Coverage

of

whole

proteome

by

structural

genomics

observed

through

protein

homology

modeling

database

We

have

been

developing

FAMSBASE

a

protein

homology-modeling

database

of

whole

ORFs

predicted

from

genome

sequences

The

latest

update

of

FAMSBASE

-LRB-

-RRB-

which

is

based

on

the

protein

three-dimensional

-LRB-

-RRB-

structures

released

by

November

contains

modeled

structures

for

open

reading

frames

-LRB-

ORFs

-RRB-

derived

from

genomes

of

species

namely

archaebacterial

eubacterial

eukaryotic

and

phage

genomes

Those

genomes

are

predicted

to

have

ORFs

in

total

and

the

current

FAMSBASE

contains

protein

structure

of

approximately

of

the

ORF

products

However

cases

that

a

modeled

structure

covers

the

whole

part

of

an

ORF

product

are

rare

When

portion

of

an

ORF

with

structure

is

compared

in

three

kingdoms

of

life

in

archaebacteria

and

eubacteria

approximately

of

the

ORFs

have

modeled

structures

covering

almost

the

entire

amino

acid

sequences

however

the

percentage

falls

to

about

in

eukaryotes

When

annual

differences

in

the

number

of

ORFs

with

modeled

structure

are

calculated

the

fraction

of

modeled

structures

of

soluble

protein

for

archaebacteria

is

increased

by

and

that

for

eubacteria

by

in

the

last

years

Assuming

that

this

rate

would

be

maintained

and

that

determination

of

structures

for

predicted

disordered

regions

is

unattainable

whole

soluble

protein

model

structures

of

prokaryotes

without

the

putative

disordered

regions

will

be

in

hand

within

years

For

eukaryotic

proteins

they

will

be

in

hand

within

years

The

structures

we

will

have

at

those

times

are

not

the

structure

of

the

entire

proteins

encoded

in

single

ORFs

but

the

structures

of

separate

structural

domains

Measuring

or

predicting

spatial

arrangements

of

structural

domains

in

an

ORF

will

then

be

a

coming

issue

of

structural

genomics

Introduction

Genome

sequencing

projects

provided

a

huge

number

of

amino

acid

sequences

without

functional

information

-LRB-

Stein

-RRB-

To

discover

biological

functions

of

those

proteins

both

computational

predictions

and

biochemical

experiments

are

necessary

-LRB-

Tsoka

and

Ouzounis

-RRB-

Most

of

the

proteins

perform

functions

after

forming

specific

structures

and

therefore

protein

structure

is

one

of

the

most

valuable

sources

of

information

to

predict

protein

function

-LRB-

Domingues

et

al.

Xie

and

Bourne

-RRB-

Protein

function

prediction

based

on

structures

especially

protein

surface

structures

with

evolutionary

and/or

physicochemical

characteristics

have

been

extensively

studied

-LRB-

Lichtarge

and

Sowa

Campbell

et

al.

Kinoshita

and

Nakamura

Laskowski

et

al.

Ota

et

al.

Pieper

et

al.

-RRB-

However

determining

protein

structures

of

all

the

function-unknown

proteins

for

applying

these

types

of

study

is

not

practical

Proteins

are

classified

into

a

large

number

of

families

based

on

the

amino

acid

sequence

similarity

-LRB-

Dayhoff

-RRB-

and

proteins

with

similar

amino

acid

sequences

are

known

to

have

similar

structures

-LRB-

Chothia

and

Lesk

-RRB-

all

because

the

proteins

in

a

family

are

evolutionary

related

-LRB-

Doolittle

-RRB-

Once

we

have

structure

of

at

least

one

of

the

proteins

in

a

family

then

structures

of

other

proteins

in

the

same

family

can

be

computationally

deduced

by

homology

modeling

-LRB-

Burley

Baker

and

Sali

-RRB-

Based

on

this

logic

structural

genomics

-LRB-

SG

-RRB-

projects

which

are

to

determine

protein

structures

of

representatives

for

each

family

have

been

proposed

and

launched

-LRB-

Vitkup

et

al.

Brenner

Burley

and

Bonnano

-RRB-

In

homology

modeling

corresponding

residues

between

an

amino

acid

sequence

of

structure

unknown

protein

-LRB-

target

-RRB-

and

that

of

structure

known

protein

-LRB-

template

-RRB-

in

the

same

family

are

determined

by

sequence

alignment

and

every

residue

in

a

template

protein

is

replaced

by

that

in

a

target

protein

-LRB-

Marti-Renoma

et

al.

-RRB-

SG

projects

have

been

providing

new

protein

structures

-LRB-

Todd

et

al.

Xie

and

Bourne

Chandonia

and

Brenner

-RRB-

Protein

Data

Bank

-LRB-

PDB

-RRB-

-LRB-

Berman

et

al.

-RRB-

now

contains

more

than

structures

for

function

unknown

or

hypothetical

proteins

-LRB-

Stark

et

al.

-RRB-

Protein

function

predictions

based

on

structures

determined

by

SG

projects

are

also

in

progress

-LRB-

Goldsmith-Fischman

and

Honig

Liu

et

al.

Petrey

and

Honig

-RRB-

There

are

some

projects

that

focus

on

a

specific

species

and

try

to

determine

the

structures

of

whole

proteins

encoded

in

the

genome

of

the

species

-LRB-

Kim

Yokoyama

et

al.

Kim

et

al.

-RRB-

Those

projects

provide

a

considerable

number

of

structures

in

a

single

protein

family

This

results

in

providing

multiple

templates

for

a

single

protein

family

and

it

can

improve

quality

of

homology

modeling

-LRB-

Contreras-Moreira

et

al.

-RRB-

We

have

developed

FAMSBASE

a

database

for

homology

modeling

structures

of

whole

proteins

predicted

on

whole

genome

sequences

since

-LRB-

Yamaguchi

et

al.

-RRB-

FAMSBASE

contains

results

of

homology

modeling

by

FAMS

a

full

automatic

modeling

software

-LRB-

Ogata

and

Umeyama

-RRB-

Sequence

alignments

between

whole

ORFs

and

proteins

in

PDB

are

based

on

GTOP

-LRB-

Kawabata

et

al.

-RRB-

We

report

here

the

update

of

the

database

including

differences

in

the

amount

of

structural

data

from

the

previous

version

estimation

of

the

time

that

whole

ORFs

predicted

out

of

genome

sequences

are

covered

by

homology

modeling

structures

and

upcoming

issues

for

utilizing

those

modeled

structures

Methods

Data

update

of

FAMSBASE

Correspondence

between

ORFs

derived

from

whole

genome

sequences

and

protein

amino

acid

sequences

whose

structures

are

known

is

provided

by

GTOP

database

-LRB-

Kawabata

et

al.

-RRB-

The

update

in

May

of

FAMSBASE

is

based

on

February

version

of

GTOP

Protein

structures

in

PDB

by

November

are

used

for

homology

modeling

templates

FAMS

-LRB-

Ogata

and

Umeyama

-RRB-

is

applied

by

Umeyama

et

al.

to

pair-wise

alignments

between

a

predicted

ORF

sequence

and

an

amino

acid

sequence

with

known

structure

and

a

structure

is

modeled

All

the

results

are

stored

in

FAMSBASE

Assessing

annual

difference

of

data

in

FASBASE

Based

on

the

amount

of

data

in

FAMSBASE

in

and

the

amount

of

increase

in

the

following

years

a

due

year

for

whole

proteome

structure

models

is

estimated

Estimation

is

done

residue-wise

not

ORF-wise

since

modeled

structures

in

FAMSBASE

are

often

limited

to

structural

domains

In

this

report

structural

domains

refer

to

SCOP

domains

-LRB-

Andreeva

et

al.

-RRB-

All

ORFs

predicted

out

of

genome

sequences

are

divided

into

soluble

and

membrane

proteins

The

division

is

carried

out

by

SOSUI

-LRB-

Hirokawa

et

al.

-RRB-

and

a

protein

with

one

or

more

transmembrane

regions

is

classified

into

a

membrane

protein

The

number

of

residues

of

whole

soluble

proteins

encoded

in

the

genome

sequence

-LRB-

G

-RRB-

of

species

i

is

denoted

as

S

and

the

number

of

residues

of

whole

membrane

proteins

is

denoted

as

M

The

number

of

residues

included

in

modeled

structures

of

soluble

and

membrane

proteins

are

denoted

as

and

respectively

For

a

certain

genome

Gi

the

coverage

of

modeled

structures

in

whole

soluble

proteins

is

then

and

the

coverage

for

whole

membrane

proteins

is

The

coverage

is

summarized

in

different

kingdoms

of

life

as

in

the

following

equations

Both

figures

are

calculated

based

on

the

data

at

the

different

times

of

FAMSBASE

update

gradients

in

figures

are

then

calculated

and

the

figures

are

extrapolated

up

to

the

year

that

coverage

reaches

to

It

is

getting

to

be

known

that

not

all

ORFs

assume

stable

structures

Some

parts

of

ORFs

are

considered

to

be

natively

disordered

-LRB-

Oldfield

et

al.

Dyson

and

Wright

-RRB-

Hence

it

is

unlikely

that

coverage

by

homology

modeling

reaches

to

We

therefore

estimate

disordered

regions

in

whole

ORFs

by

DisEMBL

-LRB-

Linding

et

al.

-RRB-

and

omit

these

disordered

regions

from

the

calculation

Non-overlap

multiple

model

structures

in

single

ORFs

Modeled

structures

in

FAMSBASE

are

often

limited

to

structural

domains

To

find

an

ORF

of

which

most

of

the

entire

structure

is

modeled

in

pieces

of

structural

domains

an

ORF

covered

by

non-overlapping

three

or

more

modeled

structures

in

eukaryotic

genome

is

surveyed

based

on

the

following

criteria

-LRB-

-RRB-

or

more

residues

in

the

ORF

are

included

in

one

of

the

modeled

structures

-LRB-

-RRB-

the

ORF

contains

three

or

more

non-overlapping

modeled

structures

and

-LRB-

-RRB-

the

sequence

identity

between

a

template

protein

and

a

target

domain

is

no

less

than

At

the

time

of

FAMSBASE

building

five

model

structures

are

at

most

built

for

each

ORF

-LRB-

Yamaguchi

et

al.

-RRB-

Therefore

the

expected

number

of

modeled

structures

in

the

above

criteria

is

between

three

and

five

Prediction

of

domain

interfaces

The

structure

in

pieces

for

a

single

ORF

needs

to

be

assembled

to

model

the

entire

structure

For

this

procedure

a

prediction

of

domain

interfaces

of

each

structure

is

needed

A

hydrophobicity

index

based

on

protein

structures

is

built

for

domain

interface

prediction

Hydrophobicity

of

amino

acid

residue

is

measured

by

buriedness

of

a

residue

inside

the

protein

structures

A

representative

chains

in

PDB

among

which

sequence

identities

are

less

than

were

selected

and

solvent

accessibility

of

each

residue

is

calculated

on

a

monomer

state

For

each

amino

acid

residue

type

i

-LRB-

i

-RRB-

the

number

of

residue

with

accessibility

no

less

than

b

-LRB-

-RRB-

is

counted

-LRB-

Sb

i

-RRB-

Database

derived

hydrophobicity

index

-LRB-

Ib

i

-RRB-

is

obtained

by

b

is

set

to

to

maximize

the

difference

of

Ib

i

among

different

residues

The

index

has

good

correlation

with

Kyte

and

Doolittle

hydrophobicity

index

-LRB-

Kyte

and

Doolittle

-RRB-

The

index

i

is

assigned

to

every

residue

on

the

surface

-LRB-

accessibility

no

less

than

-RRB-

of

a

modeled

structure

The

hydrophobicity

of

each

residue

on

a

surface

of

a

protein

is

then

obtained

by

averaging

the

assigned

values

of

residues

within

Å

from

the

residue

in

concern

A

hydrophobic

patch

on

the

surface

of

the

modeled

structure

is

found

as

a

cluster

of

surface

residues

with

the

hydrophobicity

no

less

than

Results

and

discussion

Coverage

of

whole

protein

space

by

homology

modeling

The

latest

update

of

FAMSBASE

at

May

uses

protein

structures

deposited

to

PDB

by

the

end

of

Nov.

and

ORFs

predicted

from

genome

sequences

deposited

by

February

-LRB-

Famsbase

-RRB-

The

latest

FAMSBASE

contains

modeled

structures

of

ORFs

derived

from

archaebacterial

eubacterial

eukaryotic

and

phage

genomes

in

total

genomes

Five

models

at

maximum

are

built

for

each

ORF

in

FAMSBASE

Those

five

models

are

the

structure

for

the

same

or

different

regions

in

the

ORF

When

multiple

models

are

built

for

the

same

region

of

ORF

we

can

evaluate

the

reliability

of

the

model

When

the

model

based

on

different

templates

have

the

similar

structures

then

the

structure

would

be

reliable

When

the

structures

are

different

the

modeled

structure

would

be

less

reliable

We

further

test

the

quality

of

modeled

structure

by

ProsaII

-LRB-

Sippl

-RRB-

and

find

that

about

of

the

modeled

structures

are

energetically

ranked

as

number

one

and

comparable

to

experimentally

determined

structures

Some

of

the

structures

that

fail

the

test

are

structures

of

a

part

of

a

large

protein

mostly

structural

domains

of

large

proteins

It

is

difficult

to

assess

the

quality

of

this

type

of

domain

structures

because

interfaces

of

the

domain

for

other

parts

of

the

protein

are

exposed

in

the

modeled

structures

Tendency

of

amino

acid

residue

appearance

in

the

interface

is

supposed

to

be

different

from

that

at

the

surface

as

we

discuss

down

below

In

the

genome

of

species

ORFs

are

predicted

Therefore

in

FAMSBASE

structure

of

-LRB-

-RRB-

of

ORFs

have

been

built

and

stored

-LRB-

Table

-RRB-

These

are

about

of

ORFs

in

archaebacterial

genomes

about

in

eubacterial

genomes

and

about

of

eukaryotic

genomes.Table

of

ORFs

and

those

with

modeled

structures

in

genomesSpeciesORFModel

ArchaeaArchaeoglobus

fulgidus

Aeropyrum

pernix

Halobacterium

sp.

Methanosarcina

acetivorans

Methanocaldococcus

jannaschii

Methanopyrus

kandleri

Methanosarcina

mazei

Methanothermobacter

Nanoarchaeum

equitans

Pyrococcus

abyssi

Pyrobaculum

aerophilum

Pyrococcus

furiosus

DSM

Pyrococcus

horikoshii

Sulfolobus

solfataricus

Sulfolobus

tokodaii

Thermoplasma

acidophilum

Thermoplasma

EubacteriaAquifex

aeolicus

Nostoc

sp.

PCC

Agrobacterium

tumefaciens

A.

tumefaciens

-LRB-

Dupont

-RRB-

Bacillus

anthracis

str

Buchnera

aphidicola

B.

Bordetella

bronchiseptica

Borrelia

Bacillus

cereus

ATCC

Candidatus

Blochmannia

Bacillus

halodurans

Bradyrhizobium

Bifidobacterium

longum

Brucella

melitensis

Bordetella

B.

pertussis

Tohama

Bacillus

subtilis

Brucella

suis

Bacteroides

thetaiotaomicron

Buchnera

sp.

Clostridium

acetobutylicum

Coxiella

burnetii

RSA

Chlamydophila

caviae

Caulobacter

Corynebacterium

diphtheriae

Corynebacterium

efficiens

Corynebacterium

glutamicum

ATCC

Campylobacter

Chlamydia

muridarum

Clostridium

perfringens

Chlamydophila

pneumoniae

Chlamydophila

pneumoniae

Chlamydophila

pneumoniae

Chlamydophila

pneumoniae

Chlorobium

tepidum

Clostridium

tetani

Chlamydia

trachomatis

D/UW

Chromobacterium

violaceum

ATCC

Deinococcus

radiodurans

Escherichia

coli

E.

coli

E.

coli

E.

coli

Enterococcus

faecalis

Fusobacterium

nucleatum

ATCC

Geobacter

sulfurreducens

Gloeobacter

violaceus

PCC

Haemophilus

ducreyi

Helicobacter

hepaticus

ATCC

Haemophilus

influenzae

Helicobacter

pylori

Helicobacter

pylori

Listeria

innocua

Leptospira

interrogans

Lactococcus

lactis

Listeria

monocytogenes

Lactobacillus

plantarum

Mycobacterium

bovis

subsp

Mycoplasma

gallisepticum

Mycoplasma

genitalium

Mycobacterium

leprae

Mesorhizobium

loti

Mycoplasma

Mycoplasma

pneumoniae

Mycoplasma

pulmonis

UAB

Mycobacterium

tuberculosis

Mycobacterium

tuberculosis

Nitrosomonas

europaea

ATCC

Neisseria

meningitidis

Neisseria

meningitidis

Oceanobacillus

iheyensis

Phytoplasma

asteris

OY

Pseudomonas

aeruginosa

Porphyromonas

gingivalis

Photorhabdus

luminescens

Prochlorococcus

marinus

Prochlorococcus

marinus

Prochlorococcus

marinus

Pasteurella

multocida

Pseudomonas

putida

Pseudomonas

syringae

pv

tomato

str

Pirellula

sp.

Rickettsia

conorii

Malish

Rhodopseudomonas

Rickettsia

prowazekii

Madrid

Ralstonia

solanacearum

Streptococcus

Streptococcus

agalactiae

Staphylococcus

aureus

Staphylococcus

aureus

Staphylococcus

aureus

Streptomyces

Streptomyces

coelicolor

-LRB-

-RRB-

Staphylococcus

epidermidis

ATCC

Shigella

flexneri

Shigella

flexneri

str

Sinorhizobium

meliloti

Streptococcus

mutans

Shewanella

oneidensis

Streptococcus

pneumoniae

Streptococcus

pneumoniae

Streptococcus

pyogenes

Streptococcus

pyogenes

Streptococcus

pyogenes

Streptococcus

pyogenes

Salmonella

typhi

Salmonella

typhimurium

Salmonella

enterica

subsp

Synechocystis

sp.

PCC

Synechococcus

sp.

WH

Thermosynechococcus

elongatus

Thermotoga

maritima

Treponema

pallidum

subsp

Thermoanaerobacter

tengcongensis

Tropheryma

whipplei

Tropheryma

whipplei

str

Ureaplasma

Vibrio

cholerae

Vibrio

parahaemolyticus

RIMD

Vibrio

vulnificus

Vibrio

vulnificus

Wigglesworthia

Wolinella

succinogenes

DSMZ

Xanthomonas

axonopodis

pv

citri

Xanthomonas

campestris

pv

Xylella

fastidiosa

Xylella

fastidiosa

Yersinia

pestis

Yersinia

pestis

EukaryotesArabidopsis

Caenorhabditis

Caenorhabditis

Ciona

Drosophila

Danio

Encephalitozoon

Guillardia

theta

Homo

sapiens

-LRB-

ENSEMBLE

-RRB-

Leishmania

major

Mus

Neurospora

Oryza

Plasmodium

falciparum

Rattus

Saccharomyces

Schizosaccharomyces

Takifugu

rubripes

AHJD2114.844RR2.8t2525120.2933W80911.3A11872912.5A5111100.0Aeh13315115.4APSE-154611.1

B11119.1B10317423.5Bcep7816158.2BF238112.5bIL1706423.1bIL2856258.1bIL28661711.5bIL30956610.7bIL31029413.8bIL31122627.3bIL31227311.1BK5-T6369.5

ϕCTX4748.5D29791519.0D3941111.7Rb1549612.2ϕg1e49612.2GA-13538.6

HF2114119.6HK02257814.0HK62058610.3HK97611016.4HP14137.3HP23638.3K1394449.1KVP403815715.02,38957712.3L-413C40410.0

Mycoplasma

virus

Enterobacteria

phage

P11100.0P242511.9P2236925.0P2758915.5P33549612.2P412216.7P60801316.3PA0134514.7PaP369811.6ϕKZ306258.2ϕCh19899.2ϕYeO3-12591322.0

RM

SP

βc21853317.8SPP110676.6MM153611.3ST64B56814.3ST64T65913.8720146817.4DT147714.9O12055747.0Sfi1945613.3Sfi2150918.0Stx2165116.7T3441022.7T42785820.9T7581017.2TM48955.6TP901-156712.5

Tuc200956712.5Ul365858.6VHML57814.0VpV2626746.0VT2-Sa821113.4

When

a

modeled

structure

is

counted

based

on

the

number

of

amino

acid

residues

not

on

the

number

of

ORFs

a

different

aspect

emerges

Figure

shows

the

percentage

of

amino

acid

residues

per

ORF

included

in

the

modeled

structures

ORFs

without

a

modeled

structure

are

omitted

Of

archaebacterial

and

eubacterial

genomes

in

of

ORFs

more

than

of

the

residues

are

included

in

modeled

structures

however

of

eukaryotic

genomes

only

in

of

ORFs

more

than

of

the

residues

are

included

-LRB-

red

and

blue

sections

in

Fig.

-RRB-

The

proportion

of

residues

in

modeled

structure

can

be

measured

by

the

number

of

residues

in

a

typical

structural

domain

as

shown

in

SCOP

-LRB-

Andreeva

et

al.

-RRB-

The

average

size

of

protein

domain

is

around

residues

-LRB-

Copley

et

al.

-RRB-

In

ORFs

with

modeled

structures

a

continuous

region

of

residues

with

one

domain

or

more

remains

as

structure

unknown

in

only

about

of

ORFs

of

archaebacterial

and

eubacterial

genomes

whereas

in

about

of

ORFs

of

eukaryotic

genomes

the

regions

with

one

domain

or

more

remain

as

structure

unknown.Fig

of

amino

acid

residues

included

in

modeled

structures

in

each

ORF

is

classified

by

bins

and

shown

in

pie

charts

ORFs

without

a

modeled

structure

are

not

included

A

number

of

ORFs

with

modeled

structures

and

an

average

length

of

the

ORFs

are

shown

at

the

center

of

each

pie

chart

Sections

bordered

by

thick

black

lines

indicate

that

the

unmodeled

region

in

the

ORF

is

no

less

than

the

size

of

a

domain

-LRB-

about

residues

-RRB-

Annual

difference

of

model

structures

In

FAMSBASE

of

of

amino

acid

residues

in

all

ORFs

of

archaebacterial

and

of

eubacterial

genomes

were

included

in

modeled

structures

-LRB-

Yamaguchi

et

al.

-RRB-

In

the

current

update

of

FAMSBASE

based

on

data

by

around

of

amino

acid

residues

in

all

ORFs

in

archaebacterial

and

of

eubacterial

genomes

are

included

in

modeled

structures

In

eukaryotic

genomes

of

amino

acid

residues

in

and

in

are

included

in

modeled

structures

Those

figures

can

be

used

to

estimate

the

time

when

modeled

structures

of

whole

proteins

predicted

from

genomes

are

obtained

The

estimation

for

the

time

obtaining

the

whole

soluble

and

membrane

proteins

are

treated

separately

because

the

speed

of

structure

determination

for

soluble

and

membrane

proteins

seems

to

differ

The

assumption

for

the

estimation

is

that

the

speed

for

structure

determination

would

stay

the

same

and

no

new

protein

family

would

appear

For

eubacterial

genomes

of

residues

in

whole

ORFs

are

predicted

by

SOSUI

-LRB-

Hirokawa

et

al.

-RRB-

to

encode

soluble

proteins

and

to

encode

membrane

proteins

This

ratio

is

not

so

different

from

the

previous

prediction

by

Krogh

et

al.

-LRB-

-RRB-

Of

about

of

whole

eubacterial

ORF

that

were

with

modeled

structures

in

approximately

were

soluble

proteins

and

were

membrane

proteins

Therefore

about

-LRB-

-RRB-

of

the

whole

soluble

proteins

were

modeled

Of

the

whole

membrane

proteins

in

eubacterial

genome

about

-LRB-

-RRB-

were

modeled

In

those

figures

are

grown

to

and

respectively

In

eubacterial

whole

ORFs

about

of

amino

acid

residues

are

predicted

to

be

included

in

disordered

region

by

DisEMBL

-LRB-

Linding

et

al.

-RRB-

Some

of

these

regions

are

included

in

the

modeled

structures

These

regions

are

either

incorrectly

predicted

regions

or

incorrectly

modeled

regions

Assuming

that

the

disordered

regions

without

modeled

structures

are

correctly

predicted

of

amino

acid

residues

in

soluble

proteins

were

disordered

and

we

would

never

obtain

structures

of

those

regions

Then

by

extrapolating

the

coverage

of

soluble

proteins

up

to

-LRB-

-RRB-

with

the

current

growth

rate

we

can

estimate

that

by

the

year

whole

soluble

proteins

encoded

in

eubacterial

genomes

can

be

modeled

-LRB-

Fig.

-RRB-

Whole

soluble

proteins

of

archaebacterial

genome

can

be

modeled

by

and

those

of

eukaryotic

genomes

by

Fig.

differences

of

modeled

structures

classified

by

kingdoms

of

life

The

percentage

is

the

number

of

amino

acid

residues

included

in

modeled

structures

over

the

whole

number

of

residues

in

predicted

sequences

for

soluble

and

membrane

proteins

in

each

kingdom

-LRB-

S

-RRB-

stands

for

soluble

proteins

and

-LRB-

M

-RRB-

stands

for

membrane

proteins

Some

of

the

residues

are

predicted

to

be

in

a

disordered

region

The

percentage

of

residues

in

disordered

regions

is

shown

at

the

top

Orengo

et

al.

-LRB-

-RRB-

showed

percentage

of

ORFs

with

protein

structures

as

between

and

in

The

genome

sequences

known

by

were

mostly

derived

from

prokaryotic

species

and

the

known

protein

structures

were

mostly

soluble

proteins

Therefore

the

figures

they

presented

in

should

correspond

to

the

figures

of

archaebacterial

and

eubacterial

soluble

proteins

When

we

extrapolate

the

figures

of

archaebacterial

and

eubacterial

soluble

proteins

to

the

past

in

Fig.

the

figures

are

around

in

indicating

that

their

figures

approximately

lie

on

the

extrapolated

lines

The

current

estimation

indicates

that

we

will

obtain

structures

of

whole

soluble

proteins

of

eubacteria

in

years

and

archaebacteria

in

years

This

estimation

does

not

take

into

account

the

acceleration

of

structure

determination

speed

by

automation

-LRB-

McPherson

DeLucas

et

al.

-RRB-

which

makes

the

due

days

closer

to

the

present

For

membrane

proteins

speed

of

structure

determination

has

been

drastically

accelerated

by

recent

technical

innovations

-LRB-

Kyogoku

et

al.

Lundstrom

Walian

et

al.

Dobrovetsky

et

al.

-RRB-

and

therefore

we

will

not

linearly

extrapolate

the

present

status

to

estimate

the

due

day

for

membrane

proteins

Frequency

of

template

structure

in

use

When

the

template

structures

used

in

FAMSBASE

are

classified

by

SCOP

superfamily

which

is

a

group

of

proteins

that

have

low

sequence

identities

but

whose

structural

and

functional

features

suggest

that

a

common

evolutionary

origin

is

probable

-LRB-

Lo

Conte

et

al.

-RRB-

and

frequencies

of

superfamilies

in

use

are

counted

P-loop

containing

nucleoside

triphosphate

hydrolases

superfamily

is

found

to

be

the

most

frequent

one

times

-LRB-

about

-RRB-

in

whole

archaebacterial

model

structures

-LRB-

about

-RRB-

in

eubacterial

structures

and

times

-LRB-

about

-RRB-

in

eukaryotic

structures

The

templates

that

follow

in

frequency

in

archaebacterial

and

eubacterial

protein

structures

are

NAD

-LRB-

P

-RRB-

binding

Rossmann

fold

domains

ferredxin

and

PLP-dependent

transferases

superfamilies

In

eukaryotic

protein

structures

protein-kinase

immunoglobulin

and

and

zinc

fingers

superfamilies

which

appear

specifically

in

eukaryotic

genomes

follow

the

top

Differences

in

distribution

of

frequency

of

templates

in

different

kingdoms

of

life

are

evident

when

frequencies

in

use

of

template

are

plotted

in

descending

order

-LRB-

Fig.

-RRB-

In

any

kingdoms

of

life

the

frequencies

of

the

most

and

the

second

most

used

templates

exceed

those

of

the

remaining

templates

The

frequencies

of

templates

in

use

drops

first

in

archaebacterial

protein

structures

and

then

in

eubacterial

protein

structures

The

descending

curve

of

eukaryotic

template

frequency

is

less

steep

compared

with

the

others

indicating

that

one

template

can

produce

a

large

number

of

domain

structures

in

eukaryotic

ORFs

In

other

words

a

significant

number

of

proteins

encoded

in

eukaryotic

genomes

are

originated

by

domain

duplication

as

Koonin

et

al.

-LRB-

-RRB-

demonstrated

Superfamilies

with

the

structures

and

with

many

copies

in

eukaryotic

genomes

but

seldom

in

prokaryotic

genomes

are

protein

kinase-like

immunoglobin

RNA-binding

domain

and

zinc

fingers

glucocorticoid

receptor-like

homeodomain-like

PH

domain-like

RING-box

L

domain

ankyrin

repeat

ARM

repeat

cytochrome

and

EF-hand

superfamilies

These

superfamilies

are

transcription

factors

protein

protein

interaction

mediators

and

response

factor

for

toxic

substances

mostly

known

to

be

unique

to

eukaryotes.Fig

of

template

usage

in

descending

order

Horizontal

axis

is

a

template

and

the

vertical

axis

is

a

frequency

of

templates

in

use

Red

line

is

a

template

usage

in

archaebacteria

blue

line

is

eubacteria

and

green

line

is

eukaryotes

The

P-loop

containing

nucleoside

triphosphate

hydrolases

superfamily

outnumbering

other

superfamily

in

template

frequency

corresponds

to

the

previous

finding

that

the

enzyme

is

highly

frequently

used

in

every

kingdom

of

life

-LRB-

Leipe

et

al.

-RRB-

When

biological

functions

of

these

ORFs

with

the

structure

of

P-loop

containing

nucleoside

triphosphate

hydrolases

superfamily

are

classified

about

half

of

the

proteins

are

ABC

transporters

in

archaebacterial

and

eubacterial

proteomes

but

numbers

of

G-proteins

and

motor

proteins

in

eukaryotic

proteomes

are

noticeable

-LRB-

Fig.

-RRB-

Fig.

family

distribution

of

P-loop

containing

nucleoside

triphosphate

hydrolases

superfamily

in

each

kingdom

In

the

three

pie

charts

the

section

with

the

same

color

is

a

category

of

the

same

family

except

for

the

white

section

In

the

last

two

years

new

protein

structures

were

determined

and

contributed

to

an

increase

in

the

number

of

templates

for

homology

modeling

A

part

of

those

template

structures

are

listed

in

Table

A

part

of

those

top

templates

contributed

a

lot

for

the

growth

of

modeled

structure

database

In

Table

structure

derived

from

SG

projects

is

rare

The

ratio

of

SG

products

in

Table

is

the

same

as

that

in

PDB

-LRB-

Editorial

Board

Nature

Structural

Molecular

Biology

-RRB-

As

the

SG

projects

in

US

and

Europe

have

proceeded

to

phase

-LRB-

Service

-RRB-

SG

products

are

expected

to

contribute

to

increase

in

the

number

of

templates

in

the

near

future

The

qualities

of

protein

structures

namely

size

resolution

R-factors

and

so

forth

derived

from

SG

projects

were

compared

with

those

in

PDB

and

no

obvious

compromise

in

quality

of

SG

products

were

found

-LRB-

Todd

et

al.

-RRB-

The

quality

of

homology

modeling

based

on

products

of

SG

projects

in

the

future

therefore

will

be

expected

to

be

no

less

than

the

current

quality.Table

modeling

templates

in

the

newly

determined

structures

between

and

of

uses

as

a

templateSGaProtein

NMaltose/maltodextrin

transport

ATP-binding

protein

NHypothetical

ABC

transporter

ATP-binding

protein

NABC

transporter

ATP-binding

protein

NTransport

ATP-Binding

Protein

YActin

interacting

protein

regulatory

protein

NFarnesyltransferase

α

YABC

DNA

helicase

RecQ

protein

NHaemolysin

secretion

ATP-binding

protein

ATP-binding

domain1mdbA1,745N2,3-dihydroxybenzoate-AMP

ligase

enhancer

protein

proteasome

non-ATPase

regulatory

subunit

PDB

entry

seemingly

derived

from

the

SG

projects

judged

by

description

in

PDB

file

is

tagged

Y

and

the

remaining

entry

is

tagged

N

Whole

structure

and

function

of

proteins

from

homology

modeling

of

domain

structures

Protein

function

prediction

especially

studies

on

enzyme

specificity

based

on

homology

modeling

structures

is

intensively

carried

out

in

the

field

of

drug

design

and

related

fields

-LRB-

Goldsmith-Fischman

and

Honig

Kopp

and

Schwede

-RRB-

Those

studies

are

mostly

based

on

homology

modeling

of

domain

structures

As

mentioned

above

most

of

the

eukaryotic

protein

structures

in

FAMSBASE

are

structures

of

structural

domains

not

the

entire

coding

regions

-LRB-

Fig.

-RRB-

Protein

functional

sites

are

often

located

at

a

cleft

of

domains

-LRB-

Laskowski

et

al.

-RRB-

and

therefore

understanding

relative

location

of

domains

will

be

a

critical

issue

Xie

and

Bourne

-LRB-

-RRB-

and

O'Toole

et

al.

-LRB-

-RRB-

also

pointed

out

this

problem

and

mentioned

even

if

all

the

domains

of

a

multiple-domain

query

sequence

have

determined

structures

the

individual

structures

will

not

enable

accurate

modeling

of

how

they

associate

together

in

the

structure

of

the

entire

proteins

-LRB-

O'Toole

et

al.

-RRB-

Figure

shows

all

eukaryotic

ORFs

whose

structures

are

mostly

modeled

in

pieces

There

are

three

types

of

enzymes

and

four

types

of

cell

surface

receptors

A

protein

structure

of

which

is

an

ORF

found

in

human

genome

can

be

modeled

based

on

Escherichia

coli

carbamoylphosphate

synthetase

-LRB-

CPS

-RRB-

and

Pyrococcus

abyssi

asparatate

transcarbamoylase

-LRB-

ATC

-RRB-

E.

coli

CPS

is

composed

of

a

large

subunit

and

a

small

subunit

CPS

and

ATC

are

the

first

and

the

second

enzymes

respectively

in

pyrimidine

biosynthesis

pathway

In

mammalian

genomes

those

proteins

are

coded

by

a

single

gene

and

active

in

a

hexamer

form

-LRB-

Serre

et

al.

-RRB-

Interactions

between

the

large

subunit

domain

and

the

small

subunit

domain

of

human

CPS

are

conjectured

to

be

the

same

as

those

between

the

large

and

the

small

subunits

of

E.

coli

CPS

N-terminal

residues

of

the

large

subunit

and

the

C-terminal

residues

of

the

small

subunit

are

spatially

located

close

in

E.

coli

CPS

which

permits

the

two

chains

to

be

chemically

connected

without

disrupting

subunit

interfaces

To

be

active

human

CPS

should

form

a

hexamer

supramolecule

and

the

interfaces

for

the

supramolecule

formation

should

be

predicted

from

the

modeled

structures

At

the

moment

the

interfaces

are

unknown.Fig

ORFs

with

multiple

model

structures

covering

more

than

of

entire

protein

In

each

of

the

bar

representation

of

proteins

a

black

box

is

a

region

with

structure

A

name

and

PDB

ID

of

a

template

structure

and

amino

acid

sequence

identity

between

template

and

target

domains

are

given

below

the

black

box

A

yellow

box

is

a

putative

signal

peptide

and

green

box

is

a

putative

transmembrane

region

Template

and

modeled

structures

of

were

shown

on

the

right

side

of

the

figure

Each

domain

is

colored

by

hydrophobicity

A

hydrophilic

residue

is

in

green

and

a

hydrophobic

residue

is

in

red

A

buried

residue

is

in

deep

blue

is

an

ORF

found

in

mouse

genome

and

encodes

a

putative

cell

surface

receptor

The

protein

is

predicted

to

consist

of

six

consecutive

Ig-fold

domains

There

is

a

putative

transmembrane

helix

at

the

C-terminal

region

of

the

protein

Two

consecutive

Ig-fold

domains

are

modeled

without

overlap

and

no

pieces

of

information

for

relative

orientation

of

three

modeled

structures

have

been

found

Information

of

interaction

sites

of

those

domains

is

required

to

build

the

entire

structure

of

the

protein

and

to

predict

a

target

molecule

of

this

receptor

Computational

analyses

of

domain

interfaces

and

of

protein

protein

interfaces

have

been

targets

for

extensive

study

for

a

long

time

and

some

general

characteristics

have

been

found

One

of

them

is

the

hydrophobicity

of

the

interfaces

-LRB-

Wodak

and

Janin

-RRB-

Hydrophobic

clusters

on

the

surface

of

modeled

structures

of

are

shown

in

right

side

of

Fig.

One

of

the

template

structures

ectodomain

has

hydrophilic

surface

-LRB-

green

-RRB-

around

the

C-terminal

residues

of

the

domain

however

the

modeled

structure

has

a

hydrophobic

surface

-LRB-

orange

-RRB-

at

the

corresponding

area

The

other

template

structure

has

a

hydrophilic

surface

around

the

N-terminal

residues

of

the

domain

however

the

modeled

structure

has

a

hydrophobic

surface

at

the

corresponding

area

The

surfaces

uniquely

turned

into

hydrophobic

in

modeled

structures

are

close

to

the

residues

that

are

chemically

bonded

in

the

target

protein

and

therefore

both

of

the

areas

likely

form

interfaces

of

the

two

domains

The

modeled

structure

based

on

domain

has

another

hydrophobic

surface

around

the

C-terminal

residues

which

may

interact

with

domain

located

at

the

C-terminal

side

of

the

domain

Accuracy

of

homology

modeling

There

are

at

least

three

major

issues

that

affect

accuracy

in

homology

modeling

the

best

template

selection

accuracy

of

an

amino

acid

sequence

alignment

between

template

and

target

protein

sequences

and

the

accuracy

of

structure

building

procedure

itself

-LRB-

Contreras-Moreira

et

al.

-RRB-

Accuracy

of

the

alignment

is

high

when

sequence

identity

of

template

and

target

proteins

is

higher

than

and

alignment

of

proteins

with

identity

less

than

is

known

to

be

less

reliable

thereby

accuracy

of

homology

modeling

deteriorates

-LRB-

Kopp

and

Schwede

-RRB-

FAMS

has

been

shown

to

construct

relatively

accurate

model

structures

even

with

low

sequence

identity

between

template

and

target

sequences

in

the

homology

modeling

competition

-LRB-

Iwadate

et

al.

Yamaguchi

et

al.

-RRB-

A

distribution

of

sequence

identity

between

amino

acid

sequences

of

template

and

target

proteins

in

FAMSBASE

is

shown

in

Fig.

Half

of

the

model

structures

in

FAMSBASE

rely

on

alignments

of

sequence

identity

less

than

Figure

suggests

that

the

current

structure

database

does

not

contain

good

enough

structures

for

high

quality

homology

modeling

SG

projects

will

eventually

provide

better

template

structures

and

improvement

in

target

selection

alignment

and

modeling

methods

are

also

in

pursuit

to

overcome

the

difficulties

in

homology

modeling

-LRB-

John

and

Sali

Wallace

et

al.

-RRB-

Fig.

of

sequence

identity

between

template

and

target

amino

acid

sequences

in

FAMSBASE

Conclusion

Construction

of

database

of

whole

genome

homology

modeling

clarified

that

protein

structures

of

about

of

the

protein

coding

regions

in

whole

genome

can

now

be

modeled

Maintaining

the

current

speed

of

structure

determination

it

will

take

at

most

years

to

have

enough

templates

to

cover

whole

soluble

proteins

of

eubacterial

genomes

and

years

to

cover

those

of

eukaryotic

genomes

The

current

advancement

in

technologies

of

protein

structure

determination

is

expected

to

make

these

due

times

closer

to

the

present

What

we

obtain

at

those

times

are

not

the

structures

of

entire

proteins

but

domain

structures

in

pieces

A

homology

modeled

domain

structure

is

now

in

use

of

predicting

domain

functions

but

predicting

spatial

arrangement

of

domains

in

a

protein

will

be

an

important

issue

for

function

prediction

O O O O O B I O O O B I O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O O O O O B O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B I O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B I O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

domain duplication
genome
homology modeling
structural genomics


Non-breeding habitat preference affects ecological speciation in migratory waders Models of ecological speciation predict that certain types of habitat should be more conducive to species diversification than others . In this study , I test this hypothesis in waders of the sub-order Charadrii using the number of morphological sub-species per species as an index of diversity . I classified all members of this clade as spending the non-breeding season either coastally or inland and argue that these represent fundamentally different environments . Coastal mudflats are characterised by high predictability and patchy worldwide distribution , whilst inland wetlands are widespread but unpredictable . The results show that migratory species that winter coastally are sub-divided into more sub-species than those that winter inland . This was not the case for non-migratory species . I argue that coastal environments select for more rigid migratory pathways , whilst inland wetlands favour more flexible movement patterns . Population sub-division could then result from the passive segregation of breeding sites or from the active selection for assortative mating of ecomorphs . Introduction Models of ecological speciation show that the propensity of a species to diverge is affected by the shape of the fitness landscape generated by its environment -LRB- Gavrilets 2004 -RRB- . This predicts that some environments should be more conducive to speciation than others . Recent comparative studies have supported this prediction -LRB- Funk et al. 2006 ; Phillimore et al. 2007 -RRB- . However , these studies do not discuss aspects of habitats that promote or inhibit speciation . In this study , I test this prediction for wading birds of the sub-order Charadrii . This group was chosen because its species can be divided into those that are adapted to coastal habitats and those that feed mostly inland -LRB- Piersma 1997 , 2003 -RRB- . On a year-to-year basis , coastal habitats offer a more predictable environment than inland wetlands -LRB- Roshier et al. 2001 -RRB- . Inter-tidal mudflats and ocean beaches will be located in the same place each year and offer a highly predictable pool of food resources . They are distributed patchily around the globe , and patches differ in their food resources and other conditions -LRB- Piersma et al. 1993a -RRB- . On the other hand , the inland wetlands favoured by other waders are unpredictable and include waterbodies in arid regions that flood infrequently . Flooded areas are often large , but may be located in different areas between years and shift within a single non-breeding season , promoting extensive movements of birds that rely on these habitats -LRB- Roshier et al. 2002 -RRB- . The suitability of grasslands in semi-arid regions varies from year to year due to the differences in rainfall level . Little is known about the composition of the food resources used by waders in these inland environments , but it seems likely that these may be similar on broad spatial scales . I thus predict that coastal habitats should be more favourable to species diversification than inland habitats . Many of the wader species that utilise coastal or inland habitats for the greater part of the year migrate to very different habitats , such Arctic tundras or boreal swamps , to breed . Whilst the reasons for this are beyond the scope of this study -LRB- but see Piersma 1997 -RRB- , it means that in these species reproduction is spatially separated from the feeding habitat to which they are adapted . Red knot Calidris canutus illustrate this point . For 10 months of the year , this species feeds on bivalves on mudflats around the world . The species has evolved a range of adaptations to feeding on bivalves , including a muscular stomach and sensitive bill tip -LRB- Piersma et al. 1993b , 1998 -RRB- . Nevertheless , it breeds exclusively on high Arctic tundras where feeding on abundant insect larvae and berries require few special adaptations . In such cases , local adaptation to , for example , temperate or tropical inter-tidal mudflats requires that birds from the same non-breeding area mate assortatively on the breeding grounds -LRB- Webster et al. 2002 -RRB- . Investigating ecological speciation in this group of birds thus adds two new dimensions to the general pattern found by Funk et al. -LRB- 2006 -RRB- and Phillimore et al. -LRB- 2007 -RRB- . First , as argued above , I have a priori reasons to expect that one type of habitat -LRB- coastal -RRB- should be more conducive to speciation than the other -LRB- inland -RRB- . Second , many of the species investigated reproduce away from the feeding grounds to which they are adapted and speciation thus requires differential migration to achieve assortative mating . To test these ideas , I use the number of sub-species per species as a measure of diversification . Thus , I treat sub-species as ` incipient species ' . Morphological sub-species are considered useful in estimating the patterns of divergence among populations -LRB- Phillimore et al. 2007 -RRB- . Evidence that wader sub-species represent phylogenetically distinct groups is available for Dunlin Calidris alpina -LRB- Wenink et al. 1993 -RRB- , and to a lesser extent , for Red knot -LRB- Buehler and Baker 2005 -RRB- . Because older species could have differentiated into more sub-species than younger species , I investigated whether the number of sub-species was correlated to species age . Materials and methods Data collection I collated data on the number of sub-species , migratoriness , breeding and non-breeding habitat from Del Hoyo et al. -LRB- 1996 -RRB- for all 215 species of the sub-order Charadrii -LRB- see Table 2 of S1 -RRB- . Crude estimates of species age were obtained from Thomas et al. -LRB- 2004 -RRB- . Due to high levels of polytomy in parts of the phylogeny , some of these are likely to be over-estimates ; however , no better estimates are available at present . I adopted the number of sub-species from one reputable source rather than search for the more recent updates because some of these revisions are still controversial . Migratoriness was scored as non-migrant , partial migrant or migrant -LRB- > 80 % , between 20 % and 80 % and < 20 % of non-breeding range overlaps with breeding range , respectively -RRB- . Breeding habitat was scored as one of seven classes : tundra , boreal/temperate , mountains , steppe , -LRB- sub -RRB- tropical wetlands -LRB- either inland or coastal -RRB- , -LRB- sub -RRB- tropical forest or oceanic . Non-breeding habitat was scored as coastal , inland or pelagic . For species that regularly use both coastal and inland habitats , I scored the habitat considered by Del Hoyo et al. -LRB- 1996 -RRB- to be used by the largest part of the population . Statistical analysis I first investigated whether the dependent variable -LRB- number of sub-species -RRB- showed phylogenetic auto-correlation -LRB- i.e. whether closely related species resembled each other more in their tendency to form sub-species than expected by chance -RRB- using the phylogenetic topology from Thomas et al. -LRB- 2004 -RRB- . I used runs test as implemented in the programme Phylogenetic Independence -LRB- Abouheif 1999 -RRB- to compare the observed pattern to that generated by randomly redistributing the data over the tips of the phylogeny 1,000 times . For this analysis , the number of sub-species per species was dichotomised as 1 -LRB- i.e. only the nominate form -RRB- or > 1 . Finding no evidence for phylogenetic autocorrelation -LRB- see the `` Results '' section -RRB- , I treated species as independent data points in further analysis . As the number of sub-species per species followed a Poisson distribution , I analysed the data using generalised linear models with a Poisson error distribution and a log link function . GLMStat -LRB- Beath 2001 -RRB- was used to generate glms . Significance was tested by dropping each term from the fuller model and comparing the resulting change in deviance to a chi-square distribution . Results The results of runs test showed that there was no evidence for auto-correlation in the phylogenetic distribution of the number of sub-species -LRB- runs test P = 0.46 -RRB- . In fact , 461 of the 1,000 randomly generated runs averages were greater than that of the real data set , indicating a non-significant tendency towards over-dispersion . A glm containing migratoriness , non-breeding habitat , breeding habitat and species age and their interactions showed no significant effects of three - and four-way interactions . However , there was a significant two-way interaction between migratoriness and non-breeding habitat -LRB- Table 1 -RRB- , indicating that the pattern of covariance between non-breeding habitat and sub-species richness differed between migrants and non-migrants . Individual models revealed a significant effect of non-breeding habitat on the number of sub-species in migrants , but not in non-migrants or partial migrants -LRB- Table 1 -RRB- . These results are illustrated in Fig. 1 : species wintering coastally are on average sub-divided into more sub-species than inland wintering species , but only in migrants . The results are not confounded by differences in species age -LRB- Table 1 -RRB- . Whilst there was a significant association between breeding and non-breeding habitat among migrants -LRB- , P = 0.009 -RRB- , this did not result in an association between breeding habitat and the number of sub-species -LRB- Table 1 -RRB- . Fig. 1Mean number of sub-species per species for migratory and non-migratory waders . Pelagic wintering species -LRB- N = 2 -RRB- and partial migrants -LRB- N = 31 -RRB- not shown . Error bars represent the Poisson standard errorsTable 1Results of the generalised linear model with the number of sub-species per species as independent variable Estimate ± SEDeviance -LRB- df = 1 -RRB- P valueMinimal adequate model Migratoriness − 0.10 ± 0.08 0.23 Non-breeding habitat − 0.16 ± 0.19 0.39 Breeding habitat0 .06 ± 0.04 0.17 Species age − 0.01 ± 0.007 0.11 Migratoriness × non-breeding habitat0 .27 ± 0.124.920.03 Separate models Non-breeding habitat Migrants0 .42 ± 0.157.390.007 Partial migrants0 .13 ± 0.100.070.79 Non-migrants − 0.21 ± 0.211.070.30Non-significant interaction terms were removed . P values for the main effects were obtained by using the parameter estimate divided by its standard error as the test statistic . Also shown are the results of separate models for each category of migratoriness . Discussion The prediction that coastal habitats should be more conducive to diversification in waders than freshwater habitats is supported , but only for migratory species . This is remarkable , given that the reduction in gene flow between locally adapting populations requires an extra step in these species . In non-migrants , all that is needed to mate with a locally adapted partner is not to disperse . In migrants , however , all individuals migrate away from the non-breeding grounds to breed , often to the other end of the globe . To mate with a partner from the same non-breeding grounds , the non-breeding populations must either have segregated breeding grounds or have a very sophisticated individual recognition system . There is good evidence for a number of coastally wintering migrants that the former is true -LRB- Wennerberg 2001 ; Atkinson et al. 2005 ; Buehler et al. 2006 -RRB- , whilst the latter has not been investigated . I suggest that the predictability of coastal non-breeding grounds favours rigid migratory pathways to and from the non-breeding grounds . Returning to the same non-breeding ground each year would save individuals from wasting time on searching for suitable non-breeding areas and allow them to benefit from learned aspects of local conditions -LRB- such as food distribution -RRB- . The rigidity of the migratory pathways could then result in spatial segregation on the breeding grounds because individuals would be genetically predisposed to favour the same breeding grounds each year . The formation of distinct sub-species would then be a by-product of the spatially discontinuous distribution of non-breeding grounds . By contrast , many of the species that spend the non-breeding season on inland habitats have to track spatially and temporally variable resources -LRB- Roshier et al. 2001 , 2002 -RRB- , which should select for flexible movement patterns during the non-breeding season . This could translate into flexible migration patterns , perhaps through a genetic correlation between migratory and non-migratory movements , and flexible movement patterns on the breeding grounds . If true , this would promote gene flow and inhibit diversification . Alternatively , the divergent ecology of different coastal non-breeding grounds may actively select for assortative mating of corresponding ecomorphs . For example , it may be costly for waders wintering on tropical mudflats to pair with those that winter on temperate mudflats because the resultant hybrids would be poorly adapted to either habitat . Support for this interpretation comes from the fact that many sub-species of coastal waders differ from each other in bill length , which is an ecologically relevant trait -LRB- Engelmoer and Roselaar 1998 -RRB- . By contrast , the unpredictability of inland wetlands would not allow specialisation to the specific conditions presented by any particular area of habitat . The question remains why the difference in diversification between species occupying coastal and inland habitats is not reflected in the non-migratory species that breed there ? Given that migrant species often greatly outnumber resident species at individual feeding areas during the non-breeding season , migrant species may have greater effective population sizes per non-breeding area than non-migrants . This would allow more efficient response to selection and increase the potential for local adaptive change in migrants relative to non-migrants . Given the arguments above , such change is most likely in coastal areas .

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