1. Introduction to BioinformaticsE N T R F O I G A V B M S U
Introduction to Bioinformatics
Lecture 15: Predicting Protein Function
Centre for Integrative Bioinformatics VU (IBIVU)

2. Protein Function Prediction
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!

3. Protein Function Prediction
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

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 Template sequence + Query sequence Template structure
Compatibility score
Query sequence
Template structure

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

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 the following structural features for scoring:
Secondary structure
Is environment inside or outside? – Residue accessible surface area (ASA)
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. Threading – inverse folding Map sequence to structural environments
Query
Template ?
What is the optimal thread for each local environment?
Find the best compromise over all environments
environment
Secondary structure
ASA
Polarity of environment C N
hydrophobic
hydrophilic

12. Fold recognition by threading
Fold N
Query sequence
What is the most compatible structure?
The one with the highest threading score
Compatibility scores

13. Structure-based function prediction
SCOP ( 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 similarities

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

15. Structure-based function prediction
All-alpha protein
membrane protein
Alpha-beta protein
Coiled-coil protein
All-beta protein

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

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

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

19. Structure-based function prediction
Using sequence-structure alignment method, one can predict a protein belongs to a
SCOP family, 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

20. 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

21. 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

22. 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

23. Motif-based function prediction
Search PROSITE using ScanPROSITE
The sequence has ASN_GLYCOSYLATION N-glycosylation site: NETL
MSEGSDNNGDPQQQGAEGEAVGENKMKSRLRKGALKKKNVFNVKDHCFIARFFKQPTFCSHCKDFICGYQSGYAWMGFGKQGFQCQVCSYVVHKRCHEYVTFICPGKDKGNETLIDSDSPKTQH ……..

24. 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)

25. Regular expressions Regular expression No. of exact matches in DB
D-A-V-I-D 71
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-E

26. Prosite
In addition to regular expressions, the Prosite database also contains so-called extended profiles
Extended profiles contain more explicit information than classical profiles, for example to describe expected gap lengths, etc.
This is because some patterns are better described using regular expressions (e.g. short motifs), while others are better formalised using (extended) profiles

27. 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''

28. 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”

29. Phylogenetic profile analysis
Example – phylogenetic profiles based on 60 genomes
genome
gene
orf1034:
orf1036:
orf1037:
orf1038:
orf1039:
orf104:
orf1040:
orf1041:
orf1042:
orf1043:
orf1044:
orf1045:
orf1046:
orf1047:
orf105:
orf1054:
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
Genes with similar phylogenetic profiles have related functions or functionally linked – D Eisenberg and colleagues (1999)

30. 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.

31. 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 A B C
Though gene-fusion has low prediction coverage, it false-positive rate is low

32. 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).
In insects, the polypeptide appears as GARs-(AIRs)2-GARt.
In yeast, GARs-AIRs is encoded separately from GARt
In bacteria each domain is encoded separately (Henikoff et al., 1997).
GAR: glycinamide ribonucleotide
AIR: aminoimidazole ribonucleotide

33. 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.

34. 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

35. Protein interaction database

36. 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 ( Solid lines show experimentally determined interactions, as summarized in the Database of Interacting Proteins19 ( Dashed lines show functional links predicted by the Rosetta Stone method12. Dotted lines show functional links predicted by phylogenetic profiles16. Some predicted links are omitted for clarity.

37. Network of predicted functional linkages involving the yeast prion protein20 Sup35. The dashed line shows the only experimentally determined interaction. The other functional links were calculated from genome and expression data11 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.

38. 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)

39. 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:
Genomic Context (Synteny)
High-throughput Experiments 
(Conserved) Co-expression 
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 proteins in 179 species

40. 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).

41. 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.

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

43. Functional inference at systems level

44. Functional inference at systems level

45. Functional inference at systems level
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

46. 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)

47. 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