A

global

analysis

of

NMR

distance

constraints

from

the

PDB

Information

obtained

from

Nuclear

Magnetic

Resonance

-LRB-

NMR

-RRB-

experiments

is

encoded

as

a

set

of

constraint

lists

when

calculating

three-dimensional

structures

for

a

protein

With

the

amount

of

constraint

data

from

the

world

wide

Protein

Data

Bank

-LRB-

wwPDB

-RRB-

that

is

now

available

it

is

possible

to

do

a

global

large-scale

analysis

using

only

information

from

the

constraints

without

taking

the

coordinate

information

into

account

This

article

describes

such

an

analysis

of

distance

constraints

from

NOE

data

based

on

a

set

of

NMR

PDB

entries

containing

protein

chains

In

order

to

best

represent

the

quality

and

extent

of

the

data

that

is

currently

deposited

at

the

wwPDB

only

the

original

data

as

deposited

by

the

authors

was

used

and

no

attempt

was

made

to

clean

up

and

further

interpret

this

information

Because

the

constraint

lists

provide

a

single

set

of

data

and

not

an

ensemble

of

structural

solutions

they

are

easier

to

analyse

and

provide

a

reduced

form

of

structural

information

that

is

relevant

for

NMR

analysis

only

The

online

resource

resulting

from

this

analysis

-LRB-

-RRB-

makes

it

possible

to

check

for

example

how

often

a

particular

contact

occurs

when

assigning

NOESY

spectra

or

to

find

out

whether

a

particular

sequence

fragment

is

likely

to

be

difficult

to

assign

In

this

respect

it

formalises

information

that

scientists

with

experience

in

spectrum

analysis

are

aware

of

but

can

not

necessarily

quantify

The

analysis

described

here

illustrates

the

importance

of

depositing

constraints

-LRB-

and

all

other

possible

NMR

derived

information

-RRB-

along

with

the

structure

coordinates

as

this

type

of

information

can

greatly

assist

the

NMR

community

Introduction

The

information

contained

in

the

world

wide

Protein

Data

Bank

-LRB-

wwPDB

-RRB-

-LRB-

Berman

et

al.

-RRB-

is

growing

steadily

with

increasing

numbers

of

structures

being

deposited

from

both

traditional

single

laboratory

sources

and

recent

structural

genomics

efforts

The

two

main

methods

to

determine

these

structures

are

X-ray

crystallography

and

Nuclear

Magnetic

Resonance

-LRB-

NMR

-RRB-

NMR

structures

account

for

around

of

all

entries

in

the

wwPDB

While

inherent

size

restrictions

limit

the

method

to

molecules

of

lower

molecular

weight

NMR

has

still

made

a

significant

contribution

to

protein

folds

and

molecular

interaction

data

However

NMR

structures

are

less

straightforward

to

use

than

structures

determined

by

X-ray

because

they

are

often

represented

as

ensembles

of

structures

where

the

whole

ensemble

-LRB-

and

not

individual

structures

by

themselves

-RRB-

represents

the

solution

of

the

structure

determination

problem

based

on

the

experimental

data

The

reason

for

this

is

that

information

derived

from

NMR

is

insufficient

with

respect

to

the

structure

calculation

process

and

thus

can

not

lead

to

a

single

exact

solution

For

example

measured

distance-related

NOE

data

is

ensemble

and

time

averaged

so

that

the

final

observed

NOE

data

for

a

set

of

multiple

distinct

conformations

in

fast

exchange

will

be

a

degenerate

mix

of

the

distance

information

in

each

of

those

conformations

All

calculated

structures

that

conform

to

a

set

of

criteria

based

on

the

fit

to

the

experimental

data

-LRB-

NMR

derived

constraints

-RRB-

and

physical

characteristics

encoded

in

the

calculation

process

-LRB-

overall

energy

minimum

inter-atomic

packing

-RRB-

are

therefore

in

principle

equally

valid

-LRB-

Spronk

et

al.

-RRB-

However

there

is

no

standard

set

of

criteria

and

different

programs

and

researchers

use

different

sets

when

selecting

the

best

structures

out

of

the

calculated

ones

The

quality

of

the

NMR

structures

deposited

at

the

wwPDB

therefore

varies

widely

-LRB-

Nabuurs

et

al.

-RRB-

This

problem

is

being

addressed

by

novel

structure

determination

methods

like

Inferential

Structure

Determination

-LRB-

ISD

-RRB-

-LRB-

Rieping

et

al.

-RRB-

that

perform

more

objective

structure

calculations

and

select

statistically

relevant

ensembles

as

a

representative

solution

to

the

experimental

data

These

methods

are

however

computationally

expensive

The

observed

NMR

data

is

used

in

structure

calculations

to

constrain

inter-atomic

distances

relative

bond

orientations

and/or

dihedral

angles

Only

just

over

half

of

the

NMR

entries

in

the

wwPDB

were

deposited

together

with

the

constraint

lists

used

in

the

structure

calculation

process

These

constraint

lists

provide

very

valuable

information

as

for

example

they

enable

recalculation

of

the

structures

with

better

or

different

protocols

and

a

globally

consistent

comparison

of

the

constraints

to

the

original

coordinates

for

validation

purposes

However

a

major

problem

in

using

this

information

is

that

constraint

files

come

in

many

file

formats

and

that

the

atom

naming

and

residue

numbering

in

the

coordinate

and

the

constraint

files

often

differs

To

handle

the

problem

of

different

file

formats

constraint

lists

are

now

continuously

converted

into

the

NMR-STAR

format

at

the

BioMagResBank

-LRB-

BMRB

-RRB-

-LRB-

Doreleijers

et

al.

Ulrich

et

al.

-RRB-

A

further

step

as

part

of

the

DOCR

project

-LRB-

Doreleijers

et

al.

-RRB-

addresses

the

atom

naming

and

residue

numbering

problem

by

directly

relating

the

constraint

atoms

to

the

coordinate

atoms

and

molecular

system

using

the

FormatConverter

and

CCPN

data

model

-LRB-

Fogh

et

al.

Vranken

et

al.

-RRB-

This

data

is

then

checked

for

consistency

and

redundant

constraints

are

removed

using

the

WATTOS

software

as

part

of

the

FRED

project

-LRB-

Doreleijers

et

al.

-RRB-

In

the

original

implementation

the

DOCR/FRED

project

resulted

in

a

set

of

more

than

internally

consistent

constraint

lists

molecular

system

information

and

structure

coordinates

This

data

led

to

the

construction

of

the

RECOORD

-LRB-

Nederveen

et

al.

-RRB-

and

DRESS

-LRB-

Nabuurs

et

al.

-RRB-

databanks

where

structures

from

the

PDB

were

respectively

recalculated

and

re-refined

based

on

the

cleaned

up

constraint

lists

These

efforts

as

well

as

validation

software

like

AQUA

-LRB-

Doreleijers

et

al.

-RRB-

or

Procheck-NMR

-LRB-

Laskowski

et

al.

-RRB-

are

applied

on

a

per-entry

basis

where

the

original

or

recalculated

coordinates

are

related

to

the

original

or

standardised

constraint

lists

With

the

amount

of

constraint

data

that

is

now

available

it

has

become

possible

to

do

a

global

large-scale

analysis

using

only

the

information

from

the

constraints

without

taking

the

coordinate

information

into

account

The

constraint

lists

are

the

final

product

of

the

usually

human

analysis

of

NMR

spectra

and

as

such

they

represent

the

experimental

NMR-derived

information

that

is

relevant

for

the

structure

calculation

The

constraint

lists

also

provide

a

single

set

of

data

whereas

NMR

structures

are

usually

represented

as

ensembles

that

can

be

calculated

in

many

different

ways

which

complicates

their

interpretation

Analysing

the

constraint

data

on

a

large

scale

can

thus

provide

insights

into

the

NMR

data

analysis

process

-LRB-

e.g.

which

type

of

inter-atomic

contact

is

often

derived

from

the

spectra

-RRB-

and

the

relation

of

the

constraints

to

the

coordinate

data

-LRB-

e.g.

does

the

structure

calculation

process

add

any

distance

information

that

is

relevant

for

NMR

-RRB-

The

analysis

described

here

relates

only

to

distance

constraints

derived

from

NOE

data

with

a

base

set

of

NMR

PDB

entries

containing

protein

chains

Only

constraints

between

protons

in

protein

chains

were

retained

for

analysis

and

for

validation

purposes

the

base

set

was

further

divided

into

subsets

for

entries

that

contain

intra-residue

constraints

and

entries

where

all

the

original

constraint

and

coordinate

information

was

recognised

and

linked

to

each

other

A

coordinate

data

set

based

on

the

original

coordinate

files

was

also

generated

and

used

for

comparison

This

article

explores

some

of

the

issues

surrounding

distance

constraints

and

the

NMR

data

they

are

derived

from

and

hopes

to

highlight

the

importance

of

depositing

the

constraint

lists

used

for

structure

calculations

along

with

the

molecular

coordinates

Materials

and

methods

The

data

was

obtained

from

two

sources

the

molecular

system

coordinate

secondary

structure

and

author

information

from

the

wwPDB

and

the

original

constraint

information

as

NMR-STAR

files

from

the

NMR

Restraints

Grid

at

the

BioMagResBank

Each

file

was

directly

parsed

and

combined

into

the

CCPN

data

model

via

the

FormatConverter

software

-LRB-

Vranken

et

al.

-RRB-

in

a

process

that

extends

the

procedure

used

in

the

original

DOCR/FRED

project

To

handle

the

larger

number

of

entries

an

automatic

mapping

procedure

was

developed

that

maps

the

molecule

sequence

as

derived

from

the

PDB

file

to

the

atom

information

and

sequence

code

numbering

used

within

the

constraint

lists

For

some

entries

manual

mapping

between

the

information

from

the

PDB

file

and

the

constraint

file

was

required

The

original

mappings

from

the

RECOORD

project

-LRB-

by

Aart

Nederveen

-RRB-

and

a

further

mappings

by

the

author

and

by

Jurgen

Doreleijers

were

used

to

correctly

set

the

PDB-constraint

file

mapping

for

the

entries

in

the

base

set

Using

the

automatic

or

manual

mapping

the

atom

information

from

the

constraints

was

then

connected

to

that

for

the

molecule

During

this

process

the

dependence

on

string-based

atom

names

for

the

assignment

-LRB-

as

used

in

all

constraint

files

-RRB-

was

also

removed

since

the

CCPN

data

model

is

object-based

The

final

CCPN

project

in

which

all

the

information

from

the

original

files

is

now

highly

organised

was

then

written

out

This

process

was

completed

for

PDB

entries

Three

subsets

of

the

base

set

were

generated

for

comparison

and

validation

purposes

-LRB-

Table

-RRB-

Sets

HIP

and

AHIP

contain

only

entries

with

intra-residue

constraints

sets

AHP

and

AHIP

only

include

entries

where

over

of

relevant

constraint

information

was

assigned

to

atoms

In

addition

a

set

was

generated

based

on

the

coordinates

for

the

entries

included

in

the

HP

set

Only

the

HP

HPC

and

AHIP

sets

are

further

referred

to

in

this

article

the

HIP

and

AHP

sets

are

available

online

for

reference

purposes

Table

of

the

available

data

sets

used

in

the

analysisSet

NameData

high

coordinate

data

A

workflow

based

on

Python

-LRB-

van

Rossum

-RRB-

scripts

and

dictionaries

was

then

developed

to

handle

the

information

from

the

CCPN

project

files

for

the

entries

-LRB-

Fig.

-RRB-

This

workflow

was

run

separately

for

the

base

set

and

each

subset

In

the

first

step

the

original

information

was

filtered

so

that

only

valid

constraints

between

protons

in

protein

chains

were

retained

-LRB-

Fig.

-RRB-

Entries

were

removed

for

the

following

reasons

-LRB-

Table

-RRB-

-LRB-

-RRB-

No

valid

protein

chains

the

entry

contained

only

chains

that

are

sequence

fragments

or

duplicates

of

other

chains

-LRB-

only

the

chain

with

the

highest

number

of

valid

constraints

linked

to

it

is

retained

in

the

data

set

-RRB-

-LRB-

-RRB-

No

valid

constraint

lists

the

originally

deposited

constraint

lists

could

not

be

parsed

or

handled

correctly

or

-LRB-

for

sets

HIP

and

AHIP

-RRB-

did

not

contain

any

intra-residue

constraints

-LRB-

-RRB-

Insufficient

linking

less

than

-LRB-

sets

HP

HIP

-RRB-

or

-LRB-

sets

AHP

AHIP

-RRB-

of

the

constraint

information

could

be

linked

to

the

atom

information

-LRB-

constraint

information

belonging

to

non-protein

chains

was

ignored

for

this

purpose

-RRB-

-LRB-

-RRB-

Insufficient

valid

constraints

less

then

of

constraints

remained

for

all

lists

after

removing

invalid

constraints

Constraints

were

considered

invalid

if

they

did

not

have

any

valid

items

no

upper

distance

limit

an

upper

distance

limit

of

larger

than

Å

or

had

only

items

between

invalid

atoms

-LRB-

non-proton

unlinked

or

non-protein

-RRB-

Note

that

some

entries

had

specific

distance

constraint

lists

and/or

constraints

removed

for

these

reasons

but

were

still

included

in

the

analysed

set

Fig.

of

the

workflow

employed

in

the

analysis

Grey

boxes

indicate

files

white

boxes

Python

scriptsTable

for

each

data

set

of

the

number

of

removed

and

analysed

entriesHPHIPAHPAHIPNo

valid

protein

valid

constraint

valid

included

included

In

the

second

step

the

data

from

all

relevant

entries

was

collated

-LRB-

Fig.

-RRB-

After

reading

in

the

CCPN

projects

non-relevant

information

was

removed

based

on

the

filtering

information

and

the

data

reorganised

into

a

set

of

Python

dictionaries

that

contain

the

overall

information

on

the

entry

residue

and

constraint

levels

In

the

final

analysis

step

these

dictionaries

were

read

in

areas

and

data

of

interest

were

marked

and

organised

HTML

output

was

produced

for

browsing

A

Python

dictionary

with

highly

reduced

information

that

can

be

further

used

for

validation

or

constraint

filtering

purposes

was

also

generated

The

same

entries

from

the

HP

set

were

used

to

generate

the

HPC

set

The

original

coordinates

were

analysed

using

r

distance

averaging

for

equivalent

and

prochiral

atom

sets

over

all

structures

in

the

ensemble

-LRB-

Nilges

-RRB-

Only

individual

distances

of

less

then

Å

were

retained

and

final

averaged

distances

of

less

than

Å

were

considered

to

be

equivalent

to

an

observable

constraint

contact

Atoms

without

coordinates

were

ignored

in

the

analysis

Highlights

from

the

analysis

are

described

in

the

results

section

and

complete

details

are

available

from

a

web

site

-LRB-

-RRB-

For

all

statistical

operations

the

R

package

-LRB-

Bates

et

al.

-RRB-

was

accessed

via

the

RPy

Python

module

-LRB-

Moreira

and

Warnes

-RRB-

Since

a

contact

is

either

observed

or

not

observed

it

was

possible

to

use

a

binomial

analysis

to

determine

for

example

which

secondary

structure

specific

contacts

were

significantly

less

or

more

likely

to

be

observed

Binomial

analysis

was

used

throughout

with

a

confidence

level

of

meaning

that

only

out

of

every

determined

outliers

is

a

false

positive

The

correlations

between

the

coordinate

and

the

constraint

data

within

a

data

set

and

correlations

between

data

sets

were

plotted

via

RPy

with

linear

correlation

coefficients

determined

by

both

the

Spearman

and

Pearson

methods

The

Spearman

method

-LRB-

Spearman

-RRB-

is

a

non-parametric

measure

of

correlation

which

does

not

make

assumptions

about

the

frequency

distribution

of

the

variables

and

does

not

require

a

linear

relationship

between

the

variables

The

Pearson

method

-LRB-

Pearson

-RRB-

on

the

other

hand

assesses

if

the

relationship

between

the

variables

is

linear

In

the

analysis

a

residue

is

marked

as

assigned

when

at

least

one

proton

belonging

to

it

is

linked

to

a

constraint

The

total

number

of

times

a

particular

inter-atomic

contact

is

observed

can

be

a

fraction

as

for

ambiguous

constraints

each

constraint

item

contributes

a

fraction

of

to

the

total

The

total

number

of

relevant

distances

for

a

particular

inter-atomic

contact

-LRB-

ndist

-RRB-

is

always

an

integer

The

occurrence

for

a

particular

inter-atomic

contact

is

calculated

as

where

nactual

is

the

number

of

times

the

contact

occurs

-LRB-

with

ambiguity

taken

into

account

-RRB-

and

npossible

the

total

number

of

times

this

contact

could

occur

between

the

relevant

residues

either

for

all

residues

or

only

between

assigned

residues

The

occurrence

is

given

in

percent

or

as

a

fraction

The

ambiguity

of

an

inter-atomic

contact

is

defined

as

If

the

ambiguity

is

this

means

that

all

contributing

contacts

are

unambiguously

assigned

The

more

highly

ambiguous

the

contributing

contacts

are

the

closer

this

number

will

be

to

which

also

means

it

is

less

dependable

Within

secondary

structure

combinations

the

same

definitions

are

used

except

that

npossible

and

nactual

are

now

determined

within

that

secondary

structure

combination

as

npossible

ss

and

nactual

ss

The

uniqueness

of

a

contact

for

a

secondary

structure

combination

which

indicates

how

unique

the

contact

is

within

that

combination

is

defined

as

For

prochiral

and

possible

non-equivalent

atom

sets

the

contacts

are

divided

into

the

individual

atom

names

-LRB-

e.g.

and

or

and

for

Phe

-RRB-

if

they

occur

by

themselves

in

a

constraint

If

both

the

atom

names

occur

in

different

items

within

the

same

constraint

to

the

same

other

atom

then

they

are

grouped

-LRB-

e.g.

Hβ

or

Hδ

for

Phe

-RRB-

If

they

occur

as

a

group

in

one

constraint

item

they

are

used

as

is

In

order

to

obtain

total

statistics

for

these

contacts

they

were

also

combined

-LRB-

e.g.

-LRB-

-RRB-

or

-LRB-

-RRB-

for

Phe

-RRB-

To

get

the

combined

occurrence

the

nactual

for

the

group

is

added

to

the

individual

contribution

with

the

highest

nactual

to

obtain

the

nactual

used

to

calculate

the

occurrence

for

the

combined

contact

The

combined

distance

information

is

obtained

from

all

contributions

and

the

ambiguity

is

then

calculated

from

the

total

nactual

of

all

contributions

Results

Information

from

analysis

The

analysis

of

the

data

for

each

set

is

divided

into

separate

categories

-LRB-

visible

on

the

left-hand

side

menu

in

Fig.

-RRB-

-LRB-

-RRB-

Contact

analysis

Arranges

inter-atomic

distances

by

residue

residue

combination

secondary

structure

of

those

residues

and

contact

type

-LRB-

intra-residue

-LRB-

i

i

-RRB-

sequential

-LRB-

i

i

-RRB-

medium-range

defined

here

as

up

to

residues

separation

-LRB-

i

i

n

-RRB-

and

long

range

-LRB-

i

i

-RRB-

-RRB-

-LRB-

-RRB-

Protein

secondary

structure

analysis

Groups

inter-atomic

distances

by

secondary

structure

combinations

-LRB-

-RRB-

Residue

atom

analysis

Shows

assignment

percentages

for

the

atoms

in

each

amino

acid

-LRB-

-RRB-

Unassigned

fragments

breakdown

Lists

unassigned

sequence

fragments

-LRB-

-RRB-

Fragment

analysis

by

residue

Analyses

tripeptide

fragments

based

on

the

assignment

status

of

the

central

residue

Fig.

web

page

for

Ala

Ala

i

i

contact

information

from

The

different

data

sets

comparisons

between

them

and

analysis

categories

can

be

accessed

via

the

left

hand

side

menu

In

this

contact

information

page

overall

statistics

for

the

individual

Ala

residues

involved

in

this

contact

are

shown

in

the

Residue

and

Residue

tables

at

the

top

statistics

for

the

combined

Ala

Ala

residues

in

the

Residue

combination

table

The

information

on

an

atom

level

is

listed

in

the

Breakdown

per

contact

table

Outliers

per

secondary

structure

combination

based

on

a

binomial

analysis

are

highlighted

in

red

-LRB-

higher

than

expected

-RRB-

and

blue

-LRB-

lower

than

expected

-RRB-

This

colour

coding

is

used

throughout

the

web

pages

for

other

types

of

analyses

Validation

of

data

sets

The

relevance

of

the

base

set

-LRB-

HP

-RRB-

was

validated

by

comparison

with

the

most

restricted

AHIP

set

This

is

necessary

because

the

HP

set

contains

entries

without

intra-residue

constraints

and

entries

where

less

constraint

information

is

linked

to

atoms

The

correlation

between

the

occurrences

of

contacts

between

both

sets

is

very

high

-LRB-

-RRB-

-LRB-

Fig.

-RRB-

and

in

a

breakdown

per

contact

type

no

correlation

is

less

than

-LRB-

results

on

web

pages

-RRB-

A

further

detailed

analysis

shows

that

out

of

compared

contacts

only

differ

significantly

at

a

confidence

level

of

Of

these

are

His

His

contacts

where

the

differences

are

most

likely

introduced

by

changes

in

the

number

of

His

residues

that

are

assigned

in

the

data

sets

-LRB-

from

in

HP

to

in

AHIP

-RRB-

The

other

are

long-range

contacts

involving

side

chain

protons

between

Leu

Phe

and

Phe

Val

which

are

uncommon

and

these

differences

can

be

attributed

to

accidental

variations

in

the

number

of

entries

that

have

these

contacts

and

are

included

in

the

data

set

Overall

this

data

shows

that

including

the

additional

entries

does

not

introduce

major

changes

in

the

occurrence

levels

The

HP

set

was

therefore

chosen

as

the

reference

set

as

estimates

of

for

example

significant

differences

between

contacts

in

secondary

structure

elements

tend

to

become

more

reliable

as

more

data

is

included

Information

on

correlations

between

all

data

sets

is

available

from

the

web

pages

-LRB-

Fig.

the

Comparison

link

in

the

left-hand

side

menu

-RRB-

Fig.

between

the

foccurrence

for

the

HP

and

AHIP

data

sets

-LRB-

correlation

Spearman

Pearson

-RRB-

A

further

way

to

validate

the

constraint

information

is

that

it

should

reproduce

the

typical

contacts

that

are

observed

in

secondary

structure

elements

The

secondary

structure

definitions

as

determined

by

DSSP

-LRB-

Kabsch

and

Sander

-RRB-

were

taken

from

the

PDB

file

header

for

each

entry

Because

of

the

size

of

the

data

that

is

generated

by

the

analysis

only

the

Ala

residue

is

used

here

as

an

example

The

complete

information

can

be

accessed

via

the

web

pages

-LRB-

Fig.

-RRB-

The

numbers

that

are

relevant

in

order

to

determine

whether

a

contact

is

significant

for

a

particular

secondary

structure

element

are

the

occurrence

within

that

particular

secondary

structure

element

-LRB-

which

has

to

be

significantly

higher

than

expected

from

the

overall

occurrence

-RRB-

the

ambiguity

of

the

contact

-LRB-

if

it

is

high

it

comes

by

definition

from

highly

ambiguous

constraints

and

is

therefore

unreliable

-RRB-

and

the

uniqueness

of

the

contact

-LRB-

which

indicates

how

often

the

contact

is

seen

within

that

secondary

structure

element

compared

to

the

total

number

of

times

it

is

observed

-RRB-

For

intra-residue

contacts

there

are

not

many

significant

differences

in

the

observed

occurrence

within

different

secondary

structure

elements

although

overall

fewer

contacts

are

observed

when

secondary

structure

is

absent

-LRB-

Table

-RRB-

This

is

likely

due

to

higher

signal

overlap

for

atoms

in

random

coil

fragments

which

complicates

assignment

The

information

from

the

coordinate-derived

HPC

set

is

provided

throughout

for

comparison

there

are

as

expected

when

using

a

distance

cutoff

of

Å

no

differences

for

this

HPC

set

between

the

occurrence

for

different

secondary

structure

elements

and

only

minor

differences

in

the

average

and

mean

distance

are

observed

-LRB-

data

available

on

web

pages

-RRB-

Table

for

selected

backbone

Ala

Ala

contactsTypeAtom

secondary

structurei

HHβ

HαHβ

i

i

HHβ

HαHβ

i

i

HHβ

HαHβ

Hβ

Hβ

Hβ

i

i

HHβ

HαHβ

Hβ

Hβ

Hβ

i

i

HHβ

HαHβ

Hβ

Hβ

Hβ

In

the

secondary

structure

columns

the

first

value

is

from

the

HP

set

the

second

from

the

HPC

set

A

bold

value

indicates

a

contact

that

occurs

significantly

more

than

average

a

italic

value

significantly

less

For

sequential

Ala

Ala

contacts

the

H

H

contact

is

observed

significantly

more

in

α-helices

than

in

β-sheets

-LRB-

Table

-RRB-

and

is

also

highly

unique

-LRB-

-RRB-

This

situation

is

as

expected

reversed

for

the

Hα

H

contact

which

is

observed

significantly

less

in

α-helices

and

more

in

β-sheets

Overall

however

this

contact

is

more

uniquely

observed

in

α-helices

-LRB-

compared

to

for

β-sheets

-RRB-

which

is

due

to

the

prevalence

of

Ala

Ala

fragments

in

α-helices

-LRB-

compared

to

-RRB-

Interestingly

however

an

H

Hβ

contact

is

also

observed

significantly

more

in

α-helices

than

in

β-sheets

or

when

no

secondary

structure

is

present

and

has

a

uniqueness

of

so

within

a

sequential

Ala

Ala

fragment

this

type

of

contact

is

highly

predictive

of

α-helical

structure

The

sequential

Hα

Hβ

contact

is

on

the

other

hand

highly

predictive

of

β-sheet

with

a

uniqueness

of

although

it

is

on

average

mostly

observed

when

no

secondary

structure

is

present

-LRB-

-RRB-

This

illustrates

that

the

uniqueness

of

a

contact

is

strongly

related

to

the

prevalence

of

a

particular

sequence

combination

in

a

particular

secondary

structure

element

and

is

not

necessarily

indicative

of

the

kind

of

secondary

structure

element

a

particular

contact

usually

occurs

in

The

information

from

the

constraints

and

the

coordinates

show

clear

differences

as

illustrated

by

comparison

with

some

of

the

contacts

described

earlier

The

H

Hβ

contact

occurs

quite

often

in

β-sheets

based

on

the

coordinate

data

with

a

median

distance

that

is

slightly

higher

than

in

α-helices

-LRB-

Å

compared

to

Å

-RRB-

The

Hα

Hβ

contact

which

would

generally

be

difficult

to

identify

due

to

overlap

in

the

aliphatic

region

of

a

NOESY

spectrum

is

always

present

based

on

the

coordinate

data

but

is

only

seen

in

cases

based

on

the

constraints

These

differences

illustrate

that

the

NOE

constraint

data

not

only

incorporates

distance

information

but

also

encodes

NMR-specific

information

such

as

the

difficulty

with

which

a

particular

contact

can

be

assigned

For

i

i

and

i

i

contacts

as

expected

the

typical

H

H

Hα

H

Hα

Hβ

and

Hβ

H

contacts

are

highly

prevalent

for

α-helices

-LRB-

Table

-RRB-

with

generally

high

uniqueness

and

low

ambiguity

An

H

Hβ

contact

is

also

more

often

observed

than

average

-LRB-

respectively

in

and

cases

-RRB-

while

Hβ

Hβ

contacts

are

very

rarely

seen

for

i

i

contacts

in

an

α-helix

Note

that

based

on

the

coordinate

data

the

i

i

Hβ

Hβ

contact

is

highly

relevant

for

β-sheets

-LRB-

-RRB-

but

it

is

in

practice

rarely

observed

-LRB-

-RRB-

probably

because

it

falls

in

a

densely

populated

region

of

a

typical

NOESY

spectrum

For

helices

the

percentages

are

often

similar

to

the

α-helical

ones

but

there

are

often

not

enough

data

to

determine

whether

a

difference

is

significant

-LRB-

data

on

web

pages

-RRB-

Also

of

interest

is

the

i

i

contact

between

Hβ

H

which

occurs

in

cases

for

β-sheets

and

cases

where

secondary

structure

is

absent

The

binomial

analysis

indicates

that

the

fraction

is

observed

significantly

less

while

it

does

not

mark

the

fraction

This

is

because

this

type

of

analysis

is

dependent

on

sample

size

-LRB-

only

sample

for

the

fraction

for

the

fraction

-RRB-

For

the

i

i

contacts

Hα

H

and

Hβ

H

connections

are

again

highly

prevalent

for

α-helices

while

Hα

Hβ

and

Hβ

Hβ

contacts

are

present

in

the

coordinate

set

but

are

seldom

observed

in

practice

-LRB-

Table

-RRB-

All

contacts

with

a

separation

of

residues

or

higher

are

very

rarely

observed

in

α-helices

but

become

highly

prevalent

for

β-sheets

-LRB-

data

not

shown

-RRB-

The

only

exception

to

this

are

long

range

Hα

Hβ

and

Hβ

Hβ

contacts

which

are

seen

in

significantly

higher

percentages

between

α-helices

as

compared

to

the

average

Contact

data

highlights

Traditionally

and

as

described

above

identifying

secondary

structure

contacts

is

based

on

the

commonly

observed

contacts

between

protons

from

the

backbone

and

the

β

position

-LRB-

Wüthrich

-RRB-

However

with

experience

in

assigning

NOE

peaks

comes

the

knowledge

that

other

contacts

are

also

often

observed

-LRB-

e.g.

i

i

contacts

between

side

chain

protons

in

a

β-sheet

-RRB-

In

this

analysis

such

contacts

are

readily

observed

-LRB-

Table

-RRB-

For

example

the

sequential

Trp

Gly

H

contact

is

quite

common

based

on

the

coordinate

data

but

is

in

practice

particularly

observed

in

a

β-sheet

The

sequential

Trp

Phe

Hα

contact

is

seemingly

more

often

observed

in

an

α-helix

but

the

differences

are

not

significant

This

is

a

case

that

could

be

clarified

if

more

relevant

data

were

available

The

Thr

Tyr

Hε

i

i

contact

is

clearly

observed

in

mostly

β-sheet

Interesting

in

this

case

is

that

according

to

the

constraint

data

it

can

occur

in

an

α-helix

while

this

is

not

the

case

based

on

the

coordinate

data

even

though

the

ambiguity

of

the

contact

is

This

is

possible

because

this

data

point

is

based

on

one

contact

with

an

upper

distance

limit

of

Å

whereas

the

cutoff

used

for

coordinates

is

Å

Generally

speaking

this

type

of

situation

can

occur

for

highly

ambiguous

constraints

where

other

constraint

items

satisfy

the

upper

distance

limit

An

Hα

i

i

contact

between

Ala

and

Ile

is

observed

almost

exclusively

in

an

α-helix

-LRB-

and

never

in

a

β-sheet

-RRB-

and

is

very

unique

-LRB-

-RRB-

To

be

able

to

discern

whether

this

contact

is

very

common

from

the

Hα

of

any

amino

acid

to

the

of

an

Ile

the

data

was

joined

as

Xxx

residues

to

produce

generic

information

to

and

from

each

amino

acid

-LRB-

data

on

web

pages

-RRB-

The

specific

information

for

the

i

i

Hα

contact

from

all

residues

to

Ile

is

shown

in

Table

and

shows

that

this

contact

is

common

in

α-helices

Finally

some

other

interesting

i

i

side

chain

contacts

that

often

occur

in

helices

are

listed

in

Table

Table

for

selected

secondary

structure

related

contactsTypeAtom

secondary

structurei

i

Trp

i

i

Tyr

Hε

i

i

HαIle

Xxx

HαIle

i

i

Tyr

In

the

secondary

structure

columns

the

first

value

is

from

the

HP

set

the

second

from

the

HPC

set

A

bold

value

indicates

a

contact

that

occurs

significantly

more

than

average

an

italic

value

significantly

less

It

is

not

possible

to

describe

all

information

in

detail

in

this

article

and

the

web

pages

serve

as

the

reference

resource

for

any

investigations

However

to

provide

a

better

overview

of

the

overall

trends

in

secondary

structure

elements

all

backbone

contacts

were

grouped

by

atom

type

-LRB-

H

Hα

Hβ

-RRB-

and

secondary

structure

combinations

-LRB-

Table

full

data

from

web

site

-RRB-

For

intra-residue

contacts

more

contacts

involving

Hβ

protons

are

defined

in

β

sheets

Sequential

contacts

in

α

helices

originating

from

the

H

and

Hβ

protons

are

observed

significantly

more

often

whereas

ones

originating

from

the

Hα

proton

are

less

common

This

situation

is

reversed

in

β

sheets

-LRB-

except

for

Hβ

H

contacts

-RRB-

There

are

only

for

sequential

contacts

some

discrepancies

between

the

data

from

the

HP

and

HPC

sets

with

for

example

the

rate

at

which

sequential

H

Hα

contacts

are

observed

being

reversed

in

the

HPC

set

as

compared

to

the

HP

set

The

reasons

for

this

are

not

immediately

clear

but

are

likely

related

to

overlap

Most

i

i

contacts

are

more

common

in

α

helices

except

for

ones

to

the

Hα

proton

and

between

Hβ

protons

The

latter

contact

is

more

often

observed

in

β

sheets

As

expected

most

i

i

contacts

are

commonly

observed

in

α

helices

except

for

Hβ

Hα

and

H

Hα

The

latter

is

again

more

frequently

seen

for

β

sheets

but

in

this

case

this

is

expected

to

be

between

different

strands

in

the

hairpin

area

The

trends

are

not

as

clear

for

i

i

contacts

where

Hα

H

Hα

Hβ

and

Hβ

H

contacts

are

more

frequent

in

α

helices

and

all

other

ones

more

frequent

in

β

sheets

This

is

again

likely

related

to

hairpin

contacts

All

contacts

from

i

i

and

more

are

very

infrequent

for

α

helices

but

relatively

very

frequent

for

β

sheets

Table

overview

of

general

trends

in

joint

secondary

structure

information

for

all

contactsTypeSSH

-LRB-

i

-RRB-

Hα

-LRB-

i

-RRB-

Hβ

-LRB-

i

-RRB-

HHαHβHHαHβHHαHβi

iHelix

Sheet

i

i

Sheet

i

i

Sheet

i

i

Sheet

i

i

Sheet

i

i

Helix

Sheet

Indicates

that

signals

observed

more

than

average

less

than

average

signifies

that

there

is

no

trend

The

first

character

in

each

cell

contains

the

constraint

HP

set

information

the

second

the

coordinate

HPC

set

An

analysis

of

the

percentage

of

atoms

that

were

assigned

within

each

residue

type

-LRB-

Residue

atom

analysis

on

the

website

-RRB-

shows

that

generally

atoms

are

significantly

higher

assigned

in

α-helices

and

β-sheets

and

lower

when

no

secondary

structure

is

present

This

finding

is

not

surprising

as

the

secondary

structure

elements

are

defined

by

constraints

and

the

atoms

have

to

be

assigned

to

obtain

those

Another

general

trend

is

that

prochiral

methylenes

are

within

α-helices

significantly

more

degenerated

i.e.

the

and

atoms

exists

as

a

QB

pseudoatom

or

an

HB

type

atom

set

This

type

of

analysis

can

be

significantly

improved

by

cleaning

up

the

stereospecific

assignment

status

based

on

the

coordinates

The

unique

sequence

fragments

for

which

no

assignments

were

found

are

also

listed

on

the

website

-LRB-

Unassigned

fragments

breakdown

-RRB-

An

example

from

this

data

is

that

often

no

constraints

are

found

for

His

tags

The

LEHHHHH

fragment

for

example

was

not

assigned

in

entries

A

general

overview

of

the

percentage

of

residues

that

are

assigned

confirms

that

His

residues

for

example

are

assigned

in

only

of

cases

while

Trp

is

assigned

in

of

cases

-LRB-

Fragment

analysis

by

residue

-RRB-

To

examine

this

in

more

detail

all

tripeptide

fragments

where

a

particular

amino

acid

is

the

central

residue

are

listed

-LRB-

for

the

N

and

C-terminus

these

are

dipeptide

fragments

-RRB-

Listed

for

each

fragment

are

-LRB-

-RRB-

the

total

number

of

times

the

tripeptide

fragment

occurs

in

the

data

set

-LRB-

-RRB-

the

percentage

of

times

it

is

unassigned

compared

to

the

total

number

of

times

the

amino

acid

occurs

-LRB-

-RRB-

the

number

of

times

the

amino

acid

is

not

assigned

-LRB-

-RRB-

the

number

of

times

the

tripeptide

fragment

occurs

overall

and

-LRB-

-RRB-

the

assignment

percentages

per

secondary

structure

element

The

entries

in

which

the

fragments

are

unassigned

are

also

listed

Continuing

with

His

as

an

example

it

is

not

assigned

when

part

of

the

EHH

fragment

in

of

cases

and

when

part

of

a

C-terminal

HH

fragment

it

is

not

assigned

in

of

all

times

the

fragment

occurs

To

get

a

better

overall

view

of

the

sequence

fragments

that

are

difficult

to

assign

they

were

grouped

by

joining

respectively

the

i

and

i

residues

The

results

for

His

and

some

selected

other

fragments

are

shown

in

Table

A

Ser

residue

preceded

or

followed

by

a

Gly

for

example

is

often

unassigned

This

type

of

information

could

be

useful

for

predicting

which

areas

of

a

protein

sequence

are

difficult

to

assign

from

an

NMR

perspective

Table

sequence

fragments

where

the

central

residue

is

often

unassigned

The

unassigned

percentages

are

relative

to

the

total

number

of

times

the

fragment

occursFragmentUnassigned

-LRB-

-RRB-

TotalXxx

His

His

His

His

His

Ser

Ser

Pro

Discussion

In

this

analysis

only

the

original

data

as

deposited

by

the

authors

was

used

and

no

attempt

was

made

to

clean

up

and

further

interpret

this

information

except

for

linking

the

constraint

with

the

coordinate

data

and

removing

identical

sequences

from

the

data

set

pool

-LRB-

where

only

the

entry

with

the

highest

number

of

constraints

linked

to

atoms

was

kept

-RRB-

This

approach

is

intentional

as

it

best

represents

the

quality

and

extent

of

the

data

that

is

currently

deposited

at

the

PDB

Only

the

distance

constraint

information

was

included

in

the

analysis

and

the

information

from

dihedral

H-bond

and

RDC

constraints

was

ignored

Even

though

these

constraints

contain

important

structural

information

they

were

as

experimental

data

recorded

independently

from

the

NOE

data

They

are

used

in

the

structure

determination

process

however

and

it

was

not

investigated

whether

their

presence

influences

the

quality

of

the

final

distance

constraint

lists

There

are

several

other

issues

that

can

still

be

addressed

and

although

these

are

likely

to

improve

some

of

the

aspects

of

this

type

of

study

it

is

also

important

to

start

with

the

original

information

so

that

a

comparison

point

is

available

The

first

issue

is

that

stereospecific

assignments

can

be

swapped

or

deassigned

based

on

the

original

coordinates

similar

to

the

approach

in

the

RECOORD

project

This

could

in

principle

reveal

preferences

related

to

stereospecifically

assigned

atoms

in

secondary

structure

elements

The

second

issue

concerns

the

distances

that

were

provided

with

the

constraints

These

are

often

binned

in

weak/medium/strong

classes

with

fixed

distance

cutoffs

so

that

the

resulting

distance

distributions

often

show

spikes

at

these

distances

In

Fig.

for

example

it

is

clear

that

spikes

occur

at

and

Å

Recalibrating

the

distances

based

on

the

deposited

coordinates

should

improve

the

quality

of

the

resulting

information

and

reveal

relationships

between

distance

and

occurrence

The

third

issue

concerns

the

sequences

that

are

included

the

current

data

sets

include

protein

sequences

with

a

high

homology

This

is

clearly

not

an

ideal

situation

but

there

is

currently

not

enough

data

available

for

a

cleaner

analysis

It

is

therefore

important

to

check

whether

a

particular

contact

appears

in

a

large

amount

of

entries

or

the

observed

occurrence

might

be

due

to

systematic

error

from

homologous

proteins

produced

by

the

same

laboratory

The

fourth

problem

is

the

identification

of

the

secondary

structure

fragments

This

is

now

based

on

the

PDB

DSSP

analysis

from

the

original

coordinates

for

only

the

first

or

representative

model

so

that

secondary

structure

elements

are

not

always

identified

properly

If

chemical

shifts

were

available

an

identification

based

on

CSI

-LRB-

Wishart

et

al.

-RRB-

would

become

possible

so

secondary

structure

stretches

can

be

included

that

are

more

flexible

and

less

defined

on

the

coordinate

level

More

refined

secondary

structure

identifications

from

the

coordinates

could

also

reveal

patterns

related

to

for

example

turns

Overall

the

best

way

to

improve

this

analysis

remains

to

increase

the

sample

size

by

encouraging

deposition

of

constraint

lists

and

all

related

NMR

information

-LRB-

peak

lists

chemical

shifts

spectra

-RRB-

and

ensuring

that

the

data

is

consistent

when

deposited

by

the

authors

Efforts

like

the

CCPN

project

-LRB-

Fogh

et

al.

-RRB-

which

allow

data

harvesting

from

NMR

data

collection

to

structure

calculation

should

provide

this

kind

of

data

without

requiring

any

additional

effort

by

the

scientists

who

produce

the

data

Fig.

distribution

from

the

constraint

information

for

sequential

Ala

Ala

contacts

between

backbone

H

protons

In

this

analysis

a

particular

inter-atomic

contact

between

two

residues

from

one

PDB

entry

is

either

observed

or

not

observed

The

reason

why

a

contact

is

observed

-LRB-

or

not

-RRB-

implicitly

includes

distance

information

peak

overlap

water

exchange

line

broadening

and

all

other

factors

that

can

lead

to

not

observing

or

assigning

a

contact

during

analysis

of

a

spectrum

This

is

different

from

the

traditional

meaning

of

an

assigned

atom

on

the

chemical

shift

level

where

it

means

that

the

chemical

shift

value

for

the

resonance

that

arises

from

the

atom

is

known

However

this

does

not

necessarily

mean

that

these

assigned

atoms

produce

any

valid

inter-atomic

distance

information

Thus

an

assigned

atom

-LRB-

or

residue

-RRB-

on

the

constraint

level

means

that

a

chemical

shift

assignment

also

produced

useful

and

valid

information

related

to

the

inter-atomic

distances

within

the

molecule

The

original

study

that

defined

the

inter-atomic

contacts

relevant

for

assigning

secondary

structure

elements

with

NMR

used

a

set

of

high-resolution

protein

crystal

structures

comprising

about

residues

-LRB-

Wüthrich

et

al.

-RRB-

In

this

study

the

extent

of

identification

was

defined

as

the

percentage

of

times

a

distance

was

smaller

than

a

particular

cutoff

value

within

a

secondary

structure

element

while

the

uniqueness

of

identification

is

the

percentage

of

times

the

distance

is

observed

within

a

particular

secondary

structure

element

out

of

the

total

number

of

times

it

is

observed

The

extent

is

equivalent

to

the

foccurence

used

in

this

study

for

the

constraint

sets

with

the

exception

that

no

distance

cutoff

is

used

-LRB-

although

some

distance

information

is

as

mentioned

implicitly

included

because

the

reason

a

constraint

is

observed

or

not

is

very

dependent

on

distance

-RRB-

and

that

values

are

labelled

as

significant

based

on

a

binomial

analysis

The

uniqueness

has

the

same

definition

except

that

again

it

is

based

on

the

amount

of

constraints

that

are

observed

Also

of

interest

is

the

relationship

between

the

information

that

comes

directly

from

the

deposited

constraints

and

the

information

that

comes

from

the

deposited

coordinates

Here

the

constraint

information

is

compared

to

the

distances

from

the

originally

deposited

coordinates

Although

a

set

of

recalculated

coordinates

-LRB-

as

in

RECOORD

-RRB-

or

X-ray

structures

could

have

equally

well

been

used

the

originally

deposited

coordinates

were

chosen

because

they

should

best

match

the

content

of

the

constraint

lists

All

comparisons

between

constraint

and

coordinate

information

are

intended

for

informative

purposes

only

the

constraints

represent

the

experimental

NMR

side

of

the

information

contained

in

the

coordinates

and

are

in

effect

only

a

subset

of

the

information

contained

therein

However

a

dependable

determination

of

whether

a

particular

NOE

contact

is

observed

or

not

is

not

possible

based

on

an

NMR

structural

ensemble

but

is

trivial

based

on

the

constraints

because

they

inherently

contain

NMR-specific

information

like

signal

overlap

dynamics

etc.

From

Fig.

it

is

clear

that

the

contact

occurrence

is

almost

always

higher

for

the

HPC

set

compared

to

the

HP

set

This

is

related

to

the

use

of

a

direct

distance

cutoff

of

Å

in

the

HPC

set

contacts

with

long

distances

could

give

rise

to

peaks

that

are

too

weak

to

be

seen

in

a

real

spectrum

but

are

still

included

Also

many

contacts

have

an

foccurrence

of

in

the

HPC

set

because

of

conformational

constraints

from

covalent

bonds

Not

all

of

these

contacts

are

seen

in

real

spectra

because

of

for

example

peak

overlap

or

line

broadening

The

correlations

between

the

occurrences

overall

are

not

very

high

-LRB-

Spearman

Pearson

-RRB-

with

especially

the

intra-residue

contacts

giving

bad

correlations

-LRB-

Spearman

Pearson

see

web

site

-RRB-

and

i

i

-LRB-

Spearman

Pearson

-RRB-

and

i

-LRB-

Spearman

Pearson

-RRB-

contacts

giving

the

best

results

An

indication

that

the

main

reason

for

the

bad

correlation

between

the

occurrences

is

distance

related

comes

from

the

large

improvement

that

is

observed

in

the

overall

correlations

if

only

coordinate

distances

of

less

than

Å

are

considered

-LRB-

Spearman

Pearson

-RRB-

However

results

from

using

both

the

HP

and

HPC

information

to

filter

ambiguous

constraints

lists

show

that

both

sets

essentially

give

the

same

results

-LRB-

personal

data

-RRB-

even

though

the

constraints

are

available

in

a

much

more

compressed

form

than

the

coordinates

and

no

force

field

information

was

used

Fig.

between

the

foccurrence

from

the

HPC

-LRB-

Coordinates

-RRB-

and

HP

-LRB-

Constraint

-RRB-

sets

In

the

KNOWNOE

-LRB-

Gronwald

et

al.

-RRB-

X-ray

structure

based

approach

to

obtain

probabilities

for

assignments

the

distance

distributions

for

inter-atomic

contacts

are

used

to

generate

volume-based

probabilities

in

addition

to

the

atom

identity

based

probabilities

This

approach

improves

the

probabilities

that

are

generated

but

it

does

require

that

the

original

peak

list

with

volumes

is

available

This

is

not

possible

within

the

current

analysis

although

this

will

be

pursued

if

a

meaningful

way

to

recalibrate

the

distance

constraint

bounds

is

available

This

would

also

allow

a

better

comparison

between

the

NMR

constraint

data

and

any

coordinate

data

-LRB-

from

NMR

or

X-ray

structures

-RRB-

Conclusion

A

resource

is

now

available

where

it

is

possible

to

check

how

likely

a

particular

contact

is

when

assigning

NOESY

spectra

or

if

a

particular

sequence

fragment

is

likely

to

be

difficult

to

assign

In

this

respect

it

formalises

information

that

scientists

with

experience

in

spectrum

analysis

are

aware

of

but

can

not

quantify

The

amount

of

information

provided

here

is

extensive

however

and

is

even

more

useful

when

used

as

knowledge

based

probabilities

in

automatic

assignment

strategies

to

filter

and/or

validate

ambiguous

constraint

possibilities

and

as

a

tool

to

rank

assignment

possibilities

in

spectrum

analysis

programs

These

are

being

implemented

as

part

of

the

CCPN

framework

Finally

the

NMR

constraint

lists

encompass

the

experimental

NMR

data

encoded

in

the

NMR

structural

ensembles

and

comprise

a

single

set

of

data

that

is

much

easier

to

analyse

than

an

ensemble

of

solutions

As

such

they

provide

a

reduced

form

of

structural

information

that

is

relevant

for

NMR

analysis

only

For

this

reason

and

to

allow

a

basic

level

of

scientific

reproducibility

and

validation

it

is

important

that

constraints

and

all

other

possible

NMR

derived

information

are

deposited

along

with

the

structure

coordinates

It

is

very

likely

that

a

lot

more

information

than

described

in

this

article

can

be

gained

from

it

which

in

turn

can

assist

the

NMR

community

and

can

help

to

understand

the

relationships

between

NMR

and

structure

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 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 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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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

constraint analysis
noe assignment
noe distances
nuclear magnetic resonance (nmr)