Vol

March

doi

ARTICLES

Global

landscape

of

protein

complexes

in

the

yeast

Saccharomyces

cerevisiae

Nevan

J.

Gerard

Haiyuan

Gouqing

Xinghua

Alexandr

Joyce

Shuye

Nira

Aaron

P.

Thanuja

Jose

M.

Peregri

Michael

Xin

Michael

Mark

D.

Alberto

James

E.

Anthony

Bryan

Dawn

P.

Veronica

Atanas

Frank

Peter

Andrei

Myra

M.

James

Samuel

Chris

Sean

R.

Shamanta

Robin

Jennifer

J.

Kiran

Natalie

J.

Gabe

Peter

St

Shaun

Mandy

H.

Y.

Gareth

Amin

M.

Shigehiko

Ali

Erin

Jonathan

S.

C.

James

Timothy

R.

John

Mark

Shoshana

J.

Andrew

Jack

F.

Identification

of

protein

protein

interactions

often

provides

insight

into

protein

function

and

many

cellular

processes

are

performed

by

stable

protein

complexes

We

used

tandem

affinity

purification

to

process

different

tagged

proteins

of

the

yeast

Saccharomyces

cerevisiae

Each

preparation

was

analysed

by

both

matrix-assisted

laser

desorption

ionization

time

of

flight

mass

spectrometry

and

liquid

chromatography

tandem

mass

spectrometry

to

increase

coverage

and

accuracy

Machine

learning

was

used

to

integrate

the

mass

spectrometry

scores

and

assign

probabilities

to

the

protein

protein

interactions

Among

different

proteins

identified

with

high

confidence

by

mass

spectrometry

from

successful

purifications

our

core

data

set

-LRB-

median

precision

of

-RRB-

comprises

protein

protein

interactions

involving

proteins

A

Markov

clustering

algorithm

organized

these

interactions

into

protein

complexes

averaging

subunits

per

complex

about

half

of

them

absent

from

the

MIPS

database

as

well

as

additional

interactions

between

pairs

of

complexes

The

data

-LRB-

all

of

which

are

available

online

-RRB-

will

help

future

studies

on

individual

proteins

as

well

as

functional

genomics

and

systems

biology

Elucidation

of

the

budding

yeast

genome

initiated

a

decade

of

landmark

studies

addressing

key

aspects

of

yeast

cell

biology

on

a

system-wide

level

These

included

microarray-based

analysis

of

gene

screens

for

various

biochemical

identification

of

protein

subcellular

and

identifying

effects

of

single

and

pairwise

gene

Other

efforts

were

made

to

catalogue

physical

interactions

among

yeast

proteins

primarily

using

the

yeast

two-hybrid

and

direct

purification

via

affinity

many

of

these

interactions

are

conserved

in

other

Data

from

the

yeast

protein

protein

interaction

studies

have

been

non-overlapping

to

a

surprising

degree

a

fact

explained

partly

by

experimental

inaccuracy

and

partly

by

indications

that

no

single

screen

has

been

Proteome-wide

purification

of

protein

complexes

Of

the

various

high

throughput

experimental

methods

used

thus

far

to

identify

protein

protein

tandem

affinity

purification

-LRB-

TAP

-RRB-

of

affinity-tagged

proteins

expressed

from

their

natural

chromosomal

locations

followed

by

mass

has

provided

the

best

coverage

and

To

map

more

completely

the

yeast

protein

interaction

network

-LRB-

interactome

-RRB-

S.

cerevisiae

strains

were

generated

with

in-frame

insertions

of

TAP

tags

individually

introduced

by

homologous

recombination

at

the

end

of

each

predicted

open

reading

frame

-LRB-

ORF

-RRB-

-LRB-

http

Proteins

were

purified

from

yeast

cultures

under

native

conditions

and

the

identities

of

the

copurifying

proteins

-LRB-

preys

-RRB-

determined

in

two

complementary

Each

purified

protein

preparation

was

electrophoresed

on

an

SDS

polyacrylamide

gel

stained

with

silver

and

visible

bands

removed

and

identified

by

trypsin

digestion

and

peptide

mass

fingerprinting

using

matrix-assisted

laser

desorption/ionization

time

of

flight

-LRB-

MALDI

TOF

-RRB-

mass

spectrometry

In

parallel

another

aliquot

of

each

purified

protein

preparation

was

digested

in

solution

and

the

peptides

were

separated

and

sequenced

by

data-dependent

liquid

chromatography

tandem

mass

spectrometry

-LRB-

LC-MS

MS

-RRB-

Because

either

mass

spectrometry

method

often

fails

to

Banting

and

Best

Department

of

Medical

Research

Terrence

Donnelly

Centre

for

Cellular

and

Biomolecular

Research

University

of

Toronto

College

St

Toronto

Ontario

Canada

of

Medical

Genetics

and

Microbiology

University

of

Toronto

Kings

College

Circle

Toronto

Ontario

Canada

Institute

University

College

Dublin

Belfield

Dublin

Ireland

of

Molecular

Biophysics

and

Biochemistry

Whitney

Avenue

Yale

University

PO

Box

New

Haven

Connecticut

USA

for

Sick

Children

University

Avenue

Toronto

Ontario

Canada

Pharmaceuticals

University

Avenue

Toronto

Ontario

Canada

Hughes

Medical

Institute

Department

of

Cellular

and

Molecular

Pharmacology

UCSF

Genentech

Hall

St

San

Francisco

California

USA

Genomics

Laboratory

Nara

Institute

of

Science

and

Technology

Takayama

Ikoma

Nara

Japan

of

Biochemistry

Saint

Louis

University

School

of

Medicine

South

Grand

Boulevard

St

Louis

Missouri

USA

Hughes

Medical

Institute

Department

of

Molecular

and

Cellular

Biology

Harvard

University

Divinity

Avenue

Cambridge

Massachusetts

USA

Present

address

Department

of

Cellular

and

Molecular

Pharmacology

UCSF

San

Francisco

California

USA

These

authors

contributed

equally

to

this

work

Nature

Publishing

Group

ARTICLES

NATURE

Vol

March

identify

a

protein

we

used

two

independent

mass

spectrometry

methods

to

increase

interactome

coverage

and

confidence

Among

the

attempted

purifications

of

different

proteins

-LRB-

Supplementary

Table

-RRB-

including

all

predicted

non-membrane

proteins

purifications

were

successful

-LRB-

Supplementary

Table

-RRB-

in

that

at

least

one

protein

was

identified

-LRB-

in

cases

by

MALDI

TOF

mass

spectrometry

and

in

cases

by

LC-MS/MS

Fig.

-RRB-

that

was

not

present

in

a

control

preparation

from

an

untagged

strain

In

total

different

yeast

proteins

were

identified

as

preys

with

high

confidence

-LRB-

see

Methods

-RRB-

by

MALDI

TOF

mass

spectrometry

and/or

LC-MS/MS

corresponding

to

of

the

predicted

yeast

proteome

-LRB-

Supplementary

Table

-RRB-

Smaller

proteins

with

a

relative

molecular

mass

-LRB-

M

r

-RRB-

of

were

less

likely

to

be

identified

-LRB-

Fig.

-RRB-

perhaps

because

they

generate

fewer

peptides

suited

for

identification

by

mass

spectrometry

We

were

more

successful

in

identifying

smaller

proteins

by

LC-MS/MS

than

by

MALDI

TOF

mass

spectrometry

probably

because

smaller

proteins

stain

less

well

with

silver

or

ran

off

the

SDS

gels

Our

success

in

protein

identification

was

unrelated

to

protein

essentiality

-LRB-

data

not

shown

-RRB-

and

ranged

from

for

low

abundance

proteins

to

over

for

high

abundance

proteins

-LRB-

Fig.

-RRB-

Notably

we

identified

of

the

proteins

not

detected

by

genome-wide

western

indicating

that

affinity

purification

followed

by

mass

spectrometry

can

be

more

sensitive

Many

hypothetical

proteins

not

detected

by

western

or

our

mass

spectrometry

analyses

may

not

be

expressed

in

our

standard

cell

growth

conditions

Although

our

success

rates

for

identifying

proteins

were

and

for

nuclear

and

cytosolic

proteins

respectively

and

at

least

in

most

cellular

compartments

-LRB-

Fig.

-RRB-

they

were

lower

-LRB-

and

respectively

-RRB-

for

the

endoplasmic

reticulum

and

vacuole

However

even

though

we

had

not

tagged

or

purified

most

proteins

with

transmembrane

domains

we

identified

over

of

the

membrane-associated

proteins

perhaps

because

our

extraction

and

purification

buffers

contained

Triton

Our

identification

success

rate

was

lowest

-LRB-

-RRB-

with

proteins

for

which

localization

was

not

many

of

which

may

not

be

expressed

We

had

high

success

in

identifying

proteins

involved

in

all

biological

processes

as

defined

by

gene

ontology

-LRB-

GO

-RRB-

nomenclature

or

possessing

any

broadly

defined

GO

molecular

function

-LRB-

Fig.

f

-RRB-

We

were

less

successful

-LRB-

each

about

success

-RRB-

with

transporters

and

proteins

of

unknown

function

many

of

the

latter

may

not

be

expressed

A

high-quality

data

set

of

protein

protein

interactions

Deciding

whether

any

two

proteins

interact

based

on

our

data

must

encompass

results

from

two

purifications

-LRB-

plus

repeat

purifications

if

performed

-RRB-

and

integrate

reliability

scores

from

all

protein

identifications

by

mass

spectrometry

Removed

from

consideration

as

likely

nonspecific

contaminants

were

preys

detected

in

of

the

purifications

and

nearly

all

cytoplasmic

ribosomal

subunits

-LRB-

Supplementary

Table

-RRB-

Although

the

cytosolic

ribosomes

and

pre-ribosomes

as

well

as

some

associated

translation

factors

are

not

represented

in

the

interaction

network

and

protein

complexes

we

subsequently

identified

we

previously

described

the

interactome

for

proteins

involved

in

RNA

metabolism

and

ribosome

We

initially

generated

an

intersection

data

set

of

protein

protein

interactions

based

only

on

proteins

identified

in

at

least

one

purification

by

both

MALDI

TOF

mass

spectrometry

and

LC-MS/MS

with

relatively

low

thresholds

-LRB-

-RRB-

-LRB-

Supplementary

Table

-RRB-

This

intersection

data

set

containing

proteins

was

of

reasonable

quality

but

limited

in

scope

-LRB-

Fig.

-RRB-

Our

second

approach

added

to

the

intersection

data

set

proteins

identified

either

reciprocally

or

repeatedly

by

only

a

single

mass

spectrometry

method

Figure

The

yeast

interactome

encompasses

a

large

proportion

of

the

predicted

proteome

a

Summary

of

our

screen

for

protein

interactions

PPI

protein

protein

interactions

b

f

The

proportions

of

proteins

identified

in

the

screen

as

baits

or

preys

are

shown

in

relation

to

protein

mass

-LRB-

b

-RRB-

expression

level

-LRB-

c

-RRB-

intracellular

localization

-LRB-

d

-RRB-

and

annotated

GO

molecular

function

-LRB-

e

-RRB-

and

GO

biological

process

-LRB-

f

-RRB-

Nature

Publishing

Group

NATURE

Vol

March

ARTICLES

to

generate

the

merged

data

set

The

merged

data

set

containing

proteins

and

protein

protein

interactions

-LRB-

Supplementary

Table

-RRB-

had

better

coverage

than

the

intersection

network

-LRB-

Fig.

-RRB-

To

deal

objectively

with

noise

in

the

raw

data

and

improve

precision

and

recall

we

used

machine

learning

algorithms

with

two

rounds

of

learning

All

four

classifiers

were

validated

by

the

hold-out

method

-LRB-

for

training

and

for

testing

-RRB-

and

tentimes

tenfold

cross-validation

which

gave

similar

results

Because

our

objective

was

to

identify

protein

complexes

we

used

the

handcurated

protein

complexes

in

the

MIPS

reference

as

our

training

set

Our

goal

was

to

assign

a

probability

that

each

pairwise

interaction

is

true

based

on

experimental

reproducibility

and

mass

spectrometry

scores

from

the

relevant

purifications

-LRB-

see

Methods

-RRB-

In

the

first

round

of

learning

we

tested

bayesian

inference

networks

and

different

kinds

of

decision

settling

on

bayesian

networks

and

and

boosted

stump

decision

trees

as

providing

the

most

reliable

predictions

-LRB-

Fig.

-RRB-

We

then

improved

performance

by

using

the

output

of

the

three

methods

as

input

for

a

second

round

of

learning

with

a

stacking

algorithm

in

which

logistic

regression

was

the

We

used

a

probability

cut-off

of

-LRB-

average

median

-RRB-

to

define

a

core

data

set

of

protein

protein

interactions

involving

proteins

-LRB-

Supplementary

Table

-RRB-

and

a

cut-off

of

-LRB-

average

median

-RRB-

for

an

extended

data

set

of

protein

protein

interactions

involving

proteins

-LRB-

Supplementary

Table

-RRB-

The

interaction

probabilities

in

Supplementary

Tables

and

are

likely

to

be

underestimated

because

the

MIPS

complexes

used

as

a

gold

standard

are

themselves

We

subsequently

used

the

core

protein

protein

interaction

data

set

to

define

protein

complexes

-LRB-

see

below

-RRB-

but

the

extended

data

set

probably

contains

at

least

correct

interactions

-LRB-

as

well

as

many

more

false

interactions

-RRB-

not

present

in

the

core

data

set

The

complete

set

of

protein

protein

interactions

and

their

associated

probabilities

-LRB-

Supplementary

Table

-RRB-

were

used

to

generate

a

ROC

curve

with

a

performance

-LRB-

area

under

the

curve

-RRB-

of

-LRB-

Fig.

-RRB-

Predictive

sensitivity

-LRB-

true

positive

rate

-RRB-

or

specificity

-LRB-

false

positive

rate

-RRB-

or

both

are

superior

for

our

learned

data

set

than

for

the

intersection

and

merged

data

sets

each

previous

high-throughput

study

of

yeast

protein

protein

or

a

bayesian

combination

of

the

data

from

all

these

-LRB-

Fig.

-RRB-

Identification

of

complexes

within

the

interaction

network

In

the

protein

interaction

network

generated

by

our

core

data

set

of

protein

protein

interactions

the

average

degree

-LRB-

number

of

interactions

per

protein

-RRB-

is

and

the

distribution

of

the

number

of

interactions

per

protein

follows

an

inverse

power

law

-LRB-

Fig.

-RRB-

indicating

scale-free

network

These

protein

protein

interactions

could

be

represented

as

a

weighted

graph

-LRB-

not

shown

-RRB-

in

which

individual

proteins

are

nodes

and

the

weight

of

the

arc

connecting

two

nodes

is

the

probability

that

interaction

is

correct

Because

the

successful

purifications

underlying

such

a

graph

would

represent

of

the

detectably

expressed

we

have

typically

purified

multiple

subunits

of

a

given

complex

To

identify

highly

connected

modules

within

the

global

protein

protein

interaction

network

we

used

the

Markov

cluster

algorithm

which

simulates

random

walks

within

We

chose

values

for

the

expansion

and

inflation

operators

of

the

Markov

cluster

procedure

that

optimized

overlap

with

the

hand-curated

MIPS

Although

the

Markov

cluster

algorithm

displays

good

convergence

and

robustness

it

does

not

necessarily

separate

two

or

more

complexes

that

have

shared

subunits

-LRB-

for

example

RNA

polymerases

I

and

III

or

chromatin

modifying

complexes

-LRB-

S

-RRB-

and

-LRB-

L

-RRB-

-RRB-

The

Markov

cluster

procedure

identified

distinct

-LRB-

nonoverlapping

-RRB-

heteromeric

protein

complexes

-LRB-

Supplementary

Table

-RRB-

about

half

of

which

are

not

present

in

MIPS

or

two

previous

high-throughput

studies

of

yeast

complexes

using

affinity

purification

and

mass

spectrometry

-LRB-

Fig.

-RRB-

New

subunits

or

interacting

proteins

were

identified

for

most

complexes

that

had

been

identified

previously

-LRB-

Fig.

-RRB-

Overlap

of

our

Markov-clustercomputed

complexes

with

the

MIPS

complexes

was

evaluated

-LRB-

see

Supplementary

Information

-RRB-

by

calculating

the

total

precision

-LRB-

measure

of

the

extent

to

which

proteins

belonging

to

one

reference

MIPS

complex

are

grouped

within

one

of

our

complexes

and

vice

versa

-RRB-

and

homogeneity

-LRB-

measure

of

the

extent

to

which

proteins

from

the

same

MIPS

complex

are

distributed

across

our

complexes

and

vice

versa

-RRB-

-LRB-

Fig.

-RRB-

Both

precision

and

homogeneity

were

higher

for

the

complexes

generated

in

this

study

even

for

the

extended

set

of

protein

protein

interactions

than

for

complexes

generated

by

both

previous

high-throughput

studies

of

yeast

complexes

perhaps

because

the

increased

number

of

successful

purifications

in

this

study

increased

the

density

of

connections

within

most

modules

The

average

number

of

different

proteins

per

complex

is

but

the

distribution

-LRB-

Fig.

-RRB-

which

follows

an

inverse

power

law

is

characterized

by

a

large

number

of

small

complexes

most

often

containing

only

two

to

four

different

polypeptides

and

a

much

smaller

number

of

very

large

complexes

Proteins

in

the

same

complex

should

have

similar

function

and

colocalize

to

the

same

subcellular

compartment

To

evaluate

this

we

Figure

Machine

learning

generates

a

core

data

set

of

protein

protein

interactions

a

Reliability

of

observed

protein

protein

interactions

was

estimated

using

probabilistic

mass

spectra

database

search

scores

and

measures

of

experimental

reproducibility

-LRB-

see

Methods

-RRB-

followed

by

machine

learning

b

Precision-sensitivity

ROC

plot

for

our

protein

protein

interaction

data

set

generated

by

machine

learning

Precision/sensitivity

values

are

also

shown

for

the

intersection

and

merged

data

sets

-LRB-

see

text

-RRB-

and

for

other

large-scale

affinity

and

data

sets

and

a

bayesian

networks

combination

of

those

data

all

based

on

comparison

to

MIPS

complexes

FP

false

positive

TP

true

positive

c

Plot

of

the

number

of

nodes

against

the

number

of

edges

per

node

demonstrates

that

the

core

data

set

protein

protein

interaction

network

has

scale-free

properties

Nature

Publishing

Group

ARTICLES

NATURE

Vol

March

Figure

Organization

of

the

yeast

protein

protein

interaction

network

into

protein

complexes

a

Pie

charts

showing

how

many

of

our

complexes

have

the

indicated

percentages

of

their

subunits

appearing

in

individual

MIPS

complexes

or

complexes

identified

by

other

affinity-based

purification

b

Precision

and

homogeneity

-LRB-

see

text

-RRB-

in

comparison

to

MIPS

complexes

for

three

large-scale

studies

c

The

relationship

between

complex

size

-LRB-

number

of

different

subunits

-RRB-

and

frequency

d

Graphical

representation

of

the

complexes

This

Cytoscape

GenePro

screenshot

displays

patterns

of

evolutionary

conservation

of

complex

subunits

Each

pie

chart

represents

an

individual

complex

its

relative

size

indicating

the

number

of

proteins

in

the

complex

The

thicknesses

of

the

edges

connecting

complexes

are

proportional

to

the

number

of

protein

protein

interactions

between

connected

nodes

Complexes

lacking

connections

shown

at

the

bottom

of

this

figure

have

interactions

with

any

other

complex

Sector

colours

-LRB-

see

panel

f

-RRB-

indicate

the

proportion

of

subunits

sharing

significant

sequence

similarity

to

various

taxonomic

groups

-LRB-

see

Methods

-RRB-

Insets

provide

views

of

two

selected

complexes

the

kinetochore

machinery

and

a

previously

uncharacterized

highly

conserved

complex

-LRB-

see

text

for

details

-RRB-

detailing

specific

interactions

between

proteins

identified

within

the

complex

-LRB-

purple

borders

-RRB-

and

with

other

proteins

that

interact

with

at

least

one

member

of

the

complex

-LRB-

blue

borders

-RRB-

Colours

indicate

taxonomic

similarity

e

Relationship

between

protein

frequency

in

the

core

data

set

and

degree

of

connectivity

or

betweenness

as

a

function

of

conservation

Colours

of

the

bars

indicate

the

evolutionary

grouping

f

Colour

key

indicating

the

taxonomic

groupings

-LRB-

and

their

phylogenetic

relationships

-RRB-

Numbers

indicate

the

total

number

of

ORFs

sharing

significant

sequence

similarity

with

a

gene

in

at

least

one

organism

associated

with

that

group

and

importantly

not

possessing

similarity

to

any

gene

from

more

distantly

related

organisms

Nature

Publishing

Group

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Vol

March

ARTICLES

calculated

the

weighted

average

of

the

fraction

of

proteins

in

each

complex

that

maps

to

the

same

localization

-LRB-

see

Supplementary

Information

-RRB-

Co-localization

was

better

for

the

complexes

in

our

study

than

for

previous

high-throughput

studies

but

not

unexpectedly

less

than

that

for

the

curated

MIPS

complexes

-LRB-

Supplementary

Fig

-RRB-

We

also

evaluated

the

extent

of

semantic

for

the

GO

terms

in

the

biological

process

category

for

pairs

of

interacting

proteins

within

our

complexes

-LRB-

Supplementary

Fig

-RRB-

and

found

that

semantic

similarity

was

lower

for

our

core

data

set

than

for

the

MIPS

complexes

or

the

previous

study

using

TAP

but

higher

than

for

a

study

using

protein

This

might

be

expected

if

the

previous

TAP

tag

study

significantly

influenced

the

semantic

classifications

in

GO

To

analyse

and

visualize

our

entire

collection

of

complexes

the

highly

connected

modules

identified

by

Markov

clustering

for

the

global

core

protein

protein

interaction

network

were

displayed

-LRB-

-RRB-

using

our

GenePro

plug-in

for

the

Cytoscape

software

-LRB-

Fig.

-RRB-

Each

complex

is

represented

as

a

pie-chart

node

and

the

complexes

are

connected

by

a

limited

number

-LRB-

-RRB-

of

high-confidence

interactions

Assignment

of

connecting

proteins

to

a

particular

module

can

therefore

be

arbitrary

and

the

limited

number

of

connecting

proteins

could

just

as

well

be

part

of

two

or

more

distinct

complexes

The

size

and

colour

of

each

section

of

a

pie-chart

node

can

be

made

to

represent

the

fraction

of

the

proteins

in

each

complex

that

maps

into

a

given

complex

from

the

hand-curated

MIPS

complexes

-LRB-

Supplementary

Fig

-RRB-

Similar

displays

can

be

generated

when

highlighting

instead

the

subcellular

localizations

or

GO

biological

process

functional

annotations

of

proteins

in

each

complex

Furthermore

the

protein

protein

interaction

details

of

individual

complexes

can

readily

be

visualized

-LRB-

see

Supplementary

Information

-RRB-

Evolutionary

conservation

of

protein

complexes

ORFs

encoding

each

protein

were

placed

into

nine

distinct

evolutionary

groups

-LRB-

Fig.

-RRB-

based

on

their

taxonomic

profiles

-LRB-

see

Methods

-RRB-

and

the

complexes

displayed

so

as

to

show

the

evolutionary

conservation

of

their

components

-LRB-

Fig.

-RRB-

Insets

highlight

the

kinetochore

complex

required

for

chromosome

segregation

and

a

novel

highly

conserved

complex

involved

in

degradation

of

Strong

co-evolution

was

evident

for

components

of

some

large

and

essential

complexes

-LRB-

for

example

and

proteasomes

involved

in

protein

degradation

the

exosome

involved

in

RNA

metabolism

and

the

complex

required

for

the

motility

and

integrity

of

cortical

actin

patches

-RRB-

Conversely

the

kinetochore

complex

the

mediator

complex

required

for

regulated

transcription

and

the

RSC

complex

that

remodels

chromatin

have

a

Figure

Characterization

of

three

previously

unreported

protein

complexes

and

a

novel

RNAPII-interacting

factor

a

Identification

of

three

novel

complexes

by

SDS

PAGE

silver

staining

and

mass

spectrometry

The

same

novel

complex

containing

was

obtained

after

purification

from

strains

with

other

tagged

subunits

-LRB-

data

not

shown

-RRB-

b

Identification

of

-LRB-

interacts

with

RNAPII

-RRB-

Tagging

and

purification

of

unique

RNAPII

subunits

identified

-LRB-

-RRB-

as

a

novel

RNAPII-associated

factor

-LRB-

Supplementary

Fig

-RRB-

Purification

of

is

shown

here

c

Genetic

interactions

of

with

various

transcription

factors

Lines

connect

genes

with

synthetic

lethal/sick

genetic

interactions

d

Microarray

analysis

on

the

indicated

deletion

strains

Pearson

correlation

coefficients

were

calculated

for

the

effects

on

gene

expression

of

each

deletion

pair

and

organized

by

two-dimensional

hierarchical

clustering

e

Antibody

generated

against

the

amino-terminal

amino

acid

sequence

-LRB-

DDDDDDDSFASADGE

-RRB-

of

the

Drosophila

homologue

of

-LRB-

-RRB-

and

a

monoclonal

antibody

-LRB-

-RRB-

against

RNAPII

subunit

phosphorylated

on

of

the

heptapeptide

repeat

of

its

carboxy-terminal

were

used

for

co-localization

studies

on

polytene

chromosomes

as

previously

Nature

Publishing

Group

ARTICLES

NATURE

Vol

March

high

proportion

of

fungi-specific

subunits

Previous

studies

have

shown

that

highly

connected

proteins

within

a

network

tend

to

be

more

highly

a

consequence

of

either

functional

constraints

or

preferential

interaction

of

new

proteins

with

existing

highly

connected

For

the

network

as

a

whole

and

consistent

with

earlier

studies

Fig.

reveals

that

the

frequency

of

ORFs

with

a

large

number

-LRB-

-RRB-

of

connections

is

proportional

to

the

relative

distance

of

the

evolutionary

group

Betweenness

provides

a

measure

of

how

central

a

protein

is

in

a

network

typically

calculated

as

the

fraction

of

shortest

paths

between

node

pairs

passing

through

a

node

of

interest

Figure

shows

that

highly

conserved

proteins

tend

to

have

higher

values

of

betweenness

Despite

these

average

network

properties

the

subunits

of

some

complexes

-LRB-

for

example

the

kinetochore

complex

-RRB-

display

a

high

degree

of

connectedness

despite

restriction

to

hemiascomycetes

These

findings

suggest

caution

in

extrapolating

network

properties

to

the

properties

of

individual

complexes

We

also

investigated

the

relationship

between

an

ORF

's

essentiality

and

its

conservation

degree

of

connectivity

and

betweenness

-LRB-

Supplementary

Fig

-RRB-

Consistent

with

previous

essential

genes

tend

to

be

more

highly

conserved

highly

connected

and

central

to

the

network

-LRB-

as

defined

by

betweenness

-RRB-

presumably

reflecting

their

integrating

role

Examples

of

new

protein

complexes

and

interactions

Among

the

complexes

not

in

MIPS

that

we

identified

three

are

shown

in

Fig.

One

contains

and

binds

subtelomeric

TTAGGG

repeats

and

insulates

adjacent

genes

from

telomeric

suggesting

that

this

trimeric

complex

might

be

involved

in

this

process

Consistent

with

this

a

hypomorphic

DAmP

-LRB-

untranslated

region

-LRB-

UTR

-RRB-

deletion

-RRB-

of

the

essential

gene

causes

a

synthetic

growth

defect

when

combined

with

a

deletion

of

-LRB-

data

not

shown

-RRB-

suggesting

that

and

have

a

common

function

and

are

the

only

yeast

proteins

containing

BED

Zinc-finger

domains

thought

to

mediate

DNA

binding

or

protein

protein

suggesting

that

each

uses

its

BED

domain

to

interact

with

or

enhance

DNA

binding

by

Another

novel

complex

in

Fig.

contains

and

six

other

subunits

-LRB-

also

see

Fig.

inset

-RRB-

Five

of

its

subunits

-LRB-

-RRB-

have

been

genetically

linked

to

proteasome-dependent

catabolite-induced

degradation

of

suggesting

that

the

remaining

two

subunits

-LRB-

-RRB-

hypothetical

proteins

of

hitherto

unknown

function

are

probably

involved

in

the

same

process

was

reported

to

be

in

a

complex

with

a

M

r

of

approximately

-LRB-

ref

-RRB-

similar

to

the

sum

of

the

apparent

M

r

values

of

the

subunits

of

the

complex

The

third

novel

complex

contains

and

Because

is

related

to

nucleosome

assembly

protein

and

is

involved

in

Ty

this

complex

may

be

involved

in

chromatin

assembly

or

function

Our

systematic

characterization

of

complexes

by

TAP

and

mass

spectrometry

has

often

led

to

the

identification

of

new

components

of

established

protein

complexes

-LRB-

Fig.

-RRB-

Figure

highlights

-LRB-

-RRB-

which

co-purifies

with

RNA

polymerase

II

-LRB-

RNAPII

-RRB-

along

with

general

initiation

factor

TFIIF

and

transcription

elongation

factors

and

-LRB-

TFIIS

-RRB-

-LRB-

Figs

and

-LRB-

inset

-RRB-

see

also

Supplementary

Fig

-RRB-

We

used

synthetic

genetic

array

-LRB-

SGA

-RRB-

in

a

quantified

high-density

E-MAP

to

systematically

identify

synthetic

genetic

interactions

for

with

deletions

of

the

elongation

factor

gene

the

SWR

complex

that

assembles

the

variant

histone

into

an

histone

deacetylase

complex

-LRB-

-LRB-

L

-RRB-

-RRB-

that

mediates

promoter-specific

transcriptional

the

histone

methyltransferase

complex

-LRB-

COMPASS

-RRB-

the

activity

of

which

is

linked

to

elongation

by

and

other

transcriptionrelated

genes

-LRB-

Fig.

-RRB-

Moreover

DNA

microarray

analyses

of

the

effects

on

gene

expression

of

deletions

of

and

other

genes

involved

in

transcription

by

RNAPII

followed

by

clustering

of

the

genes

according

to

the

similarity

of

their

effects

on

gene

expression

revealed

that

deletion

of

is

most

similar

in

its

effects

on

mRNA

levels

to

deletion

of

-LRB-

Fig.

-RRB-

a

subunit

of

RNAPII

with

multiple

roles

in

We

also

made

use

of

the

fact

that

is

highly

conserved

-LRB-

Supplementary

Fig

-RRB-

with

a

homologue

in

Drosophila

melanogaster

Fig.

shows

that

Drosophila

partly

co-localizes

with

phosphorylated

actively

transcribing

RNAPII

on

polytene

chromosomes

suggesting

that

is

an

evolutionarily

conserved

transcription

factor

Conclusions

We

have

described

the

interactome

and

protein

complexes

underlying

most

of

the

yeast

proteome

Our

results

comprise

protein

protein

interactions

for

proteins

in

the

core

data

set

Greater

coverage

and

accuracy

were

achieved

compared

with

previous

high-throughput

studies

of

yeast

protein

protein

interactions

as

a

consequence

of

four

aspects

of

our

approach

first

unlike

a

previous

study

using

affinity

purification

and

mass

we

avoided

potential

artefacts

caused

by

protein

overproduction

second

we

were

able

to

ensure

greater

data

consistency

and

reproducibility

by

systematically

tagging

and

purifying

both

interacting

partners

for

each

protein

protein

interaction

third

we

enhanced

coverage

and

reproducibility

especially

for

proteins

of

lower

abundance

by

using

two

independent

methods

of

sample

preparation

and

complementary

mass

spectrometry

procedures

for

protein

identification

-LRB-

in

effect

up

to

four

spectra

were

available

for

statistically

evaluating

the

validity

of

each

PPI

-RRB-

and

finally

we

used

rigorous

computational

procedures

to

assign

confidence

values

to

our

predictions

It

is

important

to

note

however

that

our

data

represent

a

snapshot

of

protein

protein

interactions

and

complexes

in

a

particular

yeast

strain

subjected

to

particular

growth

conditions

Both

the

quality

of

the

mass

spectrometry

spectra

used

for

protein

identification

and

the

approximate

stoichiometry

of

the

interacting

protein

partners

can

be

evaluated

by

accessing

our

publicly

available

comprehensive

database

-LRB-

-RRB-

that

reports

gel

images

protein

identifications

protein

protein

interactions

and

supporting

mass

spectrometry

data

-LRB-

Supplementary

Information

and

Supplementary

Fig

-RRB-

Soon

to

be

linked

to

our

database

will

be

thousands

of

sites

of

post-translational

modification

tentatively

identified

during

our

LC-MS/MS

analyses

-LRB-

manuscript

in

preparation

-RRB-

The

protein

interactions

and

assemblies

we

identified

provide

entry

points

for

studies

on

individual

gene

products

many

of

which

are

evolutionarily

conserved

as

well

as

systems

biology

approaches

to

cell

physiology

in

yeast

and

other

eukaryotic

organisms

METHODS

Experimental

procedures

and

mass

spectrometry

Proteins

were

tagged

purified

and

prepared

for

mass

spectrometry

as

previously

Gel

images

mass

spectra

and

confidence

scores

for

protein

identification

by

mass

spectrometry

are

found

in

our

database

-LRB-

-RRB-

Confidence

scores

for

protein

identification

by

LC-MS/MS

were

calculated

as

described

After

processing

database

searches

for

each

spectrum

a

score

of

corresponding

to

confidence

-LRB-

A.P.T.

and

N.J.K

unpublished

data

-RRB-

was

used

as

a

cut-off

for

protein

identification

by

MALDI

TOF

mass

spectrometry

Synthetic

genetic

interactions

and

effects

of

deletion

mutations

on

gene

expression

were

identified

as

described

Drosophila

polytene

chromosomes

were

stained

with

anti-peptide

antibody

and

monoclonal

antibody

as

previously

Identification

of

protein

complexes

Details

of

the

methods

for

identification

of

protein

complexes

and

calculating

their

overlaps

with

various

data

sets

are

described

in

Supplementary

Information

Protein

property

analysis

We

used

previously

published

yeast

protein

localization

and

yeast

protein

properties

were

obtained

from

the

SGD

-LRB-

http

-RRB-

and

GO

-LRB-

-RRB-

databases

Proteins

expressed

at

high

medium

or

low

levels

have

expression

log

values

of

or

Phylogenetic

analysis

For

each

S.

cerevisiae

sequence

a

BLAST

and

TBLASTX

Nature

Publishing

Group

NATURE

Vol

March

ARTICLES

search

was

performed

against

each

of

the

different

organism

data

sets

including

predicted

ORFs

from

fully

sequenced

genomes

expressed

sequence

tag

consensus

sequences

-LRB-

obtained

from

-RRB-

and

some

raw

genomic

sequences

Using

a

BLAST

bit

score

cut-off

of

a

taxonomic

profile

for

each

ORF

was

obtained

by

identifying

sequences

sharing

significant

similarity

to

at

least

one

organism

from

each

group

An

ORF

is

said

to

be

specific

to

each

group

only

if

it

has

a

match

to

an

organism

within

that

group

and

not

to

any

organism

deemed

to

be

more

distantly

related

Values

of

betweenness

were

calculated

using

the

software

Pajek

-LRB-

pajek

-RRB-

Received

December

accepted

February

Published

online

March

Goffeau

A.

et

al.

Life

with

genes

Science

-LRB-

-RRB-

Hughes

T.

R.

et

al.

Functional

discovery

via

a

compendium

of

expression

profiles

Cell

-LRB-

-RRB-

Martzen

M.

R.

et

al.

A

biochemical

genomics

approach

for

identifying

genes

by

the

activity

of

their

products

Science

-LRB-

-RRB-

Zhu

H.

Snyder

M.

Protein

chip

technology

Curr

Opin

Chem

Biol

-LRB-

-RRB-

Huh

W.

K.

et

al.

Global

analysis

of

protein

localization

in

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Supplementary

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is

linked

to

the

online

version

of

the

paper

at

Acknowledgements

We

thank

M.

Chow

N.

Mohammad

C.

Chung

and

V.

Fong

for

their

assistance

with

the

creation

of

the

web

resources

We

are

grateful

to

J.

van

Helden

and

S.

Brohee

for

sharing

information

on

their

comparison

of

clustering

methods

before

publication

This

research

was

supported

by

grants

from

Genome

Canada

and

the

Ontario

Genomics

Institute

-LRB-

to

J.F.G.

and

A.E.

-RRB-

the

Canadian

Institutes

of

Health

Research

-LRB-

to

A.E.

N.J.K.

J.F.G.

S.J.W.

S.P.

and

C.J.I.

-RRB-

the

National

Cancer

Institute

of

Canada

with

funds

from

the

Canadian

Cancer

Society

-LRB-

to

J.F.G.

-RRB-

the

Howard

Hughes

Medical

Institute

-LRB-

to

J.S.W.

and

E.O.

-RRB-

the

McLaughlin

Centre

for

Molecular

Medicine

-LRB-

to

S.J.W.

and

S.P.

-RRB-

the

Hospital

for

Sick

Children

-LRB-

to

J.M.P.-A

-RRB-

the

National

Sciences

and

Engineering

Research

Council

-LRB-

to

N.J.K.

T.R.H.

and

A.E.

-RRB-

and

the

National

Institutes

of

Health

-LRB-

to

A.S.

M.G.

A.P.

and

H.Y.

-RRB-

Author

Information

Protein

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information

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this

paper

has

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provided

to

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BioGRID

database

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as

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as

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International

Molecular

Interaction

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IMEx

http

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-RRB-

consisting

of

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DIP

IntAct

MINT

and

Mpact

-LRB-

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-RRB-

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jack.Greenblatt@utoronto.ca

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A.E.

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-RRB-

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