What’s next ??

1. What’s next ?? Today 3.3 Protein function 10.3 Protein secondary structure prediction 17.3 Protein tertiary structure prediction 24.3 Gene expression & Gene networks 31.3 RNA structure and function 7.4 Advances in Bioinformatics

2. Predicting Protein Function

3. protein RNA DNA

4. Biochemical function (molecular function) What does it do? Kinase??? Ligase??? Page 245

5. Function based on ligand binding specificity What (who) does it bind ?? Page 245

6. Function based on biological process What is it good for ?? Amino acid metabolism? Page 245

7. Function based on cellular location DNA RNA Where is it active?? Nucleolus ?? Cytoplasm?? Page 245

8. Function based on cellular location DNA RNA Where is the RNA/Protein Expressed ?? Brain? Testis? Where it is under expressed?? Page 245

9. GO (gene ontology)http://www.geneontology.org/ • The GO project is aimed to develop three structured, controlled vocabularies (ontologies) that describe gene products in terms of their associated • molecular functions(F) • biological processes (P) • cellular components (C) Ontology is a description of the concepts and relationships that can exist for an agent or a community of agents

10. Extracted from SGD Saccharomyces Genome Database

11. Inferring protein function Bioinformatics approach • Based on homology • Based on the existence of • known protein domains (the protein signature)

12. Inferring protein function based on sequence homology

13. Homologous proteins • Rule of thumb:Proteins are homologous if 25% identical (length >100)DNA sequences are homologous if 70% identical

14. Homologs Proteins with a common evolutionary origin Orthologs - Proteins from different species that evolved by speciation. Hemoglobin human vsHemoglobin mouse Paralogs - Proteins encoded within a given species that arose from one or more gene duplication events. Hemoglobin human vsMyoglobin human

15. COGsClustersof Orthologous Groupsof proteins > Each COG consists of individual orthologous proteins or orthologous sets of paralogs. > Orthologs typically have the same function, allowing transfer of functional information from one member to an entire COG. Refence: Classification of conserved genes according to their homologous relationships. (Koonin et al., NAR) DATABASE

16. Inferring protein function based on the protein signature

17. The Protein Signature • Signature: • Existence of a known protein domain or motif • Domain: • A region of a protein that can adopt a 3D structure • Motif (or fingerprint): • a short, conserved region of a protein • typically 10 to 20 contiguous amino acid residues examples: zinc finger domain immunoglobulin domain

18. DNA Binding domainZinc-Finger

19. Protein Domains • Domains can be considered as building blocks of proteins. • Some domains can be found in many proteins with different functions, while others are only found in proteins with a certain function.

20. Varieties of protein domains Extending along the length of a protein Occupying a subset of a protein sequence Occurring one or more times Page 228

21. Example of a protein with 2 domains: Methyl CpG binding protein 2 (MeCP2) MBD TRD The protein includes a Methylated DNA Binding Domain (MBD) and a Transcriptional Repression Domain (TRD). MeCP2 is a transcriptional repressor.

22. Result of an MeCP2 blastp search: A methyl-binding domain shared by several proteins

23. Are proteins that share only a domain homologous?

24. PROSITE • ProSite is a database of protein domains that can be searched by either regular expression patterns or sequence profiles. • Zinc_Finger_C2H2 • Cx{2,4}Cx3(L,I,V,M,F,Y,W,C)x8Hx{3,5}H

25. Pfam • > Database that contains a large collection of multiple sequence alignments of protein domains • Based on • Profile hidden Markov Models (HMMs).

26. Profile HMM (Hidden Markov Model) HMM is a probabilistic model of the MSA consisting of a number of interconnected states D19 D16 D17 D18 100% delete 100% 16 17 18 19 50% M16 M17 M18 M19 D R T R D R T S S - - S S P T R D R T R D P T S D - - S D - - S D - - S D - - R 100% 100% 50% Match D 0.8 S 0.2 P 0.4 R 0.6 R 0.4 S 0.6 T 1.0 I16 I17 I18 I19 insert X X X X

27. Pfam > Database that contains a large collection of multiple sequence alignments of protein domains Based on Profile hidden Markov Models (HMMs). • > The Pfam database is based on two distinct classes of alignments • Seed alignments which are deemed to be accurate and used to produce Pfam A • -Alignments derived by automatic clustering of SwissProt, which are less reliable and give rise to Pfam B

28. Physical properties of proteins

29. DNA binding domains have relatively high frequency of basic (positive) amino acids MKD P A A LKRARN T E A A RRS SRARKL QRM GCN4 zif268 M E R P Y A C P V E S C D RR F S R S D E L T RH I R I H T S K V N E A F E T L KR C T S S N P N Q R L P K V E I L R N A I R myoD

30. Transmembrane proteins have a unique hydrophobicity pattern

31. Physical properties of proteins Many websites are available for the analysis of individual proteins for example: EXPASY (ExPASy) UCSC Proteome Browser ProtoNet HUJI The accuracy of the analysis programs are variable. Predictions based on primary amino acid sequence (such as molecular weight prediction) are likely to be more trustworthy. For many other properties (such as posttranslational modification of proteins by specific sugars), experimental evidence may be required rather than prediction algorithms. Page 236

32. Knowledge Based Approach • IDEA Find the common properties of a protein family (or any group of proteins of interest) which are unique to the group and different from all the other proteins. Generate a model for the group and predict new members of the family which have similar properties.

33. Knowledge Based Approach Basic Steps 1. Building a Model • Generate a dataset of proteins with a common function (DNA binding protein) • Generate a control dataset • Calculate the different properties which are characteristic of the protein family you are interested for all the proteins in the data (DNA binding proteins and the non-DNA binding proteins • Represent each protein in a set by a vector of calculated features and build a statistical model to split the groups

34. ? SupportVector Machine (SVM) To find a hyperplane that maximally separates the DNA-binding from non-DNA binding into two classes DNA binding =[x1, x2, x3…] Kernel function new protein structure Non-DNA binding =[y1, y2,y3…] Input space Feature space

35. Basic Steps 2. Predicing the function of a new protein • Calculate the properties for a new protein And represent them in a vector • Predict whether the tested protein belongs to the family

36. Database and Tools for protein families and domains • InterPro - Integrated Resources of Proteins Domains and Functional Sites • Prosite – A dadabase of protein families and domain • BLOCKS - BLOCKS db • Pfam - Protein families db (HMM derived) • PRINTS - Protein Motif fingerprint db • ProDom - Protein domain db (Automatically generated) • PROTOMAP - An automatic hierarchical classification of Swiss-Prot proteins • SBASE - SBASE domain db • SMART - Simple Modular Architecture Research Tool • TIGRFAMs - TIGR protein families db