GE Healthcare
Life Sciences
Application note 28-9796-95 AA
BiacoreTM label-free interaction analysis
Fragment library screening and
characterization with Biacore™ 4000
Fragment-based drug design is increasingly used to identify
suitable structure scaffolds in drug discovery. Label-free
technologies such as x-ray crystallography and NMR have
proved useful for identification of low-affinity fragment
compounds to drug targets. Surface plasmon resonance
(SPR) biosensor is an attractive technique in this area due
to low target consumption, high-information content and
high-quality data.
Biacore 4000 equipped with Biacore 4000 LMW Extension
Package was used to meet the challenges posed by the
low-millimolar affinity levels and low molecular weights that
are typical for fragments. Recently developed methodologies
and evaluation tools were used for identification of superstoichiometric sticky binders, and automatic detection of
atypical binding behavior to rapidly focus the studies on
well behaving candidates. These analyses were applied
to screens that used either single concentrations or a
concentration series of each sample. Specificity of fragment
hits was confirmed using competition experiments. The
dedicated Affinity Screen evaluation tool facilitated the
evaluation of affinities in the millimolar range.
With throughput sufficient for most fragment libraries,
novel methodology, data evaluation features, and enhanced
sample data import and export functionality, Biacore 4000
is a potentially important tool in fragment-based drug
design. Examples are shown on the use of the screening
and affinity evaluation tools to improve selection of
fragments in a way that can significantly elevate the
success rates of subsequent co-crystallizations.
Introduction
Fragment-based lead design approaches the challenge of
developing new drugs in shorter time and when high
throughput screening fails by identifying small molecule
fragments as starting points for chemical development
programs. This approach increases the possibility to cover a
imagination at work
Clean Screen
• Rapid screen of entire library against each target
• Goal: Removal of fragments that could potentially disturb subsequent assays
Binding Level Screen
Affinity Screen
• Single concentration screen
• Steady state analysis based
on concentration series
• Goal: Rapid prioritization
of fragments
• Goal: Affinity ranking and verification
Competition assay
• Usage of inhibitor in sample and buffer
• Goal: Validation and binding site mapping
Fig 1. Fragment screening with Biacore 4000 provides three main tools to
enhance screening quality (shaded boxes) and simplify analysis. Competition
assays, which dramatically reduce the frequency of false positives, are easy
to run utilizing the general functionality in the software.
relatively large proportion of chemical space by screening a
smaller number of compounds/fragments (thousands instead
of hundred of thousands). A combination of structural and
functional binding information is used to identify promising
fragments. Accurate and rapid prioritization of these
fragments for further analysis and development is crucial.
Despite its advantages, fragment screening presents several
technical challenges. Many structural binding methods, such as
NMR and X-ray crystallography, consume large amounts of
target protein. In addition, the low affinities commonly exhibited
by fragments (0.1 to 10 mM) require high sample concentrations
to obtain high binding site occupancy. In practice, this means
that solubility limitations might force the use of suboptimal
concentrations for affinity analysis in relation to the affinity.
Biacore systems have the advantage of consuming small
amounts of both target protein and fragments at the same
time as providing sufficient throughput. SPR technology is,
however, mass dependent and the low molecular weights of
fragments (Mr 80 to 300) give low signals. Generally, fragments
have very fast on- and off-rates, resulting in square-shaped
sensorgrams. This means that steady-state affinity analysis
should be used to reveal the affinity as the kinetic rate
constants normally cannot be resolved.
In order to address these experimental challenges, Biacore 4000
LMW Extension Package provides three dedicated software
tools for screening fragments and other LMW compounds
(Fig 1). The Clean Screen tool is used to identify troublesome
fragments that show a persistent binding which may lower
the data quality for the following samples. After this clean-up
step, fragments can be prioritized for further analysis using
Binding Level Screen. A single concentration of fragments
can be run against multiple immobilized proteins: targets,
blank reference, and additional protein control targets.
Promising binders can be selected with respect to the binding
strength as well as their binding behavior through analysis of
the sensorgram shape which reveals, for example, secondary
interactions. The prioritized fragments (or the entire library)
are run in concentration series and characterized by affinity
steady-state analysis to verify binding and determine
the dissociation equilibrium constants for ranking based
on affinity and ligand efficiency. Application-tailored
fitting algorithms allow evaluation both when suboptimal
concentrations have to be used, and in situations where
secondary binding, unrelated to the target site of interest, occurs.
Materials and methods
Screening against a tyrosine kinase, K1 (Mr 31 900), was
performed using an unbiased in-house fragment library
composed of 1920 fragments. These fragments were selected
by criteria like the rule-of-three with a particular emphasis on
chemical diversity and solubility of the molecules. Molecular
weights were between 100 Da and 260 Da with an average
of 220 Da. To maximize sample throughput, all assays were
run in a sample-focused arrangement, where the same panel
of ligands is immobilized in each of the four flow cells (see
Fig 2). The targets were immobilized to Sensor Chip CM5 using
amine coupling. K1 was immobilized at a high and low level
on spots 1 and 2, respectively, and carbonic anhydrase (CA)
was immobilized as a control target on spot 5. One spot with
unmodified CM-dextran and one activated/deactivated spot
were used as references (Fig 2). Four different samples were
injected in each analysis cycle, one over each flow cell.
The generally weak binding of fragments to target molecules
meant that complete dissociation from the surface was rapid
and regeneration was not necessary. For all screens, the
evaluation tools in Biacore 4000 LMW Extension Package
were used.
K1
low level
Reference
(unmodified
CM dextran)
Reference
(blank surface,
activated/
deactivated)
K1
high level
Spot
Control CA
1
2
3
4
5
Fig 2. Target configuration for screening of the fragment library.
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28-9796-95 AA
Clean Screen
All fragments were screened at the highest concentration
that was planned to be used in subsequent analyses (2 mM),
using the same injection time. Assay buffer: 10 mM HEPES/
NaOH pH 7.40, 150 mM NaCl, 0.05% Surfactant P20, 5 mM
MgCl2, 1 mM DTT, and 2% DMSO. Fragments were screened
simultaneously for binding to the target kinase and carbonic
anhydrase (control protein), immobilized as shown in Figure 2.
Binding Level Screen
Since the Clean Screen run was designed to include a wellcharacterized active site inhibitor, Inh 1, as positive control
and buffer served as negative control, it could also be used
for Binding Level Screen evaluation. Evaluation included
reference subtraction, solvent correction, and molecular
weight adjustment.
Competition assay
The Binding Level Screen was repeated in the presence of a
very potent site-specific competitor, Inh 2, in all samples and
buffers at a concentration high enough to ensure complete
blockage of the active site throughout the run.
Affinity Screen
The same spot configuration was used as in Binding Level
Screen. In the Affinity Screen, Inh 1 was used as both positive
control and as Rmax control. Rmax was determined using a
saturating concentration, 2 µM (KD 20 nM), of Inh 1. Each
sample was diluted two-fold from 2000 µM in ten steps,
giving eleven concentrations (2000, 1000, 500, 250, 125, 63,
31, 16, 8, 4, and 2 µM). A blank for each sample was included.
Results and discussion
Clean Screen
Clean Screen aims at identifying troublesome fragments,
in particular “sticky” compounds. Fragments that exhibit
significant levels of persistant binding may lower data
quality in subsequent cycles. No control samples, reference
subtraction, or solvent correction is needed for the Clean
Screen evaluation and the simplest possible assay set up
can be used. In this study, however, Clean Screen evaluations
were done on runs also set up for Binding Level Screen
evaluations, an approach which was facilitated by the high
library quality. Out of 1920 fragments, twelve were identified
as sticky binders (0.6%) at a concentration of 2 mM. Among
these twelve compounds, both general and target-related
persistent binders were found (Fig 3). About half of the
persistant binders were target-dependent, while the others
could be classified as general binders or dextran binders.
For each target, the result of the automated software
evaluation can be visualized in a scatter plot, as the shift in
baseline response between each cycle (n) and the baseline
response in the following cycle (n+1). In this way, binding of the
sample injected in cycle n that persists in cycle (n+1) can be
quantitated. This value is then plotted against cycle number.
A)
B)
A)
Response
(RU)
B)
1400
Response
(RU)
1400
1200
1200
1000
Response
(RU)
Response
(RU)
900
700
600
500
400
300
200
100
0
200
Baseline 0
-200
-20
0
Response
(RU)
C)
500
20
80
20
5
25
-100
45
-50
5
65
25
85
45
105
65
125
Cycle number
85
105
40
120
60
140
80
160
Time
K1 High
K1 Low
50
CA
Reference
Baseline
-100
100
150
50
0
0
100
120
140
160
Time
250
150
100
60
350
250
200
-20
CA
Reference
450
350
300
-200
40
CA
Reference
Baseline
Response
(RU)
C)
450
400
K1 Low
400
0
600
K1 High
K1 Low
600
200
700
K1 High
800
No residual binding
No residual binding 600
General residual bindingGeneral residual binding
400
Selective residual binding
Selective residual binding
800
Baseline edifferences ([cycle number+1] – [cycle number])
Baseline edifferences ([cycle number+1] – [cycle number])
800
900
800
1000
-20
125
Cycle number
0
20
-50
40
K1 High
K1 Low
CA
Reference
Baseline
-20
60
0
80
20
100
40
120
60
140
80
160
Time
100
120
140
160
Time
Fig 3. Clean Screen result from one of the plates (384 samples) in the study: Panel A shows the difference in baseline response between each cycle (n) and
the baseline response in the following cycle (n + 1). In this way, binding of the sample injected in cycle (n) that persists in cycle (n+1) can be quantitated.
This value is then plotted against cycle number. In the Clean Screen result, blue data points reveal selective sticking (persistant binding) to K1 and not to
CA (carbonic anhydrase). Red data points indicate a generally sticky fragment with persistent signal elevation after injection to all included targets. The
sensorgram in panel B corresponds to the blue sample data points in panel A (encircled), and the sensorgram in panel C corresponds to the red data points
in panel A (enclosed in a rectangle).
The software automatically assigns different symbols to the
samples (Fig 3): Red squares indicate general residual binding
to all spots included in the evaluation, blue squares represent
compounds with selective residual binding (i.e., K1 or CA), and
green squares are for fragments with no residual binding.
The cut-off for residual binding was set to 10 RU.
Even though persistent binders do not block the site of
interest, as often shown by unaffected binding of a positive
control, they may still cause drifts in the assay leading to
underestimation of fragment binding levels and reduced data
quality. This may lead to lower prioritization of fragments
that follow a persistent binder in the experimental order. It is
therefore desirable to remove these sticky fragments from
further analyses.
Binding Level Screen
At early stages in fragment-based drug discovery, candidate
fragments seldom can be identified reliably from single
binding measurements alone. Therefore, Binding Level Screen
is designed to select a number of fragments for further work
rather than to provide a secure identification of binders.
Binding Level Screen supports prioritization between
fragments showing the strongest binding, based on response
levels measured shortly after the start of the sample injection.
An early report point provides an adequate measure of
binding levels, while at the same time reducing the risk of
unintentionally prioritizing fragments due to nonspecific
binding taking place during the injection. Such compounds
are frequently promiscuous binders and are efficiently
identified by the software and tagged with binding behavior
markers. The different behaviors are color-coded in the plot
(Fig 4) as follows: Samples with slow dissociation are shown
in yellow, samples with R > Rmax in turquoise blue, samples
with slope during binding in blue, and samples showing
multiple binding behaviors are shown in red. A sample
with no binding behavior markers is depicted with green
spots. Ranking fragments according to binding level reveals
potentially strong binders, and the binding behavior marker
functionality facilitates identification of the well-behaved
fragments. A Binding Level Screen thus gives adequate help
in prioritizing the most promising fragments on which further
efforts can be spent.
A cutoff is automatically set to pick out 10% of the samples,
and these fragments are indicated as prioritized. However,
in order to obtain 10% fragments with normal behavior or to
select another level, the cutoff can be adjusted accordingly.
In order to obtain additional information about site specificity,
a competition assay was run in the presence of a site-specific
competitor in all samples and buffers to ensure a complete
blockage of the active site throughout the run. The results
from this run were compared to the data from the run without
competitor. Two selection criteria were set for primary hits:
the difference in binding level should be between 20 and
50 RU, and the binding level in the presence of competitor
around 10 RU or less (Fig 5). This assay setup was very
efficient since all binding of the fragment in the presence of
the competitor is related to other sites on the target than the
site of interest and is regarded as secondary in this study.
28-9796-95 AA
3
A)
B)
Example of slope (blue marker)
30
25
Slope
20
Response
(RU)
15
115
10
None
Multiple
R > Rmax
Slow diss
Slope
95
5
0
-5
-10
0
10
20
30
40
50
60
70
80
75
C)
Example of Slow dissociation marker (yellow marker)
80
70
55
60
50
40
35
30
Adjusted relative response - binding early
20
15
10
Slow diss
0
-10
-5
0
10
20
30
40
50
60
70
80
90
-10
100
0
10
20
30
40
50
60
Cycle number
Fig 4. Binding Level Screen of 384 fragments for K1 target at 2 mM concentration. (A) A plot of the molecular weight-adjusted binding responses against cycle
number. (B) An illustration of a sensorgram that triggers a binding behavior marker for slope (i.e., increasing signal during sample injection; blue color).
(C) An illustration of a sensorgram with the binding behavior marker (yellow color) for slow dissociation (i.e., a delay in the signal return to baseline).
Response
(RU/Da × 100)
Binding without a competitor
Binding with a competitor
80
70
60
50
40
30
20
10
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Example Samples
Fig 5. Comparison of binding levels in the presence (grey) or absence (blue) of competitor, for a selection of samples.
From these data, 192 compounds, 180 hits, and 12 fragments
used as negative controls were chosen as primary hits and
were subjected to Affinity Screen. The major benefit of the
competition run was a significant decrease in the number of
false positives taken for further analysis.
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28-9796-95 AA
Affinity Screen
While Clean Screen and Binding Level Screen are based on
analysis of a single sample concentration, Affinity Screen
requires a concentration series for each sample. In a classical
steady-state affinity analysis, the concentration range should
ideally extend to at least twice the KD value for the interaction.
This is seldom feasible in practice with low affinity fragments.
Predeterminated Rmax enables analysis at
suboptimal concentrations
with responses above Rmax indicates secondary binding
causing superstoichiomeric responses (Fig 7C). On the other
hand, when responses are below Rmax, a high Rmax/constant
Rmax-ratio rather indicates a lack of information about Rmax
leading to a high degree of dependence on evaluation with
predetermined Rmax (Fig 7B).
The practice of fitting with predetermined Rmax provided by
Affinity Screen enhances the analysis quality and meets the
challenge of working with concentration series suboptimal
in relation to KD. In this study, the agreement between free
fitting and fitting with predetermined Rmax increases the
confidence in the results and confirms the high data quality,
provided through a careful selection by using Clean Screen,
Binding Level Screen, and the competition assay.
Despite its name, the multisite model only determines a
single affinity related to the predetermined Rmax; however, it
does this in the presence of secondary interactions.
Since fragment binding is expected to occur with rapid on-and
off-rates, equilibrium can be assumed to be reached almost
immediately after start of sample injection, and at this point
the data are minimally affected by secondary binding events.
Therefore, this early data point is preferred in Affinity Screen
plots rather than the point close to the end of the injection
that is commonly used in steady-state affinity determination.
To ensure a robust evaluation, a positive control that is known
to bind well to the site of interest on the target is used. The
binding capacity for the control binder is used to calculate
a molecular weight-adjusted predetermined Rmax for each
fragment automatically. This value is used as a base for the
affinity estimation and an example is shown in Figure 6.
Evaluation of affinity in the presence of secondary Selection of candidates for successful X-ray
crystallography screen
interactions
The 192 fragments selected with Binding Level Screen
and competition assays were run in dilution series of 11
concentrations in the range between 2 µM and 2 mM. The
same flow cell setup as in Binding Level Screen was used. The
predetermined Rmax was established, and the surface activity
was monitored by checking the binding levels for single injections
of inhibitor (Inh 1) at a saturating concentration of 2 µM.
Affinity Screen evaluation estimates the binding strength by
fitting binding data to a model for either single or multisite
interaction. A multisite model is suggested automatically by
the software when R > Rmax or if the sensorgram displays a
slope during binding and a slow dissociation, both of which
indicate secondary binding. An example of this is shown
in Figure 7. A high-fitted Rmax/constant Rmax ratio combined
A)
Response
(RU)
60
50
40
Fitted with free Rmax
Analyte concentrations 0.03KD - 16KD
Rmax = 57 RU
KD = 0.51 mM
30
20
10
0
-10
0
B)
1
2
4
5
6
7
8
9
Concentration (mM)
C)
Response
(RU)
20
Response
(RU)
20
15
15
10
10
Fitted with free Rmax
Analyte concentrations 0.03KD - 0.5KD
Rmax = 31 RU
KD = 0.11 mM
5
0
-5
3
0
-5
0
0.05
0.10
0.15
0.20
0.25
0.30
Concentration (mM)
Fitted with constant Rmax
Analyte concentrations 0.03KD - 0.5KD
Rmax = 60 RU
KD = 0.52 mM
5
0
0.05
0.10
0.15
0.20
0.25
0.30
Concentration (mM)
Fig 6. Principles of predetermined Rmax. The upper panel (A) shows a fit to an example data set (simulated with Rmax = 60 RU and KD = 0.5 mM) with concentrations
well above KD, resulting in a good determination of KD. (B) A fit to the first five points, representing a typical situation for fragments. Here, the KD cannot be properly
determined due to the lack of information about Rmax in the data points. (C) A fit with constant predetermined Rmax (60 RU), resulting in a good determination of
KD, close to the simulated value. In this case, the information about Rmax, lacking in the datapoints, could be provided from another source. In a real experiment,
this information can be obtained with a positive control.
28-9796-95 AA
5
The high solubility of the fragments allowing 2 mM
concentration, gave an excellent fitting quality and thereby a
very high confidence in the affinity estimation (Fig 8).
The final outcome of the study is 105 hits, with an affinity range
between 13 µM and > 3 mM. These hits fulfilled the following
criteria: 1) Site-specificity was a criterium already in the
competition assay. 2) They can be fitted to a one-site model
with a better fit than using the multisite model. 3) Surface
saturation is reached or will be reached in the predetermined
Rmax range.
The 105 hits for K1 have also been qualitatively analyzed
with ITC, and over 80% were confirmed as binders. Finally,
crystallization was performed on 48 of the 105 hits, selected
based on chemical diversity, ligand efficiency, and affinity.
Soaking and co-crystallization trials resulted in clearly defined
electron densities for 41 fragments at 2.0 – 2.9 Å resolution,
with the affinity range 43 µM to > 3 mM. This corresponds to
an X-ray success rate > 85%, reflecting the high quality in the
data selection process provided by Biacore 4000.
A)
B)
C)
Response
(RU)
120
Response
(RU)
Response
(RU)
120
120
100
100
100
80
80
80
60
60
60
40
40
40
20
20
20
0
0
0
-20
-20
-20
0
10
20
30
40
50
0
60
Time (s)
Response
(RU)
10
20
30
40
50
60
Time (s)
0
35
Fitted Rmax= N/D
KD= N/D
50
80
25
30
60
20
20
40
10
20
0
0
15
40
50
60
Time (s
0.8
1.0
1.2
Fitted Rmax= 251 RU
KD= 1.4 mM
100
40
30
30
120
60
Fitted Rmax= 112 RU
KD= 1.8 mM
40
20
Response
(RU)
Response
(RU)
45
10
10
5
0
-10
0
0.2
0.4
0.6
0.8
1.0
-1
0
1.2
0.2
0.4
0.6
35
0
0.4
0.6
Concentration (mM)
120
Predeterm. Rmax= 118 RU
KD= 1.8 mM
50
Predeterm. Rmax= 93 RU
KD= 0.5 mM
100
40
80
25
30
60
20
20
40
10
20
0
0
30
0.2
Response
(RU)
60
Predeterm. Rmax= 118 RU
KD= 2.0 mM
-20
1.2
Response
(RU)
45
40
1.0
Concentration (mM)
Concentration (mM)
Response
(RU)
0.8
15
10
5
0
-20
0
0
0.2
0.4
0.6
0.8
1.0
1.2
Concentration (mM)
0
0.2
0.4
0.6
0.8
1.0
1.2
Concentration (mM)
0
0.2
0.4
0.6
0.8
1.0
1.2
Concentration (mM)
Fig 7. The upper row shows sensorgram data with data reading frame marked in red. The middle row illustrates free fitting to the same three data sets, and
the lower row shows fitting with predetermined constant Rmax still to the same data sets. Column (A) illustrates a data set giving good fit both ways with a good
agreement between fitted and predetermined Rmax (Rmax-ratio close to 1). The middle column (B) shows an example where the determination of the parameters
using free fit fails (Rmax-ratio is high), while a fit with predetermined Rmax is successful. The right column (C) is an example where free fit and data response
indicate a superstoichiometric binding. Use of the multisite model and predetermined Rmax allows determination of the stoichiometric high affinity KD.
6
28-9796-95 AA
Ordering information
Response (RU)
40
System
30
20
Code no.
Biacore 4000
28-9643-21
Biacore 4000 LMW Extension Package
28-9664-62
KD= 1.1 mM
Rmax= 60 RU
Chi2= 0.1966 RU2
10
0
0
0.5
1.0
1.5
2.0
2.5
Concentration (mM)
Fig 8. Example of a high quality determination of 1 mM affinity at
1:1 stoichiometry.
Conclusion
Screening of fragment libraries with Biacore 4000 provides an
efficient, informative approach for use prior to, or in parallel
with, structural methods such as X-ray crystallography. This
is achieved with a sequential throughput appropriate for
fragment libraries with very low target consumption, allowing
the discovery process to be started before scaling up target
production. The novel evaluation tools produce quality data
despite the challenges of fragment screening by supporting
quick removal of aggregative and sticky binders, by efficient
selection of prioritized fragments through high assay sensitivity
and binding behavior analysis, and by facilitating affinity-based
ranking despite the use of suboptimal sample concentrations
and in the presence of secondary, unspecific binding.
Acknowledgement
The screening data were kindly provided by Dr. Jörg Bomke,
Merck-Serono, Germany. The illustrative data in figures 6 and
7 were generated by GE Healthcare.
28-9796-95 AA
7
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First published Jan. 2011
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