1. What can (many) sequences tell us?

2. Nuclear receptor function

3. Nuclear receptor family
NR1C1-PPAR
NR1C2-PPAS
NR1C3-PPAT
NR1D1-EAR1
NR1D2-BD73
NR1I3-MB67
NR1I4-CAR1-MOUSE-
NR1H2-NER
NR1H3-LXR
NR1H4-FAR
NR4A2-NOT
NR4A3-NOR1
NR4A1-NGFI
NR2F1-COTF
NR2F2-ARP1
NR2F6-EAR2
NR2E3-PNR
NR2B1-RRXA
NR2B2-RRXB
NR2A2-HN4G
NR3C1-GCR
NR3C4-ANDR
NR3C3-PRGR
NR3A1-ESTR
NR3A2-ERBT
NR3B1-ERR1
NR3B2-ERR2
NR5A1-SF1
NR5A2-FTF
NR1I1-VDR
NR1B3-RRG1
NR2E1-TLX
NR2C1-TR2-11
NR2C2-TR4
NR6A1-GCNF
NR2B3-RRXG
NR2A1-HNF4
NR2A5-HN4 d?
NR0B1-DAX1
NR0B2-SHP
NR3C2-MCR
NR1F3-RORG
NR1F2-RORB
NR1F1-ROR1
NR1A2-THB1
NR1A1-THA1
NR1I2-PXR
NR1B2-RRB2
NR1B1-RRA1

4. Nuclear receptor structure
A-B C D E F
AF-1
DNA
LBD C
DNA binding domain
highly conserved
> 90% similarity E
Ligand binding domain
conserved protein fold
> 20% sequence similarity

5. The questions As Organon is paying the bills, question one is,
of course☺, how do ligands relate to activity?
With and without ligand being present, NRs can bind co-activators and co-repressors, so what is an agonists, an antagonists, or an inverse agonists?
What is the role of each amino acid in the NR LBD?
Which data handling is needed to answer these questions?

6. 3D structure LBD
(hER)

7. Available NR data 56 structures in (PDB) (>200 now)
>500 sequences (scattered) (>1500 now)
>1000 mutations (very scattered)
>10000 ligand-binding studies (secret)
Disease patterns, expression, >1000 SNPs, genetic localization, etc., etc., etc.
This data must be integrated, sorted, combined,
validated, understood, and used to answer our questions.

8. Step 1 The first important step is a common numbering scheme.
Whoever solves that problem once and for all should get three Nobel prices.

9. Large data volumes
Large data volumes allow us to develop new data analysis techniques.
Entropy-variability analysis is a novel technique to look at very large multiple sequence alignments.
Entropy-variability analysis requires ‘better’ alignments than routinely are obtained with ‘standard’ multiple sequence alignment programs.

10. Part of the big alignment

11. Vriend’s first rule of sequence analysis
If it is conserved,
it is important

12. Vriend’s second rule of sequence analysis
If it is very conserved,
it is very important

13. QWERTYASDFGRGH QWERTYASDTHRPM QWERTNMKDFGRKC QWERTNMKDTHRVW
What is CMA?
QWERTYASDFGRGH
QWERTYASDTHRPM
QWERTNMKDFGRKC
QWERTNMKDTHRVW
Red = conserved
Green = variable
Blue = correlated

14. Wilma
Wilma Kuipers Thesis

15. Correlation analysis
Correlate sequences with ligand binding affinities
Alignments showed 100% correlation of affinity for pindolol and the absence/presence of Asn386
Obviously, Asn386 plays an important role in ligand binding
Receptor 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
...
Affinity + -
res. 386 N T A V L Y
1 =
5HT-1a
2 =
5HT-1b
3 =
5HT-1d
....

16. Wilma
Wilma Kuipers Thesis

17. Wilma
Wilma Kuipers Thesis

18. Entropy
Sequence entropy Ei at position i is calculated from the frequency pi of the twenty amino acid types (p) at position i: 20
Ei = S pi ln(pi)
i=1

19. Variability
Sequence variability Vi is the number of amino acid types observed at position i in more than 0.5% of all sequences.

20. Ras Entropy-Variability
11 Red
12 Orange
22 Yellow
23 Green
33 Blue

21. Protease Entropy-Variability
11 Red
12 Orange
22 Yellow
23 Green
33 Blue

22. Globin Entropy-Variability
11 Red
12 Orange
22 Yellow
23 Green
33 Blue

23. GPCR Entropy-Variability; signalling path
11 G protein
12 Support
22 Signaling
23 Ligand in
33 Ligand out

24. NR LBD Entropy-Variability
11 main function
12 first shell around main function
22 core residues (signal transduction)
23 modulator
33 mainly surface 33 23 12 22 11

25. Mutation data 1095 entries 41 receptors 12 species 3D numbers
7 sources

26. Mutation data

27. Mutation data

28. Ligand binding data
Ligand-binding positions extracted from PDB files (nomenclature)
Categorized in ‘very frequent’ to ‘not so frequent’ binder
Type of ligand (agonist/antagonist=inverse agonist…)

29. Ligand-binding residues
LIG 1 more than 50 of 56
LIG of 56
LIG of 56
LIG out of 56
H-bonds (~35,15,15,15)

30. Example: role of Asp 351
agonist
antagonist

31. Ligand, cofactor and dimerization data combined with entropy-variability analysis

32. Conclusions:
Data is difficult, but we need it (sic); life would be so nice if we could do without it. PDB files are the worst.
Nomenclature is not homogeneous. Ontologies….
Much data has been carefully hidden in the literature, where it can only be found back with great difficulty.
Residue numbering is difficult but very necessary.
Variability-entropy analysis is powerful, but requires very 'good' alignments.

33. A short break for a word from our sponsors
Laerte
Oliveira
Adje
Margot F L O R E N C H O R N
Our industrial sponsor:
Wilma Kuipers Weesp
Bob Bywater Copenhagen
Nora vd Wenden The Hague
Mike Singer New Haven
Ad IJzerman Leiden
Margot Beukers Leiden
Fabien Campagne New York
Øyvind Edvardsen TromsØ
Simon Folkertsma Frisia
Henk-Jan Joosten Wageningen
Joost van Durma Brussels
David Lutje Hulsik Utrecht
Tim Hulsen Goffert
Manu Bettler Lyon
David
Tim
Elmar
Krieger
Fabien
Manu
Simon Folkertsma