Six Biophysical Screening Methods Miss a Large Proportion of

Mar 30, 2016 - ment-target complexes is essential for the success of FBLD.5,6 ... Figure 1. Fragment distribution among the EP binding sites for (a) a...
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Six Biophysical Screening Methods Miss a Large Proportion of Crystallographically Discovered Fragment Hits: A Case Study Johannes Schiebel,† Nedyalka Radeva,† Stefan G. Krimmer,† Xiaojie Wang,† Martin Stieler,† Frederik R. Ehrmann,† Kan Fu,† Alexander Metz,† Franziska U. Huschmann,†,‡ Manfred S. Weiss,‡ Uwe Mueller,‡ Andreas Heine,† and Gerhard Klebe*,† †

Institut für Pharmazeutische Chemie, Philipps-Universität Marburg, Marbacher Weg 6, 35032 Marburg, Germany Helmholtz-Zentrum Berlin für Materialien und Energie, HZB, BESSY II, Abteilung Makromolekulare Kristallographie, Albert-Einstein-Str. 15, 12489 Berlin, Germany

‡

S Supporting Information *

ABSTRACT: Fragment-based lead discovery (FBLD) has become a pillar in drug development. Typical applications of this method comprise at least two biophysical screens as prefilter and a follow-up crystallographic experiment on a subset of fragments. Clearly, structural information is pivotal in FBLD, but a key question is whether such a screening cascade strategy will retrieve the majority of fragment-bound structures. We therefore set out to screen 361 fragments for binding to endothiapepsin, a representative of the challenging group of aspartic proteases, employing six screening techniques and crystallography in parallel. Crystallography resulted in the very high number of 71 structures. Yet alarmingly, 44% of these hits were not detected by any biophysical screening approach. Moreover, any screening cascade, building on the results from two or more screening methods, would have failed to predict at least 73% of these hits. We thus conclude that, at least in the present case, the frequently applied biophysical prescreening filters deteriorate the number of possible X-ray hits while only the immediate use of crystallography enables exhaustive retrieval of a maximum of fragment structures, which represent a rich source guiding hit-to-lead-to-drug evolution.

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MS, high-concentration biochemical screens (HCS), thermal shift assays (TSA), or surface plasmon resonance (SPR) in order to select fragments for a subsequent crystallographic analysis.1,4,20−22 Rather elaborate screening cascades have been suggested that start with a fast and crude assay to generate a primary fragment selection that is further filtered for promising hits by more sophisticated and demanding techniques.1,3,5,22 However, to the best of our knowledge, it has never been documented and therefore remains unclear whether such a sequential screening protocol is able to reliably predict which molecules will result in fragment-bound crystal structures and which strategy will be the most efficient one for different settings and aims.4 Such a validation is urgently needed, and therefore we started with the present quite elaborate case study, admittedly on one single target. We systematically applied and compared six different screening protocols and scenarios in a fragment-screening campaign supplemented by an exhaustive X-ray crystallographic screen to get a first impression of how well screening results might predict crystallographic hits. The goal of this documented test case is to raise the scientific discussion of whether we currently perform fragment screening campaigns optimally. Undoubtedly, more such investigations have to follow; however, to come to a final conclusion, a

ragment-based lead discovery (FBLD) plays an increasingly important role in the drug development process. Today most pharmaceutical companies complement their highthroughput screening (HTS) efforts by FBLD, in particular for challenging targets.1−3 FBLD samples the chemical space more efficiently using a reduced number of smaller compounds (MW < 250 Da) compared to HTS, simultaneously holding promise to lower attrition rates in clinical trials owing to improved pharmacokinetic properties.4 Indeed, vemurafenib was approved in 2011 as the first FBLD-discovered drug and many more candidates are in clinical trials.1 Practitioners agree that structural information about fragment-target complexes is essential for the success of FBLD.5,6 Therefore, it is not surprising that the field was initiated in the early 1990s based on results from crystallographic and NMR experiments.7−10 In the following years, these findings stimulated the foundation of several biotechnology companies such as Astex, Plexxikon, and SGX, or resulted in a focus on such approaches, for example at Abbott.6,11 In the initial phase, X-ray crystallography had been established and used for fragment screening and yielded several clinical candidates.6,12−16 Today, however, X-ray crystallography is less frequently employed as the primary screening tool mainly attributed to its putatively low throughput.6,17,18 Accordingly, its value for screening purposes is underappreciated.19 Nowadays, libraries are usually first screened by faster techniques able to detect low-affinity binders (Kd ≈ 0.1−10 mM), such as NMR, native © 2016 American Chemical Society

Received: December 15, 2015 Accepted: March 30, 2016 Published: March 30, 2016 1693

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Figure 1. Fragment distribution among the EP binding sites for (a) all X-ray hits, (b) those crystallographic binders that were suggested as hits by at least two or (c) maximal one screening assay. For clarity, the EP structure is not shown. Fragments that were identified as EP binders by exactly 0, 1, 2, 3, 4, or 5 screening methods are depicted as red, yellow, magenta, blue, brown, or gray sticks, respectively. Alternative binding poses are included for fragments that bind two or three times to EP. However, in panels b and c, those of the alternative binding modes that are further remote from the catalytic dyad, and thus likely less relevant, are represented by transparent sticks. EP subsites found to be easily or rarely occupied by fragments that were hits in at least two screening methods (which are specified in panel b) are represented by green or cyan spheres, respectively. The term “rarely” is used in the sense that at least 80% of all fragments binding to a specific site (e.g., S2) are identified as hits by no or only one screening method (not considering alternative binding modes; cf. subsite occupation in panel c vs b).

Figure 2. Crystallographic binding modes of four exemplarily chosen fragments. EP is depicted as gray surface representation. To facilitate orientation in the binding pocket, the catalytic aspartates are shown as gray sticks with color-coded heteroatoms. The chemical structures of the respective fragments are included. (a) EP in complex with fragment 218 (brown). The flap region, which is pushed away from the binding cleft by fragment 218 (orange arrow) and is therefore disordered in the complex structure, is shown for the apo enzyme as a blue cartoon model. (b) The EP-fragment 224 complex. The two bound 224 fragments and a polyethylene glycol (PEG) are highlighted as green and gray sticks, respectively. (c,d) EP in complex with fragments 42 (yellow) and 227 (cyan). The latter fragment was found to be covalently attached to Asp279 remote from the EP substrate-binding cleft.

physiologically relevant pH.24 Due to its shallow binding pocket, β-secretase is regarded as challenging with respect to inhibitor design and can only be tackled by FBLD.21 In a recent study, we found that minor changes in the chemical scaffold of EP binders induces major changes in the binding mode, complicating the establishment of structure−activity relation-

consorted effort of the community is needed to identify reliable concepts for the best strategy. As a target, we selected the aspartic protease endothiapepsin (EP, EC 3.4.23.22), which can be used as a surrogate to develop new drugs for the treatment of hypertension, Alzheimer’s disease, and malaria.23−27 For instance, the substitution of βsecretase by EP enables the production of crystals at a 1694

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Figure 3. Comparison of screening and X-ray hits. For all depicted Venn diagrams, the number of overlapping hit sets in a certain region of the plot is indicated by a gradually increasing reddening of the respective field (see legend). Red, orange, blue, cyan, purple, gray, and green lines enclose all fragments indicated as hits by the X-ray, ESI-MS, HCS, TSA, STD-NMR, MST, and RDA screens, respectively. This figure was generated with R and Inkscape. (a) Six-set Venn diagram comparing the X-ray results with the outcome of five different screening assays. The TSA was omitted for clarity since all X-ray hits predicted by this assay are covered by at least one additional method. As an example of how to read this diagram, the sum of numbers within the left half of the area surrounded by an orange line, corresponding to the overlap of this area with the red “X-ray” box, yields the number of fragments identified as common hits by the X-ray and MS screens (14 × 0 + 1 × 1 + 1 × 3 = 4; all contributing values are shown in blue; the origins of the latter two summands are indicated by blue arrows). The white arrow exemplarily highlights that no fragment was identified as a common hit by X-ray, RDA, MS, and NMR because the objects representing these methods, in contrast to MST and HCS, do include the indicated field. The number of hits identified only in the X-ray screen is highlighted by a red circle. (b) Weighted three-set Venn diagrams for two screening and X-ray hit lists. The areas of specific fields are approximately proportional to the number of contained hits. Note that 5, 10, 8, 24, 12, and 3 of all 71 possible X-ray structures could not be predicted by the ESI-MS, HCS, TSA, STD-NMR, MST, and RDA assays per se as these fragments were not subjected to the respective method or because the corresponding results were not analyzable for method-specific reasons.30

ships and thus suggesting EP as an equally challenging target in lead discovery and optimization.28 In our effort to provide a systematic comparison of different screening strategies currently applied in FBLD, we screened our in-house 361-fragment library for fragments binding to EP using six biophysical techniques in parallel, namely a fluorescence-based HCS, saturation-transfer difference NMR (STD-NMR), a reporter-displacement assay (RDA), native MS, microscale thermophoresis (MST), and TSA.29,30 While twothirds of the entire library were identified as potentially binding, only 41 hits were detected by at least two assays, and no hit was identified by all six methods.30 Due to this astonishingly low overlap and as crystal structures are key to successful FBLD, we decided to study the entire fragment library by crystallography on all entries individually.

The obtained 71 EP-fragment complexes are of high resolution (1.35 ± 0.18 Å) and provide a rich information source for subsequent medicinal chemistry efforts. The bound fragments sample the complete EP binding cleft, comprising eight subsites S2′ through S6, as well as three remote, potentially allosteric pockets (Figure 1). To illustrate the scope of the gained insights, we exemplarily describe four structures (Figure 2). Fragment 218 binds with its exocyclic guanidine moiety to the two catalytic aspartates of EP, positioning its 4-quinazolinone in a way triggering the opening of the flap region, which otherwise seals the active site. Further remote from the catalytic dyad, in the S3−S6 pockets, two copies of fragment 224 were found. Interestingly, the central secondary amine of one of these copies is surrounded by an oligo-ethylene glycol molecule from the soaking solution that is frequently found at this position, thus mimicking an ion-crown ether complex. In contrast, the cycloheptatriene-based fragment 42 displaces this PEG molecule and likely binds to EP in the cationic tropylium form, enabling a π−π stacking interaction with Phe291. Finally, fragment 227 is located in a pocket remote from the substrate binding cleft and, through substitution of its benzylic bromide, is covalently bound to Asp279. Full documentation of all 71 complex structures will be reported elsewhere. Prerequisites for Successful Crystallographic Screening. Given the high crystallographic hit rate, we asked which parameters might have been responsible for this success. At first, we investigated the influence of compound solubilities that could be experimentally determined for 324 of the 361 fragments in a standard buffer.29,30 Relative to this 324-entry subset, we found that almost all binders (97%) display solubilities of at least 1 mM (Figure S1a), whereas 15% of all sublibrary members are less soluble. This underlines that sufficient fragment solubility is critical for successful structure

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RESULTS AND DISCUSSION Exhaustive Crystallographic Fragment Screening. Whenever crystallography had been used as the initial screening technique in the past, crystals of the target have almost always been exposed to cocktails of several fragments in soaking experiments to speed up the process.31−33 Here, we refrained from using cocktails to avoid potential artifacts and screened all 361 fragments individually instead. Remarkably, we were able to identify 71 crystallographic fragment hits using this strategy. This corresponds to a success rate of 20%, which is higher than for any of the six alternative screening methods (2−17%). Given that usually 0.5−10% of the crystallographically screened fragments translate into structures,12 our hit rate is exceptionally high. Since it is known that the usage of focused libraries can increase success rates,1 we would like to emphasize that our library was designed for general purpose without a bias toward aspartic proteases.29 1695

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ACS Chemical Biology determination and highlights that 85% of our library meet this requirement, partially explaining the high hit rate. To profit from these high solubilities, we decided to soak the fragments at 90 mM concentration in the selected soaking buffer. Indeed, concentration- and time-dependent trials underline the importance of high fragment concentrations to achieve sufficient occupation at the binding site for proper electron density assignment (Figure S1b). Although 90 mM seems to be an artificially high ligand concentration, we would like to emphasize that the in-crystal affinity can be much lower compared to affinity in solution. Indeed, many researchers use similar high concentrations to ensure fragment detection.12,34,35 The applied 90 mM concentration is much higher than the above-mentioned solubilities. To achieve such a high fragmentsoaking concentration in as many cases as possible, the soaking condition contained approximately 10% DMSO, 20% glycerol, and 20% PEG 4000 (for further details, see Methods section). Particularly, DMSO and PEG are known to increase the solubility of many ligands.36 Moreover, we believe that the popular use of fragment cocktails in soaking experiments lowers the individually achievable fragment concentration and can lead to artifacts such as reduced occupancy, crystal damage, and interfragment reactions,32,37 all likely to diminish the success rate. Nevertheless, when we initiated this project in 2010, we first screened our library via HCS, yielding 55 hits. We then subjected solely this subset to crystallography.29 To speed up the campaign in those days, the crystallographic analysis of our 55 HCS hits was performed using two-fragment cocktails. The only methodological difference to the current study was that each of the two mixed fragments was used at a 50 mM concentration while the single-fragment soaks described here allowed usage of the higher 90 mM concentration. In the previous study, we had obtained 11 fragment-bound structures by cocktailing, whereas the individual soaks in the present study led to 20 crystallographic hits with respect to the same subset of 55 HCS hits. This is a first indication that the usage of cocktails might significantly lower hit rates or at least considerably influences the outcome of the crystallographic study.38 Comparison of Crystallographic and Biophysical Assay Results. The independent performance of our crystallographic and the six biophysical screening campaigns enables a systematic comparison of the different screening methods with respect to their ability to predict X-ray binders (Figure 3). Alarmingly, for almost half (44%) of the crystallographic hits (31 of the 71 hits), none of the six screening methods had indicated binding (Table 1). Moreover, 30% of the X-ray hits (21 of the 71 hits) were predicted by only one of all applied biophysical methods. This clearly demonstrates that any cascade of screening methods that builds on at least two screening techniques, would have failed to predict at least 73% of all X-ray hits (21 + 31 = 52 of the 71 hits). Thus, any screening cascade would have maximally retrieved 19 of the 71 structures, as only this number of hits appeared in two or more methods (for specific examples, see 3set Venn diagrams in Figure 3). Even more remarkably, these 19 fragments do not sufficiently occupy seven of the 11 EP pockets (S2′, S2, S4, S6, and three remote pockets) leading to a fundamental loss of information, which, in contrast, became available through our comprehensive crystallographic screen (Figure 1). Although we admittedly focus on only one target, there are hints in the literature that our findings may be valid more generally. For three different proteins, scientists at

Table 1. Comparison of Different Screening Methods and Protocols with Respect to the Prediction of the 71 Crystallographic Bindersa screening method or combination

fraction of X-ray binders among the hits of this methodb

missed fraction of possible 71 structuresc

HCS RDA STD-NMR ESI-MS TSA MST at least 1 method at least 2 methods at least 3 methods at least 4 methods at least 5 methods all 6 methods

21 out of 56 = 38% 27 out of 50 = 54% 12 out of 22 = 55% 4 out of 8 = 50% 11 out of 25 = 44% 10 out of 36 = 28% 40 out of 119 = 34% 19 out of 41 = 46% 13 out of 21 = 62% 8 out of 10 = 80% 5 out of 6 = 83% 0 out of 0 = 0%

70% 62% 83% 94% 85% 86% 44% 73% 82% 89% 93% 100%

a The first part of the table lists values for individual methods, while the second part is based on those fragments that were identified as hits by at least the given number of different methods. bNumber of structures and hit rates that would have resulted when applying X-ray crystallography to only those fragments suggested as hits by the specific method or strategy. cPercentage of all 71 structures identified in the full X-ray screen that would have not been found when investigating only the hits of the respective method or strategy in subsequent crystallographic experiments. We would also like to note that not all 361 fragments could be investigated by each method.30 With 66% (HCS), 60% (RDA), 74% (NMR), 94% (MS), 83% (TSA), and 83% (MST), the values in the first part of the table are slightly lower when referring to the subset of the 71 X-ray hits that could be investigated by the respective technique (61, 68, 47, 66, 63, and 59 of the 71 X-ray binders, respectively).

Vernalis showed that the number of resulting structures is significantly reduced when applying TSA as a postfilter to an NMR hit list.4 A study on the HIV-1 integrase core domain further underlines the potential danger of a cascade strategy.39 Here, 21 structures resulted when NMR and SPR techniques were applied separately, whereas not a single structure would be identified by an NMR/SPR cascade. While in a cascade approach the absolute number of resulting fragment-bound structures is decreasing due to the strict fragment preselection and the limited overlap of hits from different methods, the Xray hit rates (number of resulting fragment-bound structures divided by the number of fragments screened by crystallography based on the suggested screening hit list) can significantly increase.4 For instance (Figure 3b), the crystallographic investigation of the 22 NMR hits would have resulted in 12 of the 71 structures (59 missed structures, 12/22 = 55% hit rate). An NMR-TSA cascade, however, would have yielded the higher 86% (6 out of 7) hit rate but at a price of 6 structures. It therefore will largely depend on the project goals, the protein target, and the available resources whether a screening cascade can be an appropriate strategy. The inability of a cascade approach to predict the majority of crystallographic binders is a direct consequence of the poor overlap frequently observed between the results from different techniques.30,39 This is a problem arising from the varying assay requirements, inherent detection principles, the subsequent hitselection criteria, and strategies to avoid artifacts. For instance, TSA experiments require a solvatochromic fluorescence dye to record protein unfolding. The biochemical assay needs substrate turnover, while hits from a primary STD-NMR 1696

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ACS Chemical Biology screen were confirmed in our case via competitive displacement by ritonavir, a known active-site binder of EP.29,30 We found that this low overlap of screening hit lists also includes crystallography. In summary, this fact explains why so many putative hits are lost when the analysis is based on the intersection of an increasing number of assays (Table 1 and Figure 3). Indeed, 20−44% of the hits from each of our screening campaigns are method-specific and hence were never identified by any other technique (Table S1). Thus, different screening methods provide different information. Due to this complementarity, parallel rather than sequential application of screening methods may enhance the number of subsequently identified X-ray hits. Indeed, subjecting all 119 hits from the six screening methods to crystallography would have increased the number of crystal structures from 19 to 40 (Table 1). Predictive Power of Commonly Used Screening Methods. Since parallel screens can be quite time- and labor-intensive, we analyzed whether the individual biophysical methods would be able to prioritize entries from the fragment library for crystal structure analysis, which would be superior to a sole random selection. Indeed, all six screening methods would have resulted in an enrichment of X-ray hits with respect to the then considered fragment subsets (Table 1). In a timeor resource-limited situation, a preselection of fragments for the follow-up crystallographic screen may therefore be very useful. However, depending on the method, between 62 and 94% of all 71 possible structures were not predicted. In this context, we would like to note that not all library fragments could be investigated by each method due to assay-specific requirements and limitations.30 Overall, 325, 358, 206, 330, 342, and 282 of the 361 fragments could be successfully measured in the HCS, RDA, STD-NMR, TSA, ESI-MS, and MST assays, respectively. In most cases, it can be considered as an intrinsic property of the respective method that for certain fragments no data could be collected. For instance, the self-fluorescence of some fragments necessarily interfered with the readout in the HCS and MST experiments. The most important exception is STDNMR where only those 206 fragments were investigated that met predefined quality criteria.30 Nevertheless, even if only this subset of our library is considered, this method still fails to predict 35 of all now possible 47 fragment-bound structures (74%) (for further details, please see also footnote c of Table 1). One possible explanation for this observation might be that ritonavir, which was used in the secondary displacement NMR screen, is binding uncompetitively with respect to these fragments. Based on a comparison of the fragment- and ritonavir-bound EP crystal structures, this is, however, only true for six of the 35 fragments. Furthermore, of these six fragments, only two were characterized as hits in the primary STD-NMR screen without confirmation in the secondary screen (overall, the primary NMR screen predicts 19 of all 47 possible structures). In total, 181 of the 361 fragments could be fully characterized by each of the six screening methods and by X-ray crystallography. If we consider only this subset of the library, 42 structures could have been determined at most. If we limit our analysis to only this subset, a similar picture emerges as for the full library: 18 of these 42 crystallographic hits (43%) were predicted by none of the screening methods and any combined screening cascade would have failed to find 67% of those hits (28 out of 42). In order to evaluate the predictive power of the individual screening methods and its dependence on the chosen hit

threshold, we sorted the fragments by decreasing score (crude relative affinity ranking provided by each method) and analyzed how many X-ray hits would have been predicted processing this list from top-to-bottom until a certain limit was exceeded (Figures 4 and S2). In our case, MST and ESI-MS were the

Figure 4. Predictive power of the RDA with respect to the identification of crystallographic hits. To calculate the enrichment curve, depicted in blue, the list of those 358 fragments that could be investigated by the RDA was sorted by decreasing inhibition. This list was then worked through from top-to-bottom, and the number and percentage of fragments that would have been analyzed in a follow-up crystallographic study based on a certain inhibition cutoff were plotted against the number and percentage of structures retrieved from such an investigation (blue curve). The hit-definition threshold applied in this study and chosen based on previous experience is indicated by the purple dotted lines,30 while the optimal cutoff, up to which an enrichment is observed, is highlighted in orange. Crystallographic hit rates at a certain inhibition cutoff can be estimated from the intersection of the blue and gray lines (e.g., the hit rate at the 30% inhibition cutoff is 54%). The two red lines indicate how the blue curve would look like for an optimal or random selection of fragments, respectively. Similar plots for all five other methods are shown in Figure S2.

least predictive methods close to a random selection, followed by NMR, TSA, and HCS with similarly improved enhancements (Table S2). By far the most predictive method was RDA (Figure 4). The enrichment curve in Figure 4 clearly suggests that RDA maintains good predictive power up to a threshold of 19% inhibition, underlining that the choice of the hitpreselection criterion can strongly influence the screening results. Notably, the choice and success of the individual biophysical screens likely depend on the target and expertise of the involved research groups. Since the optimal method and the adequate cutoffs are not known a priori, we suggest the use of results from a well-established method in the laboratory in order to generate an affinity-sorted hit list. This list will help to optimally serialize the subsequent crystallographic experiments that should be worked through until the success rate gradually drops below a user-defined threshold. Crystallographic Screening without the Application of Any Prefilter. As even the described enrichment strategies do not manage to predict all fragments leading to crystal structures of protein-fragment complexes, we advocate direct crystallographic screening campaigns of entire fragment 1697

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ACS Chemical Biology libraries, particularly in cases where crystallographic data are readily obtained. It is commonly agreed upon that structural information is an important prerequisite for successful FBLD. For some target classes, such as GPCRs, crystallography is still challenging, and therefore the usage and further development of screening methods (e.g., NMR) can be critical for future FBLD successes.4 However, in many cases, primary screening via crystallography has been shown to be feasible after the establishment of a robust crystallization and soaking protocol.12,14−18 This is further facilitated by recent and future developments at synchrotron sources trying to streamline all aspects of structure determination ranging from miniaturized fragment-target crystallization to ultrafast fully automated data collection and processing.6,37 For instance, today’s thirdgeneration synchrotron sources provide very brilliant X-ray beams, significantly reducing data collection times.40 In addition, the preparation of cocrystals can be streamlined and miniaturized by the application of new tools such as improved liquid handling systems.37 All in all, these and future improvements will drastically reduce the, formerly very high, unit costs per crystal structure and therefore enable the immediate crystallographic screening of complete fragment libraries. In cases where X-ray crystallography will be used as a primary screening technique, biophysical methods will still be very important for the characterization of crystallographic hits.4 Given the high sensitivity of crystallography,2 one may suppose that the 31 additional X-ray hits not identified by any of the biophysical screens represent only very weak binders not really relevant for hit-to-lead-to-drug evolution (Figure 3a). We thus determined Kd values and ligand efficiencies (LE) for all 71 crystallographic binders by isothermal titration calorimetry (ITC; for details, see Methods section and refs 41 and 42), a method that is frequently used for this purpose.1 In particular, we applied displacement titrations to enable a reliable determination of the binding constants.41 These experiments revealed that almost half of the submillimolar EP binders would have not been identified by a cascade screening approach. Intriguingly, 10 of the 24 fragments with a good LE above 1.2 kJ mol−1 atom−1 (corresponding to ∼0.3 kcal mol−1 atom−1; please note that we use kJ instead of kcal throughout the manuscript according to SI convention), which is believed to be a precondition for successful hit-to-lead-to-drug development,1 were only identified by X-ray crystallography (Figure 5). Furthermore, we show that the exclusive X-ray hits occupy new and remote, potentially allosteric sites, providing a wealth of structural information that can prove essential during a medicinal chemistry project (Figure 1). The full structural data set maps the complete EP binding pocket and reveals experimental interaction hot spots that can inspire subsequent synthesis as also pointed out by researchers at AstraZeneca and Vernalis.4,22 Moreover, scientists of several pharmaceutical companies, such as Astex, Johnson & Johnson, and Plexxikon, emphasize that structural data are often more important than potency for deciding which fragments to elaborate further.2,21,22 Indeed, in a recent example from our own laboratory, we found that 3-chlorobenzamide, binding ∼20-fold weaker to the S1 pocket of thrombin relative to the analogous thiophene, nevertheless turned out to result in a ∼35-fold more potent compound after evolution of both fragments (Figure S3). For heat shock protein 90, a relatively weak millimolar binder could even be converted into a clinical candidate.4 In the case of the urokinase-type plasminogen activator the selection of a less potent fragment was shown to be detrimental to develop an

Figure 5. Affinities and ligand efficiencies. These plots classify all X-ray hits that could be investigated by ITC (59 out of 71) with respect to the total number of screening assays predicting their binding and (a) regarding their Kd value or (b) the ligand efficiency, respectively. The area of each circle is proportional to the number of fragments contained in this category, which is also given as a white number. Fragments are classified with respect to (a) their Kd and (b) their LE as very good (green), good (blue), acceptable (orange), and poor (gray), respectively. Please note that the unit kJ mol−1 atom−1 was used for the LEs. More detailed results are given in Table S5.

orally bioavailable nanomolar inhibitor.6 These examples underline that the selection of fragments for hit-to-lead-todrug chemistry should not purely be based on affinity considerations, but also on structural, pharmacokinetic, kinetic, thermodynamic, synthetic tractability, and patentability aspects.4 Concluding Remarks. In summary, we conclude that screening strategies using a sequential cascade of biophysical methods prior to X-ray crystallography can be rather misleading as they might miss an important fraction of fragments resulting in a crystal structure. Combining results from screening campaigns conducted in parallel or relying on a single wellestablished method is preferable, especially when the hitdefinition cutoffs can be optimized. However, ultimately only the direct application of X-ray crystallography, which is enabled by the declining unit costs per structure, will lead to a maximum of crystal structures driving the drug discovery process and avoiding the sole reliance on affinity data, which can be detrimental to the success of FBLD. Admittedly, our conclusions build mainly on the experience from our systematic study on the aspartic protease EP, but we hope that similar investigations of other targets will follow. Considering the effort involved, this can only be accomplished by a concerted 1698

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experimental details, we would like to refer to this work).29,30 The resulting screening as well as solubility data were taken from there and compared to the outcome of the full crystallographic screen with the aid of the statistical framework R.56 Isothermal Titration Calorimetry. For ITC measurements, EP samples obtained as described above were dialyzed against 0.1 M NaAc (pH 4.6). The protein concentration was then determined spectrophotometrically at a wavelength of 280 nm. Since fragments display only weak, mostly millimolar, binding affinities toward their target, it can be difficult to determine meaningful thermodynamic parameters by direct ITC titrations.57,58 We therefore decided to apply a strategy in which the fragment is displaced from the EP binding site by a more potent inhibitor titrated into the sample cell. In our case, we chose the strongly enthalpic EP inhibitor SAP114 as a displacement ligand (for the chemical structure, see Figure S4). As a reference, a solution of 500 μM SAP114 in 0.1 M NaAc (pH of 4.6) including 3% (v/v) DMSO was titrated into a mixture of 50 μM EP, 0.1 M NaAc (pH 4.6), and 3% (v/v) DMSO. For the displacement titrations, the same solutions were used except that the sample cell additionally contained 0.25−3.0 mM of the investigated fragment. Importantly, 12 of the 71 X-ray hits cannot be displaced by SAP114 for structural reasons (the PDB code of the EP-SAP114 complex is 4LAP),28 and thus no binding free energy can be determined via the displacement approach (for the chemical structures of these fragments see Table S5). Ten of the remaining 59 fragments were used as racemic mixtures even though in some cases only one stereoisomer was identified in the crystal structure (Table S5). However, we refrained from a correction of the resulting Kd values because the other stereoisomer might still have a similar binding strength. All titrations were carried out using an ITC200 device (GE Healthcare) at a temperature of 25 °C and a reference power of 5 μcal s−1. After a stable baseline had been reached, an initial injection with a volume of 0.3 μL was followed by 19−30 injections with spacings of 180 s and a constant injection volume ranging between 1.2 and 2.0 μL for different titrations. The obtained thermogram peaks were integrated with Nitpic 1.0.3.59 Subsequently, fitting of a single-site binding model isotherm omitting the first data point was performed using Sedphat 10.58d.60 As suggested by Zhang and co-workers, the association (and reciprocal dissociation) constant , was calculated using the of the fragment binding event, Kfragment a association constant of SAP114 as determined in the reference ), the apparent association constant obtained during titration (KSAP114 a ), as well as the fragment the displacement titration (Kdisplacement a concentration in the sample cell (eq 1).58

initiative of the community. However, taken together, this may reveal optimal strategies for successful fragment-screening campaigns.

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METHODS

X-ray Crystallographic Screening of a 361-Entry Fragment Library. The 361-fragment library was designed as described previously.29 Chemical structures of individual fragments can be found in ref 30. The isolation and crystallization of EP was performed as reported previously.29 Briefly, the buffer of Suparen samples, kindly provided by DSM Food Specialties, was exchanged to 0.1 M NaAc (pH 4.6) using a 10 kDa molecular-weight cutoff ultrafiltration device. Subsequently, the protein concentration was adjusted to 5 mg mL−1 using a spectrophotometer at a wavelength of 280 nm, assuming an extinction coefficient of 1.15 for 1 mg mL−1 solutions. Applying the vapor diffusion method at 17 °C, EP crystals were grown using a reservoir solution containing 0.1 M NH4Ac, 0.1 M NaAc (pH 4.6), and 24−30% (w/v) PEG 4000. Crystallization drops consisted of 2 μL of protein and 2 μL of reservoir solution. At a concentration of 90 mM, fragments were then soaked into native EP crystals in a buffer of 70 mM NH4Ac and 70 mM NaAc (pH 4.6), containing 16−20% (w/v) PEG 4000, 19−23% (v/v) glycerol, and 9% (v/v) DMSO, using the sitting-drop vapor diffusion method at 17 °C. If the fragment partly precipitated under these conditions, the deposit was ignored. After soaking for 2 days, the crystals were directly flash-frozen in liquid nitrogen without further addition of cryoprotectant and subjected to data collection and structure solution. Measurements were carried out at Helmholtz-Zentrum Berlin.43,44 Diffraction data were collected using the BESSY MX beamlines 14.1, 14.2, and 14.3 at cryogenic temperatures of 100 K. Data indexing, integration, and scaling were performed using XDS and XDSAPP, respectively.45,46 In rare cases, the programs HKL2000 or Imosflm/ Scala were used.47−49 On the basis of the EP model with the PDBentry code 3PCW, the phase problem was then solved via molecular replacement using Phaser.50 Alternatively, the same model was used for immediate rigid body refinement using Phenix.51 Alternate rounds of model building in Coot and refinement with Phenix.refine were performed until convergence of the R-factors.52,53 Our refinement strategy included an initial simulated annealing step to facilitate the correct placement of the flexible flap region. B-factors of protein, ligand, and possibly water atoms were treated anisotropically if this led to reduced Rfree values; otherwise, TLS groups were assigned. Resolutions of the 71 EP-fragment complex structures vary between 1.06 and 1.75 Å with a mean of 1.35 Å and a standard deviation of 0.18 Å. Geometric restraints for all fragments were calculated using Grade.54 Crystallographic details regarding this comprehensive X-ray screen will be published elsewhere. Structural figures were prepared using PyMOL.55 Time- and Concentration-Dependent Soaking Experiments. The time- and concentration-dependent soaking experiments (cf. Figures S1b and c) were performed with fragment 112 as described above for the standard soaking experiments except for the anticipated fragment concentration or soaking time that were varied between 90 mM, 9 mM, 4.5 mM, 900 μM, and 90 μM or 48 h, 20 h, 2 h, 20 min, 10 min, and 2 min, respectively. Data were collected at the BESSY MX beamlines 14.2 and 14.3 and processed using XDS.46 In order to warrant comparability to the best possible extent, we solved all resulting structures using an in-house automated refinement pipeline, which will be described in detail elsewhere. Briefly, the routine first performs a molecular replacement search using Phaser before 10 defined modeling and refinement steps are conducted using Coot and Phenix.refine leading to a model that already contains anisotropic Bfactors, (riding) hydrogen atoms, and water molecules but lacks other ligands.50,52,53 Data collection and refinement statistics are given in Tables S3 and S4. Screening of the Fragment Library Applying Six Different Biophysical Methods. The HCS, RDA, STD-NMR, ESI-MS, MST, and TSA screens were performed as we have recently described (for

⎛ K SAP114 ⎞ 1 a K afragment = ⎜⎜ displacement − 1⎟⎟ · [fragment] ⎝ Ka ⎠

(1)

, KSAP114 a

defined by the standard deviation of at least three The error in measurements, was propagated to the error in the final binding free energy of the investigated fragment as given in Table S5. For seven of the eight fragments with a Kd above 10 mM, the thus obtained experimental error in ΔG° was ≥2 kJ mol−1, while fragments with a Kd below 10 mM had much lower errors mostly below 1 kJ mol−1 (Table S5). We therefore refrained from giving exact affinity values for the former fragments but rather specified them as extremely weak binders (Kd > 10 mM).

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acschembio.5b01034. Five tables and five figures (PDF)

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. 1699

DOI: 10.1021/acschembio.5b01034 ACS Chem. Biol. 2016, 11, 1693−1701

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ACS Chemical Biology Notes

Feltell, R. E., Lewis, E. J., McMenamin, R. L., Navarro, E. F., O’Brien, M. A., O’Reilly, M., Reule, M., Saxty, G., Seavers, L. C., Smith, D. M., Squires, M. S., Trewartha, G., Walker, M. T., and Woolford, A. J. (2008) Identification of N-(4-piperidinyl)-4-(2,6-dichlorobenzoylamino)-1H-pyrazole-3-carboxamide (AT7519), a novel cyclin dependent kinase inhibitor using fragment-based X-ray crystallography and structure based drug design. J. Med. Chem. 51, 4986−4999. (17) Newman, J., Dolezal, O., Fazio, V., Caradoc-Davies, T., and Peat, T. S. (2012) The DINGO dataset: a comprehensive set of data for the SAMPL challenge. J. Comput.-Aided Mol. Des. 26, 497−503. (18) Koh, C. Y., Siddaramaiah, L. K., Ranade, R. M., Nguyen, J., Jian, T., Zhang, Z., Gillespie, J. R., Buckner, F. S., Verlinde, C. L., Fan, E., and Hol, W. G. (2015) A binding hotspot in Trypanosoma cruzi histidyl-tRNA synthetase revealed by fragment-based crystallographic cocktail screens. Acta Crystallogr., Sect. D: Biol. Crystallogr. 71, 1684− 1698. (19) Patel, D., Bauman, J. D., and Arnold, E. (2014) Advantages of crystallographic fragment screening: functional and mechanistic insights from a powerful platform for efficient drug discovery. Prog. Biophys. Mol. Biol. 116, 92−100. (20) Murray, C. W., and Blundell, T. L. (2010) Structural biology in fragment-based drug design. Curr. Opin. Struct. Biol. 20, 497−507. (21) Baker, M. (2013) Fragment-based lead discovery grows up. Nat. Rev. Drug Discovery 12, 5−7. (22) Joseph-McCarthy, D., Campbell, A. J., Kern, G., and Moustakas, D. (2014) Fragment-based lead discovery and design. J. Chem. Inf. Model. 54, 693−704. (23) Cooper, J., Quail, W., Frazao, C., Foundling, S. I., Blundell, T. L., Humblet, C., Lunney, E. A., Lowther, W. T., and Dunn, B. M. (1992) X-ray crystallographic analysis of inhibition of endothiapepsin by cyclohexyl renin inhibitors. Biochemistry 31, 8142−8150. (24) Geschwindner, S., Olsson, L. L., Albert, J. S., Deinum, J., Edwards, P. D., de Beer, T., and Folmer, R. H. (2007) Discovery of a novel warhead against beta-secretase through fragment-based lead generation. J. Med. Chem. 50, 5903−5911. (25) Cooper, J. B. (2002) Aspartic proteinases in disease: a structural perspective. Curr. Drug Targets 3, 155−173. (26) Hemmings, A. M., Foundling, S. I., Sibanda, B. L., Wood, S. P., Pearl, L. H., and Blundell, T. (1985) Energy calculations on aspartic proteinases: human renin, endothiapepsin and its complex with an angiotensinogen fragment analogue, H-142. Biochem. Soc. Trans. 13, 1036−1041. (27) Mondal, M., Groothuis, D. E., and Hirsch, A. K. H. (2015) Fragment growing exploiting dynamic combinatorial chemistry of inhibitors of the aspartic protease endothiapepsin. MedChemComm 6, 1267−1271. (28) Kuhnert, M., Köster, H., Bartholomäus, R., Park, A. Y., Shahim, A., Heine, A., Steuber, H., Klebe, G., and Diederich, W. E. (2015) Tracing binding modes in hit-to-lead optimization: chameleon-like poses of aspartic protease inhibitors. Angew. Chem., Int. Ed. 54, 2849− 2853. (29) Köster, H., Craan, T., Brass, S., Herhaus, C., Zentgraf, M., Neumann, L., Heine, A., and Klebe, G. (2011) A small nonrule of 3 compatible fragment library provides high hit rate of endothiapepsin crystal structures with various fragment chemotypes. J. Med. Chem. 54, 7784−7796. (30) Schiebel, J., Radeva, N., Köster, H., Metz, A., Krotzky, T., Kuhnert, M., Diederich, W. E., Heine, A., Neumann, L., Atmanene, C., Roecklin, D., Vivat-Hannah, V., Renaud, J.-P., Meinecke, R., Schlinck, N., Sitte, A., Popp, F., Zeeb, M., and Klebe, G. (2015) One Question, Multiple Answers: Biochemical and Biophysical Screening Methods Retrieve Deviating Fragment Hit Lists. ChemMedChem 10, 1511− 1521. (31) Larsson, A., Jansson, A., Aberg, A., and Nordlund, P. (2011) Efficiency of hit generation and structural characterization in fragmentbased ligand discovery. Curr. Opin. Chem. Biol. 15, 482−488. (32) Drinkwater, N., Vu, H., Lovell, K. M., Criscione, K. R., Collins, B. M., Prisinzano, T. E., Poulsen, S. A., McLeish, M. J., Grunewald, G. L., and Martin, J. L. (2010) Fragment-based screening by X-ray

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank the Helmholtz-Zentrum Berlin for the allocation of synchrotron radiation beamtime and travel support. The presented work was supported by the German Ministry of Science and Education in the BioChancePlus Program (Project FragScreen No. 0315161C) and the BMBF-Project Frag2Xtal (No. 05K13RM1). We also acknowledge the financial support by the European Research Council (ERC) of the European Union (grant 268145-DrugProfilBind).

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REFERENCES

(1) Scott, D. E., Coyne, A. G., Hudson, S. A., and Abell, C. (2012) Fragment-Based Approaches in Drug Discovery and Chemical Biology. Biochemistry 51, 4990−5003. (2) Jhoti, H., Williams, G., Rees, D. C., and Murray, C. W. (2013) The ’rule of three’ for fragment-based drug discovery: where are we now? Nat. Rev. Drug Discovery 12, 644−645. (3) Silvestre, H. L., Blundell, T. L., Abell, C., and Ciulli, A. (2013) Integrated biophysical approach to fragment screening and validation for fragment-based lead discovery. Proc. Natl. Acad. Sci. U. S. A. 110, 12984−12989. (4) Hubbard, R. E., and Murray, J. B. (2011) Experiences in Fragment-Based Lead Discovery. Methods Enzymol. 493, 509−531. (5) Mashalidis, E. H., Sledz, P., Lang, S., and Abell, C. (2013) A three-stage biophysical screening cascade for fragment-based drug discovery. Nat. Protoc. 8, 2309−2324. (6) Davies, T. G., and Tickle, I. J. (2011) Fragment screening using X-ray crystallography. Top. Curr. Chem. 317, 33−59. (7) Fitzpatrick, P. A., Steinmetz, A. C., Ringe, D., and Klibanov, A. M. (1993) Enzyme crystal structure in a neat organic solvent. Proc. Natl. Acad. Sci. U. S. A. 90, 8653−8657. (8) Hilpert, K., Ackermann, J., Banner, D. W., Gast, A., Gubernator, K., Hadvary, P., Labler, L., Muller, K., Schmid, G., Tschopp, T. B., et al. (1994) Design and synthesis of potent and highly selective thrombin inhibitors. J. Med. Chem. 37, 3889−3901. (9) Verlinde, C. L., Rudenko, G., and Hol, W. G. (1992) In search of new lead compounds for trypanosomiasis drug design: a protein structure-based linked-fragment approach. J. Comput.-Aided Mol. Des. 6, 131−147. (10) Shuker, S. B., Hajduk, P. J., Meadows, R. P., and Fesik, S. W. (1996) Discovering high-affinity ligands for proteins: SAR by NMR. Science 274, 1531−1534. (11) Chessari, G., and Woodhead, A. J. (2009) From fragment to clinical candidate - a historical perspective. Drug Discovery Today 14, 668−675. (12) Hartshorn, M. J., Murray, C. W., Cleasby, A., Frederickson, M., Tickle, I. J., and Jhoti, H. (2005) Fragment-based lead discovery using X-ray crystallography. J. Med. Chem. 48, 403−413. (13) Mooij, W. T., Hartshorn, M. J., Tickle, I. J., Sharff, A. J., Verdonk, M. L., and Jhoti, H. (2006) Automated protein-ligand crystallography for structure-based drug design. ChemMedChem 1, 827−838. (14) Murray, C. W., Callaghan, O., Chessari, G., Cleasby, A., Congreve, M., Frederickson, M., Hartshorn, M. J., McMenamin, R., Patel, S., and Wallis, N. (2007) Application of fragment screening by X-ray crystallography to beta-secretase. J. Med. Chem. 50, 1116−1123. (15) Antonysamy, S. S., Aubol, B., Blaney, J., Browner, M. F., Giannetti, A. M., Harris, S. F., Hebert, N., Hendle, J., Hopkins, S., Jefferson, E., Kissinger, C., Leveque, V., Marciano, D., McGee, E., Najera, I., Nolan, B., Tomimoto, M., Torres, E., and Wright, T. (2008) Fragment-based discovery of hepatitis C virus NS5b RNA polymerase inhibitors. Bioorg. Med. Chem. Lett. 18, 2990−2995. (16) Wyatt, P. G., Woodhead, A. J., Berdini, V., Boulstridge, J. A., Carr, M. G., Cross, D. M., Davis, D. J., Devine, L. A., Early, T. R., 1700

DOI: 10.1021/acschembio.5b01034 ACS Chem. Biol. 2016, 11, 1693−1701

Articles

ACS Chemical Biology crystallography, MS and isothermal titration calorimetry to identify PNMT (phenylethanolamine N-methyltransferase) inhibitors. Biochem. J. 431, 51−61. (33) Davies, D. R., Begley, D. W., Hartley, R. C., Staker, B. L., and Stewart, L. J. (2011) Predicting the success of fragment screening by X-ray crystallography. Methods Enzymol. 493, 91−114. (34) Blundell, T. L., Jhoti, H., and Abell, C. (2002) High-throughput crystallography for lead discovery in drug design. Nat. Rev. Drug Discov 1, 45−54. (35) Böttcher, J., Jestel, A., Kiefersauer, R., Krapp, S., Nagel, S., Steinbacher, S., and Steuber, H. (2011) Key factors for successful generation of protein-fragment structures requirement on protein, crystals, and technology. Methods Enzymol. 493, 61−89. (36) Oster, L., Tapani, S., Xue, Y., and Kack, H. (2015) Successful generation of structural information for fragment-based drug discovery. Drug Discovery Today 20, 1104−1111. (37) Yin, X., Scalia, A., Leroy, L., Cuttitta, C. M., Polizzo, G. M., Ericson, D. L., Roessler, C. G., Campos, O., Ma, M. Y., Agarwal, R., Jackimowicz, R., Allaire, M., Orville, A. M., Sweet, R. M., and Soares, A. S. (2014) Hitting the target: fragment screening with acoustic in situ co-crystallization of proteins plus fragment libraries on pin-mounted data-collection micromeshes. Acta Crystallogr., Sect. D: Biol. Crystallogr. 70, 1177−1189. (38) Nair, P. C., Malde, A. K., Drinkwater, N., and Mark, A. E. (2012) Missing Fragments: Detecting Cooperative Binding in Fragment-Based Drug Design. ACS Med. Chem. Lett. 3, 322−326. (39) Wielens, J., Headey, S. J., Rhodes, D. I., Mulder, R. J., Dolezal, O., Deadman, J. J., Newman, J., Chalmers, D. K., Parker, M. W., Peat, T. S., and Scanlon, M. J. (2013) Parallel screening of low molecular weight fragment libraries: do differences in methodology affect hit identification? J. Biomol. Screening 18, 147−159. (40) Ackermann, W., Asova, G., Ayvazyan, V., Azima, A., Baboi, N., Bahr, J., Balandin, V., Beutner, B., Brandt, A., Bolzmann, A., Brinkmann, R., Brovko, O. I., Castellano, M., Castro, P., Catani, L., Chiadroni, E., Choroba, S., Cianchi, A., Costello, J. T., Cubaynes, D., Dardis, J., Decking, W., Delsim-Hashemi, H., Delserieys, A., Di Pirro, G., Dohlus, M., Dusterer, S., Eckhardt, A., Edwards, H. T., Faatz, B., Feldhaus, J., Flottmann, K., Frisch, J., Frohlich, L., Garvey, T., Gensch, U., Gerth, C., Gorler, M., Golubeva, N., Grabosch, H. J., Grecki, M., Grimm, O., Hacker, K., Hahn, U., Han, J. H., Honkavaara, K., Hott, T., Huning, M., Ivanisenko, Y., Jaeschke, E., Jalmuzna, W., Jezynski, T., Kammering, R., Katalev, V., Kavanagh, K., Kennedy, E. T., Khodyachykh, S., Klose, K., Kocharyan, V., Korfer, M., Kollewe, M., Koprek, W., Korepanov, S., Kostin, D., Krassilnikov, M., Kube, G., Kuhlmann, M., Lewis, C. L. S., Lilje, L., Limberg, T., Lipka, D., Lohl, F., Luna, H., Luong, M., Martins, M., Meyer, M., Michelato, P., Miltchev, V., Muller, W. D., Monaco, L., Muller, W. F. O., Napieralski, A., Napoly, O., Nicolosi, P., Nolle, D., Nunez, T., Oppelt, A., Pagani, C., Paparella, R., Pchalek, N., Pedregosa-Gutierrez, J., Petersen, B., Petrosyan, B., Petrosyan, G., Petrosyan, L., Pfluger, J., Plonjes, E., Poletto, L., Pozniak, K., Prat, E., Proch, D., Pucyk, P., Radcliffe, P., Redlin, H., Rehlich, K., Richter, M., Roehrs, M., Roensch, J., Romaniuk, R., Ross, M., Rossbach, J., Rybnikov, V., Sachwitz, M., Saldin, E. L., Sandner, W., Schlarb, H., Schmidt, B., Schmitz, M., Schmuser, P., Schneider, J. R., Schneidmiller, E. A., Schnepp, S., Schreiber, S., Seidel, M., Sertore, D., Shabunov, A. V., Simon, C., Simrock, S., Sombrowski, E., Sorokin, A. A., Spanknebel, P., Spesyvtsev, R., Staykov, L., Steffen, B., Stephan, F., Stulle, F., Thom, H., Tiedtke, K., Tischer, M., Toleikis, S., Treusch, R., Trines, D., Tsakov, I., Vogel, E., Weiland, T., Weise, H., Wellhoffer, M., Wendt, M., Will, I., Winter, A., Wittenburg, K., Wurth, W., Yeates, P., Yurkov, M. V., Zagorodnov, I., and Zapfe, K. (2007) Operation of a freeelectron laser from the extreme ultraviolet to the water window. Nat. Photonics 1, 336−342. (41) Rühmann, E., Betz, M., Fricke, M., Heine, A., Schäfer, M., and Klebe, G. (2015) Thermodynamic signatures of fragment binding: Validation of direct versus displacement ITC titrations. Biochim. Biophys. Acta, Gen. Subj. 1850, 647−656.

(42) Rühmann, E., Betz, M., Heine, A., and Klebe, G. (2015) Fragment Binding Can Be Either More Enthalpy-Driven or EntropyDriven: Crystal Structures and Residual Hydration Patterns Suggest Why. J. Med. Chem. 58, 6960−6971. (43) Mueller, U., Darowski, N., Fuchs, M. R., Forster, R., Hellmig, M., Paithankar, K. S., Puhringer, S., Steffien, M., Zocher, G., and Weiss, M. S. (2012) Facilities for macromolecular crystallography at the Helmholtz-Zentrum Berlin. J. Synchrotron Radiat. 19, 442−449. (44) Mueller, U., Forster, R., Hellmig, M., Huschmann, F. U., Kastner, A., Malecki, P., Puhringer, S., Rower, M., Sparta, K., Steffien, M., Uhlein, M., Wilk, P., and Weiss, M. S. (2015) The macromolecular crystallography beamlines at BESSY II of the Helmholtz-Zentrum Berlin: Current status and perspectives, Eur. Phys. J. Plus 130, DOI: 10.1140/epjp/i2015-15141-2. (45) Krug, M., Weiss, M. S., Heinemann, U., and Mueller, U. (2012) XDSAPP: a graphical user interface for the convenient processing of diffraction data using XDS. J. Appl. Crystallogr. 45, 568−572. (46) Kabsch, W. (2010) Xds. Acta Crystallogr., Sect. D: Biol. Crystallogr. 66, 125−132. (47) Leslie, A. G. W. (1992) Recent changes to the MOSFLM package for processing film and image plate data, Joint CCP4 + ESFEAMCB Newsletter on Protein Crystallography, Vol 26. (48) Evans, P. (2006) Scaling and assessment of data quality. Acta Crystallogr., Sect. D: Biol. Crystallogr. 62, 72−82. (49) Otwinowski, Z., and Minor, W. (1997) Processing of X-ray diffraction data collected in oscillation mode, In Methods in Enzymology (Charles, W., and Carter, J., Ed.), pp 307−326, Academic Press. (50) McCoy, A. J., Grosse-Kunstleve, R. W., Adams, P. D., Winn, M. D., Storoni, L. C., and Read, R. J. (2007) Phaser crystallographic software. J. Appl. Crystallogr. 40, 658−674. (51) Adams, P. D., Afonine, P. V., Bunkoczi, G., Chen, V. B., Davis, I. W., Echols, N., Headd, J. J., Hung, L. W., Kapral, G. J., GrosseKunstleve, R. W., McCoy, A. J., Moriarty, N. W., Oeffner, R., Read, R. J., Richardson, D. C., Richardson, J. S., Terwilliger, T. C., and Zwart, P. H. (2010) PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr., Sect. D: Biol. Crystallogr. 66, 213−221. (52) Emsley, P., and Cowtan, K. (2004) Coot: model-building tools for molecular graphics. Acta Crystallogr., Sect. D: Biol. Crystallogr. 60, 2126−2132. (53) Afonine, P. V., Grosse-Kunstleve, R. W., Echols, N., Headd, J. J., Moriarty, N. W., Mustyakimov, M., Terwilliger, T. C., Urzhumtsev, A., Zwart, P. H., and Adams, P. D. (2012) Towards automated crystallographic structure refinement with phenix.refine. Acta Crystallogr., Sect. D: Biol. Crystallogr. 68, 352−367. (54) Smart, O. S., Womack, T. O., Sharff, A., Flensburg, C., Keller, P., Paciorek, W., Vonrhein, C., and Bricogne, G. (2011) Grade (http:// www.globalphasing.com), Global Phasing Ltd., Cambridge, United Kingdom. (55) DeLano, W. L. (2002) The PyMOL molecular graphics system on world wide web, http://www.pymol.org. (56) R Development Core Team. (2010) R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria. (57) Sigurskjold, B. W. (2000) Exact analysis of competition ligand binding by displacement isothermal titration calorimetry. Anal. Biochem. 277, 260−266. (58) Zhang, Y. L., and Zhang, Z. Y. (1998) Low-affinity binding determined by titration calorimetry using a high-affinity coupling ligand: a thermodynamic study of ligand binding to protein tyrosine phosphatase 1B. Anal. Biochem. 261, 139−148. (59) Keller, S., Vargas, C., Zhao, H., Piszczek, G., Brautigam, C. A., and Schuck, P. (2012) High-precision isothermal titration calorimetry with automated peak-shape analysis. Anal. Chem. 84, 5066−5073. (60) Houtman, J. C. D., Brown, P. H., Bowden, B., Yamaguchi, H., Appella, E., Samelson, L. E., and Schuck, P. (2007) Studying multisite binary and ternary protein interactions by global analysis of isothermal titration calorimetry data in SEDPHAT: Application to adaptor protein complexes in cell signaling. Protein Sci. 16, 30−42. 1701

DOI: 10.1021/acschembio.5b01034 ACS Chem. Biol. 2016, 11, 1693−1701