Fragments vs KAT6A: ligand efficiency in action

Epigenetic writers such as histone acetyltransferases (HATs) control gene expression, which often goes awry in cancer. The gene encoding the lysine acetyltransferase KAT6A, for example, is amplified in multiple types of cancer, and its overexpression is associated with poor clinical outcomes for patients with ER⁺ breast cancer. A recent paper in Bioorg. Med. Chem. Lett. from Andrew Buesking and colleagues at Prelude Therapeutics reports a new series of inhibitors.

 

The researchers started by considering previously reported molecules such as PF-9363. Recognizing the importance of the central sulfonamide, they generated a library of 150 fragments containing this core and screened them in a biochemical assay. Taking a similar approach as other researchers reporting on a different epigenetic target we discussed last year, the Prelude team explicitly used ligand efficiency to call hits, setting the cutoff at > 0.3 kcal per mol per heavy atom.

 

A direct deconstruction of PF-9363 led to compound 6, but, seeking something novel, the researchers were drawn to compound 8, with similar ligand efficiency. Modifications were made to both of the terminal aromatic groups, leading to compound 13, which was co-crystallized with KAT6A. This led to further structure-guided modifications, with compound 25 being the most potent.

 

Unfortunately, further improvements in potency were not forthcoming, and the ligand efficiency had dropped steadily from both the initial fragment as well as PF-9363. The researchers conclude by stating that they “decided to pursue alternative approaches.” Still, this paper is a nice, concise example of using metrics both for pursuing a chemical series and, ultimately, discontinuing it.

Nineteenth Annual Fragment-Based Drug Discovery Meeting

Last week the CHI Drug Discovery Chemistry (DDC) meeting was held in San Diego. This was the largest ever, with more than 900 participants, 95% of whom attended in person, up from 87% last year. I won’t attempt to cover all fourteen tracks but will just touch on some of the main themes.

 

Computational approaches

All four days of the conference featured dedicated sessions on machine learning and artificial intelligence, but since I was in other sessions I don’t know how relevant they were to FBLD. If you attended an interesting talk please let me know so I can watch it on-demand.

 

Among computational talks I did see, Antonina Nazarova (University of Southern California) provided an update on V-SYNTHES, which we first wrote about here. This synthon-based screening approach now covers 36 billion molecules and has been tested against eight different proteins, four of which yielded nanomolar hits when tested experimentally.

 

Computational methods have historically treated proteins as rigid, though many targets are anything but. Diane Joseph-McCarthy (Boston University) described an improvement to the pocket finding approach FTMap, called FTMove, to incorporate molecular dynamics by starting with an ensemble of different crystal structures. A further advance is E-FTMap, which expands the number of virtual probes from 16 to 119 to more finely assess ligandable sites.

 

Benjamin Walters (Genentech) described using protein dynamics to find cryptic pockets using ESP, or Experimental Structure Prediction. In this approach, experimental data from hydrogen-deuterium exchange (HDX) or chemical shift perturbations (CSPs) are used to constrain multiple parallel computational simulations, leading to better models than flexible docking, even for weak fragments.

 

Experimental approaches

Protein-detected NMR was the first practical fragment-finding method, and Steve Fesik (Vanderbilt) described using SAR by NMR to find fragments binding to the papain-like protease of SARS-CoV-2. These have been advanced to molecules with nanomolar affinity and activity in cell-based assays.

 

Andreas Lingel described the new fluorine-containing fragment library built at Novartis and how ¹⁹F NMR was used to generate inhibitors of IL-1β. We wrote about that success last year, noting that the initial fragment hit was “super-sized,” and Andreas confirmed that for trifluoromethyl-containing fragments the upper molecular weight limit was relaxed to 350 Da.

 

Sriram Tyagarajan (Merck) presented a crystallographic screen against the neurodegeneration target TTBK1 which yielded hits at 15 sites. Several potential allosteric sites were identified, but fragment growing and linking were not successful, leading them to a quick (3 month) no-go decision on the protein.

 

Virgil Woods (City University of New York) described using crystallographic screening to find hits against the challenging phosphatase PTP1B both under conventional cryogenic temperatures as well as at room temperature. As we noted about related work, there was a surprisingly poor overlap between the two sets of hits, and some fragments bound in a different manner at different temperatures.

 

Integrating FBDD and DNA-encoded libraries (DEL) for lead generation was the topic of Chaohong Sun’s talk. She noted that of some two dozen targets at AbbVie screened by both methods, 60% found hits from both, 10% found only fragment hits, and 5% found only DEL hits, with a quarter of the targets producing no hits. Hits from both approaches can be combined, as we noted here. Chaohong also noted that for both FBDD and DEL, high quality protein is essential for successful screens.

 

Covalent approaches

Covalent approaches to drug discovery are becoming ever more acceptable as more covalent drugs are approved. Understanding these in depth was the focus of Micah Niphakis (Lundbeck), who characterized 22 approved drugs containing 18 different warheads. The stability in buffer, liver microsomes, and hepatocytes varied dramatically, though more recently approved drugs tended to be more stable. Chemoproteomic studies revealed many off-targets in cells; for example, all the kinase inhibitors tested hit BTK to some extent even when this was not the primary target. The fact that the drugs are (mostly) safe and well-tolerated is a useful reminder that just because we can detect something doesn’t mean it is relevant.

 

Henry Blackwell described building a 12,000-member covalent fragment library at AstraZeneca. Due to the presence of a warhead, they relaxed rule of three parameters, with MW ranging from 250-400 Da and ClogP from 0-4. Henry also discussed the successful use of this library to identify covalent hits against the anticancer target BFL1 that were optimized to k_inact/K_I ~ 7000 M^-1s^-1. This accomplishment is all the more impressive given that screens using ASMS, DSF, ¹⁹F NMR, and SPR had all failed to yield validated hits.

 

We recently wrote about electrophilic MiniFrags, and György Keserű (Research Center for Natural Sciences, Hungary) described screening these against HDAC8 and the main protease from SARS-CoV-2. He also mentioned that the set is available for purchase from Enamine, so you can try it yourself against your favorite target.

 

As covalent modifiers become more common we will see new metrics for characterizing them, as illustrated by Benjamin Horning’s (Vividion) presentation, “Ligand Efficiency Metrics in Covalent Drug Discovery.” He described Ligand Reactivity Efficiency (LRE), defined as pTE₅₀(target, 1 hr) – pTE₅₀(glutathione, 1 hr), where TE is the target (or glutathione) engagement. LRE is analogous to LLE but focused on reactivity rather than lipophilicity. Despite my post last week, the metric could be useful, and I look forward to seeing what Dr. Saysno and friends will make of it.

 

Most covalent modifiers bind to a target and remain intact, but Nir London (Weizmann Institute) has developed Covalent Ligand Directed Release (CoLDR), in which a portion of the small molecule leaves; applications include release of fluorescent or chemiluminescent probes. Useable warheads include α-substituted methacrylamides and sulfamate acetamides.

 

Although more recent covalent drugs have targeted cysteine residues, there is growing interest in other amino acid side chains. Nir mentioned that thio-methacrylate esters can react with lysine residues, thought the kinetics are slow. And Carlo Ballatore (University of California San Diego) described hydroxy-naphthaldehyde fragments that bound reversibly to a lysine on the vascular target KRIT1.

 

Both plenary keynote speakers focused heavily on covalent chemistry. Dan Nomura (UC Berkeley) described using chemoproteomics approaches to find covalent molecules that could inhibit, degrade, or change the cellular localization of myriad proteins.

 

Finally, K. Barry Sharpless (Scripps), one of only five people to have been awarded the Nobel Prize twice, gave a rich description of sulfur (VI) fluoride exchange chemistry (SuFEx), which included drawing chemical structures on a flip chart. He presented the discovery of a fluorosulfate that is bactericidal against multiple resistant forms of Mycobacterium tuberculosis. Interestingly, the molecule works by modifying a catalytic serine residue which then cyclizes to form a β-lactam. His passion for chemistry is obvious, but he also has personal reasons for pursuing the second most deadly infectious disease: his brother died of tuberculosis before effective drugs were developed. And with the rise of extensively drug resistant TB, we’ll need new ones.

 

I’ll end on that note, but please leave comments. And mark your calendar for April 14-17 next year, when DDC returns to San Diego.

Personality tests for molecules

Long-time readers of Practical Fragments will be familiar with various metrics for measuring molecules, such as LE, LLE, and WTF. But these are all hard-edged, numerical constructs. Some folks argue that we should take a softer, more nuanced approach. This call has been heeded by Katharine Bigg and Isabel Myerrors in the form of a “Myerrors-Bigg” Type Indicator, or MBTI.

 

The MBTI consists of a series of questions which rank a molecule into four dimensions: Extroversion/Introversion, Sociable/Nonsociable, Flat/Three-dimensional, and Pretty/Janky. Defining molecules as extroverts may sound strange, but it really just comes down to a question of molecular recognition: we’ve noted that 4-bromopyrazole seems to bind to just about every protein and is thus an Extrovert while other compounds, being Introverts, fall into the category of “dark chemical matter” and never come up in screens.

 

As for the other dimensions, Practical Fragments has written previously about (Non)Sociable fragments as well as Flat fragments. This leads to the last dimension. Claims that beauty is in the eye of the beholder are undermined by the rigorous process of the MBTI, which places molecules such as curcumin squarely in the Janky category while approved drugs are self-evidently Pretty. Thus, toxoflavin is an ESFJ, while sotorasib is an INTP.

 

The utility of the MBTI remains to be established, but this has not stopped companies everywhere from applying it in their acquisition and evaluation processes. And other tests, such as the Decagram of Personality and the Big Six Personality Traits, are also becoming popular. Which do you prefer?

Spacial Scores: new metrics for measuring molecular complexity

Molecular complexity is one of the theoretical underpinnings for fragment-based drug discovery. Mike Hann and colleagues proposed two decades ago that very simple molecules may not have enough features to bind tightly to any proteins, whereas highly functionalized molecules may have extraneous spinach that keeps them from binding to any proteins. Fragments, being small and thus less complex, are in a sweet spot: just complex enough.

 

But what does it mean for one molecule to be more complex than another? Most chemists would agree that pyridine is more complex than methane, but is it more complex than benzene? To decide, you need a numerical metric, and there are plenty to choose from. The problem, as we discussed in 2017, is that they don’t correlate with one another, so it is not clear which one(s) to choose. In a new (open access) J. Med. Chem. paper, Adrian Krzyzanowski, Herbert Waldmann and colleagues at the Max Planck Institute Dortmund have provided another. (Derek Lowe also recently covered this paper.)

 

The researchers propose the Spacial Score, or SPS. This is calculated based on four molecular parameters for each atom in a given molecule. The term h is dependent on atom hybridization: 1 for sp-, 2 for sp²-, 3 for sp³-hybrized atoms, and 4 for all others. Stereogenic centers are assigned an s value of 2, while all other atoms are assigned a value of 1. Atoms that are part of non-aromatic rings are also assigned an r value of 2; those that are part of an aromatic ring or linear chain are set to 1. Finally, the n score is set to the number of heavy-atom neighbors.

 

For each atom in a molecule, h is multiplied by s, r, and n². The SPS is calculated by summing the individual scores for all the atoms in a molecule. Because there is no upper limit, and because it is nice to be able to compare molecules of the same size, the researchers also define the nSPS, or normalized SPS, which is simply the SPS divided by the number of non-hydrogen atoms in the molecule. Although SPS can be calculated manually, the process is tedious and the researchers have kindly provided code to automate the process. Having defined SPS, the researchers compare it to other molecular complexity metrics, including the simple fraction  of sp³ carbons in a molecule, Fsp³, which we wrote about in 2009. 

 

The researchers next calculated nSPS for four sets of molecules including drugs, a screening library from Enamine, natural products, and so-called “dark chemical matter,” library compounds that have not hit in numerous screens. The results are equivocal. For example, the nSPS for dark chemical matter is very similar to that for drugs. On the other hand, natural products tend to have higher nSPS scores than drugs, as expected. Interestingly, the average nSPS score for compounds in the GDB-17 database, consisting of theoretical molecules having up to 17 atoms, is also quite high.

 

The researchers assessed whether nSPS correlated with biological properties, and found that compounds with lower nSPS tended to have lower potencies against fewer proteins, as predicted by theory. That said, this analysis was based on binning compounds into a small number of categories, and as Pete Kenny has repeatedly warned, this can lead to spurious trends.

 

The same issue of J. Med. Chem. carries an analysis of the paper by Tudor Oprea and Cristian Bologa, both at University of New Mexico. This contextualizes the work and confirms that drugs do not seem to be getting more complex over time, as measured by nSPS. This may seem odd, though Oprea and Cristian note that by “normalizing” for size, nSPS misses the increasing molecular weight of drugs.

 

This observation also raises other questions, such as the fact that SPS explicitly excludes element identity. Coming back to benzene and pyridine, both have identical SPS and nSPS, which does not seem chemically intuitive. One could quibble more: why square the value of n in the calculation of SPS? Why allow s to be only 1 and 2, as opposed to 1 and 5?

 

In the end I did enjoy reading this paper, and I do think having some metric of molecular complexity might be valuable. I’m just not sure where SPS will fit in with all the existing and conflicting metrics, and how such metrics can lead to practical applications.

A rule of 1 (hydrogen bond donor) library

Hydrogen bond donors (HBDs) in ligands are troublemakers. Having more than a couple tends to decrease permeability, bioavailability, and even solubility. HBDs can also lead to efflux, which is particularly problematic for drugs that must cross the blood-brain barrier. While this is true in general, the problems become even more acute for heterobifunctional drugs such as PROTACs, which contain two moieties that each recognize a separate protein. To minimize the number of HBDs at the outset of a project, Benjamin Whitehurst and colleagues at AstraZeneca have built a “Low HBD” fragment set, which has just been described in J. Med. Chem. Soc.

 

The researchers started by examining roughly 205,000 compounds in their collection having between 11 and 19 non-hydrogen atoms and no more than one HBD, defined as “a proton bonded to an oxygen or nitrogen atom in its neutral form.” An extensive series of filters was used to winnow the molecules based on physicochemical properties, diversity, and absence of reactive groups. Consistent with our recent poll, synthetic tractability was considered explicitly. The researchers also made use of a multiparameter optimization score (see here). After quality control, which included solubility and compatibility with SPR, they ended up with a set of 551 fragments.

 

AstraZeneca has recently revamped their general purpose “Biophysics” fragment library, which consists of 2741 compounds. They also have a set of 402 “Kinase Hinge” fragments, which contain hydrogen bond donors near hydrogen bond acceptors.  Comparing the Low HBD set with the other two revealed that it was as diverse as the Biophysics set and more diverse than the Kinase Hinge set. Other parameters such as the number of hydrogen bond acceptors (HBAs), polar surface area, and molecular weight were similar between the Low HBD and Biophysics libraries. Happily, and perhaps defying expectations, lipophilicity was not higher in the Low HBD set.

 

So how does the library perform? The researchers describe five screens against an E3 ligase, a protein-protein interaction, a kinase, a histone methyltransferase, and a transcription factor. Confirmed hits (defined as having K_d < 1 mM by SPR) were obtained for all targets. Hit rates for two targets were comparable to hit rates for the Biophysics set, as were the dissociation constants and ligand efficiencies. Not surprisingly the Kinase Hinge set produced a higher hit rate for the kinase. (Two targets were only screened with the Low HBD set.)

 

The percentage of hits from the other fragment libraries having 0 or 1 HBD was 44%, 46%, and 80%, so the Low HBD set does seem to be fulfilling its role of enriching these types of compounds. Interestingly, when the researchers analyzed successful fragment-to-lead studies published between 2015 and 2021, they found that 53% of them had just 0 or 1 HBD.

 

All these results suggest that sharply curtailing the number of hydrogen bond donors in a fragment library doesn’t have negative consequences. Perhaps this isn’t surprising: an analysis we highlighted in 2021 based on 131 fragment-to-lead success stories noted that most of them only retained one or two polar interactions from the initial hit. That paper also noted that while 35% of the polar interactions were from N-H hydrogen bond donors on the ligands, an even higher percentage came from hydrogen bond acceptors. That paper and the AstraZeneca researchers also note the potential of other types of interactions, such as polarized C-H hydrogen bond donors and halogen bonds. It will be fun to watch hits from this library progress.

Efficiency metrics in action for a bromodomain inhibitor

The metrics ligand efficiency (LE) and lipophilic ligand efficiency (LLE or LipE) are frequently used during fragment-to-lead optimization. A recent paper in J. Med. Chem. by Philip Humphreys and colleagues at GSK describes how they were useful in developing an “oral candidate quality” inhibitor of BET-family bromodomains.

 

Practical Fragments has written frequently about bromodomains, which bind to acetyl-lysine residues in histones to epigenetically modulate gene transcription. Some 17 bromodomain inhibitors have entered the clinic, of which at least three (pelabresib, PLX51107, and ABBV-744) came from fragments. GSK was an early pioneer in the field, and researchers there were interested in using fragments to develop a differentiated class of molecules that would inhibit all four members (BRD2, BRD3, BRD4, and BRDT) of the BET family, each of which contains two separate bromodomains designated as either BD1 or BD2.

 

GSK already had BRD4 BD1 binding data for 50,000 compounds, and these were analyzed to find molecules with LE>0.3 kcal/mol per atom that were structurally differentiated from known bromodomain binders. Compound 9 was quite potent and had high LE as well as respectable LipE. (As the researchers note, LE is “the more relevant metric” for fragments, with LipE becoming increasingly important during later optimization.) A crystal structure of this molecule superposed onto another bromodomain inhibitor suggested that adding a methyl group to fill a small pocket could boost affinity, and this was confirmed by compound (R)-10. This molecule showed cell activity and good permeability, although hepatocyte stability was poor, likely due to the two methoxy groups. Removing these led to compound 12, the most ligand-efficient compound that had been seen. (All values in the figure below are for binding to BRD4 BD1.)

 

Compound 12 mimics the N-acetyl lysine residue of the natural ligand, and previous research had revealed two additional regions of the bromodomain that could be targeted for enhanced affinity, the so-called “WPF shelf” and the “ZA channel.” Structure-based design was used to independently explore both areas, leading to compounds such as 24 and 31. In addition to assessing LE and LipE, the researchers paid close attention to other factors such as permeability. Virtually combining the best moieties that bind at the WPF shelf and ZA channel led to 770 potential molecules to make, which were winnowed down to just 40 on the basis of predicted lipophilicity (specifically chromLogD_pH7.4), molecular weight, and TPSA. The best of these were more extensively profiled, including in pharmacokinetic studies. I-BET432 emerged as the winner.

 

I-BET432 binds tightly to both bromodomains of the four BET family proteins and is at least 80-fold selective against two dozen other bromodomains. It shows excellent oral bioavailability in rats and dogs, does not inhibit hERG, is not mutagenic in an Ames test, and does not inhibit CYP3A4. The molecule is also clean in a panel of four dozen off-target proteins. Human oral dose predictions come in at 5-18 mg per day. A crystal structure of the molecule bound to BRD2 BD2 showed the expected binding pose, and that the two alcohol substituents may be forming an intra-molecular hydrogen bond, which could explain the high permeability.

 

This is a nice case study in metric-driven optimization. As the researchers note, I-BET432 “has the highest LipE (6.2) and LE (0.43) of the candidate quality GSK pan-BET inhibitors disclosed to date.” Although the molecule does not seem to have gone forward into development, the story is nonetheless worth reading to see how metrics can yield quality molecules.

Achieving maximum diversity with minimum size

One theoretical advantage of fragment-based drug discovery is the ability to efficiently explore chemical space: there are vastly fewer possible fragment-sized molecules than lead-sized molecules. That said, even fragment space is daunting; the number of possible molecules with up to 17 non-hydrogen atoms is about three orders of magnitude larger than the largest computational screen. Maximizing diversity is thus a key goal in designing fragment libraries, but how do you actually do this? A new open-access paper in Molecules by Yun Shi and Mark von Itzstein at Griffith University provides a practical new approach.

As the researchers point out, diversity itself can be a slippery concept. Functional diversity (ie, what targets are bound) is important but hard-won knowledge. Physicochemical diversity is by definition limited for fragments. That leaves structural diversity, as defined by “molecular fingerprints.” These can be as simple as the presence or absence of a fluorine atom, or can require complicated calculations involving, say, the distance between a hydrogen bond donor and acceptor in the lowest energy conformation of a molecule. In their paper the researchers focus on “extended-connectivity” fingerprints, which take into consideration the physical connectivity between different types of atoms.

But how can you actually quantify structural diversity? One possibility is by comparing molecules to see how different they are, as used for example in Tanimoto similarity assessments. Each additional molecule would be chosen to be least similar to those in a library. Alternatively, one could consider “richness,” how much of chemical space is covered, by calculating how many unique structural features (such as specific bond connectivities) are represented. Each additional molecule would be chosen to provide as many new molecular fingerprints as possible. Shi and von Itzstein propose a third approach, “true diversity,” that considers the number of unique features as well as their proportional abundances. In other words, a library with a higher true diversity would have a “more even distribution of proportional abundances.” The researchers note that this approach has been used in ecology for decades.

To see how their approach performs, the researchers started with a set of 227,787 commercially available fragments, all of which were roughly rule-of-3-compliant and scrubbed of undesirable functionalities. They also considered a subset of 47,708 fluorine-containing fragments. For both sets, they then assessed structural diversity as a function of increasing fragment library size using Tanimoto similarity, richness, and true diversity, as well as random sampling.

Naturally, as the size of a fragment library rose, the diversity increased. As expected, applying Tanimoto similarity or richness led to greater diversity at a smaller library size than did random sampling. This was even more true for true diversity. Interestingly, true diversity reached a maximum at 8.8% or 15.7% (for the full and fluorinated libraries) and then began to decline. This conceptually makes sense because commercial compounds themselves are unlikely to be truly diverse.

More importantly, just 1% or 2.5% of fragments were sufficient to achieve the same true diversity as the full sets. This corresponds to 2052 fragments for the complete commercial set, the structures of which are provided in the supplementary material. As the researchers note, this is comparable to the size of many commonly used fragment libraries.

The method is computationally inexpensive (it runs on a desktop), and should be a useful tool for both building and curating fragment libraries, real and virtual. Of course, diversity is not everything, and it probably makes sense to include privileged pharmacophores even at the cost of lower diversity. But as Lord Kelvin said, “when you can measure what you are speaking about, and express it in numbers, you know something about it.” This paper provides a quantitative approach for measuring diversity.

Is thermodynamic data useful for drug discovery?

Just over a decade ago Ernesto Freire suggested that small molecules whose binding energy is dominated by the enthalpic – rather than the entropic – term make superior drugs. He also suggested that such molecules may be more selective for their target. But the backlash came quickly, and a couple years ago we wrote that focusing on thermodynamics probably isn’t particularly practical. A new perspective in Drug Disc. Today by Gerhard Klebe (Philipps-University Marburg) revisits this topic.

Klebe suggests that enthalpy was initially embraced “because readily accessible and easily recordable parameters are much sought after for the support of the nontrivial decision over which molecules to take to the next level of development.” (I would be interested to know whether sales of isothermal titration calorimetry (ITC) instruments spiked around 2010.) Unfortunately, both theoretical and practical reasons make thermodynamic measurements less useful than hoped.

First, and as we noted previously, “in an ITC experiment… the balance sheet of the entire process is measured.” In particular, water molecules – which make up the bulk of the solution – can affect both enthalpic and entropic terms. Klebe describes an example in which the most flexible of a series of ligands binds with the most favorable entropy to the target protein; this is counterintuitive because the ligand adopts a more ordered state once bound to the protein. It turned out that in solution the ligand traps a water molecule that is released when the ligand binds to the protein, thus accounting for the favorable entropy.

Indeed, water turns out to be a major confounding factor. We’ve previously written about “high-energy” water; Klebe notes that an individual water molecule can easily contribute more than 2 kcal/mol to the overall thermodynamic signature. And of course, proteins in solution are literally bathed in water. The structure of this water network, which may change upon ligand binding, is rarely known experimentally, but optimizing for it can improve affinity of a ligand by as much as 50-fold. Conversely, attaching a polar substituent to a solvent-exposed portion of a molecule to improve solubility sometimes causes a loss in affinity, and Klebe suggests this can be due to disruption of the water sheath.

Beyond these theoretical considerations, experimental problems abound. We’ve previously discussed how spurious results can be obtained when testing mixtures of ligands in an ITC experiment, but even with single protein-ligand complexes things can get complicated. Klebe shows examples where the relative enthalpic and entropic components to free energy change dramatically simply because of changes in buffer or temperature. This means that the growing body of published thermodynamic data needs to be treated cautiously.

So what is to be done? First, thermodynamic data should always be treated relatively: “we should avoid classifying ligands as enthalpy- or entropy-driven binders; in fact, we can only differentiate them as enthalpically or entropically more favored binders relative to one another.”

Klebe argues that collecting data on a variety of ligands for a given target under carefully controlled conditions will be useful for developing computational binding models. This is important, but not the kind of work for which people usually win grants, let alone venture funding.

He also suggests that, by collecting thermodynamic data across a series of ligands, unexpected changes in thermodynamic profiles might reveal “changes in binding modes, protonation states, or water-mediated interactions.” Maybe. But it takes serious effort to collect high-quality ITC data. Are there examples where you’ve found it to be worthwhile?

Review of 2018 reviews

As 2018 recedes into history, we are using this last post of the year to do what we have done since 2012 – review notable events along with reviews we didn’t previously cover.

This was a busy year for meetings, starting in January with a FragNet event in Barcelona, then moving to San Diego in April for the annual CHI FBDD meeting. Boston saw an embarrassment of riches, from the first US-based NovAliX meeting, to a symposium on FBDD at the Fall ACS meeting, followed closely by a number of relevant talks at CHI’s Discovery on Target. Finally, the tenth anniversary of the renowned FBLD meeting returned to San Diego. Look for a schedule of 2019 events later this month.

If meetings were abundant, the same can be said for reviews.

Lead optimization

Writing in J. Med. Chem., Dean Brown and Jonas Boström (AstraZeneca) asked “where do recent small molecule clinical development candidates come from?” For three quarters of the 66 molecules published in J. Med. Chem. in 2016 and 2017 the answer is from known compounds or HTS, though fragments accounted for four examples. Although average molecular weight increased during lead optimization, lipophilicity did not, suggesting the importance of this parameter.

The importance of keeping lipophilicity in check is also emphasized by Robert Young (GlaxoSmithKline) and Paul Leeson (Paul Leeson Consulting) in a massive J. Med. Chem. treatise on lead optimization. Buttressed with dozens of examples, including several from FBLD, they show that the final molecule is usually among the most efficient (in terms of LE and LLE) in a given series, even when metrics were not explicitly used by the project team. Perhaps with pedants like Dr. Saysno in mind, they also emphasize the complexity of drug discovery, and note that “seeking optimum efficiencies and physicochemical properties are guiding principles and not rules.”

Lipophilic ligand efficiency (LLE) is also the focus of a paper in Bioorg. Med. Chem. by James Scott (AstraZeneca) and Michael Waring (Newcastle University). This is based largely on personal experiences and provides lots of helpful tips. Importantly, the researchers note that calculated lipophilicity values can differ dramatically from measured values, and go so far as to say that “this variation is sufficient to render LLEs derived from calculated values meaningless.”

Turning wholly to fragments, Chris Johnson and collaborators (including yours truly) from Astex, Carmot, Vrije Universiteit Amsterdam, and Novartis have published an analysis in J. Med. Chem. of fragment-to-lead success stories from last year. This review, the third in a series, also summarizes all 85 examples published between 2015 and 2017, confirming and expanding some of the trends we mentioned last year.

Targets

Two reviews focus on specific target classes. Bas Lamoree and Rod Hubbard (University of York) cover antibiotics in SLAS Discovery. After a nice, concise review of fragment-finding methods, the researchers discuss a number of case studies, many of which will be familiar to regular readers of this blog, including an early example of whole-cell screening.

David Bailey and collaborators from IOTA and University of Cambridge discuss cyclic nucleotide phosphodiesterases (PDEs) in J. Med. Chem. The researchers provide a good overview of the field, including mining the open database ChEMBL for fragment-sized inhibitors. As they point out, the first inhibitors discovered for these cell-signaling enzymes were fragment-sized, so it is no surprise that FBLD has been fruitful – see here for an example from earlier this year. Interestingly though, although at least six fragment-sized PDE inhibitor drugs have been approved, none of these were actually discovered using FBLD.

PDEs are an example of “ligandable” targets, for which small molecule modulators are readily discovered. In Drug Discovery Today, Sinisa Vukovic and David Huggins (University of Cambridge) discuss ligandability “in terms of the balance between effort and reward.” They use a published database of protein-ligand affinities to develop a metric, LIG_exp, for experimental ligandability, and also describe their computational metric, Solvaware, which is based on identifying clusters of water molecules binding weakly to a protein. Comparisons with experimental data and with other predictive metrics, such as FTMap, reveal that while the computational methods are useful, there is still room for improvement.

We have previously written about how target-guided synthesis methods such as dynamic combinatorial chemistry have – despite decades of research – yielded few truly novel, drug-like ligands. Is this because the targets chosen were simply not ligandable? In J. Med. Chem., Anna Hirsch and collaborators at the University of Groningen, the Helmholtz Institute for Pharmaceutical Research, and Saarland University review some (though by no means all) published examples and examine their computationally determined ligandability scores. There seems to be no difference between these targets and a set of traditional drug targets.

Finding fragments

Crystallography continues to be a key tool for FBLD: as we noted in the review of the 2017 literature, 21 of the 30 examples made use of a crystal structure of either the starting fragment or an analog, and only 3 projects didn’t use crystallography at all. That said, FBLD is possible without crystallography, as illustrated through multiple examples in a Cell Chem. Biol. review by Wolfgang Jahnke (Novartis), Ben Davis (Vernalis), and me (Carmot).

In the absence of a crystal structure, NMR is best suited for providing structural information, and this is the subject of a review in Molecules by Barak Akabayov and colleagues at Ben-Gurion University of the Negev. The researchers provide a nice summary of NMR screening methods and success stories within a broader history of FBLD. They also include an extensive list of fragment library providers as well as a discussion of virtual screening.

Speaking of virtual screening, three reviews cover this topic. In Methods Mol. Biol., Durai Sundar and colleagues at Indian Institute of Technology Delhi touch on a number of computational approaches for de novo ligand design, though the lack of structures sometimes makes it challenging to read. A broader, more visually appealing review is published in AAPS Journal by Yuemin Bian and Xiang-Qun Xie at University of Pittsburgh. In addition to an overview and case studies, the researchers also provide a nice table summarizing 15 different computational programs. One of these, SEED, is a main focus of a review in Eur. J. Med. Chem. by Jean-Rémy Marchand and Amedeo Caflisch (University of Zürich). The researchers describe how this docking program can be combined with X-ray crystallography (SEED2XR) to rapidly identify fragments; we highlighted an example with a bromodomain. Their ALTA protocol uses SEED to generate larger, more potent molecules, as we described for the kinase EphB4. The researchers note that together these protocols have led to about 200 protein-ligand crystal structures deposited in the PDB over the past five years.

Rounding out methods, Sten Ohlson and Minh-Dao Duong-Thi (Nanyang Technological University) provide a detailed how-to guide in Methods for performing weak affinity chromatography, and how this can be combined with mass spectrometry (WAC-MS), as we noted last year.

Chemistry

One drawback of some computational approaches for fragment optimization is that they do not consider synthetic accessibility. In Mol. Inform., Philippe Roche, Xavier Morelli, and collaborators at Aix-Marseille University and Institut Paoli-Calmettes focus on hit to lead approaches that do, and provide a handy table summarizing nearly a dozen computational methods. We highlighted one from the authors, DOTS, earlier this year.

DOTS is an example of using DOS, or diversity-oriented synthesis. In Front. Chem., David Spring and colleagues at University of Cambridge review recent applications of DOS for generating new fragments, some of which we recently highlighted. Only a couple examples of successfully screening these new fragments are described, but the authors note that this is likely to increase as virtual library screening continues to advance.

Perhaps the most productive fragment of all time is 7-azaindole, the origin of three fragment-derived clinical compounds. (The moiety appears in both approved FBLD-derived drugs, vemurafenib and venetoclax.) Takayuki Irie and Masaaki Sawa of Carna Biosciences devote their attention to this little bicycle in Chem. Pharm. Bull. The researchers count six clinical kinase inhibitors that contain 7-azaindole (not all from FBLD) as well as more than 100,000 disclosed compounds containing the fragment. More than 90 kinases have been targeted by molecules containing 7-azaindole, and the paper provides a list of 70 PDB structures of 37 different kinases bound to molecules containing the moiety.

Finally, in J. Med. Chem., Brian Raymer and Samit Bhattacharya (Pfizer) survey the universe of “lead-like” drugs. Among the most highly prescribed small molecule drugs, 36% have molecular weights below 300 Da. Only 28 of 174 drugs approved between 2011 and 2017 fall into this category, consistent with the increasing size of newer drugs. The researchers discuss 16 recently approved drugs, and find that 13 have very high ligand efficiencies (at least 0.4 kcal mol^-1 per heavy atom). As noted above, optimization often entails adding molecular weight by growing or linking, and the researchers suggest that alternative strategies such as conformational restriction and truncation also be investigated.

And with that, Practical Fragments wishes you a happy new year. Thanks for reading some of our 686 posts over the past decade plus, and please keep the comments coming!

Sixteenth Annual Discovery on Target

CHI’s Discovery on Target took place in Boston last week. With >1300 attendees from over two dozen countries, this is the older, larger cousin of the San Diego DDC meeting; at some points ten tracks were running simultaneously. Although more heavily focused on biology, there were still plenty of talks of interest to fragment folks.

Michael Shultz (Novartis) provocatively asked “do we need to change the definition of drug-like properties?” Long-time readers will recall that his earlier papers on ligand efficiency led to considerable debate, which seems to have been settled to everyone’s satisfaction with the exception of Dr. Saysno.

His new study, which has just published in J. Med. Chem., analyzes the molecular properties of all 750 oral drugs approved in the US between 1900 and 2017. Contrary to what strict rule of five advocates might expect, the molecular weight has increased over the past couple decades, as has the number of hydrogen bond acceptors. In contrast, the number of hydrogen bond donors (#HBD) has remained constant, suggesting that this may be more important for oral bioavailability. (Indeed, #HBD is the only Lipinski rule not broken by venetoclax.) Although Shultz did not examine “three dimensionality,” he laudably includes all the raw data – including SMILES – in the supporting information. This will be a useful resource for data-driven debates.

Molecular properties are carefully considered by Ashley Adams, who discussed the four fragment libraries used at AbbVie. The first is a 4000-member “rule of three” compliant library. For tougher targets, a 9000-member Ro3.5 library is available, as is a specialized fluorine library for ¹⁹F NMR (2000 members) and a 1000-member “biophysics” library, in which all compounds are less than 200 Da. Fragment optimization is often challenging, and since the C-H bond is most common but perhaps least explored, the AbbVie database is annotated with references on C-H bond activation relevant to each fragment.

Anil Padyana spoke about the metabolic enzymes being targeted at Agios. As we mentioned recently, these are very difficult targets, so the researchers often use parallel (as opposed to nested) screening using different techniques to minimize false negatives. Anil also described an interesting SPR assay in which fragments were introduced to the protein after the addition of an activating substrate.

High-quality protein constructs are essential for any fragment screen, and Jan Schultz described ZoBio’s technology for generating these. The company’s “protein domain trapping” approach entails high-throughput generation and screening of tens or hundreds of thousands of truncations of a given protein and rapidly selecting stable, high-expressing, and active variants.

Trevor Perrior mentioned that Domainex has a similar technology, which has been able to produce soluble protein domains in 90% of its attempts. Trevor also described a separate project in which a 656-fragment compound library was screened using SPR against the enzyme RAS. They found fragments that bind in a previously discovered site but, unlike the earlier work, the Domainex researchers were able to optimize these to nanomolar inhibitors.

Another success story was presented by Dean Brown (AstraZeneca), who described a collaboration with Heptares to discover inhibitors of protease-activated receptor 2 (PAR2). As the name suggests, this GPCR is activated when a protease cleaves the N-terminus, allowing the remaining N-terminal residues to fold back and activate the GPCR. The researchers used a stabilized form of PAR2 in an SPR screen of 4000 fragments and obtained >100 binders in multiple series. This led to AZ8838, which blocks signaling by binding in an allosteric pocket. It also has a slow off-rate, which is often an attractive feature – particularly in the context of intramolecular activation.

A number of talks were focused on protein degraders such as PROTACs (PROteolysis-TArgeting Chimeras). These are generally two-part molecules connected by a linker: one part binds to a target of interest, while the other engages the cellular degradation machinery to destroy the target. As Shanique Alabi, a graduate student in Craig Crews's lab at Yale demonstrated, the molecules are catalytic – a single PROTAC molecule can cause the destruction of multiple copies of a target protein. This “event-driven” pharmacology is thus different from most historical drugs, which are “occupancy-driven.” Is there a role for fragments?

One of the strengths of FBLD is that if a ligandable site exists, it can be found. As Astex demonstrated, the majority of proteins seem to have secondary sites, away from the active site. Although some of these may be allosteric, others probably have no functional activity, particularly in the case of protein-protein interactions where secondary sites may be located some distance from the interface. The power of degraders is that non-functional sites can be made functional. The power of FBLD is that it can find small-molecule binding sites, which could then be used as anchoring sites for one side of a degrader. Watch this space!

Review of 2017 reviews

The year is done, and the darkness

Falls from the wings of Night.

As we've done since 2012, Practical Fragments is using the last post of the year to highlight conferences as well as reviews not previously discussed.

Significant events included the venerable CHI FBDD meeting in San Diego, the NovAliX Biophysics conference in Strasbourg, and the first-ever fragment conference in Shanghai. We discussed a special issue of Essays in Biochemistry devoted to structure-based drug design, and Teddy came out of retirement to provide an entertaining summary of his experience putting together a book on biophysics in drug discovery - well worth reading if you're ever tempted to edit one yourself.

As in years past, several reviews were devoted to the broad topic of FBDD. Below, I’ll outline the general reviews, followed by those focusing on particular targets, techniques, and other topics.

György Keserű (Hungarian Academy of Sciences) and Mike Hann (GlaxoSmithKline) ask “what is the future for fragment-based drug discovery?” in Fut. Med. Chem. After a concise summary of the topic, they answer that it “includes target discovery and validation, the development of chemical biology probes, pharmacological tools and more importantly drug-like compounds.” In other words, the future looks bright.

FBDD is more comprehensively covered by Ben Davis and Stephen Roughley (Vernalis) in Ann. Reports Med. Chem. This is a complete, self-contained guide to the field, covering everything from history, theory, fragment library design, and fragment-to-lead approaches. It is ideal for a newcomer, but there are enough insights throughout that it makes a rewarding read for experts too.

Of the thirty-plus fragment-derived drugs that have made it to the clinic, none are directed against neglected diseases. Gustavo Henrique Goulart Trossini and colleagues at Universidade de São Paulo review some of the work that has been done in this area in Chem. Biol. Drug Des.

And rounding out general reviews, Christopher Johnson (Astex) and collaborators examined all 28 successful fragment-to-lead programs published in 2016, defined as at least a 100-fold improvement in affinity to a 2 µM or better compound. This is a sequel to our analysis of the 2015 literature, also published in J. Med. Chem., and many of the trends are similar. Interestingly, many leads maintained high ligand efficiencies, and there was no correlation between the “shapeliness” (deviation from planarity) of fragments and that of the resulting leads. Consistent with our recent poll on the importance of structural information, 25 of the 28 examples used crystallography at some point.

Targets

Three of the success stories from 2016 involved bromodomains, the subject of an entire month of Practical Fragments’ posts last year. In Arch. Pharm., Mostafa Radwan and Rabah Serya (Ain Shams University, Cairo) review this target class, with a particular emphasis on the four BET family proteins.

More than 30% of enzymes are metalloenzymes, yet these are targeted by fewer than 70 FDA-approved drugs. One of the first published examples of FBDD involved a metalloenzyme, but most efforts have been focused on a limited set of metal-binding pharmacophores, such as hydroxamic acids. Seth Cohen (University of California, San Diego) has been steadily building libraries of metallophilic fragments, and in Acc. Chem. Res. he describes how this approach can lead to new classes of inhibitors.

Protein-protein interaction inhibitors are another underrepresented class of drugs, though one approved FBDD-derived molecule falls into this category. In Methods, Daisuke Kihara and collaborators at Purdue University look at in silico methods to discover PPI inhibitors, including fragment-based approaches.

Unlike PPIs, kinases have been highly successful drug targets. We recently highlighted one review of cyclin-dependent kinases (CDKs), and in Eur. J. Med. Chem. Marco Tutone and Anna Maria Almerico (Università di Palermo) provide another. Although the main focus is on in silico methods, there is a section on FBDD.

Techniques

As noted above, X-ray crystallography has played a role in most successful fragment to lead programs. In the open-access journal IUCrJ, Sir Tom Blundell (University of Cambridge) provides an engaging and personal view of protein crystallography, a field in which he has played a starring role, starting with his early involvement in determining the crystal structure of insulin. He also notes that the interchange of ideas and techniques between academia and industry has long been a crucial driver of advances.

NMR was the first practical method used for FBDD, so it is not surprising that there are several reviews on the topic. In Arch. Biochem. Biophys., Michael Reily and colleagues at Bristol-Myers Squibb provide a detailed overview of NMR in drug design. This covers not just the ligand- and protein-detection methods often used in fragment screening, but also more intensive techniques to characterize protein-ligand interactions.

A briefer look at many of these topics is provided by Yan Li and Congbao Kang (A*STAR) in Molecules. This review also highlights more unusual approaches such as NMR experiments on living cells.

Artifacts are a fact of life in both FBDD and HTS, and it is always important to recognize these early. In J. Med. Chem. Anamarija Zega (University of Ljubljana) discusses how NMR can help. This includes methods to detect aggregators and covalent modifiers. Of course, NMR methods can introduce their own artifacts, and these are also covered.

Other topics

Speaking of artifacts, PAINS are responsible for quite a few. The term “PAINS” has also been somewhat controversial, and in a new paper in ACS Chem. Biol. Jonathan Baell (Monash University) and J. Willem Nissink (AstraZeneca) examine the “utility and limitations” of the term Jonathan coined seven years ago. As they acknowledge, the PAINS filters were derived from just 100,000 compounds run in a limited set of assays. This means that not every bad actor will be recognized by PAINS filters, and some compounds that are may only be PAINful in certain assay formats. Like Lipinski’s rule of 5, it is important to recognize the limits of applicability. As the authors note, “the key is to remain evidence-based.”

Another sometimes controversial topic is ligand efficiency and associated metrics, the subject of an analysis in Expert Opin. Drug Disc. by Giovanni Lentini and collaborators at the University of Bari Aldo Moro. This includes extensive tables of rules and metrics, both common and obscure. The authors note that, while metrics can be useful, it is important not to use them as a “magic box.” As they quote William Blake, “to generalize is to be an idiot.”

Shawn Johnstone and Jeffrey Albert (IntelliSyn Pharma) discuss pharmacological property optimization for allosteric ligands in a review in Bioorg. Med. Chem. Lett. As we recently noted, fragments are particularly suited for discovering allosteric sites, and this paper discusses how to characterize these.

Finally, Jörg Rademann and collaborators at Freie Universität Berlin discuss protein-templated fragment ligations in Angew. Chem. Int. Ed. Earlier this year we highlighted some of his work, and this review provides a thorough analysis of both reversible and irreversible approaches, with good discussions of detection methods, chemistries, and case studies.

That’s it for the year. Thanks for reading, and especially for commenting.

And may 2018 be filled with music, and light.

Ligand efficiency invalidated!

Practical Fragments has had quite a few posts on ligand efficiency (see here, here, and here, for starters). Ligand efficiency (LE) is defined simply as the free energy of binding for a ligand divided by the number of heavy atoms in the ligand. One of the criticisms of LE is that the definition of free energy depends on the definition of standard state, which may be different on different planets. With the discovery of silicon-based life on Venus, this is no longer just an academic argument. Indeed, a recent paper in Venusian Analytical, Physical, & Inorganic Discoveries describes an excellent case study.

Professor Perelandra and colleagues at East Eistla University performed a crystallograhic fragment screen on the enzyme silica hydratase, which is essential for the life cycle of the viciously parasitic Crystalline Horde. Fragment 1 binds in the active site, and although it has low affinity, structure-guided medicinal chemistry rapidly led to compound 42, with low nM activity in vitro and good efficacy in a silicon resorption model.

Things get even more interesting when you calculate the ligand efficiency values. The Venusians define standard temperature and pressure very differently from us. More importantly, they don't believe that standard state concentration should be 1 M. Given the extreme conditions on their home world, they choose a standard state concentration of 10 M.

LE = - ΔG/HA

(where HA = number of non-hydrogen atoms)

Thus, LE_Venus = -RTln(K_D/[A]⁰)/HA

(where T = 737 K and [A]⁰ = 10 M)

Using our terracentric definitions, the (impressive) LE of the fragment hit stays roughly the same during optimization, suggesting that the medicinal chemists have done a good job. However, by Venusian standards, the LE decreases!

This rock-solid example shows that Dr. Saysno was right: ligand efficiency is arbitrary and should never be used – on Venus.

Many measures of molecular complexity

Molecular complexity is a fundamental concept underlying fragment-based lead discovery: fragments, being simple, can bind to more sites on proteins and thus give higher hit rates than larger, more complex molecules. The ultimate example of this is water, which – at 55 M concentration – binds to lots of sites on proteins. But although the concept is easy to describe, it is much harder to quantify: everyone can agree that palytoxin is more complex than methane, but by how much? And if complexity could be measured, could it help in optimizing libraries? This is the subject of a review by Oscar Méndez-Lucio and José Medina-Franco at the Universidad Nacional Autónoma de México published recently in Drug Discovery Today.

There are many ways to measure molecular complexity. Two of the simplest to calculate are the fraction of chiral centers (FCC) and the fraction of sp³ carbons (Fsp³). These range from 0 to 1, and larger numbers imply a higher number of unique molecules with the same formula.

More complicated methods to measure complexity abound, but many of these require specialized software. Two that are publicly available are PubChem complexity and DataWarrior complexity. In PubChem, complexity incorporates the number of elements as well as structural features such as symmetry, though stereochemistry is not explicitly considered, and aromaticity is scaled such that both benzene and cyclohexane have the same complexity – a sharp contrast to FCC and Fsp³. DataWarrior uses its own metric, though I couldn’t find the definition. (Ironically, though the software itself is open source, the paper describing it is not.)

So, do more complex molecules have lower hit rates? The researchers looked at several public databases of screening data for dozens of assays against thousands of molecules. Using each of the four metrics, they classified molecules as “simple,” “intermediate,” or “complex”. For FCC and Fsp³, simple compounds did appear to be more promiscuous, in line with theory and with previous findings. However, for PubChem and DataWarrior, the trends were not clear – and even reversed in some cases. The researchers note that the median complexity of molecules in each dataset may vary, and as Pete has also observed simple binning strategies can be misleading.

Do these different definitions of complexity even measure the same thing? The researchers plotted each pair-wise measurement of complexity for >400,000 molecules – for example, Fsp³ vs DataWarrior. Not only are there no universal correlations, those that do exist are conflicting. "For example," the authors write, “compounds with high FCC values are associated with low PubChem complexity values, whereas the same molecules have high DataWarrior complexity." 

Teddy has previously invoked Justice Potter Stewart and his famous “I know it when I see it” expression, and I think that just about sums up where things stand in terms of molecular complexity. From a practical standpoint this probably doesn’t matter; a complex molecule is not even necessarily more difficult to make, as evidenced by the ease of oligonucleotide and peptide synthesis. Still, it would be nice if someone could come up with a reliable measurement for such a fundamental property – or even demonstrate whether or not such measures are possible.

Enthalpy revisited – and retired

The relative importance of enthalpy versus entropy for protein-ligand interactions has been a subject of considerable attention. In a 2009 post we suggested that it might be worthwhile to focus on fragments that bind predominantly enthalpically, and in 2011 we highlighted a paper suggesting that enthalpic binders may be more selective than entropic binders. But the universe has a way of confounding pet models – as we acknowledged in 2012 (twice). The best way forward is often with lots of data, which is exactly what we have in a new paper in Drug Disc. Today by György Keserű and collaborators at the Hungarian Academy of Sciences, Astex, and AstraZeneca.

The data in this case are sets of 284 protein-ligand interactions with thermodynamic binding data from the literature, 782 from Astex, and about 230 from AstraZeneca. Commendably, these data are provided in 103 pages of supporting information.

In order to analyze the data, the researchers developed a new metric, the Enthalpy-Entropy Index:

I_E-E = (ΔH+TΔS)/ΔG

If I_E-E = 0, it means that enthalpy and entropy both contribute equally to the free energy of binding; if I_E-E > 0 it means that enthalpy dominates, and if I_E-E > 1 it means that enthalpy needs to overcome an unfavorable entropy. Similarly, negative values mean that entropy dominates – completely so when I_E-E < -1. (Note that, unlike enthalpy efficiency, this is a dimensionless ratio, which should please our friends over at Molecular Design.)

As it turns out, the vast majority of fragments bind to their targets with favorable enthalpy, and almost all of those that don’t are charged compounds in which desolvation of the charged bit could entail an enthalpic cost. The researchers also examined a set of 94 neutral fragment-sized and 44 larger molecules binding to 17 targets and found that, statistically speaking, enthalpy plays a more important role in the free energy of binding for fragments than for larger molecules. But things can change quickly: in one case, adding just two non-hydrogen atoms to a molecule improves the affinity by more than 4000-fold and changes the I_E-E from -1.5 to +0.5.

The paper does an excellent job describing the challenges of collecting high-quality isothermal titration calorimetry (ITC) data. In a typical experiment, the heat measured with each injection is the same as “would fall on an A4 sheet of paper in 1 second when illuminated by a 40 Watt bulb placed nearly 5 kilometers away.” Errors can be caused by inaccurate concentrations, heat of dilution, and changes in buffer concentration or protonation state. An analysis of replicate measurements at Astex found that, while most of the replications were within 1 kcal/mol of each other, some were off by nearly 3 kcal/mol. However, these larger values were all associated with different forms of the protein, and so may not be considered true replicates, though they do indicate how changes in the protein far from the active site can have an effect on what is often considered (erroneously) a local interaction.

This also emphasizes the fact that, as the researchers note, “the measured binding enthalpy is a net value and the dissection of the individual contributions might be ambiguous.” Or, as Pete has previously stated, “the contribution of an individual protein-ligand contact is not strictly an experimental observable”.

From a molecular recognition standpoint I find all this quite interesting and even intuitive in a hand-wavy sort of way. As the researchers suggest, fragments, being small, have minimal surface area with which to make (often but not always entropically-driven) hydrophobic interactions. Instead, much of the binding energy comes from hydrogen-bond interactions, which are (again often but not always) predominantly enthalpic. Moreover, since the entropic cost of locking down any ligand onto a protein is on the order of 3-5 kcal/mol, fragments are already fighting against entropy, and this is exacerbated by low affinity.

But from a practical perspective, my earlier suggestion to focus on enthalpic fragments may have been simplistic: if you’ve found a fragment, its enthalpy of binding is almost certainly favorable, and even if it’s not, this could change with the slightest tweak. So unless we see something truly new, don’t expect many new posts on this topic.