Applications

Small Molecules and Fragment Screening

Where the usual rules have to be renegotiated, and exactly why the reversal is justified.

22 min read 4 sections 9 sources cited (1 verified)

The signal problem, quantified#

Response scales with the mass of the analyte. A 250 Da fragment binding a 40 kDa protein produces, at full saturation, a response of 250/40 000 = 0.6% of the immobilised level. That single ratio drives every design decision that follows.

Set analyte MW to 250 Da and read off the immobilisation level required. Then set it back to 25 000 Da and see the difference.

DMSO, and why it needs its own procedure#

Compound libraries live in DMSO. DMSO has a refractive index far from water’s, so a mismatch of 0.1% between sample and running buffer produces a bulk shift that can exceed the entire binding signal you are trying to measure (Myszka, 2004).

The standard remedy is a calibration procedure rather than a subtraction:

Include DMSO in the running buffer at the same nominal concentration as the samples — typically 1–5%.
Inject a series of solvent standards bracketing that concentration, for example 0.5% steps from 1.0% to 3.0%.
From the responses of those standards, build a correction curve relating the reference-subtracted response to the DMSO mismatch.
Apply the correction to every sample injection based on its own reference-channel response.
Re-run the solvent standards periodically through a long screen, since evaporation shifts DMSO concentration over hours.

The screening cascade#

Fragment campaigns are structured as a funnel, with each stage cheaper per compound than the next and each removing a specific class of false positive (Navratilova & Hopkins, 2010).

Stage	Question	What it removes
Clean screen	Does the compound bind the reference surface?	Promiscuous binders and aggregators, before they waste target time (Giannetti et al., 2008)
Binding-level screen	At a single high concentration, is there any response?	The ~95% of a library that does nothing
Superstoichiometry filter	Is the response above the theoretical R_max?	Aggregators and non-specific binders that passed the clean screen
Curve-shape triage	Is the shape consistent with binding?	Compounds giving square, non-saturating or erratic responses
Affinity screen	Does it titrate sensibly across a concentration series?	Anything whose response does not depend on concentration properly (Hämäläinen et al., 2008)
Site specificity	Does it compete with a known site binder?	Compounds binding somewhere irrelevant
Off-rate ranking	Which hits have the longest residence times?	Nothing — this prioritises what survives

A fragment screening cascade. Each stage answers one question.

The superstoichiometry filter is worth dwelling on because it is cheap and decisive. You know the target’s molecular weight, the immobilised level and the compound’s molecular weight, so you know exactly what a 1:1 saturation response would be. A compound giving three times that is not binding one site on your protein, whatever its curve looks like (Giannetti et al., 2008).

Assay formats for small analytes#

The main assay arrangements. Formats lower in the table trade directness for the ability to handle analytes too small to detect directly.

When the analyte is simply too small to see — below roughly 150–200 Da, or where the target cannot be immobilised at sufficient density — the response can be inverted. In a solution competition assay, the compound is pre-equilibrated with a soluble binding partner and the free partner is measured on the chip; compound binding therefore reduces the signal. Karlsson and colleagues developed the direct and competitive formats side by side for thrombin inhibitors and showed where each is preferable (Karlsson et al., 2000).

Small-molecule SPR is also used well outside hit-finding. Frostell-Karlsson and colleagues immobilised human serum albumin and used SPR to predict plasma protein binding for drug candidates — an ADME measurement rather than a target-engagement one (Frostell-Karlsson et al., 2000). The lesson generalises: once you can measure weak binding to an immobilised protein reproducibly, a lot of pharmacology becomes accessible.

Sources cited on this page

Listed alphabetically. Each badge records whether the bibliographic record was confirmed against Crossref. unverified marks a real, deliberately chosen source whose volume and page numbers we have not yet machine-checked — it is not a comment on the science.

Frostell-Karlsson et al., 2000Å. Frostell-Karlsson, A. Remaeus, H. Roos, et al. (2000). Biosensor analysis of the interaction between immobilized human serum albumin and drug compounds for prediction of human serum albumin binding levels. Journal of Medicinal Chemistry 43, 1986–1992. unverified
Giannetti et al., 2008A. M. Giannetti, B. D. Koch, M. F. Browner (2008). Surface plasmon resonance based assay for the detection and characterization of promiscuous inhibitors. Journal of Medicinal Chemistry 51, 574–580. doi:10.1021/jm700952v unverified
The primary source for using superstoichiometric, non-saturating responses to identify aggregating and promiscuous compounds.
Hämäläinen et al., 2008M. D. Hämäläinen, H. Nordin, E. Pol, et al. (2008). Label-free primary screening and affinity ranking of fragment libraries using parallel analysis of protein panels. Journal of Biomolecular Screening 13, 202–209. doi:10.1177/1087057107313329 unverified
Karlsson & Fält, 1997R. Karlsson, A. Fält (1997). Experimental design for kinetic analysis of protein–protein interactions with surface plasmon resonance biosensors. Journal of Immunological Methods 200, 121–133. doi:10.1016/S0022-1759(96)00195-0 verified
Where the low-density / high-flow-rate / analyte-range design rules come from.
Karlsson et al., 2000R. Karlsson, M. Kullman-Magnusson, M. D. Hämäläinen, et al. (2000). Biosensor analysis of drug–target interactions: direct and competitive binding assays for investigation of interactions between thrombin and thrombin inhibitors. Analytical Biochemistry 278, 1–13. unverified
Myszka, 2004D. G. Myszka (2004). Analysis of small-molecule interactions using Biacore S51 technology. Analytical Biochemistry 329, 316–323. doi:10.1016/j.ab.2004.03.028 unverified
Establishes the surface-density and referencing requirements specific to low-molecular-weight analytes.
Navratilova & Hopkins, 2010I. Navratilova, A. L. Hopkins (2010). Fragment screening by surface plasmon resonance. ACS Medicinal Chemistry Letters 1, 44–48. unverified
Perspicace et al., 2009S. Perspicace, D. Banner, J. Benz, F. Müller, D. Schlatter, W. Huber (2009). Fragment-based screening using surface plasmon resonance technology. Journal of Biomolecular Screening 14, 337–349. doi:10.1177/1087057109332595 unverified
A full fragment cascade run by SPR, including the artefact filters.
Squires et al., 2008T. M. Squires, R. J. Messinger, S. R. Manalis (2008). Making it stick: convection, reaction and diffusion in surface-based biosensors. Nature Biotechnology 26, 417–426. doi:10.1038/nbt1388 unverified
The general transport analysis for any surface-based sensor, in dimensionless form. The clearest statement of when a measured rate is chemistry and when it is delivery.