15 April 2024

Detailing hot spots with atomic consensus sites

Practical Fragments has written frequently about hot spots, regions on proteins that are predisposed to bind ligands such as drugs. Determining whether a protein has a hot spot can help prioritize a target for screening, and one of the more established computational approaches to do so is FTMap, which we wrote about most recently just a couple months ago.
While FTMap can tell you whether a protein has one or more hot spots, it provides few further details, such as which regions might prefer a hydrogen bond donor or acceptor. This has now been addressed in a new J. Chem. Inf. Mod. paper by Sandor Vajda and collaborators at Boston University, Stony Brook University, and Acpharis. (Diane Joseph-McCarthy presented some of this work at the CHI DDC conference earlier this month.)
The original version of FTMap started with a collection of 16 very small molecule probes: these were docked all over a protein, with hot spots being identified as consensus sites where many probes bound. To get more information about each hot spot, the researchers have extended the method – now called E-FTMap – by increasing the number of probes to 119 covering key functional groups. For example, whereas FTMap included dimethyl ether as a probe, E-FTMap also includes 2-methoxypropane, 2-methoxy-2-methylpropane, and tetrahydropyran. If all these probes bind with the oxygen in the same part of the hot spot, this suggests a predilection for a hydrogen bond acceptor, and also provides information about nearby hydrophobic contacts.
By using a sufficiently diverse group of virtual probes, E-FTMap is able to more finely detail hot spots, tallying the “atomic consensus sites” within them. This is reminiscent of an approach we wrote about several years ago, though that method used just three different probes.
To benchmark E-FTMap, the researchers took 109 fragment-to-lead pairs with published crystallographic information and assessed whether the program could identify interactions that had been experimentally observed. The results were encouraging and far superior to the original version of FTMap. The highest ranked atomic consensus sites generally overlapped with appropriate atoms in fragments and leads. Interestingly, the results for fragments were better than those for leads, and the researchers suggest this is because the fragment “core is responsible for the bulk of the binding free energy in a ligand and that larger ligands bind by forming additional interactions at weaker hot spots that surround the fragment binding site.”
Next, E-FTMap was tested against five proteins for which between 31 and 353 fragment-bound crystal structures were available. Here too the program was broadly successful, though some fragments bound regions of the protein that E-FTMap overlooked, particularly in cases where there were conformational changes. This is not surprising given that the program assumes the protein remains rigid. (Other computational approaches such as SWISH, which we wrote about here, are starting to account for protein flexibility.)
E-FTMap looks qualitatively at specific atomic interactions, and one question I had was how well the atomic consensus sites matched up with binding affinities of known fragments; perhaps some crystallographically identified fragments bind so weakly one would not expect to find them computationally, as we discussed here and here. This hypothesis might be tested by focusing on comparisons with experimentally characterized fragments with the highest ligand efficiencies.
Also, I was struck by the fact that the virtual probes in E-FTMap are roughly the size of MiniFrags or MicroFrags, and I couldn’t help but wonder how well the atomic consensus sites from the virtual screens would correlate with the binding modes of these tiniest of fragments.
One nice feature of E-FTMap is that it can be accessed through a simple web server, so if you’re interested in these and other questions you can test it for yourself. If you do, please share your experiences.

08 April 2024

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 kinact/KI ~ 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.

01 April 2024

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?