Basic Assay Principles for Fluorescence

Basic assay principles for fluorescence

• donor transfers energy to acceptor • Result: donor fluorescence is reduced (quenced), acceptor fluorescence is increased

• Main application : quenced protease substrate, fluorescence increases when protease cleaves substrate and donor and acceptor get too far separated for energy transfer

1 Fluorescence anisotropy Excitation Dipole of Dye Excitation - photoselection

+ HN O OH OH O t

• during absorption a photo- Emission: selection occurs • After average fl. Life-time molecules emits light of different orientation. • This is used as a measure for how fast molecule rotates, i.e. how big it is. Anisotropy measurment setup

Anisotropy setup D vertical

Fluorescence D lens beam splitter horizontal

I - I Ivv - IVH vv VH P = r = I + 2 I Ivv + IVH vv VH

3 How to use Anisotropy for ligand binding Assay principle Orientation Orientation Labeled ligand of dipole of dipole at moment of protein at moment of Test ligand excitation emission

1. complex: High MW Anisotropy setup D vertical Slow rotation Fluorescence D lens beam splitter horizontal

2. ligand replaced: Low MW D vertical Fast rotation Fluorescence D lens beam splitter horizontal

4 How well does a ligand bind?

Main indicators used: [R] x [L] 1. Dissociation constant, Kd: = Kd unit: concentration e.g. 1 uM [RL] If ligand is at concentration of Kd, 50% of receptor is bound to ligand.

2. IC50: Half maximum effect (any assay). unit: concentration e.g. 1 uM The relationship to the Kd is the following: IC50 Where SL is the surrogate ligand used Kd = in the assay [SL] + 1 [Kd of SL]

Note: only the Kd is indipendent of the assay and can be used to compare compounds. IC50 values (e.g. for cellular assays can only be compared for the same assay).

5 Ligand-receptor interactions: Thermodynamics (Kd)

+ ligand Protein

In reality there is a thermodynamic equilibrium between unbound and bound form of the ligand. The «better « the ligand is i.e. the stronger it’s binding, the more ligand molecules will be bound to the protein at a given ligand and protein concentration. This is expressed as «dissociation constant» Kd and is a parameter measuring the «strength « or affinity of a ligand to its protein.

[P] x [L] Where P, L are free protein and free ligand Kd = PL is the complex [PL] [] denotes «concentration» thereof

6 A weak ligand:

Protein + Protein Protein ligand Protein Protein Protein

[2] x [5] [P] x [L] Kd = = = 10 [PL] [1]

7 A strong ligand:

Protein + Protein Protein ligand Protein Protein Protein

[1] x [1] [P] x [L] Kd = = = 0.5 [PL] [2]

• As seen from the last two slides: the lower Kd, the better i.e. higher affinity the ligand. • Dissociation constants are expressed as concentrations [M] • Classification: Kd > 10-3 M «low affinity» Kd < 10-3 M > 10-6 «medium affinity» kd < 10-9 M «high affinity» 8 How affinites are determined:

• To a solution of Ligand are added increasing amounts of P P the Protein and the protein-ligand complex formation is P detected. (or vice versa). L L -> requirement: there needs to be an «assay principle» to L L measure complex formation. • Then, the measured effect is plotted vs. concentration of added ligand and non-linear curve fitting is done Note: in reality one knows only the total amount of 0.12 ligand and protein and the amount of complex 0.1 formed during the reaction, therefore the actual free

0.08 and bound ligand and protein concentrations are not directly known. This is why the simplified calculation 0.06 shown above is not used in reality but curve-fitting is

Kd = 273 +/- 8 nM Fluorescence Anisotropy Fluorescence 0.04 needed.

0.02 0 2000 4000 6000 8000 10000 Concentration of unlabeled Grb2-SH2 [nM]

9 How to determine binding Kd

Normally, via a titration experiment :

2. Protein in portions [100 nM] -> final [1000 nM] readout 1. Ligand [100 nM] [c] ligand

[R] x [L] = Kd Is simplified, because the true meaning [RL] of the concentrations is [R]free and [L]free However, we only know total ligand concentration and total protein concentration we put into the well [c]tot ligand, [c]tot protein Answer : non-linear curve fitting 10 Non-linear curve fitting

Direct binding reaction: labeled ligand -> protein

protein + protein

We have to build up equations from the reaction:

[R]f x [L]f / [RL]= Kd

Mass balances: Substituting Rf and Lf [R]f + [RL] = [R]tot for Rtot and Ltot [L]f + [RL] = [L]tot ([R]tot – [RL] ) x ([L]tot – [RL])/[RL] = Kd

-> quadratic equation as fit-function for Kd -> now Kd can be determined with fit fuction applied to data generated with total protein & ligand [c]

-> same doable for inhibition testing -> leads to a cubic equation -> higher models only numerically solvable.... 11 [A] ×[B] A + B AB Kd = f f [AB]

[A0 ]= [A]f +[AB]

[B0 ]= [B]f +[AB] ([A ]-[AB])×([B ]-[AB]) [AB]= 0 0 Kd

2 [AB]× Kd = [A0 ]×[B0 ]- [B0 ]×[AB]- [A0 ]×[AB]+ [AB]

2 [AB] - [AB]×([A0 ]+ [B0 ]+ Kd )+ [A0 ]×[B0 ]= 0

Quadratic equation: [A ]+ [B ]+ Kd [A ]+ [B ]+ Kd [AB]= 0 0 - 0 0 -[A ]×[B ] 2 2 0 0

12 To use the equation built up before we need a system, where the Signal is linearly proportional to the amount of complex in solution.

Option 1: Signal is increasing

DF [AB] =

DFMAX B0

[AB] F = FMIN + (FMAX - FMIN )× B0

Substitute term for [AB]

13 Option 2 Signal is decreasing

DF F - F [AB] = MAX =

DFMAX FMAX - FMIN [B0 ]

[AB] F = FMAX - (FMAX - FMIN )× B0

Substitute term for [AB]

14 IC50 fitting

Imax S = slope factor (hill coefficent) y= I max = starting value 1+( [c] Ligand )s IC50

100

60 signal signal 40

0 1 10 100 c [ligand] 15 Non-linear curve fitting

Advantages: • Exact equations based on the law of mass action • Data can directly be fittet (no linearisation etc, .. ) • The end-point does not have to be reached but can be used as parameter • Simulations help to guide experimental planning • Quick

Disadvantages:

• For n-interaction partner equation is of n+1 order : i.e. A + B <=> AB -> quadratic equaction A + B <=> AB A + C <=> AC -> cubic equation

Build-up is analogous from mass balances & interaction equations: e..g. Kd1 = [A] + [B] /[ AB] variables: Kd1, [A], [B], [C], [AC] Kd 2 = [A] + [C]/ [AC] Kd2 determined separately [C]tot = [C] + [AC] [AB] dependent variable [B]tot = [B] + [AB] [A]tot = [A] + [AB] + [AC] 16 Inhibition curve Fitting:

protein

100 Direct binding 80 Kd1< Kd 2 Kd1 > Kd2 60

20 Fluorescence anisotropy Fluorescence

0 0 1 2 3 Concentration of unlabeled target [µM]

17 Application of nonlinear curve fitting for elucidation of binding mechansim Meisner, Hintersteiner et al. Experiment Nat. Chem. Biol. 2007, 3 (8), 508

0.24 ARE IL-2 + HuR Kd = 15.82 nM 0.2 + cpd at 1 µM + cpd at 5 µM 0.16 + cpd at 20 µM Anisotropy

0.12

0 20 40 60 80 100 HuRfl [nM]

Protein RNA (fl labeled) cpd

18 19 Single molecule detection technologies

Ensemble Single molecule

20 21 Principle of confocal detection

Laser Optics f

Pinhole Ø = 50 µm Detector Detector

22 Fluorescence only from the confocal focus: the size of an E. coli bacterium

FCS and HTS Screening

• Insensitive to Miniaturisation (Volume 10-15 l) • Potential to Cover ALL Fluorescence Techniques • Improves Well to Well and • Assay to Assay Quality Control • Concentrations Free of Artefacts • On-line Mechanistic Characterisation • Throughput > 100 000 a day.

23 In general, same setup as for bulk fluorescence is used for single molecule methods except: - light source is laser/diode - detector is APD (avalanche photo detector) Two types of techniques: a) Single channel

Fluorescence D Pin-hole beam splitter

b) Multiple channel e.g. polarized D vertical or color Or Color 1 Fluorescence D Polarization splitter horizontal Pin-hole Or color split Or Color 2 24 FCS: Fluorescence Correlation Spectroscopy FCS provides information about intermolecular interactions from tiny fluctuations.

FCS can retrieve

1/N - local concentrations tdiff - motility coefficients - rate constants: Kd, Ka - enzyme kinetics - …

tdiff : transl. diffusion time 1/N 1/N : particle number tdiff

• Refs: Magde et al., Phys. Rev. Let.29, 1972; Rigler et al., Eur. Biophys. J. 22, 1993 25 FIDA: Fluorescence Intensity Distribution Analysis FIDA allows identifying multiple species and their concentrations by analyzing the molecular-specific brightness

FIDA Binding Study - Case 1: FIDA Binding/Aggregation Study - Case 2: binding-induced change in dye (label) fluorescence intensity binding-induced accumulation of dye fluorescence

qI qI

qII > qII > q : molecular brightness q q : molecular brightness II qI qII qI

- fluorescence quenching / enhancement The molecular brightness parameter can be perturbed by - induced spectral shift of fluorophore • Refs: Kask et al. PNAS, 1996; Chen et al. Biophys. J. 77, 1996; - fluorescence resonance energy transfer Buehler et al., Springer Series on Fluorescence 1, 2001 - multiple binding sites for labelled molecules

- … - two-dimensional data acquisition -

SAMPLE Possible Applications Beam expander • 1 color excitation / 2 color emission (FRET, Spectral Shift, etc.) Microscope objective LASER DICHROIC MIRROR • 2 color excitation / 2 color emission Optical filter (Coincidences like Tubus lens Cross Correlation, 2C-2D-FIDA etc.)

• Polarized excitation / Polarized emission (Polarization, Anisotropy, etc.)

PINHOLE Beamsplitter or DETECTOR #2 Polarization cube Color filters DETECTOR #1 2D-FIDA

27 2D-FIDA – Analysis of molecular brightness in 2 channels

• 2D-FIDA is a general method that provides multi-dimensional analysis of two simultaneously recorded brightness distributions. • 2D-FIDA may be configured for - polarization► molecular rotation -> single molecule analog of Anisotropy - two-color ► molecular brightness - FRET ► molecular energy transfer

• Refs: Schwille et. al., Biophys. J. 72, 1878-1886, 1997; Kask et al., Biophys. J. 78, 1703-1713, 2000

• Example: Two-Color-FIDA simultaneously measures the molecular brightness in two user-defined spectral band (colors).

(A) Dual-color “raw data” time traces of detected red and blue photons. (B) 2D “scatter-plot” generated by 2-D histograming the count rates of the red and blue raw data traces.

► 2D-FIDA/color using two fluorescent labels emitting in different spectral bands enables to determine the concentrations and specific brightness values of multiple molecular and different labeled species .

Images retrieved from „evotec-technologies“ website (www.evotec-technologies.com)

28 FIMDA: Fluorescence Intensity Multiple Distribution Analysis

• FIMDA allows distinguishing fluorescent species via both their specific molecular

brightness ( q ) and their translational diffusion time ( tdiff ). • FIMDA analyzes FIDA histograms at varying photon counting time intervals (time bins). • FIMDA extracts the characteristic readout parameters of both FCS and FIDA

- translational diffusion times ( tdiff ) - molecular brightness ( q ) - concentration ( C ) The shape of FIDA distributions depends on the time-bin specific mean photon count rate and the • Refs: Palo et. al., Biophys. J. 79, 2858-2866, 2000 molecular diffusion time tdiff .

Detected Photon FIDA Time 2 5 7 5 3 1 2 5 [ µs ] Frequency n = 3 Photons / Bin(50µs) Time Bins = 50µs Photons / Bin FIDA Time 0 7 12 4 7 [ µs ] 24 Frequency Bin Time48 Index 80 Time Bins = 60µs Photons / Bin(60µs) 72 120 96 FIDA 120 200 Photons Time 14 9 [ µs ]

Frequency ( Davis et. al, Proc- Time Bins = 70µs SPIE, Vol 4966, 2003 ) Photons / Bin(70µs) 29 FIMDA: Fluorescence Intensity Multiple Distribution Analysis

time (t) Nr. of Photonscounts (n) Photonscounts of Nr. Bin 3 16 n Bin 2 11 Bin 1 8 7 3 4 2 time (t) Bin 7+8 Bin 4+5+6 P(n) P(n)

Number of Photon Counts (n)

30 FILDA: Fluorescence Intensity Lifetime Distribution Analysis

• FILDA combines FIDA and fluorescence lifetime analysis (FLA).

• FILDA allows discriminating and quantifying molecular species according to their - specific brightness q, and their - fluorescence lifetime t

• Refs: Palo et. al., Biophys. J. 83, 605-618, 2002

FILDA Histogram Detected Photon of 1nM Bodipy t = 3.3 ns Time q = 23.2 kHz [ µs ] 2 5 7 5 3 1 2 5 7 FIDA Frequency Time Bins n = 5 Frequency n (~10µs ) Photons / Bin Photons / Bin

Laser Pulses Time FLA [ ns ] Q = 4 Q = 8 Q = 5 Q n Fluorescence Time Frequency Q Photons [ ns ] ( Palo et. al, BioPhys. J., Lifetime Bin Vol 83, 2002 )

31 What to get out from single-molecule assays

• FCS: Fluorescence correlation spectrocopy: 0.4 µm translational diffusion time 1.9 i.e. how long is molecule in Focus? µm • FIDA: Fluorescence Intensity Distribution Analysis molecular brightness

• 2D-FIDA-Anisotropy: molecular rotation – equivalence to anisotropy

• FCCS: Fluorescence cross correlation spectrosocpy coincidence of two different colored species diffusing through focus V = 0.24 fl N = 1.5 for 10-8 M • 2C-2D-FIDA: 2 color – 2D FIDA coincidence of 2 color species in rotational mode

32 The pitfalls of screening – a factory concept of the early 2000s uHTS Factory: ! ultra-high throughput screening capacity (> 1 Mio wells/24 hours) ! integrated compound reformatting capacity (384wè1536w) ! fully automated system (factory design concept) ! set-up for biochemical and cell-based assays ! set-up for fluorescent and luminescent readout technologies (imagers) ! unattended operation for up to 48 hours

33 28 November 2019 The pitfalls of screening

• Bad/non descriptive assays (e.g. Only one type) • Non-reproducible hits • Throughput higher valued than quality • False positives often recognized too late • Bad compound quality

-> screening is very valuable, but care needs to be taken how it is set-up and how it will be followed-up

34 Case study HuR: 4 different screening methods

1. Competition screening 2. Reporter gene assay: of HuR with fluorescent RNA HuR inhbiting compounds will lead to a stabilization of luciferase tagged mRNA

RNA

HuR

HuR stablizes mRNA; no degradiation -> high luciferase

HuR

Compound inhibits HuR -> mRNA degradation -> low luciferase 35 Case study HuR: 4 different screening methods

3. Affinity based screening 4. Cellular shuttling R2

N N O R4

O O NH N O R1 N OH R2 O HuR helps shuttle mRNA NH From nucleus to cytoplasm. Staining reveals where it is. NH AIDA + Linker

Bead based libraries 36 1 regulatory system homogeneous living cells solution ® multiple targets ® multiple assay systems ® multiple regulatory levels

HuR12– AREs theoretical X-clustering experimental X-testing

specific inhibitors ? mode of action ? solid surface mechanistics

37 Compound repository Idialized Drug Discovery Archive & development flow chart Disease area Assay strategy Hit confirmation, basic research; Assay development Screening Hit validation Medical disease Assay validation «counter screening» understanding

Lead Selection Hit Selection Med. Chem. based on Hit to «Lead» optimization «lead criteria» early chemistry

Animal Models Process development, IND Preclinical in-vivo Disease marker, Investigational New Clinical Phase I profiling endpoint definition Drug application

NDA NEW DRUG Clinical Phase III Clinical Phase II APPLICATION 38 Compound Logistics

Automated Solution Archive Automated Plate Replication

Automated Solution Production Automated “Cherry Picking“

Slide: NIBR – DT LDC 39 28 November 2019 Liquid Handling (1) - Compound Transfer

Pin-Tools (384) Piezo-Electric (8)

Standard Tips (384) Capillary Effect (384)

Slide: NIBR – DT LDC 40 28 November 2019 Liquid Handling (2)- Reagent Dispensing

Solution Dispensing (1536w) Cell Dispenser (1536w)

41 Liquid Handling (3) – Automated Systems

Biomek FX (µL) MITONA System (24 nL)

Cybi-Well (µL) NPRS System (50nL)

Slide: NIBR – DT LDC 42 28 November 2019 Compound repository Idialized Drug Discovery Archive & development flow chart Disease area Assay strategy Hit confirmation, basic research; Assay development Screening Hit validation Medical disease Assay validation «counter screening» understanding

Lead Selection Hit Selection Med. Chem. based on Hit to «Lead» optimization «lead criteria» early chemistry

Animal Models Process development, IND Preclinical in-vivo Disease marker, Investigational New Clinical Phase I profiling endpoint definition Drug application

NDA NEW DRUG Clinical Phase III Clinical Phase II APPLICATION 43 Compound side of screening

Q1: Where do we get the compounds from for screening? Q2: Which compounds do we screen? A1: a) Historical compound collections -> each of the big companies has collections of compounds (archive) -> archives today are between 1 million and 5 million cpds + very broad range of diverse compounds (with past success - very costly to maintain – takes looooong to build up b) Random cobinatorial libraries: small molecules or peptides, sometimes starting from a known binding motif + cost efficient (esp. if combined with d) - very limited chemical space, usually too many similar compounds c) Natural products (extracts): from fungi, plants, bacteria, etc.. + broad diversity of plants – evolved for some remarkable bioactivity (see Rapamycin) - Very often too complex chemistry, too reactive, too sensitive d) Natural starting points: known binding molecules from co-factors, co-enzymes, peptides etc.. Or from previously related target classes + often very efficient, molecules with proven activity - per definition «nothing new»; known binding sites, known compounds, ... 44 Which compounds do we need to consider?

45 A chemical space model for drug classification

peptides, natural products

Ottl et al. Molecules, 2019 24, 1629 46 Priviledged structure • Concept is based on the recognition that certain structural elements seem to be recurring among different pharmcologically active compounds. • Hence, this concept has been proposed as a selection tool for compound archives • Q: is it real? Is there an argument for it from evolution or is it because Medicinal chemists use known structures/same screening collection etc.. (historical bias)

Evolutionary argument: 5-hydroxytryptamine-6 (5HT6) receptor:

Different proteins have similar binding pockets melanocortin-4 (MC4) receptor: and bind similar structures.

47 Examples of privileged structures

Welsch et al. Curr. Op. Chem. Biol.

48 49 50 51 52 53 54 55 56 Not very convincing for the argument 57 Is there a natural pendant for chemical space?

- We target (for the most part) proteins - All proteins perform the «active» functional roles we are looking at from a mecinal chemistry/pharmacology/chemical biology point of view - Essentially, 20 amino acids represent the «chemical space of functional groups, etc..» ? How many functional groups in the 20? Amino Acids

59 Acidic amino acids & amides

Acids: - Often Surface exposed - Solubility & charge - Ionic interactions «salt bridges» Aspartic acid Glutamic acid

Amides: - Hydrogen bond donor & acceptors - Anchoring sites for Glycosylation Asparagine Glutamine

Na, K, salts: flavoring agent; «asia food» binds to glutamate receptors -> main excitatory neurotransmitter

60 Aliphatic, hydrophobic aa

- main consitutents of hydrophobic surfaces e.g. Isoleucine Valine Leucine Hydrophobic “face” of helices or beta sheets

- «Deepest reaching» hydrophobic chains

- FormlyMEt is first aa in prokaryotes Methionine - Met can be oxidized on S: relevance is not compl. clear

61 Alcohols and Thiols

- Hydrophilic & solublizing - Strong H-bonds - sites of seondary modification (phosphorylation, sulfonation) - Part of active sites: serin/thr proteases - Anchoring sites for Glycosylation

- Tyr: aromat. Alcohol: «Phenol» - Cystein: - simultaneous: aromatic & ionic &Hbond - Most important for structural interaction stability (Disulfides), highly - Site of secondary modification reactive ; often part of active site - Most prominent «recognition» motif: of enzymes: «proteases» phosphotyrosine 62 Aromatic aa

- Very often central motifs in protein-protein interactions. - Different sizes/electronic properties/H-bonding ability

- Size: His

- His and Tyr often implicated in Metal binding sites.

63 Basic AA

Arginine: Lysine: - Predominant contributor to positive charges on - most important aa for protein surface, constitutively charged protein modification. - due to «guanidine» group positive charge is delocalized, very strong interaction of Arg with aromatic acids

64 Special AA

Glycine: Proline: Tryptophan: - only non-chiral aa - Turn inducer - The only fluorescent amino acid - flexibility means it acts - This property is used in protein as structure-breaking detection, assays, etc... element and turn-inducer - Metabolically: most costly aa - «Linker»

65 Special AA II

Evolutionary «flexible» variants.

Selenocystein

Found in special Selenoproteins in all Pyrrolysine kingdoms. Translation Found in of UGA is controlled methyltransferases of by special «elongation certain archae bacteria factors»

66 Secondary modification of aa

4% of all eukaryotic Sulfonation in analogous proteins 67 Is there a natural pendant for chemical space?

? Is their diversity random?

? What is the maximum diversity possible for life? i.e. Do we have 20 because we need 20 or do we have 20 because there were not more around in the pre-historic soup? 68 Why amino acids? Why 20 (21) & Why these?

• Prof. Peter Schultz: «What would God have done if he had not rested on the 7th day?» • -> enlarged the set of amino acids

• Alternative position: We have 20 aa because it is the most efficient, compact and smallest code representing «chemical space and functionality . • -> if so, we should be able to proof it.

69 70 71 72 20 aa represent indeed a small 20 aa code is indeed economically subset: optimized in terms of «synthetic cost»

73 74 The approach: a) definition of possible pools of aa based on evidence from meteorite samples and biosynthetic pathways. B) random selection of sets c) Analysis of set properties with respect to i) charge ii) size iii) hydrophobicity in comp to the std. 20 aa.

75 Result: 3 types of sets analyzed:

- 20 aa out of pool of 50 abiotic aa - 8 out of 50 abiotic aa - 20 out of the total pool 76 aa

Values represent the % of sets which perform better than the std. set of 20.

-> As seen, for the two 20 set analyses 0% outperformed the standard alphabet of 20 aa. For sets of 8, there were 0.1 % of sets outperforming the std. set.

-> The set of 20 std. aa represents a highly optimized, non-random selection!

76 Conclusion

• All target functions/interactions etc. are performed by small set of functional groups.

• Amino acid example credibly shows that finite, chemical diversity space concept seems to exist/be meaningful

• Essential questions: a) can we use this concept to select/design compounds for screening collections? b) can we use this concept to filter/evaluate screening hits?

77 Alternatives to compound archives – in-vitro molecular evolution

• Compound archives are hard to maintain • Take long to build up • Their conent is biased (if historically grown) • Selecting content is not easy (no hard selection criteria)

Alternative – no compound archive & screening but in-vitro selection (evolution)

Basis: DNA + Enzyme Peptide

Problem: of non-compatibility with non-natural cpds 78 Alternative to compound archive: DNA tagged libraries - molecular evolution,

1. during synthesis, for each building block a DNA tag is used

2. affinity selection is performed using immobilized target protein -> “hits stick to column when they bind to protein”

3. active compounds are identified by sequencing the DNA code

Ottl et al. Molecules, 2019 24, 1629 79 Broadening the scope to non-natural building blocks

• artificial amino- acylated tRNA allows non-natural building blocks to be incorporated

-> true molecular evolution possible, because of functional connection between Information (DNA) and compound (via tRNA)

80 Success examples & advantages/disadvantages of DNA encoded libraries

advantages disadvantages

+ large number of compounds 109-1013 - only affinity no info on function of cpd - interference of tag with DNA/RNA + number of hits obtaind gives information binding targets on structure-activity relationship - large hydrophilic tag (DNA) can + fast, generic and good to obtain tool compounds interfer/change molecular properties - limited number of reactions compatible with tag 81 DNA + Enzyme Problem of non-compatibility Peptide with non-natural cpds

Separate reaction from information step Oligo-directed sorting

NH2 NH2

Solid support Chemical conjugation

82 83 84 85 86 Summary:

• Harbury et al. have provided one of the most advanced approaches for molecular evolution • Similar approaches followed by others e.g. use of oligo-tagged libraries (tag is used for identification and purification by ether preciptiation) • Advantage: huge number of compounds screenable

• Disadvantage: still, chemical diversity is limited to simple oligomers (compatibility of reactions with DNA etc.. )

• Disadvantage: influence of the oligonucleotide on molecular recognition

• Disadvantage: stability of the process

-> Technologies like this one will revolutionize part of the early stage discovery

-> Still small molecules are the «golden egg» of drug discovery (we will get back to that later in the course) 87