Centre for Integrative Bioinformatics VU (IBIVU)

1. Bioinformatics master courseDNA/Protein structure-function analysis and predictionLecture 13: Protein Function • Centre for Integrative Bioinformatics VU (IBIVU) • Faculty of Sciences / Faculty of Earth & Life Sciences

2. Sequence-Structure-Function Sequence Structure Function Folding: impossible but for the smallest structures Ab initio Threading Function prediction from structure – very difficult BLAST

3. Experimental • Structural genomics • Functional genomics • Protein-protein interaction • Metabolic pathways • Expression data

6. Protein function categories • Catalysis (enzymes) • Binding – transport (active/passive) • Protein-DNA/RNA binding (e.g. histones, transcription factors) • Protein-protein interactions (e.g. antibody-lysozyme) (experimentally determined by yeast two-hybrid (Y2H) or bacterial two-hybrid (B2H) screening ) • Protein-fatty acid binding (e.g. apolipoproteins) • Protein – small molecules (drug interaction, structure decoding) • Structural component (e.g. -crystallin) • Regulation • Signalling • Transcription regulation • Immune system • Motor proteins (actin/myosin)

7. Catalytic properties of enzymes Vmax Km kcat • E + S ES E + P E = enzyme S = substrate ES = enzyme-substrate complex (transition state) P = product Km = Michaelis constant kcat = catalytic rate constant (turnover number) Kcat/Km = specificity constant (useful for comparison) Moles/s Vmax/2 Km [S] Vmax × [S] V = ------------------- Michaelis-Menten equation Km + [S]

8. Protein interaction domains http://pawsonlab.mshri.on.ca/html/domains.html

9. Energy difference upon binding Examples of protein interactions (and functional importance) include: • Protein – protein (pathway analysis); • Protein – small molecules (drug interaction, structure decoding); • Protein – peptides, DNA/RNA  (function analysis) The change in Gibb’s Free Energy of the protein-ligand binding interaction can be monitored and expressed by the following;  G =  H – T S      (H=Enthalpy, S=Entropy and T=Temperature)

10. Protein function • Many proteins combine functions • Some immunoglobulin structures are thought to have more than 100 different functions (and active/binding sites) • Alternative splicing can generate (partially) alternative structures

11. Protein function Protein-protein interaction Active site / binding cleft Shape complementarity

12. Protein function evolution Chymotrypsin

13. How to infer function • Experiment • Deduction from sequence • Multiple sequence alignment – conservation patterns • Homology searching • Deduction from structure • Threading • Structure-structure comparison • Homology modelling

14. Cholesterol biosynthesis primarily occurs in eukaryotic cells. It is necessary for membrane synthesis, and is a precursor for steroid hormone production as well as for vitamin D. While the pathway had previously been assumed to be localized in the cytosol and ER, more recent evidence suggests that a good deal of the enzymes in the pathway exist largely, if not exclusively, in the peroxisome (the enzymes listed in blue in the pathway to the left are thought to be at least partly peroxisomal). Patients with peroxisome biogenesis disorders (PBDs) have a variable deficiency in cholesterol biosynthesis Mevalonate plays a role in epithelial cancers: it can inhibit EGFR

15. Epidermal Growth Factor as a Clinical Target in CancerIntroduction:A malignant tumour is the product of uncontrolled cell proliferation. Cell growth is controlled by a delicate balance between growth-promoting and growth-inhibiting factors. In normal tissue the production and activity of these factors results in differentiated cells growing in a controlled and regulated manner that maintains the normal integrity and functioning of the organ. The malignant cell has evaded this control; the natural balance is disturbed (via a variety of mechanisms) and unregulated, aberrant cell growth occurs. A key driver for growth is the epidermal growth factor (EGF) and the receptor for EGF (the EGFR) has been implicated in the development and progression of a number of human solid tumours including those of the lung, breast, prostate, colon, ovary, head and neck.

16. Energy housekeeping: Adenosine diphosphate (ADP) – Adenosine triphosphate (ATP)

29. Metabolic networksGlycolysis and Gluconeogenesis Kegg database (Japan)

30. Gene Ontology (GO) • Not a genome sequence database • Developing three structured, controlled vocabularies (ontologies) to describe gene products in terms of: • biological process • cellular component • molecular function in a species-independent manner

31. The GO ontology

32. Gene Ontology Members