1. Protein families, domains and motifs in functional prediction
June 6, 2017

2. Outline Usefulness of protein domain analysis
Types of protein domain databases
SMART, HMMER and Interpro protein domain database
Uniprot protein annotation
Predicting post-translational modifications

3. Protein families
Groups of homologous sequences (within and across species) that share similar functions and domains
Examples:
Carbonic anhydrases (14 in humans)
Chitin synthases (8 in C. neoformans)
Ser/Thr kinases

4. Protein domains
Conserved part of protein sequence that can evolve, function and exist independent of the rest of the protein chain
Often independently stable and folded
Can recombine or evolve from gene duplications into proteins with different combinations of domains

5. Protein motifs
Short linear peptide sequences that serve a specific function for the protein, but will not be stable or fold independent of the rest of chain
Protein-protein interaction, ligand interactions, cleavage sites, targeting
Examples:
14-3-3: Interaction with kinases
KELCH: ubiquitin targeting
SUMO: site recognized for modification by SUMO
Often found within intrinsically disordered regions

6. Predicting function for unknown proteins
Do they belong (by sequence homology) to a protein family?
Do they contain known protein domains?
Do they have motifs that suggest a specific function?

7. When annotation is NOT enough
You’ve got a list of genes, most of which have been annotated with gene ontology and a potential protein function
Why would you want to go on and look more specifically at the protein domains?

8. Limitations of annotation
Even in a model organism with large amount of resources, most genes are still annotated by similarity
Often, the name given is based on the BEST match to a particular domain or known protein
But…

9. Limitations of BLAST Likelihood of finding a homolog to a sequence:
>80% bacteria
>70% yeast
~60% animal
Rest are truly novel sequences
~900/6500 proteins in yeast without a known function
NAME: Similar to yeast protein YAL7400 not very informative

10. Limitations of similarity
Proteins with more than one domain cause problems.
Numerous matches to one domain can mask matches to other domains.
Increased size of protein databases
Number related sequences rises and less related sequence hits may be lost
Low-complexity regions can mask domain matches

11. Proteins are modular
Individual domains can and often do fold independently of other domains within the same protein
Domains can function as an independent unit (or truncation experiments would never work)
Thus identity of ALL protein domains within a sequence can provide further clues about their function

12. Proteins can have >1 domain
The name: protein kinase receptor UFO doesn’t necessarily tell you that this protein also contains IgG and fibronectin domains or that it has a transmembrane domain

13. Domains are not always functional
If a critical residue is missing in an active site, it’s not likely to be functional
A similarity score won’t pick that up

14. Protein signature databases
Identify domains or classify proteins into families to allow inference of function
Approaches include:
regular expressions and profiles
position-specific scoring matrix-based fingerprints
automated sequence clustering
Hidden Markov Models (HMMs)

15. PROSITE Regular expression patterns describing functional motifs
M-x-G-x(3)-[IV]2-x(2)-{FWY}
Enzyme catalytic sites
Prosthetic group attachment sites
Ligand or metal binding sites
Either matches or not
Some families/domains defined by co-occurrence
x any amno acid
[] any of the amino acids within square braces
{} any amino acid except those within the curly braces
Numbers at the end of a given pattern indicates the number of times that pattern is repeated

16. G-[FYAV]-[GA]-H-x-[IV]-x(1,2)-[RKTQ]-x(2)-[DV]-[PS]-R
Citrate synthase
G-[FYAV]-[GA]-H-x-[IV]-x(1,2)-[RKTQ]-x(2)-[DV]-[PS]-R

17. Profile-HMMs
Models generated from alignments of many homologues then counting frequency of occurrence for each amino acid in each column of the alignment (profile).
Profile-HMMs used to create probabilities of occurrence against background evolutionary model that accounts for possible substitutions.
Provides convenient and powerful way of identifying homology between sequences.
Find domains in sequences that would never be found by BLAST alone

18. HMM domain databases PFAM SMART TIGRFAMs PANTHER
Classify novel sequences into protein domain profiles
Most comprehensive; >16,000 protein families (v29)
SMART
Signaling, extracellular and chromatin proteins
Identification of catalytic site conservation for enzymes
TIGRFAMs
Families of proteins from prokaryotes
PANTHER
Classification based on function using literature evidence

19. PFAM Manually curated profiles
a statistical measure of the likelihood that an alignment occurred by chance alone
Does not indicate functionality

20. PFAM Summary

21. PFAM Domain Organization

22. SMART database SMART: Simple Modular Architecture Research Tool Use?
Focus on signaling, extracellular and chromatin-associated proteins
Curated models for >1200 domains
Use?
I have several kinase domains in my protein list and want to know which ones are functional.
What other signaling proteins are in my list?
What other domains are found in signaling proteins?

23. SMART: Search interface
Uniprot or Ensemble Protein Accession number
Add other searches

24. SMART Output

25. HMMER Fast, sensitive protein homology searches using HMMs
Results include taxonomic distribution of matches
PFAM domains
Transmembrane, coiled-coild, signal peptide and intrinsically disordered regions
Much faster than BLAST or InterProScan

26. TARDBP PHMMER search

27. PHMMER PFAM details

28. InterPro Scan Combines search methods from several protein databases
Uses tools provided by member databases
Uses threshold scores for profiles & motifs
Interpro convenient means of deriving a consensus among signature methods
Interpro records integrated with Uniprot.
Slow to return search results

29. TARDBP Interpro match

30. TARDBP – Uniprot record

31. Function from sequence
Membrane bound or secreted?
GPI anchored?
Cellular localization?
Post-translational modification sites?

32. CBS prediction services
Protein sorting
SignalP, TargetP, others
Post-translational modification
Acetylation, phosphorylation, glycosylation
Immunological features
Epitopes, MHC allele binding, ect
Protein function & structure
Transmembrane domains, co-evolving positions

33. Transmembrane domain prediction

34. Phosphorylation prediction

35. O-glycosylation

36. EMBOSS Open source software for molecular biology
Predict antigenic sites
Useful if want to design a peptide antibody
Look for specific motifs, even degenerate
Known phosphorylation motifs
Find motifs in multiple sequences with one submission
Get stats on proteins/nucleic acid sequences
Sequence manipulation of all kinds

37. Today in lab Tutorials on protein information sites
From a sublist generated using DAVID, generate a list of protein IDs and obtain the sequences
Obtain protein accession numbers for the cluster using Uniprot
Submit to SMART database to characterize/analyze the domains for signaling proteins
Pick 2 proteins to do additional predictions