Network analysis of
proteomes
Peter Andras
School of Computing Science
University of Newcastle
peter.andras@ncl.ac.uk
Overview
Introduction
 The data
 Network analysis
 Protein interaction networks
 Analysis of protein interaction networks
 Computational drug target discovery

Motivation
Search for new antibiotics, drugs for
genetic and prion diseases
 Destroying and restoring the functionality
of cells
 How to do this ?
 eXSys project

Cells – 1
Cells – 2
Analysing cells
Analysing and understanding cells by
analysing their protein interaction network
 Ideally: dynamic analysis
 Simplified version: static analysis

Proteomics data
Yeast-two-hybrid data
 Gene co-expression based predicted data
 Other experimental data

Web databases





DIP (Database of Interacting Proteins) –
experimentally validated data, mostly for yeast
STRING (Search Tool for the Retrieval of
Interacting Genes/Proteins) – large amount of
predicted data based on gene expression data
KEGG – metabolic cycle descriptions
EBI – Proteome – full proteomes
Swiss – Prot – general protein information
Data collection
eXSys data management engine
 Collects and updates automatically data
from web databases
 Extracts information about protein
interactions and stores this in proprietary
format
 Allows to get specific data about selected
proteins from web databases

Networks

Graphs: nodes and edges
Scale-free networks
Damaging scale-free networks
Robustness to random damage
 High sensitivity to targeted damage

Important nodes

Hubs: high connectivity nodes

Bottlenecks: nodes connecting clusters
Other important nodes
Elementary cycle number of nodes
 Effect of deletion on the characteristic
polynomial

Integrity measures

Average minimum path length: how close
are in average the nodes of the graph

Clustering coefficient: how densely
clustered is the graph

Number of isolated clusters
Other integrity measures

Comparison of characteristic polynomials
of the damaged and non-damaged
networks

Informational measures
Comparative integrity measures
Integrity measures calculated for well
specified targeted damage or random
damage
 E.g., top 10% hub nodes deleted, average
damage by 10% of randomly selected
nodes deleted

Network analysis

Evaluation and categorisation of nodes

Evaluation of damaging capacity of nodes
and node combinations

Selection of nodes to achieve a desired
level of damage
Protein interaction systems

Protein interaction systems can be viewed
as networks

Static picture of the cell, ignores the
temporal activation of sub-networks of the
full protein interaction network
Protein interaction networks
E. coli
P. aeruginosa
Analysis of protein interaction
networks – 1

Protein interaction networks are scale-free
networks  high sensitivity to targeted
damage, low sensitivity to random damage

Earlier work shows that hub proteins are
likely to be essential proteins
Analysis of protein interaction
networks – 2

Conjecture: graph theoretic network
integrity is related to functional integrity of
the protein interaction system

Objective: determine important nodes and
node combinations that can cause
significant integrity damage
Analysis of protein interaction
networks – 3

Lists of hubs, bottlenecks, elementary
cycle nodes and other important nodes

Calculation of comparative damage
measures
Analysis of protein interaction
networks – 4

Calculation of optimal combination of
nodes that have damage potential above a
pre-specified limit

Cocktails of target proteins; blocking the
activity of target proteins causes
significant integrity damage to the protein
interaction network
Analysis of protein interaction
networks – 5

Checking potential targets for toxicity

BLAST comparison of targets with
important proteins of host organism

Selection of admissible targets and target
combinations
Protein network analysis

eXSys network analysis engine

Takes data files generated by the eXSys
data management engine

Performs network analysis and generates
suggested target protein cocktails of
admissible targets
Analysis of B. subtilis
B. subtilis
Important nodes for B. subtilis – 1
Hub nodes
Id
Swiss
-Prot
Id
Protein Name
Gene
Name
Function
355
P351
64
Sensor protein
resE
RESE
Member of the twocomponent regulatory
system resd/rese involved
in the global regulation of
aerobic and anaerobic
respiration. Probably
phosphorylates resd.
378
P164
97
Sporulation
kinase A
KINA
Phosphorylates the
sporulation-regulatory
proteins spo0a and spo0f.
It also autophosphorylates
in the presence of atp.
391
Q456
14
Sensor protein
yycG
YYCG
392
O316
61
YKRQ protein
YKRQ
393
P397
64
Sporulation
kinase C
KINC
Essential
Member of the twocomponent regulatory
system yycG/yycF
involved in the regulation
of the ftsAZ operon.
Probably phosphorylates
yycF.
Phosphorylates the
sporulation-regulatory
protein spo0a.
Important nodes for B. subtilis – 2
Bottleneck nodes
Id-
SwissProt Id
Protein Name
Gene Name
Significance
Function
121
P05652
DNA gyrase
subunit B
GYRB
Essential
DNA gyrase negatively supercoils closed
circular double-stranded DNA in an ATPdependent manner and also catalyzes the
interconversion of other topological isomers of
double-stranded DNA rings, including
catenanes and knotted rings.
122
Q45066
Topoisomerase IV
subunit A
PARC/GRLA
Essential
Topoisomerase IV is essential for chromosome
segregation. It has relaxation of supercoiled
DNA activity. Performs the decatenation events
required during the replication of a circular
DNA molecule
123
P05653
DNA gyrase
subunit A
GYRA
Essential
DNA gyrase negatively supercoils closed
circular double-stranded DNA in an ATPdependent manner and also catalyzes the
interconversion of other topological isomers of
double-stranded DNA rings, including
catenanes and knotted rings.
124
Q59192
Topoisomerase IV
subunit B
PARE
Essential
Topoisomerase IV is essential for chromosome
segregation. It has relaxation of supercoiled
DNA activity. Performs the decatenation events
required during the replication of a circular
DNA molecule
453
O07622
Hypothetical
protein yhw
YHFW
Important nodes for B. subtilis – 3
Other important nodes
Id
SwissProt Id
Protein Name
Gene
Name
Significance
Function
52
P16336
Preprotein translocase secY
subunit
SECY
Essential
Involved in protein export.
Interacts with secA and secE to
allow the translocation of
proteins across the plasma
membrane, by forming part of a
channel.
34
P42920
50S ribosomal protein L3
RPLC
Essential
This protein binds directly to
23S ribosomal RNA and may
participate in the formation of
the peptidyltransferase center of
the ribosome
35
P42921
50S ribosomal protein L4
RPLD
Essential
This protein binds directly and
specifically to 23S rRN
36
P42924
50S ribosomal protein L23
RPLW
Essential
Binds to a specific region on the
23S rRNA
37
P42919
50S ribosomal protein L3
RPLC
Essential
This protein binds directly to
23S ribosomal RNA and may
participate in the formation of
the peptidyltransferase center of
the ribosom
Target list for B. subtilis
Target nodes validated against human proteome
Id
Swiss-Prot
Id
Protein Name
Gene Name
Significance
55
P05647
50S ribosomal protein L34
RPMH
Essential
56
O06492
Glutamyl tRNA amidotransferase subunit C
GATC
Essential
374
Q45614
Sensor protein yycG
YYCG
Essential
410
P42924
Preprotein translocase secY subunit
SECY
Essential
776
P42060
50S ribosomal protein L22
RL22
Essential
eXSys proteome analysis system – 1

Components:
eXSys data management engine
 eXSys network analysis engine
 eXSys user interface and network
visualisation tool


Performs data collection, analysis of
protein interaction networks, provides user
interface and network visualisation
eXSys proteome analysis system – 2
Computational search for new
antibiotic targets

Bacterial proteome + host proteome

Analysis of bacterial proteome with BLAST
validation against the host proteome

List of potential antibiotic targets that can
cause significant damage to the bacteria
while are likely to not damage the host
New antibiotics

Usual antibiotics target a single protein or
a related class of proteins (e.g., penicillin
targeting PBPs, ribosomal antibiotics
targeting ribosomal subunits)

New antibiotics: multiple target proteins,
achieving effect by combined damage
Computational search for drug targets
for prion and genetic diseases – 1
Prions and mutated genes produce wrong
protein interactions within the protein
interaction network
 Restoring the functionality of the cells
might be done by adding or changing
existing proteins such that the functional
integrity of the protein interaction system is
restored

Computational search for drug targets
for prion and genetic diseases – 2

Analysing protein interaction systems of
diseased cells can lead to the prediction of
likely interventions that may lead to the
restoration of functional integrity of the
protein interaction system
Summary – 1
Cells can be perceived as protein
interaction systems
 Protein interaction systems can be
analysed as networks
 Protein interaction networks are scale-free
networks, which are resistant to random
damage but highly sensitive to targeted
damage

Summary – 2
The eXSys protein interaction network
analysis system can collect data about
proteomes and analyse them to detect
potential new drug target proteins
 Computational drug target discovery may
lead to new antibiotics and new drugs to
restore the functionality of diseased cells

eXSys project team

Project leaders:
Peter Andras
 Malcolm P Young


Project members:
Olusola Idowu
 Steven Lynden
 Panos Periorellis
