1. Bioinformatics and Computational Molecular Biology Geoff Barton http://www.compbio.dundee.ac.uk

2. Practical Tutorial Dr David Martin practical tutorial on the use of pymol molecular graphics software. In this lecture I will show lots of protein structures – use www.ebi.ac.uk/msd to find them, and/or scop domains database (find with google).www.ebi.ac.uk/msd

3. Similarities in Proteins Lecture 1 –Overview of data in molecular biology –Protein modelling –Similarities of Protein Sequence, Structure, Function

4. Introduction to Sequence Comparison Lecture 2: –Why compare sequences? –Methods for sequence comparison/alignment. –Multiple alignment –Database searching - FASTA/BLAST –Iterative searching - PSI-BLAST

5. Practical/WWW references Organised by Drs Martin –Good preparation would be to look at: http://www.ebi.ac.uk/Tools and http://www.ncbi.nlm.nih.gov –Look at BLAST and FASTA on these sites as well as database access facilities.

6. Private Data Past Experiments. Lab note books. Group discussions. Traditional biological research Analysis Reading. Talking. Thinking. Hypothesis! Experiment Design. Execution. Publish! Public Data Journals Conferences

7. Private Data Past Experiments. Lab note books. Group discussions. DNA sequences Protein Sequences Genetic maps Transcripts 3D structures proteomics results SNP data etc Bioinformatics/Computational Biology and biological research Analysis Reading. Talking. Thinking. Computational Analysis Software Development Hypothesis! Computer aided. Experiment Design. Execution. Computational experiments Simulation Publish! Database submission Database management Public Data Journals Conferences DNA sequences Protein Sequences Genetic maps Transcripts 3D structures proteomics results SNP data etc

8. EMBL Nucleotide Sequence Database Growth (to 2nd Oct 2006) Taken from: www.ebi.ac.uk

9. Protein Sequences Approx 3,500,000 known for all species (Oct. 2006.) 25,000 for Human (not counting splice variants and post-translational modifications)

10. Protein 3D Structures Approx 39,000 known (much duplication)

11. Biological data in context

12. DNA RNA Protein Sequence Protein 3D structure Molecular function Overview of Biological Hierarchy... Whole organism animal, plant, etc. Tissue/organ brain, heart, lungs blood,... Ecosystem many different organisms Population group of the same type of organism Family group with known common lineage Cell nerve,muscle,etc.. Organelle nucleus, mitochondria, etc... Nucleus Chromosome Gene Molecular Levels

13. DNA RNA Protein Sequence Protein 3D structure Molecular function Whole organism animal, plant, etc. Tissue/organ brain, heart, lungs blood,... Ecosystem many different organisms Population group of the same type of organism Family group with known common lineage Cell nerve,muscle,etc.. Organelle nucleus, mitochondria, etc... Nucleus Chromosome Gene Expression Data (Transcriptomics) Which of the genes are switched on in which cells/tissues and when? What are the effects of drugs and disease on expression patterns DNA ‘CHIP’ TECHNOLOGY Technology and data in biology

14. DNA RNA Protein Sequence Protein 3D structure Molecular function Whole organism animal, plant, etc. Tissue/organ brain, heart, lungs blood,... Ecosystem many different organisms Population group of the same type of organism Family group with known common lineage Cell nerve,muscle,etc.. Organelle nucleus, mitochondria, etc... Nucleus Chromosome Gene Protein Expression Data (Proteomics) Which proteins are being produced in which cells/tissues when? Which modified forms are present? What are the effects of drugs and disease on these patterns 2D Gels + Mass Spectrometry. Technology and data in biology

15. DNA RNA Protein Sequence Protein 3D structure Molecular function Whole organism animal, plant, etc. Tissue/organ brain, heart, lungs blood,... Ecosystem many different organisms Population group of the same type of organism Family group with known common lineage Cell nerve,muscle,etc.. Organelle nucleus, mitochondria, etc... Nucleus Chromosome Gene Protein 3D Structure - the bridge to chemistry (Structural Genomics) What is the atomic level structure of the protein? What other molecules does it interact with? What small molecules - potential drugs - does it interact with? What are the effects of point mutations on the structure? X-ray crystallography, NMR spectroscopy, single particle, cryo-electron microscopy. Technology and data in biology

16. Whole organism animal, plant, etc. Tissue/organ brain, heart, lungs blood,... Ecosystem many different organisms Population group of the same type of organism Family group with known common lineage Cell nerve,muscle,etc.. Organelle nucleus, mitochondria, etc... Nucleus Chromosome Gene DNA RNA Protein Sequence Protein 3D structure Molecular function Overview of Biological Hierarchy... Macroscopic Levels

17. Biology is now a data intensive science To do good science, you need to know how to use (and not abuse) computational tools.

18. Protein Structure Prediction ‘Homology’ modelling –Relies on the fact that similarity of sequence implies similarity of 3D structure.

19. Lysozyme (1lz1)  -lactalbumin (1alc) ? Imagine we don’t know the 3D structure of  -lactalbumin, but we do know its amino acid sequence and that of lysozyme

20. Lysozyme (1lz1)  -lactalbumin (1alc) 37.7% Identity, Z=17.6 ?

21. Protein structure prediction (Homology Modelling) Align sequence of protein of unknown structure to sequence of protein of known structure. In ‘conserved core’ of protein, substitute the amino acid types into the known structure. Deal with ‘loops’ between the core elements of structure.

22. Lysozyme (1lz1)  -lactalbumin (1alc) 37.7% Identity, Z=17.6

23. Protein structure prediction (Homology modelling) Problems: –Need protein of known structure that is similar in sequence. –Building loops where there are deletions. –Verifying model. Key is getting a good alignment in the first place –Bad alignment => bad model.

24. Good alignment on its own can: Identify key residues (absolutely conserved) Identify likely protein core (conserved hydrophobic residues) Help predict protein secondary structure (not this lecture).

25. Sequence alignment is a fundamental technique in molecular biology. May predict proteins of common function even when no 3D structure is known. May be used to predict 3D structure and so help understanding of mutants. Some examples of where this is right and wrong...

26. Prediction of structure and function by similarity to known sequences and structures Assumption is that similar sequence implies similar structure and function. But what do we mean by “similar”? Does similarity of sequence really imply similarity of function?

27. Protein Sequence/Structure/Function Network Sequence3D StructureFunction Similar Different

28. Protein Sequence/Structure/Function Network Sequence3D StructureFunction Similar Different

29. Similar Sequence, Similar Structure, Similar Function. e.g. Trypsin-like Serine Proteinases Same fold, same catalytic mechanism. But DIFFERENT specificity. e.g. Immunoglobulin variable domains. Same fold, similar binding function. But DIFFERENT specificity. True of all examples. Similarities only give clues to function, differences in specificity can be regarded as differences of function.

30. Immunoglobulin Variable Domains e.g. see: 1a2y

31. Tryptophan at core of Ig variable domain

32. Protein Sequence/Structure/Function Network Sequence3D StructureFunction Similar Different

33. Lysozyme (1lz1)  -lactalbumin (1alc) 37.7% Identity, Z=17.6

34.  -crystallin/ L-Lactate Dehydrogenase

35. Protein Sequence/Structure/Function Network Sequence3D StructureFunction Similar Different

36. Trypsin (3ptn)Subtilisin (2sec)

37. Trypsin (3ptn) Subtilisin (2sec)

38. Trypsin (3ptn) Subtilisin (2sec) His- 57, Asp-102, Ser-195 Asp- 32, His- 64, Ser-221

39. Protein Sequence/Structure/Function Network Sequence3D StructureFunction Similar Different

40. Nature 398,84-90, 1999 PDB: 1b47

41. 11% sequence ID rmsd 1.47 Å over 70 residues PDB: 1b47

42. Protein Sequence/Structure/Function Network Sequence3D StructureFunction Similar Different

43. Russell, R. B. and Barton, G. J. (1993), "An SH2-SH3 Domain hybrid", Nature, 364, 765. PDB: 1bia PDB: 2ptk

44. PDB:2aai PDB:1bas

45. Matthews, S., et al. (1994), "The p17 Matrix Protein from HIV-1 is Structurally Similar to Interferon-gamma", Nature, 370, 666-668.

46. Protein Sequence/Structure/Function Network Sequence3D StructureFunction Similar Different Does this ever happen?

47. HIV Reverse Transcriptase (RT)

49. HIV Reverse Transcriptase (RT) - domain linkers

50. Protein Sequence and Structural Similarity

52. Barton, G. J. et al, (1992), "Human Platelet Derived Endothelial Cell Growth Factor is Homologous to E.coli Thymidine Phosphorylase", Prot. Sci., 1, 688-690.

53. Protein Sequence and Structural Similarity

54. Barton, G. J., Cohen, P. T. C. and Barford, D. (1994), "Conservation Analysis and Structure Prediction of the Protein Serine/Threonine Phosphatases: Sequence Similarity with Diadenosine Tetra-phosphatase fromE. coli Suggests Homology to the Protein Phosphatases", Eur. J. Biochem.,220, 225-237.

55. Protein Sequence and Structural Similarity

56. Russell, R. B. and Barton, G. J. (1993), "An SH2-SH3 Domain hybrid", Nature, 364, 765.

57. Reading material for this lecture: This lecture itself. pdf’s for “Barton” papers: www.compbio.dundee.ac.uk/ftp/pdf/ Database statistics: http://www.ebi.ac.uk/embl/ Structure of the amino-terminal domain of Cbl complexed to its binding site on ZAP-70 kinase Wuyi Meng, Sansana Sawasdikosol, Steven J. Burakoff, Michael J. Eck Nature 398, 84 - 90 (04 March 1999) (available on-line at www.nature.com - search for ZAP-70 kinase - republished in December on-line) Protein recognition: An SH2 domain in disguise John Kuriyan, James E. Darnell Nature 398, 22 - 25 (04 March 1999) (news and views article for above paper) Russell, R. B. and Barton, G. J. (1993), "An SH2-SH3 Domain hybrid", Nature, 364, 765. Matthews, S., et al. (1994), "The p17 Matrix Protein from HIV-1 is Structurally Similar to Interferon-gamma", Nature, 370, 666-668. Barton, G. J., Cohen, P. T. C. and Barford, D. (1994), "Conservation Analysis and Structure Prediction of the Protein Serine/Threonine Phosphatases: Sequence Similarity with Diadenosine Tetra-phosphatase fromE. coli Suggests Homology to the Protein Phosphatases", Eur. J. Biochem.,220, 225-237.

58. The end of Lecture 1 Lecture 2 will be on sequence comparison methods.