Category Archives: fragments

Practical Fragments blog has reviewed our paper!

Our latest fragment-based drug discovery paper against the p97 ATPase has been noticed and reviewed favorably by the widely-read Practical Fragments blog.

Here is an excerpt from that review:

“The protein p97 is important in regulating protein homeostasis, and thus a potential anti-cancer target. But this is no low-hanging fruit: the protein has three domains and assembles into a hexamer. Two domains, D1 and D2, are ATPases. The third (N) domain binds to other proteins in the cell. All the domains are dynamic and interdependent. Oh, and crystallography is tough. Previous efforts have identified inhibitors of the D2 domain, but not the others. Not to be put off by difficult challenges, a group of researchers at the University of California San Francisco (UCSF) led by Michelle Arkin and Mark Kelly have performed fragment screening against the D1 and N domains, and report their adventures in J. Biomol. Screen.

FTMap: fast and free* druggable hotspot prediction

*free to academics

FTMap is a useful and fast online tool that attempts to mimic experimental fragment-screening methodologies (SAR-by-NMR and X-ray crystallography) using in silico methods.   The algorithm is based on the premise that ligand binding sites in proteins often show “hotspots” that contribute most of the free energy to binding.

Often, fragment screening will identify these hotspots when clusters of different types of fragments all bind to the same subsite of a larger binding site.   In fact, x-ray crystallography studies of protein structures solved in a variety of organic solvents demonstrate that small organic fragments often form clusters in active sites.

In the FTMap approach, small organic probes are used for an initial rigid-body docking against the entire protein surface.  The “FT” of FTMap stands for the use of fast Fourier transform (FFT) methods to quickly sample billions of probe positions while calculating accurate energies based on a robust energy expression.

Following docking of each probe, thousands of poses are energy-minimized and clustered based on proximity.  The clusters are then ranked for lowest energy.   Consensus sites (“hot spots”) are determined by looking for overlapping clusters of different types of probes within several angstroms of each other.   If several consensus sites appear near each other on the protein surface, that is a strong indication of a potentially druggable binding site.

10 Common Mistakes in Fragment Screening

There is an excellent review paper from Dan Erlanson and Ben Davis that came out last year detailing some of the more common mistakes and artifacts that can arise in fragment-based screening campaigns (so-called “unknown knowns”).  I encourage readers to go read the original paper.  I have summarized some of the key points below:

1) Not checking compound identity to make sure what you think you purchased is what you actually have.

2) Low-level impurities in compound stocks can cause problems at the high concentrations used in fragment screens.

3) DMSO, commonly used to store fragments in plates, can act as a mild oxidant and is also hygroscopic.

4) Pan-assay interference compounds (PAINS) are common in many libraries and are found to give false positives to many targets.

5) Reactive functional groups in fragment hits can cause covalent binding or aggregation of the target.

6) Many fragments can show binding or inhibition while acting as aggregators rather than reversible binders.  Including a small % of detergent can help eliminate these kinds of fragments from giving positive signals.

7) STD-NMR is very sensitive to weak binders, but because it relies on a relatively fast disassociation rate for the ligand, tight binders (<1 uM) can be missed by this method.

8) X-ray crystallographic structures are often taken as the “truth” when they are in fact a model of an electron density.  Fragments can often be modeled into the density in incorrect orientations or in place of solvent atoms.

9) SPR methods are very sensitive to fragment binding, but can be confounded by non-specific binding of fragment to the target or chip, as well as compound-dependent aggregation.

10) Fragment hits should be validated by more than one method before embarking on optimization.  They should also be screened for being aggregators by DLS or other methods.

Tackling challenging targets with Chemotype Evolution

Carmot Therapeutics, a small company located in San Francisco’s Mission Bay, has developed a very innovative drug discovery technology, called Chemotype Evolution (CE), that relies on fragment-based discovery but is different from traditional FBDD and HTS approaches in important ways.

The first important innovation is that CE relies on a “bait” molecule as a starting point for screening.  The bait can be a known ligand, cofactor, or inhibitor.  The bait is then derivatized with a linker moiety that allows it to become chemically bonded with every fragment in a proprietary library.  This process generates a screening library that contains thousands of bait-fragment hybrids.

The most powerful aspect of CE is the ability to iterate over chemical space, allowing access to an exponential number of possible fragment-bait hybrids.

These hybrids are then screened against the target for binding using either biophysical or biochemical screening techniques in a high-throughput plate format.
The most powerful aspect of CE is the ability to iterate over chemical space, allowing access to an exponential number of possible fragment-bait hybrids.  The method can be iterated with new “baits” derived from the best fragment hits of the previous round.  Thus, instead of having 7,000 fragments in your library, after 3 iterations you access 7,000^3 possible combinations (343 billion possible compounds), selecting only the most target-relevant chemotypes at each stage.

Schematic of the Chemotype Evolution process through 3 iterations. Note that at any point after each iteration, the hit molecules can be taken into hit-to-lead optimization.

The CE approach is similar in concept to the “tethering” approach pioneered at Sunesis, but differs in the fact that no protein engineering of cysteine residues needs to be performed.  The bait molecule performs the role of the engineered cys, providing a “handle” that binds to the target and selects for complementary fragment binders.

Carmot Therapeutics just embarked upon their first major industry collaboration with the January 2014 announcement of a partnership with Amgen

Carmot Therapeutics just embarked upon their first major industry collaboration with the January 2014 announcement of a partnership with Amgen to use CE technology against two challenging targets.  Identifying leads and developing hits will be carried out jointly between the companies, while clinical trials will proceed at Amgen.  I think Carmot is definitely a company to watch given its innovative and potentially paradigm-shifting discovery technology and increasing interest from big pharma.




Improve your docked poses with receptor flexibility

I have noticed that rigid docking methods, even when run with high-precision force fields, don’t always capture the correct poses for your true positives.  Sometimes a hit will be docked somewhere other than into the site that you specified because the algorithm could not fit the molecule into the rigid receptor.  This will cause true positives to be buried at the bottom of your ranked list.

You may want to try introducing receptor flexibility to improve the poses of your true positives.  There are two main ways to do this:  scale down the Van der Waals interactions to mimic flexibility (i.e., make the receptor atoms “squishy”) or use induced-fit docking (IFD) methods.  I have found that while setting a lower threshold for VdW scaling can rescue false negatives (poorly docked true binders), at least in one case, it does not improve the overall ranking of all of the true positives.  So it is not a panacea.

Induced fit methods work by mutating away several side chains in the binding pocket, docking a compound, mutating the side chains back, and energy minimizing the structure.  Then the compound is re-docked to the minimized structure using a high-precision algorithm.  There are two main applications for IFD: (1) improving the pose of a true positive that cannot be docked correctly by rigid docking and (2) rescuing false negatives.

My experience has been that IFD improves the docking scores of true positives and false positives by about the same amount, so the value of running the method on an entire library remains unclear.  However, there is much value in running IFD on a true hit where you are not sure the rigid pose is optimal.  Often, the improvement in the shape complementarity and number of interactions will be dramatic.

Also, you can use the alternative receptor conformations generated by IFD to a true positive to rescreen your library with faster rigid docking methods.  If you are screening on a prospective basis, this approach could help you identify other chemotypes that may bind well but are missed in a first pass rigid docking screen.

Practical Fragments: Natural Products as Fragments

There’s an interesting post at Practical Fragments regarding how natural products have been assembled into a small fragment library for screening and lead selection.   Natural products appear to have some advantages over synthetic fragments, including their “3D-ness” and the fact that they were screened by evolution to be protein-binders.