1. Bioinformatics

2. Not only small molecules and QM, MM techniques rule the world.

3. Central dogma of molecular biology Term is due to Francis Crick The conversion DNA → protein is not direct, RNA is involved DNA is the information store, RNA is messenger (mRNA), transporter (tRNA), biomolecular nanomachine (rRNA) source: wikipedia.org

4. Nucleic acids four letters (DNA, RNA) sequence - AACTAACG (5’ → 3’) DNA – double helix RNA – “single stranded” helix, folding (double helical regions, C2’ -OH → secondary and tertiary motifs)

5. nucleoside nucleotide

6. B-DNAA-DNAZ-DNA B A Z

7. RNA secondary motifs Nowakowski and Tinoco, Seminars in Virology 8, 153, 1997.

8. RNA source: http://complex.upf.es/~josep/RNA.jpg, http://www.biosci.ki.se/groups/ljo/images/phe_trna_large.jpg, http://rna.ucsc.edu/rnacenter/images/70s_atrna.jpg

9. Proteins 20 letters primary structure - sequence AMNTSSTVG (N-end → C- end) Alberts, Molecular Biology of the Cell, 5th Ed.

10. secondary structure (random coil,  -helix, β-sheet, loops) several secondary structure elements form motifs e.g. greek key, β-α-β, HTH

11. tertiary structure (the arrangements of motifs into domain/s) quartenary structure (multimeric complexes)

12. Proteins source:http://calstate.fullerton.edu/news/arts/2003/photos/protein-art.jpg

13. Proteins source: Petsko, Ringe – Protein structure and function

14. http://www.cellsignal.com/reference/pathway/NF_kappaB.html

15. Systems biology focuses on the systematic study of complex interactions in biological systems using a new perspective - holism instead of reductionism holism – the properties of a system cannot be determined or explained by its component parts alone one of the goals of systems biology is to discover new emergent properties new field, boom since 2000, very little covered in CZ

16. Systems biology source: wikipedia.org

17. Systems biology based on mathematical modelling of systems, control theory, cybernetics engineering view on complex biological systems e.g. answers questions about robustness of the given system when one of its part fails or about response of a systems upon the change of the environmental conditions

18. quantum chemistry molecular dynamics bioinformatics systems biology

19. Bioinformatics application of information technology to the field of molecular biology, genomics and related biological disciplines tremendous amount of data the creation and advancement of databases, algorithms, computational and statistical techniques, and theory to solve problems arising from the management and analysis of biological data

20. Podle definičního třídění ruských vědců rozlišujeme dva obory paranormálních jevů: bioinformatika a bioenergetika. Bioinformatika (tzn. mimosmyslové vnímání, ESP) zahrnuje získávání a výměnu informací mimosmyslovou cestou (nikoli normálními smyslovými orgány). V podstatě rozlišujeme následující formy bioinformace: hypnózu (kontrolu vědomí), telepatii, dálkové vnímání, prekognici, retrokognici, mimotělní zkušenost, "vidění" rukama nebo jinými částmi těla, inspiraci a zjevení. zdroj: http://www.esoterika.cz/clanek/2992-mimosmyslova_spionaz_dalkove_pozorovani_i_.htm

21. Bioinformatics sequence analysis (sequence bioinformatics) structural analysis (structural bioinformatics) functional analysis (systems biology)

22. genetic code gene genome, genomics large data sets high throughput human genome DNA localized mainly in nucleus, each nucleus carries the whole genetic information 3.2 billions bp 25 000 – 30 000 genes ca 1,5 % codes for proteins, the rest - junk DNA what is proteome? proteomics Is it more difficult to study genome or proteome?

23. Sequential bioinformatics reconstruction of sequence fragments searching of genes and other interesting regions in the genome junk DNA – 95% of human genome is made by non-coding sequences, either no function, or not yet understood querying huge genomes for a given sequence comparison of genes within a specie – similarities between protein functions comparison of genes between species – organism's evolutionary relationships (phylogenetic analysis)

24. Sequence alignment Procedure of comparing sequences Point mutations – easy More difficult example However, gaps can be inserted to get something like this ACGTCTGATACGCCGTATAGTCTATCT ACGTCTGATTCGCCCTATCGTCTATCT ACGTCTGATACGCCGTATAGTCTATCT CTGATTCGCATCGTCTATCT ACGTCTGATACGCCGTATAGTCTATCT ----CTGATTCGC---ATCGTCTATCT gapless alignment gapped alignment insertion × deletion indel

25. Flavors of sequence alignment pair-wise alignment × multiple sequence alignment

26. Flavors of sequence alignment global alignment × local alignment global local align entire sequence stretches of sequence with the highest density of matches are aligned, generating islands of matches or subalignments in the aligned sequences

27. Identity matrix Scoring systems I DNA and protein sequences can be aligned so that the number of identically matching pairs is maximized. Counting the number of matches gives us a score (3 in this case). Higher score means better alignment. This procedure can be formalized using substitution matrix. A T T G - - - T A – - G A C A T ATCG A1 T01 C001 G0001

28. Scoring systems II For nucleotide sequences identity matrix is usually good enough. For protein sequences, identity matrix is not sufficient to describe biological and evolutionary proceses. It’s because amino acids are not exchanged with the same probability as can be conceived theoretically. For example substitution of aspartic acids D by glutamic acid E is frequently observed. And change from aspartic acid to tryptophan W is very rare. Why is that? 1. Triplet-based genetic code GAT (D) → GAA (E), GAT (D) → TGG (W) 2. Both D and E have similar properties, but D and W differ considerably. D is hydrophylic, W is hydrophobic, D → W mutation can greatly alter 3D structure and consequently function.

29. Substitution matrices small, polar small, nonpolar polar or acidic basic large, hydrophobic aromatic Zvelebil, Baum, Understanding bioinformatics. Positive score – frequency of substitutions is greater than would have occurred by random chance. Zero score – frequency is equal to that expected by chance. Negative score – frequency is less than would have occurred by random chance.

30. Sequence database search BLAST Google of sequence world

31. Phylogenetic analysis

32. Structural bioinformatics the function of chemical moiety is given by its structure while DNA structure is “given” (double-helix), RNA and proteins can accommodate very different conformations (i.e. specific arrangements of atoms in 3D space) structural bioinformatics covers analysis of the NA and proteins structure prediction of structure from the sequence

33. Protein structure prediction secondary structure prediction the conformational state of each residue is predicted as H (helix), E (extended, β-sheet), C (coil) accuracy: 80% tertiary structure prediction why? many sequences are known, not that many 3D structures has been solved some proteins (e.g. transmembrane) are difficult to characterize experimentally many proteins have known function, but unknown structure (which is however needed to understand their behavior in detail) ab initio, threading, homology modelling

34. CASP Critical Assessment of Structure Prediction http://predictioncenter.org/ since 1994, every 2 years, CASP10 in preparation predict solved, but not publicly released structures competition of individual groups in 3D prediction: human groups – answer in 14 days software (automated prediction) – answer in 48 hours