Benchmark Your Process

So, not everybody agrees with me on what a fragment is.  As has been pointed out years ago, FBDD can be a FADD.  In this paper, from earlier this year, a group from AZ discusses how FBDD was implemented within the infectious disease group. Of course, because of the journal, it emphasizes how computational data is used, but you skim over that and still enjoy the paper :-). They break their process into several steps.

Hot Spots: This is a subject of much work, particularly from the in silico side.  In short, a small number of target residues provide the majority of energy for interaction with ligands.  Identifying these, especially for non-active site targets (read PPI), is highly enabling, for both FBDD and SBDD. To this end, the authors discuss various in silico approches to screening fragments.  They admit they are not as robust as would be desired (putting it kindly).  As I am wont to say, your computation is only as good as your experimental follow up.  The authors indicate that the results of virtual screens must be experimentally tested.  YAY!  They also state that NMR is the preferred method; 1D NMR in particular being the AZ preferred method.  [This is something (NMR as the first choice for screening) that I think has become true only recently.  Its something I have been saying for more than a decade, but I guarantee my cheerleading is not why.] They do note that of the two main ligand-based experiments, STD is far less sensitive than WaterLOGSY.  There is no citation, so I would like to put it out there, is this the general consensus of the community?  Has anyone presented data to this effect?  Specifically, they screen fragments 5-10 per pool with WaterLOGSY and relaxation-edited techniques.  2D screening is only done for small proteins (this is in Infection) and where a gram or more of protein is available.

Biophysics:  They have SPR, ITC, EPIC, MS, and X-ray.  They mention that SPR and MS require high protein concentrations to detect weak binders and thus are prone to artifacts.  They single out the EPIC instrument as being the highest throughput.  [As an aside, I have heard a lot of complaints about the EPIC and wonder if this machine is still the frontline machine at AZ.]  60% of targets they tried to immobilize were successful.  They also use "Inverse" SPR, putting the compounds down; the same technology NovAliX has in their Chemical Microarray SPR.  In their experience, 25% of these "Target Definition Compounds" still bind to their targets. 

They utilize a fragment-based crystallography proof of principle (fxPOP).  Substrate-like fragments (kinda like this?) are screened in the HTS, hits [not defined] are then soaked into the crystal system, and at least one structure of a fragment is solved.  This fragment is then used for in silico screening, pharmacophore models, and the like.  So, this would seem to indicate that crystals are required before FBDD starts.  They cite the Astex Pyramid where fragments of diverse shape are screened and the approach used at JnJ where they screen similar shaped fragments and use the electron density to design a second library to screen.

As I have always said, there are non-X-ray methods to obtain structural information.  AZ notes that SOS-NMR, INPHARMA, and iLOE are three ways.  These are three of the most resource intensive methods: SOS-NMR requires labeled protein (and not of the 15N kind), INPHARMA requires NOEs between weakly competitive ligands (and a boatload of computation), while iLOE requires NOEs of simultaneously binding ligands.  I think there are far better methods, read as requiring fewer resources, to give structural information more quickly (albeit at lower resolution).

The Library:  The describe in detail how they generated their fragment libraries.  They have a 20,000 fragment HCS library.  The only hard filter is to restrict HA less than 18.  I fully support that.  They also generated a 1200 fragment NMR library biased towards infection targets.

The Process:   The authors list three ways to tie these methods together:

1. Chemical Biology: Exploration of binding sites/development of pharmacophores.  I would add that this is also for target validation.  As shown by Hajduk et al. and Edfeldt et al., fragment binding is highly correlated to advancement of the project. 
2. Complementary to HTS.  At the conference I am at today, one speaker (from Pfizer) said that HTS was for selectivity, FBDD was for efficiency (or Lord, here comes Pete with that one).  I really like that approach.
3. Lastly, stand alone hit generation.  

I think this paper is a nice reference for those looking to see how one company put their FBDD process in place. Not every company will do it the same, nor should they.  But there is a FBDD process for every company.

Another (impractical) NMR Screening Method

NMR has a checkered history in drug discovery. In the 90s, it promised to deliver structures just like X-ray. Strike 1! After that, especially after the advent of SAR by NMR, it promised to deliver boatloads of hits from screening. Strike 2! After that, pharmaceutical NMR worked hard to make sure that it was impactful and value-added. It found niches in which it thrives, e.g. a variety of -omics. In drug discovery, NMR still needs to realize it is living with two strikes. How can NMR survive and even thrive? Quite simply. NMR needs to provide rapid, robust, and easily understandable data to medchemists that leads to decisions. Data that results in no action has no value.

In this paper, Salvia et al. present a ligand-based NMR screening method using "long-lived states (LLS)" of the ligand to boost the sensitivity of ligand-based screening. This new method provides 25x better signal-to-noise than established (T1rho) methods and uses less protein. One of the benefits of this method is it allows NMR to study interactions as tight as 100nM and up to 1 mM.

The graphical abstract (above) shows that while this method is very similar in concept to other ligand-based methods (TOP: equilibrium between NMR differentiated states) it requires much more work than these other methods (Bottom: titrations of ligands). The data (Below) does generate quite satisfying curves, and as noted by the authors, are in agreement with previously published values.
I think this work, while an interesting application of Long Lived States, has really no practical value to the screening world. The strength of the binding can be too strong, making the bound lifetime too long, and thus there is a practical floor for Kd. Of course, because it is based upon kinetics, it can be very different for every system.

If you want to determine Kds for a complex < 10uM there are better, far more robust methods (SPR, for one). The amount of time and effort required to generate Kds from this method seems to run contrary to the tenets I described above (rapid, robust, and (most importantly) easily understandable). To me, the title of the paper simply does not deliver. This method is NOT a screening application. A screening application is one experiment (NMR or otherwise) from which you can determine whether a compound is binding or not, ideally from a mixture of compounds.

I would be curious to see in the comments if anyone (especially our NMR savvy readers, you know who you are) think that this method has practical applications.

Nuclear Magnetic Crystallography part II

In what can only be seens as a cosmic convergence, a second paper has appeared on NMR-X-ray hybridization. This one is a collaboration from Medivir, The University of Florence, and Bruker Biospin. This method is aimed at generating structural information for a family of related proteins (in this case MMPs). The authors argue that the cost of 13C and 15N labeling is so low that such samples should be readily available, making this method widely applicable. The thrust of their method is the use of X-filtered NOESY spectroscopy to generate distance constraints, then use Autodock to determine binding. I expect that this will get covered on our FriendBlog, the FBDD-Lit Blog, so I won't go into many details.
Instead I would like to make this a discussion of the perceived value of methods such as this to the FBDD community. I, despite being an NMR jock by trade, don't feel that labeled protein methods, give enough bang for the buck, compared to ligand-based methods. Will methods such as described Isaksson et al. change that cost-benefit analysis?
What do other people think about the value and the proper role of NMR?