29 August 2012

Poll Results: Do you Need Structure

The poll question, Do you Need Structure To Prosecute Fragments is closed. We had 27 votes. 6 (22%) people voted for "Absolutely. No X-ray, no fragments." 6 (22%) voted for "Nope". And the majority 55% (15/27) voted for "Yes, but I am flexible as to what structure is." So, ~80% of the people who voted (self-selection?) think you do not need a X-ray structure to move forward.

So, what kind of methods can we put in the "structure" bin that is not X-ray? I think we need to define structure to get to that first question. In my eyes (and I hope this generates some dialog in the comments), structural information is data that informs on how the ligand and target interact. In my eyes, non-structural structural information is SAR without the guessing. Typical SAR: let's walk this methyl (hopefully its a magic methyl) around this ring, then ethyl, propyl, butyl, futile. Change the ring from phenyl to pyridyl...and so on.

Epitope mapping is one well established method to establish interactions between ligand and target. I am partial to this, but I am an NMR jock. To me, this is as instructive as a crystal structure. It tell you which part of the molecule is in closest contact with the target, which aren't. It leads to imminently testable medchem hypotheses.

Hydrogen-deuterium exchange (HDX) is another method which could inform on the target side of the ligand-target interaction. HDX is most robustly performed by Mass spectrometry, but can also be done by NMR (if the protein is amenable, blah^3). Is this a robust (and SENSITIVE!!!) enough method for routine mapping of ligand-protein interactions?

I ask this out of ignorance, not out of general gadfly-ness, but what methods do those people who are "flexible to structure" use to generate non-structure structural information?

24 August 2012

ACS Fall Meeting 2012

I recently returned from Philadelphia, where the American Chemical Society held its 244th national fall meeting. As always this was a massive affair, but fragments were well-represented, particularly in a nice session organized by Percy Carter, Debbie Loughney, and Romyr Dominique.

I opened the session by giving an introduction to fragment-screening, as well as an overview of some of the work we’re doing at Carmot. Andrew Good had perhaps the best title (“Fragment fat wobbles too”), and discussed some of the work done at Genzyme on Pim-1 kinase. Eric Manas next described some of the computational tools being used at GlaxoSmithKline, in particular strategies to deal with water. He also discussed the utility of looking for fragment analogs early in a project. In the last talk before the intermission, Chris Abell from the University of Cambridge described a number of projects from his group, starting with antimicrobial targets (such as this one); we’ll cover another in a separate post. Chris is unabashedly going after difficult targets, not just protein-protein interactions, but oligonucleotides – specifically riboswitches. There is only limited precedent for targeting RNA with fragments, so it will be fun to see how this progresses.

Francisco Talamas next described a nice example from Roche using FBLD to discover hepatitis C NS5B Palm I allosteric inhibitors. An HTS campaign of around 900,000 molecules yielded just 3 hits, none of which were advanced. A fragment screen of about 2700 fragments gave a better hit rate (5.9%), but of the 29 co-crystal structures attempted only a single structure was obtained. However, by combining the information from this crystal structure with information from other crystal structures, both proprietary and public, the researchers put together a set of rules to design a de novo fragment library tailored to this protein. This effort ultimately yielded compounds that were optimized to a clinical candidate.

Next, Nick Wurtz from Bristol-Myers Squibb described his company’s approach to discover neutral Factor VIIa inhibitors. The researchers used a combination of computational, functional, and biophysical approaches to find uncharged fragments that would bind in the P1 pocket, leading to a couple dozen crystal structures. Despite the low affinities of these fragments (typically mM), many of them could successfully be merged onto an existing series, replacing a positively charged moiety to yield potent molecules with better permeability. This is the first time I’ve seen a fragment story out of BMS, so I'm glad to see that they’re active in this area. This is also a prime example of what has been described as fragment-assisted drug discovery.

Finally, Prabha Ibrahim of Plexxikon gave a lovely overview of the discovery and development of vemurafenib, including a more detailed description of the SAR than has been presented in their earlier papers.

In addition to this dedicated session, there was a scattering of other talks and posters, including a notable poster from Timothy Rooney at the University of Oxford using fragment-based approaches to discover bromodomain inhibitors, a target class we’ve previously discussed.

A session entitled “A medicinal chemist’s toolbox” ranged over several topics of interest. Ernesto Freire of Johns Hopkins gave a great overview of thermodynamics in drug discovery, a topic we’ve previously covered. Most readers are probably familiar with the concept of enthalpy-entropy compensation, in which (for example) an added hydrogen bond fails to achieve the desired boost in potency due to unfavorable entropy. Recognizing this, he suggested that one should target groups in proteins that are already well-structured, so you don’t have to pay the cost of structuring a disordered part of the protein. He also suggested that if you introduce one hydrogen bond, you might be better off introducing a second one too, as the incremental entropic cost is likely to be low.

György Keserű of Gedeon Richter discussed the importance of avoiding lipophilicity by using tools such as LELP, which we’ve covered here and here. Continuing this theme, Kevin Freeman-Cook of Pfizer described two examples of using LLE in lead discovery programs, in particular calculating LLE values before making compounds. Although this may seem obvious, what was quite striking was the dramatic effect subtle changes in structure could make to ClogP values.

Of course, these are just a few of thousands of presentations. Please feel free to point out any that caught your eye, or expand on some of those mentioned above. And just a reminder, it’s only 4 weeks to FBLD 2012 in San Francisco – the biggest fragment event of the year!

15 August 2012

Two types of hot spots

Practical Fragments has previously written about the concept of hot spots – regions on a protein where fragments are particularly prone to bind. It’s always nice to have one of these when starting a program, since it’s a good indication that the protein is ligandable.

But there’s another type of hot spot too. When two proteins interact, they often do so through very large interfaces comprising dozens of amino acid residues. This is daunting from a molecular recognition perspective, but it turns out that most of these residues contribute very little energetically to the binding affinity, as assessed by alanine scanning mutagenesis. The few residues that do matter often cluster into hot spots, which are generally much smaller than the entire interface.

In a new paper in J. Chem. Inf. Model., Sandor Vajda, Adrian Whitty, Dima Kozakov, and colleagues at Boston University ask how these two types of hot spots are related.

The researchers used the program FTMap to look for fragment-binding hot spots on the protein ribonuclease A (RNase A). They found four in the vicinity of the binding site for the protein ribonuclease inhibitor (RNI), three of which had also been shown experimentally to bind small organic (solvent) molecules.

Having shown that FTMap can find fragment hot spots, they next turned to a set of 15 protein-protein complexes for which alanine scanning data were available; amino acid side chains were considered hot spots if mutation to alanine decreased binding by more than 2 kcal/mol. Applying FTMap to the receptor of each protein-protein pair showed that 92% of alanine-scanning hot spot residues map onto FTMap hot spots. Moreover, there were very few false negatives: 92% of “unimportant” residues did not map onto FTMap hot spots. In other words, the two types of hot spots seem to overlap considerably.

Although these results may make sense intuitively, the researchers discuss several reasons why this was not a foregone conclusion. First, a residue identified as a hot spot by alanine scanning might not be important for binding per se, but may instead be important for imposing long-range structure on the protein. Second, alanine scanning only identifies important side chains; backbone atoms are not considered, so a fragment may bind to a hot spot that is invisible by alanine scanning. Finally, hot spots identified by alanine scanning reflect the interactions between two proteins, whereas hot spots identified by fragments (or their virtual equivalents) look at only a single protein. This is important because if a residue protrudes from one protein into a cavity on the other, the protruding residue may be a hot spot for interactions but, because of its geometry, not be a good binding site for small molecules. As the authors put it:

A convex surface site on a protein typically will not bind small molecules strongly no matter how much binding energy the region generates in an interaction with a complementary concave site on its protein binding partner. Thus, observation of a hot spot by alanine scanning mutagenesis does not necessarily imply the existence of a small molecule fragment consensus site at that region.

And of course, as we’ve noted before, proteins can be wriggly little things, with new pockets opening up where you least expect them. So despite all these caveats, it’s reassuring to see that, for the most part, there is some constancy in the hotness of spots.

13 August 2012

Deadlines approaching for FBLD 2012

It is just under six weeks to the start of the conference – which will be the fragment event of the year. Please see here for the fantastic lineup of speakers and exhibitors assembling September 23-26 in San Francisco.

This post is to remind you of two deadlines:

September 1 is the closing date for poster abstracts. This is to allow time for the booklet to be assembled and plan for poster space.

Registrations will continue to be accepted until the meeting; however, the much reduced rate on hotel rooms is not guaranteed beyond September 1.

Looking forward to a great conference and hoping to meet many of you again or for the first time. If you want a sense of what to expect please see here and here.

01 August 2012

From nanomolar to picomolar, via fragments

It’s not every day that you see a picomolar inhibitor. This is all the more true for membrane proteins. And fragment-based lead discovery is rarely attempted with membrane proteins. For all these reasons, a new paper by Guang-Fu Yang at Central China Normal University, Jia-Wei Wu at Tsinghua University, and co-workers in J. Am. Chem. Soc. caught my eye.

The researchers were interested in the cytochrome bc1 complex, which is essential for cellular respiration and a validated antifungal target. Starting with the co-crystal structures of molecules such as azoxystrobin bound to the enzyme complex, they computationally replaced the pyrimidine-containing moiety (red in figure below) with a library of 1735 fragments and calculated the binding energies, in a process called pharmacophore-linked fragment virtual screening (PFVS). Several of the top ten hits were synthesized and tested, and all of these had nanomolar potency. Compound 4 was further optimized, again with the aid of computational chemistry, leading ultimately to picomolar inhibitors such as compound 4f.

Those of you of a suspicious nature may be concerned that the methoxyacrylate moiety looks like a PAINfully reactive electrophile. Happily, the researchers were able to obtain a crystal structure of a molecule in this series bound to the cytochrome bc1 complex, showing that the molecule binds non-covalently and in close agreement to the predicted structure.

In some ways PFVS is reminiscent of Silverman’s fragment hopping, another computational screening and linking approach. Such techniques work best when the protein-ligand complex is relatively rigid, making modeling more straightforward than it would be for a more flexible system

A medicinal chemist could argue that traditional techniques may well have arrived at similarly potent molecules without fragments or fancy modeling. Still, the fact remains that fragments and modeling were used to discover impressively tight-binding compounds, illustrating again the versatility and increasing application of fragment-based techniques.