Predicting protein ligandability and conservation of fragment binding modes

Say you have a protein target, and you want to know whether you will be able to find small molecules that bind to it. A fragment screen can give you a good idea as to the likelihood of success: if you find lots of different fragments with high affinities (say, better than < 0.1 mM), your protein is likely to be highly “ligandable.” On the other hand, if you get very few fragments, and most of them are weak (> 1mM), be prepared for a slog.

Of course, it would be even better if you didn’t have to do a physical screen at all, and two recent papers show how a computational approach may be sufficient. The first, by Dima Kozakov, Sandor Vajda, and their collaborators at Boston University and Acpharis is a detailed how-to guide in Nature Protocols. The second, in Proc. Nat. Acad. Sci. USA by Dima Kozakov, Adrian Whitty, and Sandor Vajda and their collaborators at Boston University, Northeastern University, and Acpharis, addresses some interesting questions about fragment binding.

The main program is called FTMap (also highlighted here); it and several related programs are accessible through a free web server. It is remarkably easy to use: just provide a protein data bank (PDB) ID or upload your own structure and away it goes.

The program works by docking a set of 16 virtual probes (such as ethanol, acetonitrile, acetamide – the largest molecule is benzaldehyde) against a protein and looking for “hot spots” where many fragments cluster. Previously the researchers demonstrated that known ligand-binding sites in proteins tend to be computational hot spots, where at least 16 probes bind. (Note that due to their small size, multiple probes of the same type – acetone, for example – can bind within the same hot spot simultaneously.) In other words,

The strongest hot spot tends to bind many different fragment structures, acting as a general “attractor.”

On the other hand, a hot spot with fewer probe molecules is unlikely to have enough inherent binding affinity to bind to ligands with low micromolar or better affinity.

A related program is called FTSite, which focuses on more thoroughly characterizing the best binding sites. Other programs allow for protein side chain flexibility, docking custom probes, or docking against ensembles of protein models such as generated by NMR structural methods.

The PNAS paper goes further to ask about ligand deconstruction. Specifically, why is it that when a larger ligand is dissected into component fragments, sometimes the fragments recapitulate the binding modes seen in the larger molecule, and sometimes they do not? The answer:

Because a substantial fraction of the binding free energy is due to protein-ligand interactions within the main hot spot, a fragment that overlaps well with this hot spot and retains the interacting functional groups will retain its binding mode when the rest of the ligand is removed.

The researchers support this assertion by examining eight literature examples in which structural information was available for fragments and larger ligands (some of which we’ve covered here, here, and here). In cases where the isolated fragments overlapped with 80% of atoms in probe molecules within a given hot spot, the fragment binding mode remained conserved. Also, these fragments tended to have high ligand efficiency values.

This is neat stuff, and it will be fun to see how general it is. I’m especially happy to see that all of the software is free and open access. Even though I’m hardly a computational chemist, I tried playing around with it and found it remarkably fast and easy to use. So if you have a protein with no known ligands, FTMap can find hot spots, and if they’re particularly promising, this should embolden experimental work.

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.

ACS Spring Meeting 2010

The spring national meeting of the American Chemical Society has just concluded in (uncharacteristically sunny) San Francisco. The main fragment event was a full day session organized by Rachelle Bienstock of the NIH. The theme was “Fragment based drug discovery: success stories due to novel computational methods applications.” Rachelle is planning on getting some of the speakers to write chapters for a book, so I won’t do more than give a very brief overview here.

The session was very multinational, with speakers from France, Germany, Russia, and the UK, in addition to the US, and a good mix of companies and academics. On the computational corporate side John MacCuish from Mesa Analytics described the molecular shape fingerprints approach, Carsten Detering of BioSolveIT provided several examples of applying his company’s methods for fragment linking and scaffold hopping, and Francois Delfaud of MEDIT described mining the pdb for protein-fragment interactions and applying this to Eg5 inhibitors. On the computational academic side, Tobias Lippert of the Center for Bioinformatics in Hamburg discussed the Qsearch program, Vladimer Poroikov of the Institute of Biomedical Chemistry in Moscow discussed PASS, which relies on a large training set to predict actives and inactives, and Dima Kozakov of Boston University presented the FTMap approach for predicting fragment-binding pockets in protein-protein interactions.

Moving away from the purely computational, Yongjin Xu of Novartis described the application of virtual fragment screening to identify p38 and BRaf inhibitors, Vicki Nienaber of Zenobia described iterative fragment screening to identify potent and selective LRRK2 inhibitors, and I presented Carmot’s Chemotype Evolution approach. Finally, GPCRs appear to be increasingly amenable to FBLD; Richard Law of Evotec presented a number of applications of computational methods to various programs including histamine receptors, while Miles Congreve of Heptares presented their StaR Technology for generating stabilized GPCRs suitable for SPR, NMR, and crystallography and discussed applications to the adenosine A2A receptor and the beta-1 adrenoreceptor. In the later case, the researchers were able to obtain 9 co-crystal structures and found that agonists and antagonists bound somewhat differently.

There were also a few other relevant posters and talks throughout the conference. For example, I learned that Locus Pharmaceuticals has transformed itself into Ansaris; Fouzia Machrouhi presented a poster on developing nanomolar inhibitors of the protein kinase AMPK.

Finally, Andrew Woodhead presented an update on Astex’s CDK2 program. One of the earliest posts on Practical Fragments described Astex’s fragment-based discovery of AT7519, which is in clinical trials for cancer. However, with an oral bioavailability of less than 1%, this compound is administered intravenously. Extensive medicinal chemistry ultimately revealed that a relatively minor change – capping the secondary amine with a methyl sulfonamide – led to a molecule with dramatically improved oral bioavailabilty. This molecule, AT9311, also retains good activity in mouse xenograft models. This is a useful reminder that fragment-based methods are not a replacement for solid (and inevitably subsequent) medicinal chemistry.

Journal of Computer-Aided Molecular Design Special FBDD Issue

Our friends over at FBDD-Literature have already highlighted this, but it bears repeating that the entire August issue of J. Comp. Aid. Mol. Des. is devoted to FBDD. For aficionados of all things silicon, there are articles on computational chemistry applied to FBDD generally as well as on more specific topics such as MCSS, NovoBench, FTMap, and two papers on Glide (here and here).

But don’t be put off by the name of the journal: with 14 articles covering close to 200 pages, there is something here for almost everyone, even for those whose interest in computers ends at using them to read this blog! A brief editorial outlines the challenges of FBDD, and a longer introductory piece gives an overview of the field. Several articles focus largely on specific targets such as p38alpha, heparanase, and Eg5, while one is devoted to assessing druggability.

Finally, two articles address the important topic of designing fragment libraries, one from the perspective of big pharma (nicely summarized here), the other from biotech.