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Modeling Protein Function

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Modeling Protein Function MED260 Philip E. Bourne Department of Pharmacology, UCSD pbourne_at_ucsd.edu http://www.sdsc.edu/pb Slides on-line at: – PowerPoint PPT presentation

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Title: Modeling Protein Function


1
Modeling Protein Function
  • MED260
  • Philip E. Bourne
  • Department of Pharmacology, UCSD
  • pbourne_at_ucsd.edu
  • http//www.sdsc.edu/pb
  • Slides on-line at
  • http//www.sdsc.edu/pb/edu/med260/med260.ppt

2
Agenda
  • Why model protein function?
  • Where does it fit as a technique in modern
    medical research?
  • The data deluge as a motivator
  • The extent of what can be modeled
  • Ontologies establishing order from chaos
  • Examples of what can be learnt
  • Accuracy a word of caution

3
Why Model Protein Function
  • The rate of discovery of new proteins far
    outweighs our ability to functionally
    characterize them
  • Functional discovery of new proteins has
    implications in
  • Drug discovery
  • Biomarker identification
  • Understanding of biological processes
  • Identification of disease states and treatment
    regimes

Why model protein function?
4
EXAMPLE UNITS
REPRESENTATIVE DISCIPLINE
REPRESENTATIVE TECHNOLOGY
MRI
Heart
Neuron
Structure Sequence
Protease Inhibitor
Where does it fit as a technique in modern
medical research?
5
EXAMPLE UNITS
REPRESENTATIVE DISCIPLINE
REPRESENTATIVE TECHNOLOGY
MRI
Heart
Translational Medicine
Neuron
Structure Sequence
Protease Inhibitor
Where does it fit as a technique in modern
medical research?
6
The Ability to Model Protein Function Influences
and can be Influenced by Any Level of Biological
Complexity - Examples
  • Genome - rapid increase in sequenced genomes
    provides new raw material
  • Proteome large increase in the number of 3D
    structures highlights new functions
  • Interactome identification of a binding partner
    points to a new function
  • Metabolome isolation of a protein within a
    metabolic pathway
  • Cell - localization points to function
  • Organ gene expression in heart tissue points to
    function
  • Organism different physiology observed in
    species can be related to protein functions

Where does it fit as a technique in modern
medical research?
7
EXAMPLE UNITS
REPRESENTATIVE DISCIPLINE
REPRESENTATIVE TECHNOLOGY
MRI
Heart
Neuron
We will focus here
Structure Sequence
Protease Inhibitor
8
At All Levels We Are Being Driven By Data
Biological Experiment Data
Information Knowledge Discovery
Collect Characterize Compare
Model Infer
Complexity
Technology
Data
Higher-life
1
10 100
1000
100000
Computing Power
Organ
Brain Mapping
Cardiac Modeling
Virtual Communities
Cellular
Model Metaboloic Pathway of E.coli
Sub-cellular
102
106
1
Neuronal Modeling
People/Web Site
Ribosome
Assembly
Virus Structure
Genetic Circuits
Structure
Human Genome Project
E.Coli Genome
C.Elegans Genome
Yeast Genome
1 Small Genome/Mo.
Sequencing Technology
ESTs
Gene Chips
Human Genome
Sequence
90
05
95
00
Year
The Data Deluge
9
Metagenomics A First Look
  • New data (and lots of it) and new types of data
  • 17M new (predicted proteins!) 4-5 x growth in
    just few months and much more coming
  • New challenges and exacerbation of old challenges
  • New type of genomics

The Data Deluge
10
Metagenomics First Results
  • More then 99.5 of DNA in very environment
    studied represent unknown organisms
  • Culturable organisms are exceptions, not the rule
  • Most genes represent distant homologs of known
    genes, but there are thousands of new families
  • Everything we touch turns out to be a gold mine
  • Environments studied
  • Water (ocean, lakes)
  • Soil
  • Human body (gut, oral cavity, human microbiome)

The Data Deluge
11
Metagenomics New DiscoveriesEnvironmental (red)
vs. Currently Known PTPases (blue)
1
2
3
4
Higher eukaryotes
The Data Deluge
12
The Good News and the Bad News
  • Good news
  • Data pointing towards function are growing at
    near exponential rates
  • IT can handle it on a per dollar basis
  • Bad news
  • Data are growing at near exponential rates
  • Quality is highly variable
  • Accurate functional annotation is sparse

The Data Deluge
13
Genomes - 2004
  • We all know about the human what is not so well
    known is
  • 191 completed microbial genomes
  • 44 archaea
  • 727 bacteria
  • 785 eukaryotes (complete or in progress)
  • Viroids .

The Data Deluge
14
Proteome
  • We are reasonably good at finding proteins in
    genomes with intergenic regions but not perfect
    eg alternative initiation codons
  • Regulatory elements provide a different set of
    challenges
  • We are not so good at assigning functions to
    those proteins
  • Moreover the devil is in the details

The Extent of What Can Be Modeled
15
Estimated Functional Roles (by of Proteins) of
the Proteome in a Complex Organism
The Extent of What Can Be Modeled
16
Functional Nomenclature Needs to be Consistent
for Orderly Progress Enter EC and GO
  • EC classifies all enzymes - http//www.chem.qmul.a
    c.uk/iubmb/enzyme/
  • Gene Ontology Consortium characterizes by
    molecular function, biochemiscal process and
    cellular location http//www.geneontology.org/

Ontologies establishing order from chaos
17
Functional Coverage of the Human Genome
40 covered
http//function.rcsb.org8080/pdb/function_distrib
ution/index.html
The Extent of What Can Be Modeled
18
Step 1. Learn What You Can from the Protein
Sequence
  • Find it
  • Pay attention to the quality of the functional
    annotation errors are transitive
  • Understand its 1-D structure domain
    organization, signatures, fingerprints

Examples of what can be learnt
19
Step 2. Is there a 3D Structure? If so What Can
You Learn from That?
  • Find it
  • Understand it
  • Characterize it
  • Understand its function(s) these follow a power
    law at the fold level some folds are
    promiscuous (many functions) others are solitary
    or of unknown function

Examples of what can be learnt
20
(a) myoglobin (b) hemoglobin (c) lysozyme (d)
transfer RNA (e) antibodies (f) viruses
(g) actin (h) the nucleosome (i) myosin
(j) ribosome
Courtesy of David Goodsell, TSRI
21
First Why Bother with Structure?An Example
Protein Kinase A
This molecular scene for cAMP dependant
protein kinase depicts years of collective
knowledge. Beyond basics, only the atomic
coordinates are captured by the PDB. Functional
annotation requires the literature
Examples of what can be learnt
22
What Did that Picture Tell Us?
  • Two domains with associated functions
  • ATP binding substrate binding
  • Through conserved residues and their spatial
    location details of the ATP and substrate binding
    and mechanism of the phospho transfer reaction
  • So is structure the answer to functional modeling?

Examples of what can be learnt
23
Question So is structure the answer to
functional modeling? Answer Partly - The
number of unique protein sequences still
outnumbers the number of unique structures by
1001
  • Enter Structural Genomics
  • Enter Structure Prediction

Examples of what can be learnt
24
The Structural Genomics Pipeline (X-ray
Crystallography)
Basic Steps
  • Crystallomics
  • Isolation,
  • Expression,
  • Purification,
  • Crystallization

Target Selection
Data Collection
Structure Solution
Structure Refinement
Functional Annotation
Publish
Examples of what can be learnt
25
Structural Genomics Will Give Us..
  • Good news
  • More structures (definitely)
  • New folds (some but not as anticipated)
  • New understanding of specific diseases and
    pathways (maybe)
  • Representatives from each major protein family
    (maybe)
  • Bad news
  • Many new structures that are functionally
    unclassified (definitely)

Examples of what can be learnt
26
What About Structure Prediction?
  • Current rule
  • We will be able to predict a structure when we
    know all the structures ?

Examples of what can be learnt
27
Why is Structure Prediction so Hard?
Random 1000 structurally similar PDB polypeptide
chains with z gt 4.5 ( sequence identity vs
alignment length)
Twilight Zone
Midnight Zone
Examples of what can be learnt
28
Approaches to Structure Prediction
  • Homology modeling
  • Threading (aka fold recognition)
  • Ab initio
  • How well do we do? see CASP
  • Consensus servers
  • Eva - http//cubic.bioc.columbia.edu/eva/
  • LiveBench - http//bioinfo.pl/meta/

Examples of what can be learnt
29
Step 3. What Can Be Got from Structure When You
Have it?
From Structural Bioinformatics Ed Bourne and
Weissig p394 Wiley 2002
Examples of what can be learnt
30
Specific Example
  • Mj0577 putative ATP molecular switch
  • Mj0577 is an open reading frame (ORF) of
    previously unknown function from Methanococcus
    jannaschii. Its structure was determined at 1.7Å
    (Figure 7a) (Zarembinski et al, 1998). The
    structure contains a bound ATP molecule, picked
    up from the E. coli host. The presence of bound
    ATP led to the proposition that Mj0577 is either
    an ATPase, or an ATP-binding molecular switch.
    Further experimental work showed that Mj0577
    cannot hydrolyse ATP by itself, and can only do
    so in the presence of M. jannaschii crude cell
    extract. Therefore it is more likely to act as a
    molecular switch, in a process analogous to
    ras-GTP hydrolysis in the presence of GTPase
    activating protein.

From Structural Bioinformatics Ed Bourne and
Weissig p402 Wiley 2002
Examples of what can be learnt
31
Step 4. Proteins Do Not Function in Isolation But
are Part of Complex Interaction Networks
http//www.genome.jp/kegg/
Examples of what can be learnt
32
Accuracy - A Word of Caution
  • Errors are transitive
  • Proteins A and B are observed to have similar
    functions through sequence homology
  • Proteins B and C are observed to have similar
    functions through sequence homology
  • Is protein A related to protein C?
  • Up to 30 of current annotation may be wrong

Accuracy - A Word of Caution
33
Questions?
34
Demo of Steps 1-4
  • Step 1. Learn What You Can from the Protein
    Sequence
  • Step 2. Is there a 3D Structure? If So, What Can
    You Learn from That?
  • Step 3. What Can Be Got from Structure When You
    Have it?
  • Step 4. Proteins Do Not Function in Isolation But
    are Part of Complex Interaction Networks
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