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Lecture 14:
Protein domains, function and
associated prediction
Introduction to Bioinformatics
Functional Genomics – Systems
Biology
Genome
Expressome
Proteome
TERTIARY STRUCTURE (fold)
Metabolomics
fluxomics
TERTIARY STRUCTURE (fold)
Metabolome
Experimental
• Structural genomics
• Functional genomics
• Protein-protein interaction
• Metabolic pathways
• Expression data
Issue when elucidating function
experimentally
• Typically done through knock-out
experiments
• Partial information (indirect interactions) and
subsequent filling of the missing steps
• Negative results (elements that have been
shown not to interact, enzymes missing in an
organism)
• Putative interactions resulting from
computational analyses
Protein function categories
• Catalysis (enzymes)
• Binding – transport (active/passive)
– Protein-DNA/RNA binding (e.g. histones, transcription factors)
– Protein-protein interactions (e.g. antibody-lysozyme) (experimentally
determined by yeast two-hybrid (Y2H) or bacterial two-hybrid (B2H)
screening )
– Protein-fatty acid binding (e.g. apolipoproteins)
– Protein – small molecules (drug interaction, structure decoding)
• Structural component (e.g. -crystallin)
• Regulation
• Signalling
• Transcription regulation
• Immune system
• Motor proteins (actin/myosin)
Catalytic properties of enzymes
Michaelis-Menten equation:
Vmax × [S]
V = ------------------Km + [S]
Km
•
•
•
•
•
•
•
kcat
Moles/s
Vmax
Vmax/2
E+S
ES
E+P
E = enzyme
K
S = substrate
ES = enzyme-substrate complex (transition state)
P = product
Km = Michaelis constant
Kcat = catalytic rate constant (turnover number)
Kcat/Km = specificity constant (useful for comparison)
m
[S]
Protein interaction domains
http://pawsonlab.mshri.on.ca/html/domains.html
Energy difference upon binding
Examples of protein interactions (and of functional
importance) include:
• Protein – protein
(pathway analysis);
• Protein – small molecules
(drug interaction, structure decoding);
• Protein – peptides, DNA/RNA
The change in Gibb’s Free Energy of the protein-ligand
binding interaction can be monitored and expressed by
the following equation:
G=H–TS
(H=Enthalpy, S=Entropy and T=Temperature)
Protein function
• Many proteins combine functions
• Some immunoglobulin structures are thought to
have more than 100 different functions (and
active/binding sites)
• Alternative splicing can generate (partially)
alternative structures
Protein function & Interaction
Active site /
binding cleft
Shape complementarity
Protein function evolution
Chymotrypsin
From a simple ancestral
active site for cutting protein
chains...
... to a more elaborate active
site with four different
features, all helping to
optimise proteolysis
(cleavage)
Gene duplication has resulted
in two-domain protein
Protein function evolution
Chymotrypsin
The active site lies between the two domains. It consists of residues on the
same two loops (firstly between beta-strands 3 and 4, secondly between
beta strands 5 and 6) of each of the two barrel domains. Four features of the
active site are indicated in the figure.
The Substrate Specificity Pocket
Main Chain Substrate-binding
The Oxyanion Hole (white)
Catalytic triad
Chymotrypsin cleaves peptides at
the carboxyl side of tyrosine,
tryptophan, and phenylalanine
because those three amino acids
contain phenyl rings.
How to infer function
• Experiment
• Deduction from sequence
– Multiple sequence alignment – conservation
patterns
– Homology searching
• Deduction from structure
– Threading
– Structure-structure comparison
– Homology modelling
A domain is a:
• Compact, semi-independent unit
(Richardson, 1981).
• Stable unit of a protein structure that can
fold autonomously (Wetlaufer, 1973).
• Recurring functional and evolutionary
module (Bork, 1992).
“Nature is a tinkerer and not an inventor” (Jacob, 1977).
• Smallest unit of function
Delineating domains is essential for:
• Obtaining high resolution structures (x-ray but
particularly NMR – size of proteins)
• Sequence analysis
• Multiple sequence alignment methods
• Prediction algorithms (SS, Class, secondary/tertiary
structure)
• Fold recognition and threading
• Elucidating the evolution, structure and function of
a protein family (e.g. ‘Rosetta Stone’ method)
• Structural/functional genomics
• Cross genome comparative analysis
Domain connectivity
linker
Structural domain organisation can be nasty…
Pyruvate kinase
Phosphotransferase
b barrel regulatory domain
a/b barrel catalytic substrate binding
domain
a/b nucleotide binding domain
1 continuous + 2 discontinuous domains
Domain size
•The size of individual structural domains varies
widely
– from 36 residues in E-selectin to 692 residues in
lipoxygenase-1 (Jones et al., 1998)
– the majority (90%) having less than 200 residues
(Siddiqui and Barton, 1995)
– with an average of about 100 residues (Islam et al.,
1995).
•Small domains (less than 40 residues) are often
stabilised by metal ions or disulphide bonds.
•Large domains (greater than 300 residues) are
likely to consist of multiple hydrophobic cores (Garel,
1992).
Analysis of chain hydrophobicity in
multidomain proteins
Analysis of chain hydrophobicity in
multidomain proteins
Domain characteristics
Domains are genetically mobile units, and
multidomain families are found in all three
kingdoms (Archaea, Bacteria and Eukarya)
underlining the finding that ‘Nature is a tinkerer and
not an inventor’ (Jacob, 1977).
The majority of genomic proteins, 75% in unicellular
organisms and more than 80% in metazoa, are
multidomain proteins created as a result of gene
duplication events (Apic et al., 2001).
Domains in multidomain structures are likely to
have once existed as independent proteins, and
many domains in eukaryotic multidomain proteins
can be found as independent proteins in
prokaryotes (Davidson et al., 1993).
Protein function evolution
- Gene (domain) duplication Active site
Chymotrypsin
Pyruvate phosphate dikinase
• 3-domain protein
• Two domains catalyse 2-step reaction
A B  C
• Third so-called ‘swivelling domain’
actively brings intermediate enzymatic
product (B) over 45Å from one active site
to the other
/
Pyruvate phosphate dikinase
• 3-domain protein
• Two domains catalyse 2-step reaction
A B  C
• Third so-called ‘swivelling domain’ actively
brings intermediate enzymatic product (B)
over 45Å from one active site to the other
The DEATH Domain
http://www.mshri.on.ca/pawson
• Present in a variety of Eukaryotic
proteins involved with cell death.
• Six helices enclose a tightly
packed hydrophobic core.
• Some DEATH domains form
homotypic and heterotypic dimers.
Detecting Structural Domains
• A structural domain may be detected as a
compact, globular substructure with more
interactions within itself than with the rest of the
structure (Janin and Wodak, 1983).
• Therefore, a structural domain can be determined
by two shape characteristics: compactness and
its extent of isolation (Tsai and Nussinov, 1997).
• Measures of local compactness in proteins have
been used in many of the early methods of
domain assignment (Rossmann et al., 1974;
Crippen, 1978; Rose, 1979; Go, 1978) and in
several of the more recent methods (Holm and
Sander, 1994; Islam et al., 1995; Siddiqui and
Barton, 1995; Zehfus, 1997; Taylor, 1999).
Detecting Structural Domains
•However, approaches encounter problems
when faced with discontinuous or highly
associated domains and many definitions
will require manual interpretation.
•Consequently there are discrepancies
between assignments made by domain
databases (Hadley and Jones, 1999).
Detecting Domains using
Sequence only
• Even more difficult than prediction from structure!
Integrating protein multiple sequence
alignment, secondary and tertiary structure
prediction in order to predict structural domain
boundaries in sequence data
SnapDRAGON
Richard A. George
George R.A. and Heringa, J. (2002) J. Mol. Biol., 316, 839-851.
Protein structure hierarchical levels
PRIMARY STRUCTURE (amino acid sequence)
SECONDARY STRUCTURE (helices, strands)
VHLTPEEKSAVTALWGKVNVDE
VGGEALGRLLVVYPWTQRFFE
SFGDLSTPDAVMGNPKVKAHG
KKVLGAFSDGLAHLDNLKGTFA
TLSELHCDKLHVDPENFRLLGN
VLVCVLAHHFGKEFTPPVQAAY
QKVVAGVANALAHKYH
QUATERNARY STRUCTURE
TERTIARY STRUCTURE (fold)
Protein structure hierarchical levels
PRIMARY STRUCTURE (amino acid sequence)
SECONDARY STRUCTURE (helices, strands)
VHLTPEEKSAVTALWGKVNVDE
VGGEALGRLLVVYPWTQRFFE
SFGDLSTPDAVMGNPKVKAHG
KKVLGAFSDGLAHLDNLKGTFA
TLSELHCDKLHVDPENFRLLGN
VLVCVLAHHFGKEFTPPVQAAY
QKVVAGVANALAHKYH
QUATERNARY STRUCTURE
TERTIARY STRUCTURE (fold)
Protein structure hierarchical levels
PRIMARY STRUCTURE (amino acid sequence)
SECONDARY STRUCTURE (helices, strands)
VHLTPEEKSAVTALWGKVNVDE
VGGEALGRLLVVYPWTQRFFE
SFGDLSTPDAVMGNPKVKAHG
KKVLGAFSDGLAHLDNLKGTFA
TLSELHCDKLHVDPENFRLLGN
VLVCVLAHHFGKEFTPPVQAAY
QKVVAGVANALAHKYH
QUATERNARY STRUCTURE
TERTIARY STRUCTURE (fold)
Protein structure hierarchical levels
PRIMARY STRUCTURE (amino acid sequence)
SECONDARY STRUCTURE (helices, strands)
VHLTPEEKSAVTALWGKVNVDE
VGGEALGRLLVVYPWTQRFFE
SFGDLSTPDAVMGNPKVKAHG
KKVLGAFSDGLAHLDNLKGTFA
TLSELHCDKLHVDPENFRLLGN
VLVCVLAHHFGKEFTPPVQAAY
QKVVAGVANALAHKYH
QUATERNARY STRUCTURE
TERTIARY STRUCTURE (fold)
SNAPDRAGON
Domain boundary prediction protocol using sequence information
alone (Richard George)
1. Input: Multiple sequence alignment (MSA)
and predicted secondary structure
2. Generate 100 DRAGON 3D models for the
protein structure associated with the MSA
3. Assign domain boundaries to each of the 3D
models (Taylor, 1999)
4. Sum proposed boundary positions within 100
models along the length of the sequence,
and smooth boundaries using a weighted
window
George R.A. and Heringa J.(2002) SnapDRAGON - a method to delineate protein structural domains from
sequence data, J. Mol. Biol. 316, 839-851.
SnapDragon
Folds
generated by
Dragon
Multiple alignment
Boundary
recognition
(Taylor, 1999)
Predicted
secondary structure
CCHHHCCEEE
Summed and
Smoothed
Boundaries
SNAPDRAGON
Domain boundary prediction protocol using sequence information
alone (Richard George)
1. Input: Multiple sequence alignment
(MSA)
1. Sequence searches using PSI-BLAST (Altschul et
al., 1997)
2. followed by sequence redundancy filtering using
OBSTRUCT (Heringa et al.,1992)
3. and alignment by PRALINE (Heringa, 1999)
• and predicted secondary structure
4. PREDATOR secondary structure prediction
program
George R.A. and Heringa J.(2002) SnapDRAGON - a method to delineate protein structural domains from
sequence data, J. Mol. Biol. 316, 839-851.
Domain prediction using DRAGON
Distance Regularisation Algorithm for
Geometry OptimisatioN
(Aszodi & Taylor, 1994)
•Folded protein models based on the requirement
that (conserved) hydrophobic residues cluster
together.
•First construct a random high dimensional Ca
distance matrix.
•Distance geometry is used to find the 3D
conformation corresponding to a prescribed target
matrix of desired distances between residues.
SNAPDRAGON
Domain boundary prediction protocol using sequence information
alone (Richard George)
2. Generate 100 DRAGON (Aszodi & Taylor, 1994)
models for the protein structure associated
with the MSA
–
–
–
–
DRAGON folds proteins based on the requirement that
(conserved) hydrophobic residues cluster together
(Predicted) secondary structures are used to further
estimate distances between residues (e.g. between the first
and last residue in a b-strand).
It first constructs a random high dimensional Ca (and pseudo
Cb) distance matrix
Distance geometry is used to find the 3D conformation
corresponding to a prescribed matrix of desired distances
between residues (by gradual inertia projection and based
on input MSA and predicted secondary structure)
DRAGON = Distance Regularisation Algorithm for Geometry OptimisatioN
Multiple alignment
Ca distance
matrix
N
Target
matrix
3
N
100 randomised
initial matrices
100 predictions
N
N
Predicted secondary
structure
CCHHHCCEEE
N
Input data
•The Ca distance matrix is divided into smaller clusters.
•Separately, each cluster is embedded into a local centroid.
•The final predicted structure is generated from full
embedding of the multiple centroids and their
corresponding local structures.
Lysozyme 4lzm
PDB
DRAGON
Methyltransferase 1sfe
PDB
DRAGON
Phosphatase 2hhm-A
PDB
DRAGON
Taylor method (1999)
DOMAIN-3D
3. Assign domain boundaries to each of
the 3D models (Taylor, 1999)
•
•
•
Easy and clever method
Uses a notion of spin glass theory (disordered magnetic
systems) to delineate domains in a protein 3D structure
Steps:
1.
2.
3.
4.
Take sequence with residue numbers (1..N)
Look at neighbourhood of each residue (first shell)
If (“average nghhood residue number” > res no) resno = resno+1
else resno = resno-1
If (convergence) then take regions with identical “residue
number” as domains and terminate
Taylor,WR. (1999) Protein structural domain identification. Protein Engineering 12 :203-216
Taylor method (1999)
repeat until convergence
5
if 41 < (5+6+56+78+89)/5
78
56
6
41
then Res 41 42 (up 1)
else Res 41 40 (down 1)
89
Taylor method (1999)
initial situation
Iterate until
convergence
continuous
discontinuous
SNAPDRAGON
Domain boundary prediction protocol using sequence information
alone (Richard George)
4. Sum proposed boundary positions within 100
models along the length of the sequence,
and smooth boundaries using a weighted
window (assign central position)
Window score = 1≤ i ≤ l Si × Wi
Wi
i
Where Wi = (p - |p-i|)/p2 and p = ½(n+1).
It follows that l Wi = 1
George R.A. and Heringa J.(2002) SnapDRAGON - a method to delineate protein structural domains from
sequence data, J. Mol. Biol. 316, 839-851.
SNAPDRAGON
Statistical significance:
• Convert peak scores to Z-scores using
z = (x-mean)/stdev
• If z > 2 then assign domain boundary
Statistical significance using random models:
• Test hydrophibic collapse given distribution of
hydrophobicity over sequence
• Make 5 scrambled multiple alignments (MSAs) and
predict their secondary structure
• Make 100 models for each MSA
• Compile mean and stdev from the boundary
distribution over the 500 random models
• If observed peak z > 2.0 stdev (from random models)
then assign domain boundary
SnapDRAGON prediction
assessment
• Test set of 414 multiple alignments;183 single and
231 multiple domain proteins.
• Boundary predictions are compared to the region
of the protein connecting two domains (maximally
10 residues from true boundary)
SnapDRAGON prediction assessment
• Baseline method I:
• Divide sequence in equal parts based on number of
domains predicted by SnapDRAGON
• Baseline method II:
• Similar to Wheelan et al., based on domain length
partition density function (PDF)
• PDF derived from 2750 non-redundant structures
(deposited at NCBI)
• Given sequence, calculate probability of onedomain, two-domain, .., protein
• Highest probability taken and sequence split equally
as in baseline method I
Average prediction results per protein
Continuous set
Discontinuous set
Full set
Coverage
63.9 (± 43.0)
35.4 (± 25.0)
51.8 (± 39.1)
Success
46.8 (± 36.4)
44.4 (± 33.9)
45.8 (± 35.4)
Coverage
43.6 (± 45.3)
20.5 (± 27.1)
34.7 (± 40.8)
Success
34.3 (± 39.6)
22.2 (± 29.5)
29.6 (± 36.6)
Coverage
45.3 (± 46.9)
22.7 (± 27.3)
35.7 (± 41.3)
Success
37.1 (± 42.0)
23.1 (± 29.6)
31.2 (± 37.9)
SnapDRAGON
Baseline 1
Baseline 2
Coverage is the % linkers predicted (TP/TP+FN)
Success is the % of correct predictions made (TP/TP+FP)
Average prediction results per protein
Protein-protein interaction networks
Protein Function Prediction
How can we get the edges (connections)
of the cellular networks?
•We can predict functions of genes or
proteins so we know where they would fit in a
metabolic network
•There are also techniques to predict whether
two proteins interact, either functionally (e.g.
they are involved in a two-step metabolic
process) or directly physically (e.g. are
together in a protein complex)
Protein Function Prediction
The state of the art – it’s not
complete
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!
How then do we build a reasonably complete networks
when the parts list is so incomplete?
Protein Function Prediction
For all these reasons, improving automated
protein function prediction is now a cornerstone
of bioinformatics and computational biology
New methods will need to integrate signals
coming from sequence, expression, interaction
and structural data, etc.
Classes of function prediction
methods (recap)
• 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
Phylogenetic profile analysis
• Function prediction of genes based on “guilt-byassociation” – 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'‘
• This means that such proteins have a good chance of being
physically or metabolically connected
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”
Phylogenetic profile analysis
• Example – phylogenetic profiles based on 60 genomes
genome
gene
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
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)
Phylogenetic profile analysis
• Phylogenetic profiles contain great amount of functional
information
• Phlylogenetic profile analysis can be used to distinguish
orthologous genes from paralogous genes
• Example: Subcellular localization: 361 yeast nucleusencoded mitochondrial proteins were 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 might have evolved through one
of several mechanisms of convergent evolution.
• Phylogenetic profiling typically has low accuracy
(specificity) but can have high coverage.
Domain fusion example
Vertebrates
have a multi-enzyme protein (GARsAIRs-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
Using observed domain fusion for
prediction of protein-protein interactions
Rosetta stone method
• 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 multidomain protein C, A and B are predicted to have interaction
A
B
C
Though gene-fusion has low prediction coverage,
its false-positive rate is low (high specificity)
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.
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
Protein interaction database
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://genomewww.stanford.edu/Saccharomyces). Solid lines show experimentally determined
interactions, as summarized in the Database of Interacting Proteins19
(http://dip.doe-mbi.ucla.edu). 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.
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.
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 (UU), Martijn Huynen (RUN) and the group of Peer Bork (EMBL,
Heidelberg)
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.
2.
3.
4.
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 736429 proteins in 179 species
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.
Wrapping up
• Understand chymotrypsin example: evolution via gene
duplication of an optimised two-domain barrel enzyme with
active site residues from either domain.
• Understand domain issues: structural and functional
• Understand the basic steps of the Snap-DRAGON method for
domain boundary prediction – but no need to memorize it all
• Understand phylogenetic profiling and the Rosetta Stone
method (guilt-by-association)
• Understand that conservation patterns in the order of genes
that are nearby on the genome (synteny) indicate functional
relationships (used in STRING method)
• Also co-expression (genes being expressed (or not) at the
same time) indicates a functional relationship (used in
STRING method)