Fragment merging – and flipping – on the leucine zipper of MITF

Transcription factors can be difficult drug targets, particularly those whose primary structure is a “leucine zipper” in which two α-helices gently coil around each other. Their three-dimensional structure provides few pockets suitable for binding small molecules. In a new (open-access) paper in Nat. Commun., Deborah Castelletti, Wolfgang Jahnke, and a large group of multinational collaborators at Novartis and elsewhere present progress toward one of these, microphthalmia-associated transcription factor (MITF), which has been implicated in melanoma.

 

Most of MITF is believed to be disordered, but the DNA-binding domain (DBD) homodimerizes as a basic helix-loop-helix leucine zipper. Unlike related transcription factors, the helices in MITF contain a small kink that keeps them from heterodimerizing and also creates a small “kink pocket.”

 

The researchers expressed the DNA-binding domain of MITF and screened it using ¹⁹F NMR against the LEF4000 library, which we described here. This yielded just 9 hits that confirmed in protein-observed NMR, a hit rate the researchers note “is amongst the lowest that we have observed across multiple FBS campaigns,” consistent with expectations for a difficult target. Two chemical series, represented by compounds 1 and 2, were prioritized, and analogs from the Novartis compound collection were screened to find more-potent compounds 3 and 4.

 

Crystallography revealed that compounds 3 and 4 both bound in the kink pocket. Excitingly, the binding modes are similar and overlapping, inviting fragment merging. This proved successful, yielding a compound that bound 100-fold more tightly than either fragment. Further optimization ultimately led to compounds 7 and 8, with low or sub-micromolar affinity as assessed by isothermal titration calorimetry (ITC).

 

The bound structures of compounds 7 and 8 were determined by crystallography. Compound 7 (gray, left) superimposes nicely onto compounds 3 (cyan) and 4 (magenta), showing successful fragment merging. Compound 8 (green, right), however, is flipped 180 degrees compared to compound 7, despite having similar structure and affinity. Although surprising, this is not too uncommon; we’ve written about previous flippers here, here, and here.

The MITF homodimer is asymmetric, with one helix kinked and the other straight. NMR experiments and molecular dynamics show that both compounds 7 and 8 slow the interconversion between kinked and straight forms, though it is unclear whether this has functional implications. The compounds do not seem to affect DNA binding, and with at best high nanomolar affinity towards MITF no cell data are reported with the molecules.

 

Nonetheless, the successful identification of ligands against a leucine zipper is exciting. The binding pocket is small; as shown in the figure above, the best compounds already stick out on either side of the helices. Further affinity improvements may be difficult, though perhaps covalent approaches could help. Alternatively, perhaps these molecules could be starting points for induced proximity strategies such as PROTACs. It will be fun to watch this story develop.

How best to assess molecular shapeliness?

The shape of a molecule influences its properties. While this is true on a per-compound level, things get a little more controversial when discussing molecules in general. Back in 2009 researchers argued that “three dimensional” molecules have better drug-like properties, though this assertion has been challenged, repeatedly. But how do you assess the shape of a molecule in the first place? In a recent (open-access) Drug Discov. Today paper, Iwan de Esch and collaborators at Vrije Universiteit Amsterdam compare the main metrics.

 

The researchers focus on three metrics: fraction of sp³-hybridized carbons (F_Csp³), which we wrote about here; plane of best fit (PBF), which we wrote about here; and principal moment of inertia (PMI), which we wrote about here. F_Csp³, which ranges from 0 to 1, is simple to calculate based on the chemical structure alone, while the other two metrics rely on the three-dimensional shape of the molecule, requiring calculations and indeed choices since many molecules can assume multiple conformations. PBF is measured in angstroms with a minimum of 0 Å and no maximum; a protein, for example, could easily have a PBF above 10 Å. PMI is represented by two normalized PMI ratios, and these are often added to give a number (3D Score or ΣNPR) between 1 and 2.

 

The researchers calculated F_Csp³, PBF, and ΣNPR for a set of nearly half a million commercially available fragments which we discussed here; PBF and ΣNPR were calculated based on the single lowest energy conformation for each molecule. As noted above, PBF is somewhat size-dependent. For example, adamantane and buckminsterfullerene have PBF scores of 0.79 and 1.76 Å but identical ΣNPR scores. Nonetheless, the researchers found a correlation between these two metrics, and this correlation increased when PBF was divided by the root of the molecular volume to attempt to normalize for size.

 

In contrast, no correlation was found between F_Csp³ and PMI, making the former “a poor descriptor for predicting 3D molecular shape.” Is there a simple alternative? F_Csp³ only considers carbon atoms, so the researchers proposed F_HAsp³, which includes nitrogen, oxygen, and sulfur atoms. Perhaps not surprisingly, this didn’t improve the correlation.

 

Three years ago we wrote about “spacial scores,” which were developed to assess molecular complexity. The researchers calculated normalized spacial scores (nSPS) for their set of compounds, but these also showed no correlation to PMI.

 

The researchers conclude that, “once corrected for size, PBF captures three-dimensionality similarly to ΣNPR values. However, unlike a PMI analysis, it is not capable of further distinguishing between rod- and disc-shaped molecules, giving PMI a higher resolution in capturing shape diversity.” Interestingly, this is the opposite conclusion of an analysis Teddy wrote about in 2014. My take is that, if you want to assess shapeliness, steer clear of F_Csp³, but both PBF and PMI are fine.

Fragment events in 2026

Lots of interesting events coming up this year - hope to see you at one!

February 17-19:  The Twelfth NovAliX Conference will be held for the first time in San Diego! You can read my impressions of the 2018 Boston event here, the 2017 Strasbourg event here, and Teddy's impressions of the 2013 event here, here, and here. 

 

April 13-16: CHI’s Fragment-Based Drug Discovery turns 21, old enough to legally drink in the US! The longest-running annual fragment event returns as usual to San Diego. This is part of the larger Drug Discovery Chemistry meeting. You can read impressions of the 2025 meeting, the 2024 meeting, the 2023 meeting, the 2022 meeting, the 2021 virtual meeting, the 2020 virtual meeting, the 2019 meeting, the 2018 meeting, the 2017 meeting, the 2016 meeting; the 2015 meeting here, here, and here; the 2014 meeting here and here; the 2013 meeting here and here; the 2012 meeting; the 2011 meeting; and the 2010 meeting. 

May 10-12: Industrial Biostructures of America will be held in Cambridge, MA. The meeting covers all aspects of structural biology including FBLD, membrane proteins, allostery, cryo-EM, machine learning, and more. 

September 14-16: Fragments X, RSC-BMCS Tenth Fragment-based Drug Discovery Meeting, will be held in Cambridge, UK.  You can read my impressions of the 2024 meeting, the 2013 meeting, and the 2009 meeting.

September 28 to October 1: CHI’s Twenty-Fourth Annual Discovery on Target will be held as always in Boston. As the name implies this event is more target-focused than chemistry-focused, but there are always plenty of FBDD-related talks. You can read my impressions of the 2025 meeting, the 2024 meeting, the 2023 meeting, the 2022 meeting, the 2021 meeting, the 2020 virtual meeting, the 2019 meeting, and the 2018 meeting.

 

November 10-12: CHI holds its third Drug Discovery Chemistry Europe in beautiful Barcelona. This will likely include tracks on lead generation, protein-protein interactions, degraders and glues, and machine learning, with multiple fragment talks throughout. 

Know of anything else? Please leave a comment or drop me a note.

A new tool for covalent ligands: kinact/KI made easy with dDRTC

As covalent drug discovery becomes increasingly common, researchers are becoming more rigorous in how they characterize their molecules. The simple IC₅₀ values used for reversible inhibitors are meaningless for irreversible ligands unless the incubation times are also disclosed. And as molecules become more potent during the course of optimization, the incubation time may need to be shortened. An early hit might require treatment overnight to give 50% protein modification, while a potent lead might completely modify the protein in seconds. How do you quantitatively compare these?

 

The most rigorous parameter to characterize irreversible ligands is k_inact/K_I, sometimes called covalent efficiency, which we recently discussed here and here. Unfortunately, determining k_inact/K_I is a pain: it requires running multiple dose-response studies at multiple time points, and is thus typically only done for key compounds. In a new (open-access) Nat. Commun. paper, Robert Everley and colleagues at Frontier Medicines (including yours truly) provide a shortcut.

 

The new method relies on the fact that, especially for low-affinity fragments, much of the data collected in a conventional dose-response time course (DRTC) is redundant, providing little additional value. For example, if a compound at one concentration gives virtually no modification after 8 hours, it also won’t modify after 1 hour. The trick is to collect just the most informative data in a “diagonal” dose-response time course, or dDRTC.

 

I won’t go into the mathematics and full implementation details since the paper is open-access, but suffice it to say that dDRTC lowers the number of required data points by a factor of eight, thus saving both time and reagents – including precious protein.

 

The paper appropriately notes limitations, such as the fact that for compounds with better affinities (K_I < 50 µM), the values derived from dDRTC can underestimate the true k_inact/K_I. However, this situation is uncommon for fragments, and indeed the potencies for even some clinical compounds such as sotorasib and VVD-133214 are largely driven by (specific) k_inact rather than K_I. The paper shows good agreement for k_inact/K_I values determined using dDRTC with those determined using the conventional approach for compounds having k_inact/K_I from 1 to 2000 M^-1s^-1.

 

Perhaps most relevant for this blog, dDRTC is a practical solution for collecting important data. The next time you’re running a covalent program, give it a try!