undue focus on ‘druggability’ has
grave consequences for the pharmaceutical industry, because the temptation to pursue targets amenable to modulation may lead to unsuccessful attempts to design drugs
to targets which are of poor quality with respect to disease
rationale [2].
On the other hand, the realisation that high affinity may
have been bought at the cost of low selectivity has stimulated debate on whether alternative strategies of lead discovery might allow a re-evaluation of the druggability concept.
Targeting protein–protein interfaces (PPIs) of multiprotein
complexes that mediate cell regulation – long regarded by
many as undruggable – has become a subject of intense
activity in both academia and industry [3]. Indeed, PPI
Corresponding author: Blundell, T.L. ([email protected]).
targeting may offer new opportunities for drug discovery
in an industry where large companies output on average less
than one approved novel molecular entity per year [2].
Although structural biology has shown that PPIs are
generally large (1500–3000 Å2), flat and relatively featureless [4], making the design of small molecule antagonists a difficult task, there is hope that these difficulties
can be overcome. Not only has it been recognised that
energetic hotspots contribute much of interface interaction
free energy in many PPIs [5] but also there is increased
appreciation that many interactions involve the concerted
folding and binding of a continuous epitope into a defined
groove or series of small specific pockets [6]. Such observations have stimulated the development of stapled a-helical
peptides and other proteomimetic approaches to drugging
interfaces [7]. They have also led to the realisation that
fragments might gain low-affinity ‘footholds’ on PPIs, and
that these might be elaborated to useful modulators of
multiprotein assemblies where knowledge of the structures of the complexes is available.
Here, we discuss the nature of PPIs with respect to
druggability. Using examples from our laboratory, we
review fragment-based PPI drug discovery and assess
the opportunities this approach presents. We argue that
structural biology is and will continue to be central to
successful for targeting protein–protein interactions. We
are of the opinion that these recent successes, coupled with
further understanding of the nature of PPIs and of small
molecule binding that disrupts such interactions, will provide a path to the development of novel therapeutics which
drug the ‘undruggable’.
Understanding the nature and classes of PPIs for drug
discovery
Protein–protein interactions may be characterised as obligate, in which the subunits are not observed on their own
in vivo, or as non-obligate, in which they are. This is a
function of both the binding energy of the interaction
(including cooperative, competitive and allosteric effects)
and coexpression/localisation of the interacting partners
[8]. Coexpression is a requisite for obligate pairing, and
thus antibody–antigen, enzyme–inhibitor and receptor–
protein ligand interactions are never obligate. Nooren
and Thornton distinguished obligation of interactions from
complex lifetime [8]. Permanent complexes maintain stable interactions, remaining in complex for a long time once
bound; thus, all obligate interactions are permanent, as are
a subset of nonobligate interactions such as antibody:antigen binding. Transient interactions may be in dynamic
0165-6147/$ – see front matter ß 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.tips.2012.03.006 Trends in Pharmacological Sciences xx (2012) 1–8
1
TIPS-957; No. of Pages 8
Opinion
Trends in Pharmacological Sciences xxx xxxx, Vol. xxx, No. x
equilibrium, or in stronger associations dependent on
modulation by other molecules such as cofactors. We are
of the opinion that understanding where a PPI lies on the
binding energy, modulation and localisation continuum,
and how the structure and nature of interactions vary
between these classes will be important for identifying
multiprotein complexes more amenable to drug targeting.
Another descriptor of PPIs relevant to drug discovery is
whether a continuous region of polypeptide is involved in
interface pairing. Continuous epitopes often comprise a
single secondary structure element, as opposed to discontinuous epitopes in which interacting regions of polypeptide originate from different regions of the primary
structure [6]. Many continuous epitopes involve flexible
or disordered (regions of) polypeptide chains that assemble
with a classical globular protein to give a globular complex;
so-called concerted folding and binding, developed beautifully by Wright and Dyson [9]. Globular partners of
concerted folding-and-binding interactions tend to have
better defined, preformed binding sites as grooves to accommodate helices (e.g. Bcl [10]), or small pockets that
bind side chains but can be exploited to bind fragments
(e.g. RAD51 interactions with BRCA2 [11,12], involved in
homologous recombination). These successes, discussed
below, give promise for drug discovery against PPI targets
that exhibit disorder–order transitions on binding in vivo.
The concept of PPI ‘hotspots’ first suggested by Clackson and Wells [11] and supported by alanine-scanning
mutagenesis studies [5] has proved helpful. Hotspots
often comprise side chains of tyrosine, tryptophan and
arginine, which all allow adaptive conformational change
to accommodate small molecules as well as strong hydrogen-bonding potential through nitrogen and oxygen
atoms and other interaction potential through aromatic
or guanidinium moieties [3]. The PPI hotspot concept is
useful in drug discovery for identifying regions on a PPI
protomer to which a ligand may bind with high efficiency.
Combinations of energetic, structural and evolutionary
information, for example using Bayesian Networks based
on training sets from experimental data [12] and support
vector machine (SVM) classifiers [13] may assist hotspot
identification. There is some evidence that protein–ligand
binding hotspots in PPIs correlate with protein–protein
RAD51–BRC4 interaction
(a)
hotspots, and this has been an active area of investigation.
Most algorithms measuring the ability of a site to bind a
ligand – better called ‘ligandability’ [14] than druggability
– depend on knowledge of the structures of the interfaces,
and databases that define interatomic interactions based
on structures are helpful in this endeavour [15]. Small
grooves and pockets in the interface region that might bind
a small molecule can be identified on the basis of their
shape and sequence conservation [16]. Although such
methods are successful for classical targets, they are retrospective and are less useful for PPIs. This is partly
because pockets at PPI interfaces tend to be unlike the
classical large cavities of druggable proteins [17] and
partly because PPI interface cavities which accommodate
molecules are induced or adaptive, depending on flexibility
of side chains and to a very much lesser extent small loops
[3]. Alternative approaches geared toward PPIs include
ANCHOR [18], which identifies preformed recognition
motifs, and Dr PIAS (Druggable Protein–protein Interaction Assessment System) [19], which adds information on
pockets at the interface. In a recent computational study,
Kozakov et al. investigated the relationship between ‘hotspots’ and druggability in six well-studied PPI targets [20].
They found that by mapping a target protein using different types of probe molecules, consensus sites could be
found where the majority of test molecules bound. The
fact that these sites were indeed known to bind PPI
inhibitors lead to the conclusion that these ‘hotspots’ relate
to the druggability of this site. Moreover, this algorithm
would be able to use structural data alone to find probable
druggable sites in a new target.
These computational methods are best complemented
by experimental methods that measure the hit rate from
an X-ray crystallographic fragment screen or, as originally
proposed, from an NMR-based screen [21]. Recent experimental approaches include development of a traffic light
ligandability score based on hit rate, affinity and hit
diversity [14]. In the RAD51/BRC4 interface, defined in
our laboratory [22] (Figure 1), computational methods
identify the pocket that binds phenylalanine of FxxA,
but only ANCHOR predicts the small alanine pocket as
druggable as well. Experimental fragment screening gives
(b) BRC repeats
TRENDS in Pharmacological Sciences
Figure 1. X-Ray diffraction studies targeting PPI involving human RAD51 interactions with BRCA2. (a) The structure of RAD51 in complex with the BRC4 region of BRCA2,
demonstrating the existence of two well-defined pockets on RAD51 that are occupied by side chains of the conserved FxxA motif. RAD51 is shown as grey van der Waals
surface. BRC4 is shown in purple cartoon form. Residues within a 4 Å radius of the FxxA motif are highlighted in red on the RAD51 surface. The interface on the RAD51 is
fairly flat, but there are distinct pockets for the phenylalanine and alanine side chains that bind the FxxA motif. (b) Sequences of homologous repeats in human BRCA2
demonstrating the conservation of the FxxA motif. (Figure is reproduced with permission from D.E. Scott, M. Marsh and M. Hyvonen.)
2
TIPS-957; No. of Pages 8
Opinion
Trends in Pharmacological Sciences xxx xxxx, Vol. xxx, No. x
(a)
(b)
(c)
(d)
(e)
(f)
TRENDS in Pharmacological Sciences
Figure 2. Mode of interaction of fragments binding to the phenylalanine-binding pocket of RAD51 in complex with the BRC4 peptide of BRCA2 as observed using X-ray
crystallography. (a) Indazole 5, (b) 4-methylester indole 4, (c) naphth-1-ol 9, (d) L-methylester tryptophan 8, (e) 2-aminobenzothiazole 6, (f) naphth-2-ol 10. The fragments
exploit both the lipophilic nature of the pocket as well as opportunities to form well-defined H-bonds, particularly with the main chain carbonyl of Leu 214 and the nearby
side chain of Gln 217. The affinities are in the range 400 to 1000 mM. This shows that small side chain pockets in PPIs may act as hotspots that undergo little conformational
rearrangement on ligand binding. (Figure is reproduced with permission from D.E. Scott, M. Marsh and M. Hyvonen.)
a predominance of hits in the phenylalanine pocket of
RAD51 (Figure 2).
Although debate continues as to whether PPI hotspots
are always indicative of ligand-efficient ‘footholds’ for small
molecule binding [3], it is clear that competitive small
molecule inhibitors can often be developed at these sites.
Screening and design of PPI inhibitors
Several studies have focused on small molecules that
disturb PPIs, comparing them with classical drugs and
popular screening libraries [3,23,24]. These studies show
that PPI inhibitors tend to be larger and more lipophilic
than molecules that bind classically druggable sites [25].
Furthermore, they are poorly represented in commonly
used chemical-screening libraries. It seems clear that drug
discovery is in an uncharted area with these challenging
targets, and that new methods will be required.
When the structure of the target or a close analogue is
known, classical computational approaches have been used
with success to find small molecules binding to PPIs. These
approaches include pharmacophore searches [26] and
docking of standard catalogues such as NCI [27] and
ACD [28], in addition to less conventional molecules such
as traditional herbal medicines [29].
Proteomimetics
A successful experimental approach for PPI inhibition is to
mimic elements of the binding site surface structure of
proteins, antibodies or peptides which bind protein targets;
particularly, when the binding site is a secondary structure
element such as a helix or b-turn. Yin and Hamilton have
used a range of chemistries, including porphyrins and
a-helical mimetics to achieve this objective [30]. An alternative approach is to use stapled peptides that target
a-helical peptide binding sites. These are stabilised by
crosslinking adjacent residues in the helix to make them
resistant to proteolysis. Successful examples have included
the p53:MDM2/X [31] interaction, and the MAML-1:Notch
[32] interaction, both of which involve helices at their
interaction interfaces.
Small molecule fragment screening
To explore the chemical space defined by PPIs, a more
efficient approach is required, one that is not biased by
earlier drug discovery campaigns. One approach is to
decrease the size of the screening molecule. This in principle offers the possibility of exploring chemical space more
efficiently, using chemical libraries comprising thousands
rather than hundreds of thousands or millions of compounds [26,27].
Because initial hits have low affinity, they will not
normally disrupt PPIs, unless they are tethered. Tethering, pioneered by Wells and coworkers [33], exploits a
thiol-containing fragment linked through a reversible
disulfide bond with a cysteine residue in a protein and
identified using mass spectrometry [34]. This often
requires engineering in cysteine residues local to the
target site and has been developed by using ‘extenders’
that irreversibly alkylate the cysteine but provide a thiol
group to capture the fragments [35]. There are several
examples of successes where tethering has been used to
antagonise PPIs [36–38].
3
TIPS-957; No. of Pages 8
Opinion
Trends in Pharmacological Sciences xxx xxxx, Vol. xxx, No. x
An alternative approach to tethering is to stabilise the
uncomplexed protein components of the multiprotein system in solution and to employ conventional fragmentbased screening [27,31–33]. The fragment-based approach
uses a library of small molecules (fragments) with molecular weights of 100–300 Da to explore chemical space of the
target protein-binding site. In our hands the Maybridge
library has proved useful with PPIs, giving multiple hits on
a range of different multiprotein complexes. As the affinity
is low, PPI fragment screening requires sensitive biophysical methods – nuclear magnetic resonance (NMR), X-ray
crystallography, surface plasmon resonance (SPR), differential scanning fluorimetry (DSF) or isothermal calorimetry (ITC) – to screen and validate fragment binding.
Fragment hits are elaborated by crosslinking or ‘growing’,
while maintaining strong interactions for each group
added. This approach has the advantage of being able to
explore new binding sites, such as those found in PPIs [39].
Problems with solubility, stability, polydispersity and
binding site occlusion by crystal contacts for biophysical
screening have been avoided by engineering the protein
target to produce a stable monomer. An excellent example
of this has been the engineering of human RAD51/RadA by
Hyvönen and coworkers [39]. Although human RAD51
cannot be expressed in a stable, monomeric and unliganded form, the archaeal homologue of RAD51 (RadA)
Target protein
can be used as a surrogate, and engineered to have humanised interaction surfaces giving stable monomeric, unliganded species. New crystallographic forms with open
binding sites for screening have been generated by targeting packing interaction sites for mutagenesis [39].
Biophysical and structural methods for screening of
fragments
Fragment-based screening is usually carried out in two
steps (Figure 3). In our experience, it is best to have a
battery of techniques available for initial high throughput
screening (HTS), because the choice of technique is hugely
dependent on the physical, chemical and structural properties of the target protein. Given that monomerised subunits of multiprotein assemblies are often difficult to
prepare, the first assays should be the simplest and have
the lowest demands on quantities of the protein. Fluorescence-based thermal shift assay [40–42] and SPR [36,37]
are both suited to PPIs, and Figure 4 exemplifies them in
the study of the Met receptor interaction with a protein
ligand hepatocyte growth factor/scatter factor (HGF/SF).
However, protein-observed NMR spectroscopy [43], the
first experimental method used for screening fragments,
has also been used successfully for targeting protein–protein interactions of Bcl-XL with an a-helical peptides
derived from pro-apoptotic molecules BAK and BAD
Hit validation
SPR
Fragment screening
Thermal shift
ITC
Cheminformatics
Substructure search
Similarity search
Bioassays
in vivo and in vitro
X-ray
Compound concentration
NMR
SPR
X-ray
Chemical optimisation
Fragment growing
Fragment library
Molecular weight
< 300 Da
cLogP
≤3
Hydrogen bond donors
≤3
Hydrogen bond acceptors
≤3
Number of rotatable bonds
≤3
NMR
Fragment linking
TRENDS in Pharmacological Sciences
Figure 3. Strategy for a fragment-based screening campaign. Methods for initial high throughput fragment screening include thermal shift (DSF), SPR, ligand-based NMR
and X-ray crystallography. Validation of potential fragment hits can be achieved by nanospray mass spectrometry, the kinetic parameters such as association and
dissociation rates defined using SPR, and the major thermodynamic parameters derived using ITC. X-Ray crystallography and NMR are used to define binding modes of the
fragment and to guide fragment optimisation or purchase of related compounds (analogue search). Assessment of biological activity assists in the chemical optimisation of
the fragments on their way to a drug lead.
4
TIPS-957; No. of Pages 8
Opinion
Trends in Pharmacological Sciences xxx xxxx, Vol. xxx, No. x
(b)
(a)
1e+5
Fluorescence signal
2.5e+4
8e+4
2.0e+4
1.5e+4
6e+4
1.0e+4
4e+4
5.0e+3
2e+4
0.0
0
25
30
35
40
45
50
55
30
Temperature (degrees Celcius)
40
50
60
80
90
(b)
(a)
Fragment I
600
Relative SPR response units [%RU]
70
Temperature (degrees Celcius)
500
NH
400
NH
300
Fragment V
200
O
100
S
S
OH
0
-100
tI
m
ag
Fr
II
I
tI
en
tI
en
a
Fr
gm
en
en
gm
a
Fr
tV
V
tI
en
gm
gm
a
Fr
a
Fr
(b) 10
(a)
Key:
SPR response units [RU]
8
Key:
Fragment 1
Fragment 2
Fragment 3
Fragment 4
6
4
2
0
K d < 100 μM
K d < 500 μM
K d > 500 μM
no binding
-2
0
1e–4
2e–4
3e–4
4e–4
5e–4
Concentration [M]
TRENDS in Pharmacological Sciences
Figure 4. Targeting the Met receptor interaction with NK1, a splice form of HGF/SF that is a partial agonist/antagonist. (Top panel) Thermal shift curves of Met and NK1. (a)
The fragments interacting with Met give good unfolding curves suitable for screening, whereas (b) demonstrates that melting curves of NK1 obtained using different buffers
at varying pH [30 mM phosphate, pH 7, 150 mM NaCl (——), 50 mM Bis Tris, pH 6.7 (), CAPS pH 10.5 (
)] have complex melting processes, possibly related to the
presence of two domains and disulfide bridges, as well as a tendency to dimerise through domain swapping. One of the curves ( ) shows a fragment that interacts with
the fluorescent dye. (Middle panel) Surface plasmon resonance (SPR) for fragment screening of binding the Met receptor, NK1 (N-terminal and Kringle1) and SPH (serine
protease homology) domains of HGF/SF. (a) Relative binding levels of five fragments showing fragment hits (fragment I as a hit for Met), nonbinders (fragment II),
overstoichiometric binders and promiscuous binders to Met (black), NK1 (light grey) and SPH domain (medium grey). Overstoichiometric binders or aggregating fragments
were identified by extremely high responses (fragment III), and promiscuous binders show binding to all three proteins (for example fragments IV and V). (b) Structures of a
fragment hit (fragment I) and a promiscuous binder (fragment V). (Bottom panel) Fragment hits for Met using SPR. (a) Distribution of steady-state binding constants (Kd)
among fragment hits for Met. (b) Concentration-dependent binding of selected fragments to immobilised Met. Steady-state binding levels were fitted using a global fit
resulting in Kd values from low micromolar to low millimolar range. (Figure is reproduced with permission from A. Winter, A. Sigurdardottir, E. Gherardi and T.L. Blundell.)
5
TIPS-957; No. of Pages 8
Opinion
Trends in Pharmacological Sciences xxx xxxx, Vol. xxx, No. x
[10]. NMR screening has been generally successful in
fragment-based drug discovery [44].
For validation of the hits derived from the initial HTS,
false negatives are minimised by repeating the assay or
better by using a second independent approach. False
positives are identified through careful analysis of the
kinetics, affinity (Kd) and structures of protein–fragment
complexes. In principle, rate constants for association and
dissociation of protein–fragment complexes can be directly
measured using SPR and used to estimate affinity [45–47].
However, most fragments show a squared response curve
due to fast on- and off-rates with little or no curvature, and
thus steady-state experiments are carried out using different concentrations of the compound and their respective
equilibrium binding levels determined [48]. Nevertheless,
we have used these methods to validate the binding of
fragments targeting the PPI of the Met complex with HGF/
SF (Figure 4). ITC, which requires large quantities of
protein, can be used to titrate the change of enthalpy upon
binding, from which affinity, free energy and change of
entropy can be calculated. Kd values obtained by ITC can
differ from those of SPR but are probably more accurate,
because the protein is immobilised in SPR experiments on
a surface, possibly introducing restrictions in flexibility or
induced conformational changes upon ligand binding. Both
ITC and SPR can be used to perform competition experiments with a known ligand to confirm that compounds
compete for binding at a specific site.
Three-dimensional structures, which are best obtained
by X-ray crystallography but can also be generated from
NMR spectroscopy, give essential insights into compound-binding modes. Although protein-based NMR is
not high throughput and usually does not give a detailed
picture of ligand binding, it is very sensitive and valuable
for screening weakly binding ligands that otherwise
might be missed. X-Ray crystallography has the advantage of providing information on not only where but also
how the ligand binds [49,50]. This information can then
be used to monitor every iterative round of the optimisation process. The ability to use structural information
dramatically increases the success of the fragment based
drug design process [27,42]. An example of the successful
use of fragment-based approaches in targeting protein–
protein interactions is illustrated for the interface between the human recombinase RAD51 and the hub protein BRCA2 (Figure 2). In this case, the analysis has
progressed by two routes: the exploration of short peptides based on the natural ligand and fragment screening.
(a)
(b)
(c)
(d)
TRENDS in Pharmacological Sciences
Figure 5. Progression of two different fragments bound simultaneously to Bcl-XL into a helix mimetic with nanomolar affinity, ABT737. The protein Bcl-XL is represented as
grey surface. (a) Two different fragments in cyan cocrystallised with Bcl-XL (1YSG). (b) Molecule in green sticks (1YSI) is generated from merging and optimising previous
fragments (from 1YSG), both are superposed using the protein chain as a guide and are represented by fine lines. (c) Optimised compound ABT737 in purple sticks is bound
to Bcl-XL (2YXJ), the molecule from 1YSI is superposed using the protein and is represented by fine lines. (d) Compound ABT737 in purple sticks is bound to Bcl-XL (2YXJ),
BAD peptide in orange is superposed using the protein and is represented by the orange helix. Dotted lines represent polar interactions between the protein and BAD in
orange and between protein and small molecules in purple. Figures generated with the program PyMol (www.pymol.org).
6
TIPS-957; No. of Pages 8
Opinion
Around 20 structures of peptide–protein complexes were
studied by X-ray crystallography and these demonstrated
subtle changes of conformation that allow peptide Hbonds to be optimised as the sequence changes. These
structures along with structures of around 80 protein–
fragment complexes (for examples, see Figure 2) have
been used to design fragment–peptide chimaeric molecules, which served as templates for the design of other
molecules.
Growing and linking fragments
With structural information in hand, fragment growing
or fragment linking can be employed to identify larger
compounds from one or more fragment starting points.
Monitoring ligand efficiency [51] or group efficiency [52]
allows estimation of an individual group contribution toward the free energy of binding. Recent efforts seem to
have been characterised by the optimisation of affinity by
increasing lipophilicity [53]. The ligand lipophilicity efficiency (LLE) index [54] and other ligand efficiency indices,
including polarity of molecules [55], can be used to monitor
and hopefully avoid this trend.
In general, we have found fragment growing more
straightforward, because only one starting point is required [48,49]. However, when more than one fragment
is bound in the target site, structural information of one
fragment can be used to guide the growing of another.
Fragment linking is often limited by linkers that perturb
the optimal binding geometries of the fragments, introduce
excessive conformational flexibility or lose ligand efficiency
by not making efficient interactions [56].
X-Ray crystal structures solved at each iteration of the
growth process continue to be central to successful exploitation of this approach [32,33]. Biochemical and cell-based
assays are high throughput and low cost. In some fragment-based screening campaigns, biological assays have
proved remarkably successful even in the initial screening,
but fragments are often too low affinity to evoke a response.
However, assays become central to the campaign as fragments are developed.
Abbott Laboratories have used these approaches to
design molecules with affinities of up to 5 nM that mimic
the key a-helix that binds Bcl-XL. Figure 5 shows the
published progression of the design process [10], in which
two different fragments were bound simultaneously to BclXL (Figure 5a) and were evolved/grown into a single molecule [57]. The smaller contact regions with the protein
meant that the ligand efficiency was almost twofold higher
than that of a small helix. Although this is a particularly
challenging example with impressive success, the optimised molecules are highly lipophilic and do not mimic
available specific polar contacts that BAD engages
(Figure 5d). Growth of fragments targeting the PPI between the human recombinase RAD51 and the hub protein
BRCA2 has advanced using the structure of the RAD51 in
complex with the BRC4 region of BRCA2 as a guide
(Figure 1) [22].
Concluding remarks and future prospects
Although the number of preclinical or clinical stage candidate compounds derived from targeting PPIs is still small,
Trends in Pharmacological Sciences xxx xxxx, Vol. xxx, No. x
there continues to be extensive activity in the design of
inhibitors in both academia and industry. In the past 4
years, improvements in proteomimetics and fragmentscreening technologies have been pushing the boundaries
of what is druggable and what is not.
We are beginning to gain useful knowledge by computational analysis of the increasing numbers of reports of
compounds that modify protein–protein complexes. Much
could be achieved by enriching fragment and other chemical libraries with chemical structures that have been successful in targeting PPIs as well as by providing a greater
variety of three-dimensional shapes [58]. Improving specific contacts in the earlier phase of the discovery, instead
of optimising purely for binding affinity, should decrease
the tendency to increase lipophilicity and nonspecific hydrophobic interactions [59].
Success in targeting PPIs clearly depends on target
type. We have commented on the characteristically different interfaces involving proteins that undergo concerted
folding and binding at the ‘receptor’ protein [6]. These tend
to have better defined binding sites, either as grooves to
accommodate helices, for example Bcl, or small pockets
that bind side chains but can be exploited to bind fragments, for example RAD51. Globular partners that bind
flexible/disordered peptides tend to have relatively little
induced conformational change on fragment or even larger
ligand binding. They constitute many of the most promising targets for fragment-based approaches.
By contrast, interactions between globular proteins
tend to involve large, flat and featureless interfaces. The
greatest success here has been with tethering methods
that allow the fragments to induce pockets at hotspots [37];
noncovalent interactions stabilise the pocket conformation, if the fragment is covalently tethered to give an
entropic advantage. Nevertheless, the evidence from the
Notch receptor ankyrin domain indicates that untethered
fragments can be observed to bind on such surfaces (N.
Abdel-Rahman, PhD thesis, University of Cambridge,
2010). However, at this time little progress has been made
to link or grow them.
Acknowledgements
We thank Marko Hyvonen, Chris Abell, Ashok Venktaraman, Grahame
McKenzie, Anna Sigurdardottir, May Marsh, Duncan Scott, Antony
Coyne, Ermanno Gherardi, Alessio Ciulli and Will Pitt for helpful
discussions. T.L.B. thanks the Wellcome Trust for support through a
Programme Grant and for a Seeding Drug Discovery Initiative Award.
A.P.H. and H.J. thank UCB and the Biotechnology and Biological
Sciences Research Council (BBSRC) for CASE Studentships, T.L.B. and
A.W. thank the EU FP7 for support.
References
1 Hopkins, A.L. and Groom, C.R. (2002) The druggable genome. Nat. Rev.
Drug Discov. 1, 727–730
2 Bunnage, M.E. (2011) Getting pharmaceutical R&D back on target.
Nat. Chem. Biol. 7, 335–339
3 Wells, J. and McClendon, C. (2007) Reaching for high-hanging fruit in
drug discovery at protein–protein interfaces. Nature 450, 1001–1009
4 Blundell, T.L. et al. (2000) Protein–protein interactions in receptor
activation and intracellular signalling. Biol. Chem. 381, 955–959
5 Bogan, A.A. and Thorn, K.S. (1998) Anatomy of hot spots in protein
interfaces. J. Mol. Biol. 280, 1–9
6 Blundell, T.L. et al. (2006) Structural biology and bioinformatics in
drug design: opportunities and challenges for target identification and
lead discovery. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 361, 413–423
7
TIPS-957; No. of Pages 8
Opinion
7 Fletcher, S. and Hamilton, A.D. (2005) Protein surface recognition and
proteomimetics: mimics of protein surface structure and function.
Curr. Opin. Chem. Biol. 9, 632–638
8 Nooren, I.M. and Thornton, J.M. (2003) Diversity of protein–protein
interactions. EMBO J. 22, 3486–3492
9 Wright, P.E. and Dyson, H.J. (2009) Linking folding and binding. Curr.
Opin. Structural Biol. 19, 31–38
10 Oltersdorf, T. et al. (2005) An inhibitor of Bcl-2 family proteins induces
regression of solid tumours. Nature 435, 677–681
11 Clackson, T. and Wells, J.A. (1995) A hot spot of binding energy in a
hormone-receptor interface. Science 267, 383–386
12 Segura, J. and Fernandez-Fuentes, N. (2011) PCRPi-DB: a database of
computationally annotated hot spots in protein interfaces. Nucleic
Acids Res. 39, D755–D760
13 Lise, S. et al. (2011) Predictions of hot spot residues at protein–protein
interfaces using support vector machines. PLoS ONE 6, e16774
14 Edfeldt, F.N.B. et al. (2011) Fragment screening to predict druggability
(ligandability) and lead discovery success. Drug Discov. Today 16,
284–287
15 Bickerton, G.R. et al. (2011) Comprehensive, atomic-level
characterization of structurally characterized protein–protein
interactions: the PICCOLO database. BMC Bioinform. 12, 313
16 Capra, J.A. et al. (2009) Predicting protein ligand binding sites by
combining evolutionary sequence conservation and 3D structure. PLoS
Comput. Biol. 5, e1000585
17 Fuller, J.C. et al. (2009) Predicting druggable binding sites at the
protein–protein interface. Drug Discov. Today 14, 155–161
18 Meireles, L.M. et al. (2010) ANCHOR: a web server and database for
analysis of protein–protein interaction binding pockets for drug
discovery. Nucleic Acids Res. 38, W407–W411
19 Sugaya, N. and Furuya, T. (2011) Dr. PIAS: an integrative system for
assessing the druggability of protein–protein interactions. BMC
Bioinform. 12, 50
20 Kozakov, D. et al. (2011) Structural conservation of druggable hot
spots in protein–protein interfaces. Proc. Natl. Acad. Sci. U.S.A. 108,
13528–13533
21 Hajduk, P.J. et al. (2005) Druggability indices for protein targets
derived from NMR-based screening data. J. Med. Chem. 48, 2518–2525
22 Pellegrini, L. et al. (2002) Insights into DNA recombination from the
structure of a RAD51–BRCA2 complex. Nature 420, 287–293
23 Pagliaro, L. et al. (2004) Emerging classes of protein–protein
interaction inhibitors and new tools for their development. Curr.
Opin. Chem. Biol. 8, 442–449
24 Fry, D.C. (2006) Protein–protein interactions as targets for small
molecule drug discovery. Biopolymers 84, 535–552
25 Higueruelo, A.P. et al. (2009) Atomic interactions and profile of small
molecules disrupting protein–protein interfaces: the TIMBAL
database. Chem. Biol. Drug Des. 74, 457–467
26 Galatin, P.S. and Abraham, D.J. (2004) A nonpeptidic sulfonamide
inhibits the p53-mdm2 interaction and activates p53-dependent
transcription in mdm2-overexpressing cells. J. Med. Chem. 47,
4163–4165
27 Enyedy, I.J. et al. (2001) Discovery of small-molecule inhibitors of Bcl-2
through structure-based computer screening. J. Med. Chem. 44, 4313–
4324
28 Wang, J-L. et al. (2000) Structure-based discovery of an organic
compound that binds Bcl-2 protein and induces apoptosis of tumor
cells. Proc. Natl. Acad. Sci. U.S.A. 97, 7124–7129
29 Nikolovska-Coleska, Z. et al. (2004) Discovery of embelin as a cellpermeable, small-molecular weight inhibitor of XIAP through
structure-based computational screening of a traditional herbal
medicine three-dimensional structure database. J. Med. Chem. 47,
2430–2440
30 Yin, H. and Hamilton, A.D. (2005) Strategies for targeting protein–
protein interactions with synthetic agents. Angew Chem. Int. Ed. Engl.
44, 4130–4163
31 Bernal, F. et al. (2010) A stapled p53 helix overcomes HDMX-mediated
suppression of p53. Cancer Cell 18, 411–422
32 Moellering, R.E. et al. (2009) Direct inhibition of the NOTCH
transcription factor complex. Nature 462, 182–188
8
Trends in Pharmacological Sciences xxx xxxx, Vol. xxx, No. x
33 Erlanson, D.A. et al. (2000) Site-directed ligand discovery. Proc. Natl.
Acad. Sci. U.S.A. 97, 9367–9372
34 Erlanson, D. et al. (2004) TETHERING: fragment-based drug
discovery. Annu. Rev. Biophys. Biomol. Struct. 33, 199–223
35 Erlanson, D. et al. (2003) In situ assembly of enzyme inhibitors using
extended tethering. Nat. Biotechnol. 21, 308–314
36 Hyde, J. et al. (2003) Discovery and characterization of cooperative
ligand binding in the adaptive region of interleukin-2. Biochemistry 42,
6475–6483
37 Raimundo, B.C. et al. (2004) Integrating fragment assembly and
biophysical methods in the chemical advancement of small-molecule
antagonists of IL-2: an approach for inhibiting protein–protein
interactions. J. Med. Chem. 47, 3111–3130
38 Sadowsky, J.D. et al. (2011) Turning a protein kinase on or off from a
single allosteric site via disulfide trapping. Proc. Natl. Acad. Sci. U.S.A.
108, 6056–6061
39 Valkov, E. et al. (2012) Targeting protein–protein interactions and
fragment-based drug discovery. Top. Curr. Chem. 317, 145–179
40 Brandts, J.F. and Lin, L.N. (1990) Study of strong to ultratight protein
interactions using differential scanning calorimetry. Biochemistry 29,
6927–6940
41 Holdgate, G.A. and Ward, W.H. (2005) Measurements of binding
thermodynamics in drug discovery. Drug Discov. Today 10, 1543–
1550
42 Leung, S.M. et al. (1996) Thermal activation of the bovine Hsc70
molecular chaperone at physiological temperatures: physical
evidence of a molecular thermometer. Cell Stress Chaperones 1, 78–89
43 Shuker, S. et al. (1996) Discovering high-affinity ligands for proteins:
SAR by NMR. Science 274, 1531–1534
44 Campos-Olivas, R. (2011) NMR screening and hit validation in
fragment based drug discovery. Curr. Top. Med. Chem. 11, 43–67
45 Cole, D.C. et al. (2010) Identification and characterization of
acidic mammalian chitinase inhibitors. J. Med. Chem. 53, 6122–
6128
46 Geitmann, M. et al. (2011) Identification of a novel scaffold for allosteric
inhibition of wild type and drug resistant HIV-1 reverse transcriptase
by fragment library screening. J. Med. Chem. 54, 699–708
47 Nordin, H. et al. (2005) Kinetic studies of small molecule interactions
with protein kinases using biosensor technology. Anal. Biochem. 340,
359–368
48 Giannetti, A.M. (2011) From experimental design to validated hits a
comprehensive walk-through of fragment lead identification using
surface plasmon resonance. Methods Enzymol. 493, 169–218
49 Blundell, T.L. et al. (2002) High-throughput crystallography for lead
discovery in drug design. Nat. Rev. Drug Discov. 1, 45–54
50 Murray, C.W. and Blundell, T.L. (2010) Structural biology in fragmentbased drug design. Curr. Opin. Struct. Biol. 20, 497–507
51 Hopkins, A. et al. (2004) Ligand efficiency: a useful metric for lead
selection. Drug Discov. Today 9, 430–431
52 Saxty, G. et al. (2007) Identification of inhibitors of protein kinase B
using fragment-based lead discovery. J. Med. Chem. 50, 2293–2296
53 Hann, M.M. (2011) Molecular obesity, potency and other addictions in
drug discovery. Med. Chem. Comm. 2, 349–355
54 Leeson, P. and Springthorpe, B. (2007) The influence of drug-like
concepts on decision-making in medicinal chemistry. Nat. Rev. Drug
Discov. 6, 881–890
55 Abad-Zapatero, C. et al. (2010) Ligand efficiency indices for an effective
mapping of chemico-biological space: the concept of an atlas-like
representation. Drug Discov. Today 15, 804–811
56 Chung, S. et al. (2009) Impact of linker strain and flexibility in the
design of a fragment-based inhibitor. Nat. Chem. Biol. 5, 407–413
57 Petros, A.M. et al. (2006) Discovery of a potent inhibitor of the
antiapoptotic protein Bcl-xL from NMR and parallel synthesis. J.
Med. Chem. 49, 656–663
58 Hung, A.W. et al. (2011) Route to three-dimensional fragments
using diversity-oriented synthesis. Proc. Natl. Acad. Sci. U.S.A. 108,
6799–6804
59 Keserü, G.M. and Makara, G.M. (2009) The influence of lead discovery
strategies on the properties of drug candidates. Nat. Rev. Drug Discov.
8, 203–212