Protein Bioinformatics Course
Matthew Betts & Rob Russell
AG Russell (Protein Evolution)
Course overview
Day 1 - Modularity
Day 2 - Interactions
Day 3 - Modularity & Interactions
Day 4 - Structure
Day 5 - Structure & Interactions
Daily schedule
10:00-11:00 lecture
11:00-12:00 work on exercises in pairs
12:00-13:00 lunch
13:00-15:30 work on exercises in pairs
16:00-17:00 presentations by you
Protein Sequence Databases
Database Searching
• Homologues = proteins with a common ancestor
• Homology --> similar function
• Sequence similarity --> homology
• Find homologues using:
• BLAST
• Profile Searching
www.proteinmodelportal.org
Scores and E-values
How similar is my sequence to
one in the database?
• Alignment
• Substitution matrix
• Gap penalties
How much would I expect
to get >= this score by
chance alone?
• cf. random sequences
• E = 1: one such match by chance
• E < 0.01: significant
• Depends on database:
• size: larger = better
• composition (random assumed)
Homology comes in two main types:
Orthology and Paralogy
What is the difference and why does this matter?
Duplication -
Speciation Duplication -
Paralogues
Orthologues
- Speciation
Paralogues
Different Fates
Orthologues:
• Both copies required (one in each species)
• conservation of function (‘same gene’)
• adaptation to new environment
Easier to transfer
knowledge of function
between orthologues
Paralogues:
• Both copies useful
• conservation of function
• One copy freed from selection
• disabled
• new function
• Different parts of each free from selection
• function split between them
Assignment of orthology / paralogy can be complicated by:
• duplication preceding speciation
• lineage-specific deletions of paralogs
• complete genome duplications
• many-to-one relationship
• multi-domain proteins
Homology usually found by sequence similarity, but
…proteins with dissimilar sequences can still be homologous
Betts, Guigo, Agarwal, Russell, EMBO J 2001
Proteins are modular
Since the early 1970s it has been observed that protein
structures are divided into discrete elements or domains that
appear to fold, function and evolve independently.
Given a sequence, what should you look for?
• Functional domains (Pfam, SMART, COGS, CDD, etc.)
• Intrinsic features
–
–
–
–
Signal peptide, transit peptides (signalP)
Transmembrane segments (TMpred, etc)
Coiled-coils (coils server)
Low complexity regions, disorder (e.g. SEG, disembl)
• Hints about structure?
Given a sequence, what should you look for?
“Low sequence complexity”
(Linker regions? Flexible? Junk?
Signal peptide
(secreted or membrane
attached)
Transmembrane segment
(crosses the membrane)
Immunoglobulin domains
(bind ligands?)
Tyrosine kinase
(phosphorylates Tyr)
SMART domain ‘bubblegram’ for human
fibroblast growth factor (FGF) receptor 1
(type P11362 into web site: smart.embl.de)
Protein Modularity
• discrete structural and functional units
• found in different combinations in different proteins
Receptor-related tyrosine-kinase
Non-receptor tyrosine-kinases
consider separately in predictions
Finding Protein Domains
• through partial matches to whole sequences:
match
query sequence:
match
match
• compare to databases of domains (Pfam, SMART, Interpro)
• can be separated by:
• low-complexity and disordered regions (SEG)
• trans-membrane regions (TMAP)
• coiled-coils (COILS)
Repeat searches using each domain separately
12 000 domain alignments make
sequence searching easier
WPP domain alignment
Alignments provide more information about a protein
family and thus allow for more sensitive sequences than a
single sequence.
Domain alignments also lack low-complexity or disorder
(normally) and other domains that can make single
sequence searches confusing.
Finding domains in a sequence
Cryptic domains:
at the border of sequence detectability
Identified using more sensitive fold
recognition methods that use
structure to help find weak members
of sequence families.
If Pfam or SMART or similar do not
find a domain, and the region is
probably not disordered, then fold
recognition might help.
Gallego et al, Mol Sys Biol 2010
Domain peptide interactions
Recognition of ligands
or targeting signals
Post-translational
modifications
Linear motifs
Peptides interacting with a common domain often show a
common pattern or motif usually 3-8 aas.
3BP1_MOUSE/528-537
APTMPPPLPP
PTN8_MOUSE/612-629
IPPPLPERTP
“instance”
SOS1_HUMAN/1149-1157
VPPPVPPRRR
NCF1_HUMAN/359-390
SKPQPAVPPRPSA
PEXE_YEAST/85-94
MPPTLPHRDW
SH3-interacting motif
PxxP
“motif”
“perpetrator”
“victim”
Puntervol et al, NAR, 2003; www.elm.org (Eukaryotic Linear Motif DB)
Linear motifs versus domains
Domains: large globular segments of the proteome that
fold into discrete structures and belong in sequence
families.
Linear motifs: small, non-globular segments that do not
adopt a regular structure, and aren’t homologous to each
other in the way domains are.
Motifs lie in the disordered part of the proteome.
Intrinsically unstructured or disordered
proteins or protein fragments
Disorder predictors
(IUPred, RONN, DisORPred, etc)
Linear motif mediated interactions
are everywhere
Include motifs for:
• Targeting – e.g. KDEL
• Modifications – e.g.
phosphorylation
• Signaling – e.g. SH3
About 200 are currently
known, likely many more
still to be discovered
Neduva & Russell, Curr. Opin. Biotech, 2006
Finding linear motifs in a sequence
Linear motifs are much harder to find than domains.
Short (typically < 8AA), simple
patterns, e.g. PxxP will occur in
most sequences randomly.
Long (>30 AA), belong
to sequence families
that help detect new
family members
www.russelllab.org/wiki