1. Introduction to Bioinformatics Lecture 13: Predicting Protein Function Centre for Integrative Bioinformatics VU (IBIVU)

2. The deluge of genomic information begs the following question: what do all these genes do? Many genes are not annotated, and many more are partially or erroneously annotated. Given a genome which is partially annotated at best, how do we fill in the blanks? Of each sequenced genome, 20%-50% of the functions of proteins encoded by the genomes remains unknown! Protein Function Prediction

3. We are faced with the problem of predicting protein function from sequence, genomic, expression, interaction and structural data. For all these reasons and many more, automated protein function prediction is rapidly gaining interest among bioinformaticians and computational biologists Protein Function Prediction

4. Outline Sequence-based function prediction Structure-based function prediction –Sequence-structure comparison –Structure-structure comparison Motif-based function prediction Phylogenetic profile analysis Protein interaction prediction and databases Functional inference at systems level

5. Classes of function prediction methods Sequence based approaches –protein A has function X, and protein B is a homolog (ortholog) of protein A; Hence B has function X Structure-based approaches –protein A has structure X, and X has so-so structural features; Hence A’s function sites are …. Motif-based approaches –a group of genes have function X and they all have motif Y; protein A has motif Y; Hence protein A’s function might be related to X Function prediction based on “guilt-by-association” –gene A has function X and gene B is often “associated” with gene A, B might have function related to X

6. Sequence-based function prediction Homology searching Sequence comparison is a powerful tool for detection of homologous genes but limited to genomes that are not too distant away uery: 2 LSDGEWQLVLNVWGKVEADIPGHGQEVLIRLFKGHPETLEKFDKFKHLKSEDEMKASEDL 61 LSD + V +W K+ G + L R+ +P+T F + D S ++ Sbjct: 3 LSDKDKAAVRALWSKIGKSSDAIGNDALSRMIVVYPQTKIYFSHWP-----DVTPGSPNI 57 Query: 62 KKHGATVLTALGGILKKKGHHEAEIKPLAQSHATKHKIPVKYLEFISECIIQVLQSKHPG 121 K HG V+ + + K + + L++ HA K ++ + ++ CI+ V+ + P Sbjct: 58 KAHGKKVMGGIALAVSKIDDLKTGLMELSEQHAYKLRVDPSNFKILNHCILVVISTMFPK 117 Query: 122 DFGADAQGAMNKALELFRKDMASNYK 147 +F +A +++K L +A Y+ Sbjct: 118 EFTPEAHVSLDKFLSGVALALAERYR 143 We have done homology searching (FASTA, BLAST, PSI-BLAST) in earlier lectures

7. Structure-based function prediction Structure-based methods could possibly detect remote homologues that are not detectable by sequence-based method –using structural information in addition to sequence information –protein threading (sequence-structure alignment) is a popular method Structure-based methods could provide more than just “homology” information

8. Threading Query sequence Template sequence + Template structure Compatibility score

9. Threading Query sequence Template sequence + Template structure Compatibility score

10. Structure-based function prediction Threading Scoring function for measuring to what extend query sequence fits into template structure For scoring we have to map an amino acid (query sequence) onto a local environment (template structure) We can use structural features for this: o Secondary structure o Is environment inside or outside? – Residue accessible surface area (ASA) o Polarity of environment The best (highest scoring) “thread” through the structure gives a so-called structural alignment, this looks exactly the same as a sequence alignment but is based on structure.

11. Fold recognition by threading Query sequence Compatibility scores Fold 1 Fold 2 Fold 3 Fold N

12. Structure-based function prediction SCOP (http://scop.berkeley.edu/) is a protein structure classification database where proteins are grouped into a hierarchy of families, superfamilies, folds and classes, based on their structural and functional similaritieshttp://scop.berkeley.edu/

13. Structure-based function prediction SCOP hierarchy – the top level: 11 classes

14. Structure-based function prediction All-alpha protein Coiled-coil protein All-beta protein Alpha-beta proteinmembrane protein

15. Structure-based function prediction SCOP hierarchy – the second level: 800 folds

16. Structure-based function prediction SCOP hierarchy - third level: 1294 superfamilies

17. Structure-based function prediction SCOP hierarchy - third level: 2327 families

18. Structure-based function prediction Using sequence-structure alignment method, one can predict a protein belongs to a –SCOP familiy, superfamily or fold Proteins predicted to be in the same SCOP family are orthologous Proteins predicted to be in the same SCOPE superfamily are homologous Proteins predicted to be in the same SCOP fold are structurally analogous folds superfamilies families

19. Structure-based function prediction Prediction of ligand binding sites –For ~85% of ligand-binding proteins, the largest largest cleft is the ligand-binding site –For additional ~10% of ligand-binding proteins, the second largest cleft is the ligand-binding site

20. Structure-based function prediction Prediction of macromolecular binding site –there is a strong correlation between macromolecular binding site (with protein, DNA and RNA) and disordered protein regions –disordered regions in a protein sequence can be predicted using computational methods http://www.pondr.com/

21. Motif-based function prediction Prediction of protein functions based on identified sequence motifs PROSITE contains patterns specific for more than a thousand protein families. ScanPROSITE -- it allows to scan a protein sequence for occurrence of patterns and profiles stored in PROSITE

22. Motif-based function prediction Search PROSITE using ScanPROSITE The sequence has ASN_GLYCOSYLATION N-glycosylation site: 242 - 245 NETL MSEGSDNNGDPQQQGAEGEAVGENKMKSRLRK GALKKKNVFNVKDHCFIARFFKQPTFCSHCKDFIC GYQSGYAWMGFGKQGFQCQVCSYVVHKRCHEY VTFICPGKDKG IDSDSPKTQH ……..

23. Regular expressions Alignment ADLGAVFALCDRYFQ SDVGPRSCFCERFYQ ADLGRTQNRCDRYYQ ADIGQPHSLCERYFQ Regular expression [AS]-D-[IVL]-G-x4-{PG}-C-[DE]-R-[FY]2-Q {PG} = not (P or G) For short sequence stretches, regular expressions are often more suitable to describe the information than alignments (or profiles)

24. Regular expressions Regular expressionNo. of exact matches in DB D-A-V-I-D71 D-A-V-I-[DENQ]252 [DENQ]-A-V-I-[DENQ]925 [DENQ]-A-[VLI]-I-[DENQ]2739 [DENQ]-[AG]-[VLI]2-[DENQ]51506 D-A-V-E1088

25. Phylogenetic profile analysis Function prediction of genes based on “guilt-by- association” – a non-homologous approach The phylogenetic profile of a protein is a string that encodes the presence or absence of the protein in every sequenced genome Because proteins that participate in a common structural complex or metabolic pathway are likely to co-evolve, the phylogenetic profiles of such proteins are often ``similar''

26. Phylogenetic profile analysis Phylogenetic profile (against N genomes) –For each gene X in a target genome (e.g., E coli), build a phylogenetic profile as follows –If gene X has a homolog in genome #i, the ith bit of X’s phylogenetic profile is “1” otherwise it is “0”

27. Phylogenetic profile analysis Example – phylogenetic profiles based on 60 genomes orf1034:1110110110010111110100010100000000111100011111110110111010101 orf1036:1011110001000001010000010010000000010111101110011011010000101 orf1037:1101100110000001110010000111111001101111101011101111000010100 orf1038:1110100110010010110010011100000101110101101111111111110000101 orf1039:1111111111111111111111111111111111111111101111111111111111101 orf104: 1000101000000000000000101000000000110000000000000100101000100 orf1040:1110111111111101111101111100000111111100111111110110111111101 orf1041:1111111111111111110111111111111101111111101111111111111111101 orf1042:1110100101010010010110000100001001111110111110101101100010101 orf1043:1110100110010000010100111100100001111110101111011101000010101 orf1044:1111100111110010010111010111111001111111111111101101100010101 orf1045:1111110110110011111111111111111101111111101111111111110010101 orf1046:0101100000010001011000000111110000010100000001010010100000000 orf1047:0000000000000001000010000001000100000000000000010000000000000 orf105: 0110110110100010111101101010111001101100101111100010000010001 orf1054:0100100110000001100001000100000000100100100001000100100000000 Genes with similar phylogenetic profiles have related functions or functionally linked – D Eisenberg and colleagues (1999) By correlating the rows (open reading frames (ORF) or genes) you find out about joint presence or absence of genes: this is a signal for a functional connection gene genome

28. Phylogenetic profile analysis Phylogenetic profiles contain great amount of functional information Phlylogenetic profile analysis can be used to distinguish orthologous genes from paralogous genes Subcellular localization: 361 yeast nucleus-encoded mitochondrial proteins are identified at 50% accuracy with 58% coverage through phylogenetic profile analysis Functional complementarity: By examining inverse phylogenetic profiles, one can find functionally complementary genes that have evolved through one of several mechanisms of convergent evolution.

29. Prediction of protein-protein interactions Rosetta stone Gene fusion is the an effective method for prediction of protein-protein interactions –If proteins A and B are homologous to two domains of a protein C, A and B are predicted to have interaction Though gene-fusion has low prediction coverage, it false-positive rate is low A B C

30. Domain fusion example Vertebrates have a multi-enzyme protein (GARs- AIRs-GARt) comprising the enzymes GAR synthetase (GARs), AIR synthetase (AIRs), and GAR transformylase (GARt) 1. In insects, the polypeptide appears as GARs- (AIRs)2-GARt. However, GARs-AIRs is encoded separately from GARt in yeast, and in bacteria each domain is encoded separately (Henikoff et al., 1997). 1GAR: glycinamide ribonucleotide synthetase AIR: aminoimidazole ribonucleotide synthetase

31. Protein interaction database There are numerous databases of protein-protein interactions DIP is a popular protein-protein interaction database The DIP database catalogs experimentally determined interactions between proteins. It combines information from a variety of sources to create a single, consistent set of protein-protein interactions.

32. Protein interaction databases BIND - Biomolecular Interaction Network Database DIP - Database of Interacting Proteins PIM – Hybrigenics PathCalling Yeast Interaction Database MINT - a Molecular Interactions Database GRID - The General Repository for Interaction Datasets InterPreTS - protein interaction prediction through tertiary structure STRING - predicted functional associations among genes/proteins Mammalian protein-protein interaction database (PPI) InterDom - database of putative interacting protein domains FusionDB - database of bacterial and archaeal gene fusion events IntAct Project The Human Protein Interaction Database (HPID) ADVICE - Automated Detection and Validation of Interaction by Co-evolution InterWeaver - protein interaction reports with online evidence PathBLAST - alignment of protein interaction networks ClusPro - a fully automated algorithm for protein-protein docking HPRD - Human Protein Reference Database

33. Protein interaction database

34. Network of protein interactions and predicted functional links involving silencing information regulator (SIR) proteins. Filled circles represent proteins of known function; open circles represent proteins of unknown function, represented only by their Saccharomyces genome sequence numbers ( http://genome- www.stanford.edu/Saccharomyces). Solid lines show experimentally determined interactions, as summarized in the Database of Interacting Proteins 19 (http://dip.doe- mbi.ucla.edu). Dashed lines show functional links predicted by the Rosetta Stone method 12. Dotted lines show functional links predicted by phylogenetic profiles 16. Some predicted links are omitted for clarity.

35. Network of predicted functional linkages involving the yeast prion protein 20 Sup35. The dashed line shows the only experimentally determined interaction. The other functional links were calculated from genome and expression data 11 by a combination of methods, including phylogenetic profiles, Rosetta stone linkages and mRNA expression. Linkages predicted by more than one method, and hence particularly reliable, are shown by heavy lines. Adapted from ref. 11.

36. STRING - predicted functional associations among genes/proteins STRING is a database of predicted functional associations among genes/proteins. Genes of similar function tend to be maintained in close neighborhood, tend to be present or absent together, i.e. to have the same phylogenetic occurrence, and can sometimes be found fused into a single gene encoding a combined polypeptide. STRING integrates this information from as many genomes as possible to predict functional links between proteins. Berend Snel en Martijn Huynen (RUN) and the group of Peer Bork (EMBL, Heidelberg)

37. STRING - predicted functional associations among genes/proteins STRING is a database of known and predicted protein- protein interactions. The interactions include direct (physical) and indirect (functional) associations; they are derived from four sources: 1.Genomic Context (Synteny) 2.High-throughput Experiments 3.(Conserved) Co-expression 4.Previous Knowledge STRING quantitatively integrates interaction data from these sources for a large number of organisms, and transfers information between these organisms where applicable. The database currently contains 736429 proteins in 179 species

38. STRING - predicted functional associations among genes/proteins Conserved Neighborhood This view shows runs of genes that occur repeatedly in close neighborhood in (prokaryotic) genomes. Genes located together in a run are linked with a black line (maximum allowed intergenic distance is 300 bp). Note that if there are multiple runs for a given species, these are separated by white space. If there are other genes in the run that are below the current score threshold, they are drawn as small white triangles. Gene fusion occurences are also drawn, but only if they are present in a run (see also the Fusion section below for more details).

39. Functional inference at systems level Function prediction of individual genes could be made in the context of biological pathways/networks Example – phoB is predicted to be a transcription regulator and it regulates all the genes in the pho-regulon (a group of co- regulated operons); and within this regulon, gene A is interacting with gene B, etc.

40. Functional inference at systems level KEGG is database of biological pathways and networks

41. Functional inference at systems level

43. By doing homologous search, one can map a known biological pathway in one organism to another one; hence predict gene functions in the context of biological pathways/networks

44. Wrapping up We have seen a number of ways to infer a putative function for a protein sequence To gain confidence, it is important to combine as many different prediction protocols as possible (the STRING server is an example of this)

45. Homework Give an example of two proteins having the same structural fold but different biological functions through searching SCOP and Swiss-prot What is the biological function of phoR in the two- component system of prokaryotic organism based on KEGG database search