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Understanding families, domains and
function using InterPro
Alex Mitchell
[email protected]
Overview
• Why do we need tools to annotate protein function?
• Ways in which we can transfer annotation:

pairwise approaches

signature based-approaches
• What can we predict using these techniques?
• Real world application of this knowledge to sequence
data
In an ideal world…
• All proteins would be expressed and characterised in
laboratories
• Their sequences, structures and functions would be
determined experimentally
• This information would be stored, in a standardised
human readable and computable form, in a freelyaccessible database
The Swiss-Prot database
Essentially, this was the idea behind the Swiss-Prot
database:
• Created in 1986 by Amos Bairoch
• Contains protein sequence data which is manually
annotated and reviewed
• Information about protein function, structure,
interactions, etc, extracted from the scientific literature
by expert curators
• Steadily grown to ~550k entries over 30 years
Difficulties in experimentally characterising proteins
• Can take many years in the lab to identify protein
function
• Lab experiments are expensive
• Assays don’t exist for all functions
• Difficulty in expressing some proteins
Do something!
Falling sequencing data costs
Wetterstrand KA. DNA Sequencing Costs: Data from the NHGRI Large-Scale Genome Sequencing
Available at: www.genome.gov/sequencingcosts.
Growing sequence data volumes
Growing sequence data volumes
Making sense of sequence data
Sequence data generation is no longer a major challenge
in biology
We have bucket loads of protein sequences (literally)
• 10s of millions of sequences from over ½ a million
species already in the databases
• Can produce 100s of millions of predicted protein
sequences from a single run on a sequencing machine
Making sense of sequence data
Inferring information from other proteins - BLAST
• BLAST: Basic Local Alignment and Search Tool
 Pairwise comparison of proteins
 Widely used
 User friendly
• Very good at recognising similarity between closely related
sequences
Using BLAST for annotation
Using BLAST for annotation
Using BLAST for annotation
Using BLAST for annotation
• Issues of scalability (comparison of 1 sequence against UniProt
requires ~ 60 million pairwise comparisons)
• Issues of sensitivity (detection of distant homologues)
Using BLAST for annotation
BLAST alignment of 2 proteins:
• 60S acidic ribosomal protein P0 from 2 closely-related
species
60S acidic ribosomal protein P0: multiple sequence alignment
Protein signatures
Alternatively, model the pattern of conserved amino acids at specific
positions within a multiple sequence alignment
• Patterns
• Profiles
• Profile HMMs
Use these models (signatures) to infer relationships with the
characterised sequences from which the alignment was constructed
Approach used by a variety of databases: Pfam, TIGRFAMs,
PANTHER, Prosite, etc
,,,
Different protein signature approaches
Single motif
methods
Patterns
Full alignment
methods
Profiles &
Hidden
Markov
models
(HMMs)
Multiple motif
methods
Fingerprints
Patterns
Many important sequence features, such as binding sites or the active
sites of enzymes, consist of only a few amino acids that are essential for
protein function
Sequence alignment:
Motif
Extract pattern sequences:
Build regular
expression:
Pattern
signature:
ALVKLISG
AIVHESAT
CHVRDLSC
CPVESTIS
[AC] – x -V- x(4) - {ED}
PS00001
Fingerprints: a multiple motif approach
Sequence alignment:
Motif 1
Motif 2
Motif 3
Define motifs:
Extract motif
sequences:
Fingerprint
signature:
xxxxxx
xxxxxx
xxxxxx
xxxxxx
PR00001
Weight
matrices
xxxxxx
xxxxxx
xxxxxx
xxxxxx
xxxxxx
xxxxxx
xxxxxx
xxxxxx
Fingerprints
• Very good at modeling the often small differences between closely
related proteins
• Can distinguish individual subfamilies within protein families,
allowing functional characterisation of sequences at a high level
of specificity
Fine-grained analyses
Profiles & HMMs
Whole protein
Sequence alignment:
Define coverage:
Use entire alignment of
domain or protein family
Build model:
Profile or HMM signature:
Entire domain
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Profiles
Start with a multiple
sequence alignment
Amino acids at each position
in the alignment are scored
according to the frequency
with which they occur
Scores are weighted
according to
evolutionary distance
using a BLOSUM matrix
Good at identifying homologues
HMMs
Start with a multiple
sequence alignment
Occupancy and amino acid
frequency at each position in
the alignment are encoded
Insertions / deletions
can be modelled
Best path calculated
Can model very divergent regions of alignment
Very good at identifying evolutionarily distant homologues
Homology search sensitivity
Functional inference
However, homology does not necessarily imply conserved
function
GHMP kinase superfamily includes:
• Galactokinases (EC 2.7.1.6)
• Homoserine kinases (EC 2.7.1.39)
• Mevalonate kinases (EC 2.7.1.36)
• Diphosphomevalonate decarboxylases (EC 4.1.1.33)
Protein signatures
• Different methodologies with different areas of application
• A range of different sequence analysis databases that use these
different methods exist
• Combining these databases together would allow us to capitalise
upon their individual strengths
• This was the idea behind InterPro
InterPro - integrated classification of protein families
The aim of InterPro
InterPro
Features of InterPro
• Manually annotated with literature referenced abstracts and Gene
Ontology terms
• Public releases every 2 months
• Manually checked and updated against the Swiss-Prot database
• Errors are identified and fixed
GO annotation in InterPro: why stability does not indicate accuracy in a
sea of changing annotations
Sangrador-Vegas et al., Database (2016)
doi: 10.1093/database/baw027
Searching the database
Searching the database
Searching the database
Type
Name
Identifier
Contributing
signature
Annotation
InterPro entry types
Family
Proteins share a common evolutionary origin, as reflected in their
related functions, sequences or structure
Domain
Distinct functional, structural or sequence units that may exist in a
variety of biological contexts
Repeats
Short sequences typically repeated within a protein
Sites
PTM
Active
Site
Binding
Site
Conserved
Site
Searching the database
Side
menu
Searching the database
Searching the database
Searching the database
Type
Annotation
Searching the database
Searching the database
Type
Annotation
Searching the database
Searching the database
Searching the database
Relationships
to other entries
InterPro relationships: families
InterPro relationships: domains
Protein kinase-like
domain
Protein kinase
catalytic domain
Serine/threonine
kinase catalytic
domain
Tyrosine
kinase catalytic
domain
Searching the database
Relationships
to other entries
Searching the database
Searching the database
Searching the database
Viral rhodopsin-like GPCRs?
Searching the database
Searching the database
What have we learned?
Built up a lot of information very quickly, based on the
sequence alone
Membrane protein with 7 TM structure
Retinal binding site in the C-terminus
Member of the rhodopsin-like GPCRs
• Broad class of membrane-bound receptors
• Involved in signal transduction via binding to G-proteins
• Mainly found in eukaroytes, but certain viruses have co-opted them
• Structure solved for some family members (and shown to dimerise)
What have we learned?
Built up a lot of information very quickly, based on the
sequence alone
More specifically, member of the red/green sensitive opsin
subfamily:
• Found only in animals
• Expressed in cone cells in the eye
• Involved in phototransduction and colour vision
• Absorption maximums at 560 & 530nm light wavelengths
• Mutations/deficiencies can cause types of colour blindness
Bulk sequence analysis
This is useful for detailed analysis of individual proteins
But for bulk analyses?
• Genomes?
• Proteomes?
• Comparative analyses?
GO term annotation
GO terms
Functional annotation: Gene Ontology
• Grew out of the model organism community
• Aims to unify the representation of gene and gene product
attributes across species
• Allows cross-species and/or cross-database comparison
http://geneontology.org/
The Gene Ontology
Less specific concepts
• A way to capture
biological knowledge
in a written and
computable form
• A set of concepts
and their relationships
to each other arranged
as a hierarchy
More specific concepts
www.ebi.ac.uk/QuickGO
The Concepts in GO
•
•
1. Molecular Function
An elemental activity or task or job
protein kinase activity
insulin receptor
activity
2. Biological Process
A commonly recognised series of events
•
3. Cellular Component
Where a gene product is located
cell division
• mitochondrion
• mitochondrial matrix
• mitochondrial inner
membrane
InterPro GO terms
• GO terms are manually assigned to InterPro entries, based on
scientific literature
• Sequences searched against the database are annotated with GO
terms, depending on the entry they match
InterPro
InterPro entry specificity determines the GO
terms assigned
GO:0007186 G-protein coupled receptor signaling
GO:0016021 integral to membrane
GO:0007186 G-protein coupled receptor signaling
GO:0016021 integral to membrane
GO:0007601 visual perception
InterPro GO terms
Using GO’s standardised & controlled vocabulary allows largescale consistent & human readable annotation of data sets,
enabling comparison
Uses of InterPro
UniProtKB
Member
Databases
Uses of InterPro
• InterPro is the largest source of GO terms in UniProt
• Provides ~ 120 million GO terms to >40 million distinct
UniProtKB sequences
GO term
Evidence
Source
Uses of InterPro
Protein discovery, functional characterisation and protein family
analysis
Uses of InterPro
Analysis & comparison whole genomes, proteomes &
transcriptomes
Uses of InterPro
Analysis of metagenomes from environmental samples
Summary
InterPro allows scientists to quickly amass information about
individual protein sequences:
• the domains and sites that they contain
• the families to which they belong
• the species in which these type of proteins are found, the
pathways they are involved in, GO term annotation, etc
The database also allows bulk analysis of genomes,
proteomes and metagenomes, enabling:
• comparative analyses
• data discovery for translational research
Acknowledgements
The Protein Families
team:
Hsin-Yu Chang
Sara El-Gebali
Aurelien Luciani
Matthew Fraser
Jaina Mistry
Gift Nuka
Sebastien Pesseat
Simon Potter
Matloob Qureshi
Neil Rawlings
Lorna Richardson
Gustavo Salazar-Orejuela
Amaia Sangrador
Siew-Yit Yong
Group Head:
Rob Finn
Cluster Head:
Alex Bateman
Hands-on
• Exploring InterPro to find predicted information about protein
sequences
• Protein functional inference using InterPro and BLAST
 Mini group research project, using InterPro and other databases