Networks of Protein Interactions

Construction of Networks from Diverse Data Sources

Neda Nategh

CS 374

Lecture 16

November 7, 2006

What we have learned about interaction networks in CS374

• Properties of interaction networks (Susan)• Comparison of networks across species (Chuan Sheng) Network alignment• Construction of networks from diverse data sources (Neda) Network integration

Biological aspects

Basics of protein Interaction Networks

Types of interactions

• Physical interactions

Protein pairs are in direct contact

• Complex interaction

Protein pairs participate in the same functional module Metabolic pathway Signaling network Multiprotein complex

Eukaryote-like glycosylation system of Campylobacter jejuni

Cell division machinery of Caulobacter crescentus

Protein Complex

A protein complex is a group of two or more associated

proteins.

Networks of proteins

• Topological properties

• Functional organization

news.uns.purdue.edu/UNS/images/cramer.photo2.jpeg

Metabolic Pathway

• Metabolic pathway is a series of chemical reactions occurring within a cell catalyzed by enzymes

formation of a metabolic product

initiation of another metabolic pathway

• Metabolic Networks

http://en.wikipedia.org/wiki/Metabolic_pathway

Signaling network

Signal transduction

Process by which a cell converts one kind of signal or stimulus into another.

A sequence of biochemical reactions inside the cell, which are carried out by enzymes and linked through second messengers.

http://en.wikipedia.org/wiki/Signal_transduction

High-throughput data

• Co-expression

• Co-location

• Co-inheritance

• Co-evolution

• Co-citation

• Rosetta stone(Gene-fusions)

Expression

Gene expression, or simply expression, is the process by which a gene's DNA sequence is converted into the structures and functions of a cell.

Indirectly, the expression of particular genes may be assessed with DNA microarray technology, which can provide a rough measure of the cellular concentration of different

messenger RNAs;

http://en.wikipedia.org/wiki/DNA_microarray

Inheritance

Proteins are clustered according to the similarity of their phylogenetic profiles. Similar profiles show a correlated pattern of inheritance and, by implication, functional linkage.

Evolution

Evolution is the change in the heritable traits of a population over successive generations, as determined by shifts in the allele frequencies of genes.

Gene fusion

• A fusion gene is a hybrid gene formed from two previously separate genes. translocation interstitial deletion chromosomal inversion

• By creating a fusion gene of a protein of interest and green fluorescent protein, the protein of interest may be observed in cells or tissue using fluorescence microscopy. The protein synthesized when a fusion gene is expressed is called a fusion protein.

http://en.wikipedia.org/wiki/Gene_fusion

Experiments

• Microarray analysis of gene expression

• Systematic protein interaction mapping

• Mass spectrometry

• Yeast two hybrid

• Synthetic lethal screens

Microarray analysis of gene expression

DNA microarray or gene/genome chip, DNA chip, or gene array

Collection of microscopic DNA spots attached to a solid surface, such as glass,plastic or silicon chip forming an array for the purpose of expression profiling, monitoring expression levels for thousands of genes simultaneously.Applications:• Identification of sequence• Determination of expression level of genes

http://phys.chem.ntnu.no/~bka/images/MicroArrays.jpg

Affinity purification/Mass spectrometry

• For characterization of proteins

• Using quantitative mass spectrometry

to analyze the composition of a partially

purified protein complex.

• Interacting proteins can be

distinguished from nonspecifically

co-purifying proteins by their abundance

ratios.

• Complexes can be analyzed after a

single step purification Better detection

of weakly associated proteins

http://en.wikipedia.org/wiki/Image:Mass_spectrom.gif

Yeast Two Hybrid

Two-hybrid screening is a molecular biology technique used to discover protein-protein interactions by testing for physical interactions (such as binding) between two proteins.

Susan Tang’s presentation, CS374 algorithms in biology, Stanford University

Synthetic lethal screening

• To interpret genetic networks by

examining the effects on the cell when pairs of genes are

knocked out simultaneously. Knocking out each gene separately may have no phenotypic

effect because of robustness provided by genetic redundancy, but knocking out both genes has a severe, possibly lethal effect.

Basics of protein Interaction Networks

Computational aspects

Statistics terminology

• Probability

• Probability density

• Conditional probability

• Prior/Postrior probability

• Bayes’ rule

Statistics terminology

True False

Positive True positive False positive(error type I)

Negative True negative False negative(error type II)

Graph theory

We map interaction networks to graphs

Vertex (node)

EdgeCycle

-5

Directed Edge (Arc)

Weighted Edge7

10

Networks in our model

• Undirected graphs

• Nodes correspond to proteins

• Edges represent the interactions

• Edge weights represent interaction probabilities

Network Clustering7000 Yeast interactionsamong 3000 proteins

Training sets

• KEGG(Kyoto Encyclopedia of Genes and Genomes)

• GFP(Green Fluorescent Protein)

• GO(Gene Ontology)

• COG(Cluster of Orthologous Groups of proteins)

Genomics• Genomics

• 1 genome• Assembly, Gene Finding

• Comparative Genomics• N genomes• Sequence Alignment

• Functional Genomics• 1 assay• Microarray Analysis

• Integrative Genomics• N assays• Network Integration

A probabilistic functional network of yeast genes

Insuk Lee, Shailesh V. Date, Alex T. Adai,Edward M. Marcotte

Motivation

Knowledge of gene networks’ structure

• Complex roles of individual genes

interplay between many systems in a cell

Problem

Heterogeneous functional genomics data• Microarray analyses of gene expression• Systematic protein interaction mapping measure different aspects of gene or protein associations• Mass spectrometry measure the tendency for proteins to be components of the

same physical module• Yeast two-hybrid assays indicate direct physical interaction(stable or transient) between

proteins• Synthetic lethal screens measure the tendency for genes to compensate for the loss of

other genes

Idea of integration

Constructing a more accurate and extensive gene network

Considering functional rather than physical associations

• genetic

• biochemical

• computational

probabilistic gene-gene linkages

Single coherent network

Scoring scheme

• Based on a Bayesian statistics approach• Log-likelihood score

• Frequencies of linkages (L) observed in the given experiment (E) between annotated genes operating in

• the same pathway is P(L|E)• different pathways is ~P(L|E)Total frequency of linkages between all annotated yeast genes

operating in • the same pathway is P(L)• different pathways is ~P(L)

Scoring scheme

• LLS > 0 Experiments tend to link genes in the same pathway

• Higher scores More confident linkages

• proportional to the accuracy of the experiments

• Different experiments’ scores are directly comparable

Data sources

Benchmarked accuracy and extent of functional genomics data sets and the integrated networks

Results

• Evidence from diverse sources

• Estimating the functional coupling between yeast genes

• A view of relations between yeast proteins distinct from their physical interactions

Probabilistic gene network

Future directions

Application of this strategy to other organisms such as human

• (i) assemble benchmarks for measuring the accuracy of linkages between human genes

• (ii) assemble gold standard sets of highly accurate interactions for calibrating the benchmarks

• (iii) benchmark functional genomics data for their ability to correctly link human genes

• then integrate the data as described.

Integrated protein interaction networks for 11 Microbes

Balaji S. Srinivasan, Antal F. Novak, Jason A. Flannick, Serafim Batzoglou, Harley H.

McAdams

Motivation

There are different methods to predict the interactions but the network generated by eah method are often contradictory

Objective:constructing a summary network for each species which

uses all the evidence at hand to predict which proteins are functionally linked

Data sourceCo-expression

1

.81

1

-.6

-.7Gene A

Gene B

Gene C

Ge

ne

B

Ge

ne

A

Ge

ne

C

Pearson Correlation

=.8

-.7 -.6

Genes

Arrays

Microarray data

Expression

Balaji S. Srinivasan

Data sourcesCo-location

0

.060

0

.25

.25Protein A

Protein B

Protein C

Pro

tein

B

Pro

tein

A

Pro

tein

C

Average chromosomal distance

.06

.25 .25

=.6.2.3.1

.5

.1.3.2.4

.25 .25 .05

Protein A

Protein B

Protein C

Ch

rom

2

Ch

rom

1

Ch

rom

4

Ch

rom

3

Location

Balaji S. SrinivasanAssembled Genomes

Data sourcesCo-evolution

1

.91

1

-.7

-.8Prt Fam A

Prt Fam B

Prt Fam C

Prt

Fa

m B

Prt

Fa

m A

Prt

Fa

m C

Tree Distances

.9

-.8 -.7

=

C’’

Evolution

A A’ A’’ A’’’

B’ B’’ B’’’B C’ C’’’C

Multiple Alignments Balaji S. Srinivasan

Data sourcesCo-inheritance

1

.951

1

-.95

-1Protein A

Protein B

Protein C

Pro

tein

B

Pro

tein

A

Pro

tein

C

.95

-1 -.95

=

Spearman Correlation

600200300100

500

100300200400

250 250 50

Protein A

Protein B

Protein C

Sp

ec

ies

2

Sp

ec

ies

1

Sp

ec

ies

4

Sp

ec

ies

3

Inheritance

Balaji S. SrinivasanBLAST bit scores

Integration of two predictors

• Previous work

• Recent work

• Method presented in this paper

Previous work

We can integrate two given networks by

• intersection

• union

• average

+

€

G1 = (V1,E1)coexpression

€

V1,V2 ∈ (V , set of all proteins)

coinheritance

€

G2 = (V2,E2)

=

€

E isc =1 if (E1 > T1) || (E2 > T2)

Eunion =1 if (E1 > T1) & & (E2 > T2)

Eavg = .5(E1 +E2)

Recent work

Bayesian Networks (Troyanskaya 2003) Decision Trees (Wong 2004)

Naïve Bayes + Boosting (Lu 2005)Likelihood Ratios (Lee 2004)

Training sets

• COG• GO• KEGG

From up COG to GO to

KEGG Fraction of annotated proteins in a given organism decreases Annotation quality is increases

1D Bayes’ rule

∑=

L

LPLEP

LPLEPELP

)()|(

)()|()|(

Bayes’ Rule: Calculateconditional probability oflinkage given evidence

Balaji S. Srinivasan

ID Bayes’ rule

Bayes error rate= min. error rate of classifier

A B

L=?E known

Different Function

A B

L=0

Same Function

A B

L=1

P(L|E)

Balaji S. Srinivasan

2D network integration

• 2D scatter plot Separates linked pairs from unlinked pairs more efficiently

• co-expression vs. co-inheritence

2D network integration

• Estimate densities Kernel density estimation Gray-Moore dual tree algorithm

2D network integration

2D network integration

• Posterior probability of interactions

• P(L=1|E)

• visual, geometric interpretation

€

P(L =1 | E) = .9

€

P(L =1 | E) = .5

€

P(L =1 | E) = .1Balaji S. Srinivasan

likely to interact

may interact

unlikely to interact

2D network integration

• Joint density reveals hidden biology

• moderate evidence

from multiple sources is

better than strong evidence

from one source

• subtle interactions

missed using one

evidence

• 30-60% of interaction data falls in this region!

Balaji S. Srinivasan

N predictors

Given all evidences

N=3)1|,,( 321 =LEEEP)0|,,( 321 =LEEEP ),,|1( 321 EEELP =

coex

pres

sion

(E1)

colocation (E2)

coinheritance (E3)

Conclusion and Future directions

This algorithm can be generalized to apply to discrete,ordinal or categorical data sets and is applicable to larger eukaryotic genomes

• Possibility of comparative modular biology

Align subgraphs of interaction networks

Network alignment algorithm scalable to large data sets and comparing many species simultaneously.

Question

?