1. Picormatics Today’s goal: Give you an overview of some recent technological bioinformatics developments that can be applied to picornaviruses. Where possible in less than a day's work, I have applied those techniques, as an example, to 'my' virus: R14. This seminar is available (without © ) from: http://swift.cmbi.ru.nl/gv/seminars/

2. Some notes up front Your community is not very WWW oriented. This is concluded from a low number of cross pointers, high numbers of dead links and incomplete sites, and from a lack of update dates,contact addresses, references, etc. Your community is not very bioinformatics oriented either. Example, www.iah.bbsrc.ac.uk holds a beautifully complete list of VP1 sequences, one-by-one....

3. Your 'simple' bioinformatics options Many protein structures Many protein sequences This allows for structure based sequence alignments that are very precise, and therefore allow for novel sequence analysis techniques 1) Correlated mutation analysis 2) Sequence variability analysis

4. Structure-based alignment This is top left corner of alignment of ~1000 sequences of ~300 residues

5. Correlated mutations APGADSFGDFHKM Gray is conserved ALGADSFRDFRRL Black is variable ARGLDPFGMNHSI Red/green are AGGLDPFRMNRRV correlated mutations Correlated mutations guarantee a function. Function is determined by the position in the structure; not by the residue type.

6. Correlated mutations Pilot indicates this works for VP1,2,3 too.

7. Correlated mutations and drug design

9. Correlations between residues and ligands. Correlated mutations and drug design

10. Automatic structure comparison Rhino 9 Polio 2 FMDV 1 Mengo 1

11. Automatic structure comparison

13. R14 drug placed in R16

14. antagonist agonist Automatic structure comparison Example from nuclear hormone receptor drug design study

15. Back to sequences First rule of sequence analysis: If a residue is conserved, it is important.

16. Sequence analysis (continued) Second rule of sequence analysis: If a residue is very conserved, it is very important.

17. But what about the variable residues 20 E i =  p i ln(p i ) i=1

18. Sequence variability is the number of residues that is present in more than 0.5% of all sequences. But what about the variable residues

19. Entropy - Variability Entropy = Information Variability = Chaos

20. 11 main function 12 first shell around main function 22 core residues (signal transduction) 23 modulator 33 mainly surface Entropy - Variability

21. Most information about mutations is carefully hidden in the literature. Automatic extraction of this information is no longer science-fiction. More than 90% of the 2226 mutations used for the previous few slides were extracted automatically from the literature. We extracted160 more mutations 'by hand'. Problems are mainly related to protein/gene nomenclature, residue numbering, and unclear description of the effects. Mutation information

24. Mutation data

26. A PubMed search gives: picornavirus mutation1176(2) rhinovirus mutation101(62) poliovirus mutation600(144) mengovirus mutation30(29) About 1 in 5 (in a small manually checked subset) contained identifiable mutation information in the abstract. But unfortunately often with nomenclature that 'our' software doesn't understand yet. Picorna mutation information

27. Now something totally different Motion is the main ingredient for protein function. Even if that function is as 'dumb' as being a container for the RNA. For example, all early Rhino directed drugs were aimed at reducing the mobility of its VP1…

28. The simulation of protein motion is normally called molecular dynamics, or MD. MD is commonly known as a very difficult technique for which you need the help of an army of mathematicians. That is no longer true. Dynamite (based on Bert de Groot's CONCOORD software) predicts protein motions via the WWW. Protein dynamics calculation

30. A short break for a word from our sponsors Laerte Oliveira Our industrial sponsor: FLORENCEFLORENCE HORNHORN Wilma KuipersWeesp Bob Bywater Copenhagen Nora vd WendenThe Hague Mike SingerNew Haven Ad IJzermanLeiden Margot BeukersLeiden Fabien CampagneNew York Øyvind EdvardsenTroms Ø Simon FolkertsmaFrisia Henk-Jan JoostenWageningen Joost van DurmaBrussels David Lutje HulsikUtrecht Tim HulsenGoffert Manu BettlerLyon Elmar Krieger Simon Folkertsma David Tim AdjeMargot Fabien Manu