3D-EM constrained modelling of macromolecular...
14
Agnel Joseph Interpretation of 3D EM maps, fitting of atomic structures EMBO course on image processing for cryo EM September 2017 Lecture 18 Aims of this lecture • To understand 3D EM density fitting and what we can achieve. • To describe the different types of density fitting methods (rigid, flexible, assembly). • To be aware of different software tools used for visualization and density fitting. • To be aware of the errors involved in density fitting and understand how to critically assess the fitted models. 2 3 EMDB Statistics https://www.ebi.ac.uk/pdbe/emdb/statistics_main.html/ 3D-EM constrained modelling of macromolecular assemblies 1
Transcript of 3D-EM constrained modelling of macromolecular...
Agn
el J
osep
hIn
terp
reta
tion
of 3
D E
M m
aps,
fit
ting
of a
tom
ic s
truct
ures
EM
BO
cou
rse
on im
age
proc
essi
ng
for c
ryo
EM
Sep
tem
ber 2
017
Lect
ure
18A
ims
of th
is le
ctur
e
• To
unde
rsta
nd 3
D E
M d
ensi
ty fi
tting
and
wha
t we
can
achi
eve.
• To
desc
ribe
the
diffe
rent
type
s of
den
sity
fitti
ng m
etho
ds (r
igid
, fle
xibl
e, a
ssem
bly)
.
• To
be a
war
e of
diff
eren
t sof
twar
e to
ols
used
for v
isua
lizat
ion
and
de
nsity
fitti
ng.
• To
be a
war
e of
the
erro
rs in
volv
ed in
den
sity
fitti
ng a
nd u
nder
stan
d ho
w to
crit
ical
ly a
sses
s th
e fit
ted
mod
els.
2
3
EM
DB
Sta
tistic
s
http
s://w
ww
.ebi
.ac.
uk/p
dbe/
emdb
/sta
tistic
s_m
ain.
htm
l/
3D-E
M c
onst
rain
ed m
odel
ling
of
mac
rom
olec
ular
ass
embl
ies
Villa
& L
aske
r, C
urr O
pin
Stru
ct B
iol,
2014
.
Map
feat
ures
vs
reso
lutio
n
Wha
t can
we
mod
el a
nd in
terp
ret
5
Rea
l-spa
ce
refin
emen
tN
o
Fitti
ng a
ll kn
own
fold
s
No
3D-E
M m
ap
Com
pone
nt
Sequ
ence
Segm
enta
tion
Com
pone
nt
stru
ctur
e kn
own?
Wha
t re
solu
tion?
NoTe
mpl
ate
dete
cted
?Fo
ld
assi
gnm
ent
from
seq
uenc
e
De
novo
cha
in
trac
ing
Hom
olog
y m
odel
ling
Rig
id fi
tting
fit d
iffer
ent
from
map
?
Mul
tiple
co
nfor
mat
ions
ENM
/ N
MA
‘tem
plat
e-fr
ee’
mod
ellin
g
Yes
No
Yes
4 Å
< 20
Å<
4.5Å
~4.5
-10Å
Yes
SSE
assi
gnm
ent
Com
pone
nt
stru
ctur
e
3D-E
M m
ap
6
-Aca
dem
ic p
rogr
ams:
•Chi
mer
a (U
CS
F)
•Vis
ion
(Scr
ipps
) •V
MD
(U Il
linoi
s U
rban
a-C
ham
paig
n)
•Vol
Rov
er (U
T A
ustin
) •G
orgo
n (N
CM
I & W
ashi
ngto
n U
ni)
• Coo
t (U
niv
of Y
ork)
• O
(Upp
sala
Uni
v)
-Com
mer
cial
pro
gram
s:
•PyM
OL
(Sch
rodi
nger
) •A
mira
(TG
S, S
an D
iego
, CA
) •I
ris E
xplo
rer (
NA
G, D
owbe
r Gro
ve, I
L)
7
Rea
l-spa
ce
refin
emen
tN
o
Fitti
ng a
ll kn
own
fold
s
No
3D-E
M m
ap
Com
pone
nt
Sequ
ence
Segm
enta
tion
Com
pone
nt
stru
ctur
e kn
own?
Wha
t re
solu
tion?
NoTe
mpl
ate
dete
cted
?Fo
ld
assi
gnm
ent
from
seq
uenc
e
De
novo
cha
in
trac
ing
Hom
olog
y m
odel
ling
Rig
id fi
tting
fit d
iffer
ent
from
map
?
Mul
tiple
co
nfor
mat
ions
ENM
/ N
MA
‘tem
plat
e-fr
ee’
mod
ellin
g
Yes
No
Yes
4 Å
< 20
Å<
4.5Å
~4.5
-10Å
Yes
SSE
assi
gnm
ent
Com
pone
nt
stru
ctur
e
3D-E
M m
ap
Segm
enta
tion
8
-Id
entif
y bo
unda
ries
betw
een
3D re
gion
s th
at re
pres
ent s
truct
ural
com
pone
nts
in th
e co
ntex
t of s
truct
ural
, bio
chem
ical
and
bio
info
rmat
ic k
now
ledg
e.
-Th
e id
entif
ied
boun
darie
s ca
n be
use
ful i
n de
tect
ing
the
posi
tions
of k
now
n co
mpo
nent
st
ruct
ures
in th
e m
ap.
-Th
e si
ze o
f the
seg
men
ted
com
pone
nts
is re
late
d to
the
map
reso
lutio
n.
20 Å
4.5
Å10
Å prot
ein
seco
ndar
y st
ruct
ure
elem
ents
shap
edo
mai
ns
back
bone
Seg
men
tatio
n to
ols
9
-Man
ual (
UC
SF
Chi
mer
a)
Mas
kB
ox a
roun
d m
arke
r/ato
ms
Han
d er
asin
g
Seg
men
tatio
n to
ols
-Kno
wle
dge-
base
d se
gmen
tatio
n:
• Ant
ibod
y la
belin
g; g
old
clus
ters
; sub
unit/
dom
ain
dele
tion
-> d
iffer
ence
map
ping
(Chi
mer
a).
• Rec
ogni
tion
of s
truct
ural
com
pone
nts
- den
sity
fitti
ng.
10
- Aut
omat
ed: b
ased
on
dens
ity a
lone
(with
or w
ithou
t the
use
of s
ymm
etry
info
rmat
ion)
Seg
geR
:Pin
tilie
et a
l, J
Stru
ct B
iol 2
011
Seg
men
tatio
n to
ols
Mov
e in
des
cend
ing
orde
r ->
Ass
ign
non
neig
hbou
ring
max
ima
as a
sep
arat
e re
gion
-> a
ssig
n bo
undi
ng p
oint
to
the
regi
on w
ith m
ore
conn
ectio
ns
20000000000000000000000000000000000000000001111111111111111111111111111111111111111111111
expe
cted
seg
men
t si
ze11
Som
e se
gmen
tatio
n m
etho
ds
-Aut
omat
ed s
egm
enta
tion
base
d on
den
sity
alo
ne:
•D
ensi
ty th
resh
oldi
ng: p
rote
in a
nd R
NA
(Spa
hn e
t al.
2000
).
•W
ater
shed
met
hods
(Vol
kman
n 20
02),
Seg
geR
(Pin
tilie
et a
l. 20
10).
•Le
vel s
et (B
aker
et a
l 200
6)
•E
igen
valu
e m
etho
ds (F
rang
akis
& H
eger
l 200
2)
•Fa
st m
arch
ing
met
hod
(Fra
ngak
is &
Heg
erl 2
002,
Baj
aj 2
003)
.
•In
fere
nce
met
hods
to fi
nd c
onse
rved
regi
ons
(Sah
a et
al.
2010
, Xu
et a
l. 20
11)
Mat
ure
bact
erio
phag
e P
22 a
t 9.5
Å re
solu
tion
Bak
er e
t al.
J S
truct
Bio
l 200
6
Ass
embl
yC
ompo
nent
Som
e ex
ampl
e pr
ogra
ms:
Vol
ume
Rov
er, S
egge
r, A
mira
, IM
OD
Rea
l-spa
ce
refin
emen
tN
o
Fitti
ng a
ll kn
own
fold
s
No
3D-E
M m
ap
Com
pone
nt
Sequ
ence
Segm
enta
tion
Com
pone
nt
stru
ctur
e kn
own?
Wha
t re
solu
tion?
NoTe
mpl
ate
dete
cted
?Fo
ld
assi
gnm
ent
from
seq
uenc
e
De
novo
cha
in
trac
ing
Hom
olog
y m
odel
ling
Rig
id fi
tting
fit d
iffer
ent
from
map
?
Mul
tiple
co
nfor
mat
ions
ENM
/ N
MA
‘tem
plat
e-fr
ee’
mod
ellin
g
Yes
No
Yes
4 Å
< 20
Å<
4.5Å
~4.5
-10Å
Yes
SSE
assi
gnm
ent
Com
pone
nt
stru
ctur
eFi
tting
all
know
n fo
lds
Wha
t re
solu
tion?
reso
lutio
n?
De
novo
cha
in
trac
ing
No
< 20
Å<
4.5Å
~4.5
-10Å
SSE
assi
gnm
ent
13
Fold
reco
gniti
on fr
om d
ensi
ty
Bak
er e
t al.
Stru
ctur
e 20
07
< ~4
.5-1
0 Å
: Sec
onda
ry s
truct
ure
elem
ent d
etec
tion
(SS
EH
unte
r)
Pro
gram
s: S
SE
hunt
er (G
orgo
n), S
SE
Trac
er, E
mat
ch, E
M-fo
ld, R
oset
ta, P
athw
alke
r, C
oot,
Buc
cane
er
~3.0
-4.5
Å:d
e no
vo Cα
traci
ng
Bak
er e
t al.
Stru
ctur
e 20
12
14
< ~1
5-20
Å:
Fit d
omai
ns fr
om a
non
-red
unda
nt p
rote
in d
omai
n da
taba
se (e
.g. C
ATH
); • C
alcu
late
a Z
-sco
re.
Fi
tting
of a
dom
ain
from
1.20
.106
0.10
(mai
nly
alph
a)
into
1.1
0.53
0.10
(mai
nly-
alph
a).
SP
I-EM
: Vel
azqu
ez-M
urie
l et a
l. JM
B 2
005
12 Å
Fold
reco
gniti
on fr
om d
ensi
ty
BA
LBE
S–M
OLR
EP
pipe
line
(Bro
wn
et a
l. 20
15)
D
etec
tion
of b
acte
rioph
age
Lam
bda
FRE
DS
: Kha
yat e
t al.
JSB
201
0
7 Å
FOLD
-EM
: Sah
a et
al.
Bio
info
rmat
ics
2012
Succ
ess
depe
nds
on:
Res
olut
ion
Feat
ures
/sha
pe o
f pro
tein
/dom
ain
fold
S
earc
h sp
ace
15
Rea
l-spa
ce
refin
emen
tN
o
Fitti
ng a
ll kn
own
fold
s
No
3D-E
M m
ap
Com
pone
nt
Sequ
ence
Segm
enta
tion
Com
pone
nt
stru
ctur
e kn
own?
Wha
t re
solu
tion?
Tem
plat
e de
tect
ed?
Fold
as
sign
men
t fr
om s
eque
nce
De
novo
cha
in
trac
ing
Hom
olog
y m
odel
ling
Rig
id fi
tting
fit d
iffer
ent
from
map
?
Mul
tiple
co
nfor
mat
ions
ENM
/ N
MA
‘tem
plat
e-fr
ee’
mod
ellin
g
Yes
No
Yes
4 Å
< 20
Å<
4.5Å
~4.5
-10Å
Yes
SSE
assi
gnm
ent
Com
pone
nt
stru
ctur
e
No
NoTe
mpl
ate
dete
cted
?Fo
ld
assi
gnm
ent
from
seq
uenc
eH
omol
ogy
mod
ellin
gg‘te
mpl
ate-
free
’m
odel
lingg
sYe
s
16
Anabaena 7120
Anacystis nidulans
Condrus crispus
Desulfovibrio vulgaris
Evo
lutio
n (r
ules
)Th
read
ing
H
omol
ogy
Mod
ellin
g E
volu
tiona
ry c
oupl
ings
GFC
HIK
AYTR
LIM
VG
Fold
ing
(phy
sics
)
Ab
initi
o (d
e no
vo) p
redi
ctio
n
Zhan
g, Cu
rr Op
in St
ruct
Biol
2008
; Mar
ks et
al. N
at B
iotec
hnol.
2012
Fold
reco
gniti
on fr
om s
eque
nce
Pro
gram
s: H
Hpr
ed, F
ugue
, Phy
re2
(tem
plat
e ba
sed)
iT
asse
r, R
oset
ta (a
b-in
itio
/ hyb
rid)
17
Rea
l-spa
ce
refin
emen
tN
o
Fitti
ng a
ll kn
own
fold
s
No
3D-E
M m
ap
Com
pone
nt
Sequ
ence
Segm
enta
tion
Com
pone
nt
stru
ctur
e kn
own?
Wha
t re
solu
tion?
Tem
plat
e de
tect
ed?
Fold
as
sign
men
t fr
om s
eque
nce
De
novo
cha
in
trac
ing
Hom
olog
y m
odel
ling
Rig
id fi
tting
fit d
iffer
ent
from
map
?
Mul
tiple
co
nfor
mat
ions
ENM
/ N
MA
‘tem
plat
e-fr
ee’
mod
ellin
g
Yes
No
Yes
4 Å
< 20
Å<
4.5Å
~4.5
-10Å
Yes
SSE
assi
gnm
ent
Com
pone
nt
stru
ctur
e
Com
pone
nt
stru
ctur
e kn
own?
Yes
N
Com
pone
nt
stru
ctur
e
No
NoTe
mpl
ate
dete
cted
?Fo
ld
assi
gnm
ent
from
seq
uenc
eH
omol
ogy
mod
ellin
gg‘te
mpl
ate-
free
’m
odel
lingg
sYe
s
Rea
l-spa
ce
refin
emen
tN
o
Rig
id fi
tting
fit d
iffer
ent
from
map
?
Mul
tiple
co
nfor
mat
ions
ENM
/ N
MA
Yes
Fitti
ng a
n at
omic
stru
ctur
e w
ithin
the
enve
lope
(an
isoc
onto
ur) o
f the
den
sity
usi
ng
visu
alis
atio
n pr
ogra
ms.
Pros
:- H
uman
bra
in in
effi
cien
t in
certa
in p
atte
rn re
cogn
ition
task
s.- Im
med
iate
feed
back
and
inte
llige
nt c
hoic
es b
y th
e us
er.
- Ofte
n go
od fo
r the
initi
al p
lace
men
t of t
he c
ompo
nent
in th
e m
ap.
Con
s:- H
igh
leve
l of s
ubje
ctiv
ity m
ay le
ad to
err
or, e
spec
ially
if th
e m
ap d
oes
not h
ave
suffi
cien
t di
stin
ctiv
e fe
atur
es fo
r an
unam
bigu
ous
plac
emen
t of t
he c
ompo
nent
.- D
epen
ds o
n co
ntou
r lev
el.
- Con
form
atio
nal r
earr
ange
men
ts c
anno
t be
mod
elle
d (m
isfit
s an
d st
eric
cla
shes
).
Man
ual f
ittin
g
19
Aut
omat
ed fi
tting
All
auto
mat
ed fi
tting
met
hods
requ
ire:
1. a
way
of r
epre
sent
ing
both
the
stru
ctur
e an
d th
e de
nsity
map
(rep
rese
ntat
ion)
.
2. a
way
of m
easu
ring
the
good
ness
-of-f
it (s
corin
g).
3. a
met
hod
of fi
ndin
g th
e be
st fi
t (an
opt
imis
atio
n al
gorit
hm).
Opt
imis
atio
n ba
sed
on
good
ness
-of-f
it
Den
sity
map
Com
pone
nt a
tom
ic
stru
ctur
e
Com
pone
nt
repr
esen
tatio
n an
d pl
acem
ent
Villa
& L
aske
r, C
urr O
pin
Stru
ct B
iol,
2014
.20
ρ cal
c
Blu
r ato
mic
st
ruct
ure
(m)
ρ obs
Com
pare
with
E
xper
imen
tal m
ap
rigid
fitti
ngX-
ray
stru
ctur
e
Rep
rese
ntat
ion
and
scor
ing
A go
od m
odel
con
firm
s to
sta
ndar
d ge
omet
ry a
nd “f
its w
ell”
in th
e de
nsity
Cro
ss C
orre
latio
n C
oeffi
cien
t (C
CC
) C
ross
Cor
rela
tion
Co
Cha
con
& W
rigge
rs, 2
002.
Ato
m in
clus
ion/
over
lap
scor
eFi
tted
atom
s sh
ould
occ
upy
sam
ple
dens
ity
22ht
tp://
ww
w.e
bi.a
c.uk
/pdb
e/en
try/e
mdb
/EM
D-3
061/
anal
ysis
Vasis
htan &
Topf,
J St
ruct
Biol
2011
, Far
abell
a et a
l. J A
ppl C
ryst.
2015
, Jos
eph e
t al. J
SB 20
16
•M
utua
l inf
orm
atio
n-ba
sed
scor
e (M
I)
i
ii
p(x)
, p(y
)
I(X;Y)=
p(x,y)log
p(x,y)
p(x)p(y)
y∈Y∑
x∈X∑
iii
p(x,y)
(
Use
ful a
t int
erm
edia
te re
solu
tions
; noi
sy m
aps;
less
sen
sitiv
e to
re
lativ
e in
tens
ity le
vels
•C
ross
-cor
rela
tion
coef
ficie
nt (C
CC
)
SCC
C =
23
Den
sity
-bas
ed s
corin
g fu
nctio
nsT
EMPy
: http
://te
mpy
.ism
b.lo
n.ac
.uk/
Loca
l sco
ring
Rose
man,
Acta
Cry
stallo
gr D
2000
; Pan
dura
ngan
et a
l., J S
truct
Biol
2014
•S
egm
ent-b
ased
cro
ss-c
orre
latio
n co
effic
ient
(SC
CC
)
Targ
et d
ensi
tyY
Pro
be d
ensi
tyX
SSCC
C =
Use
ful t
o ca
lcul
ate
CC
C o
n an
y de
fined
loca
l seg
men
t
TEM
Py
: http
://te
mpy
.ism
b.lo
n.ac
.uk/
Loca
l sco
ring
Jose
ph et
al. M
ethod
s 201
6
Segm
ent-b
ased
man
ders
’ ove
rlap
coef
ficie
nt (S
MO
C)
Sco
re c
alcu
late
d on
ove
rlapp
ing
segm
ents
alo
ng t
he s
eque
nce
and
assi
gned
to c
entra
l res
idue
so
that
eac
h re
sidu
e ha
s a
scor
e.
Use
ful t
o ca
lcul
ate
loca
l fit
per
resi
due
(seg
men
t)
TEM
Py
: http
://te
mpy
.ism
b.lo
n.ac
.uk/
Nor
mal
Vec
tor s
core
(NV
)
Sur
face
-bas
ed s
corin
g fu
nctio
ns
Ceule
mans
& R
usse
ll, J
Mol
Biol
2004
; Va
sishta
n & To
pf, J
Stru
ct Bi
ol 20
11
•N
orm
al v
ecto
r sco
re (N
V)
TEM
Py
: http
://te
mpy
.ism
b.lo
n.ac
.uk/
Use
ful m
ainl
y at
low
reso
lutio
ns
26
Opt
imis
atio
n: ri
gid
fittin
g
Rot
ate
and
tran
slat
e th
e co
mpo
nent
to s
earc
h th
roug
h al
l pos
sibl
e co
nfig
urat
ions
in
the
dens
ity m
ap s
o as
to m
axim
ise
the
fit b
etw
een
the
com
pone
nt a
nd th
e m
ap.
6D s
earc
h
eth
e co
mpo
nent
to s
earc
h th
roug
h al
l pos
sib
o as
to m
axim
ise
the
fit b
etw
een
the
com
pone
27
....
....
....
..
....
....
....
..
....
....
....
..
....
....
....
..
....
....
....
..
....
....
....
..
....
....
....
..
....
....
....
..
....
....
....
..
....
....
....
..
....
....
....
..
....
....
....
..
....
....
....
..
....
....
....
..
....
....
....
..
....
....
....
..
....
....
....
..
....
....
....
..
....
....
....
..
....
....
....
..
....
....
....
..
....
....
....
..
Exh
aust
ive
sear
ch
- L
ocal
fitti
ng -
Sea
rch
exha
ustiv
ely
a gi
ven
sub-
regi
on in
the
map
.
...
....
...
....
....
...
....
...
.. ....
...
....
....
...
....
....
....
....
....
....
....
....
...
...
....
....
....
....
....
...
....
....
...
....
....
....
....
....
....
....
....
....
....
....
...
....
....
...
....
....
....
....
...
....
...
....
....
...
....
....
....
...
....
....
...
....
...
....
....
...
....
...
....
....
...
....
...
....
....
....... ....
....
....
....... ...
.......
....
....
....
....
....
....
....
...
...
....
...
...
....
...
...
.....
... .. ..
Pros
:Get
the
glob
al s
olut
ion
in re
spec
t to
a gi
ven
scor
ing
func
tion.
Con
s: T
he s
earc
h in
real
spa
ce is
too
larg
e fo
r mos
t sco
res
(ver
y ex
pens
ive)
.
- Acc
eler
atio
n:FF
T (tr
ansl
atio
nal m
oves
) (C
OLO
RE
S, D
OC
KE
M);
Sph
eric
al h
arm
onic
s (r
otat
iona
l mov
es) (
AD
P-E
M).
j
Villa
& L
aske
r, C
urr O
pin
Stru
ct B
iol,
2014
.28
Pros
: Fas
t; ea
sy to
impl
emen
t diff
eren
t sco
ring
func
tions
.C
ons:
The
mod
el c
an b
e “tr
appe
d” in
loca
l min
ima
(e.g
. gra
dien
t met
hods
) or
mig
ht m
iss
the
min
ima
(e.g
. ran
dom
sam
plin
g)
6D ro
tatio
nal &
tr
ansl
atio
nal s
earc
h
Sto
chas
tic/ra
ndom
and
gra
dien
t met
hods
Pro
gram
s: M
odE
M, C
him
era,
GM
fit (g
auss
ian
appr
oxim
atio
n),
Villa
& L
aske
r, C
urr O
pin
Stru
ct B
iol,
2014
.29
Li
mita
tions
of r
esol
utio
n
2 Å
10 Å
20 Å
Cor
rect
fit
Flip
ped
180
Solu
tions
:
-Im
prov
e sc
orin
g of
goo
dnes
s-of
-fit.
-C
oars
e-gr
aini
ng (c
hang
e re
pres
enta
tion)
-Fi
t/mod
el a
sses
smen
t.
Prob
lem
s:
-A
t low
reso
lutio
n: m
any
loca
l opt
ima
with
si
mila
r num
eric
al v
alue
s.
-Lo
cal r
esol
utio
n, n
oise
, sca
ling,
filte
ring,
m
aski
ng.
-B
lurr
ing
of th
e at
omic
stru
ctur
e.
Con
form
atio
nal v
aria
bilit
y
Solu
tion:
cha
nge
the
conf
orm
atio
n of
the
atom
ic m
odel
dur
ing
the
fittin
g pr
oces
s —
fle
xibl
e fit
ting.
Prob
lem
: Con
form
atio
ns o
bser
ved
by 3
D E
M o
ften
devi
ate
from
the
conf
orm
atio
ns o
f the
ato
mic
mod
els
we
fit.
-D
ynam
ics.
-
Cry
stal
pac
king
effe
cts.
-
Err
ors
in s
truct
ure
pred
ictio
n.
31
Rea
l-spa
ce
refin
emen
tN
o
Fitti
ng a
ll kn
own
fold
s
No
3D-E
M m
ap
Com
pone
nt
Sequ
ence
Segm
enta
tion
Com
pone
nt
stru
ctur
e kn
own?
Wha
t re
solu
tion?
NoTe
mpl
ate
dete
cted
?Fo
ld
assi
gnm
ent
from
seq
uenc
e
De
novo
cha
in
trac
ing
Hom
olog
y m
odel
ling
Rig
id fi
tting
fit d
iffer
ent
from
map
?
Mul
tiple
co
nfor
mat
ions
ENM
/ N
MA
‘tem
plat
e-fr
ee’
mod
ellin
g
Yes
No
Yes
4 Å
< 20
Å<
4.5Å
~4.5
-10Å
Yes
SSE
assi
gnm
ent
Com
pone
nt
stru
ctur
e
Rea
l-spa
ce
refin
emen
t
gM
ultip
le
conf
orm
atio
ns
ENM
/ N
MA
Yes
-Id
entif
y on
e of
the
mos
t acc
urat
e m
odel
s fro
m a
dec
oy s
et b
ased
the
qual
ity o
f fit
Topf
et a
l, J
Stru
ct B
iol 2
005
Fitt
ing
mul
tiple
con
form
atio
ns
Bak
er e
t al.
Plo
S C
ompu
t Bio
200
6
Pro
gram
s:M
OD
ELL
ER
, Ros
etta
33
With
out a
ny re
stra
ints
a m
odel
may
fit w
ell w
ith a
hig
h sc
ore
in
near
-ato
mic
-to-
low
reso
lutio
n de
nsity
: P
erfe
ctly
ove
rfitte
d m
odel
(e
.g. F
aulk
ner e
t al.
2013
)
The
resu
lting
mod
el h
owev
er w
ill n
ot h
ave
stan
dard
pro
tein
ge
omet
ry :
ba
ckbo
ne to
rsio
ns: p
hi/p
si (R
amac
hand
ran
spac
e), p
eptid
e pl
anar
ity, c
hira
lity
(tran
s/ci
s)
bond
leng
ths
and
angl
es
side
cha
in to
rsio
ns /
rota
mer
s
The
refin
emen
t met
hods
try
to m
aint
ain
stan
dard
geo
met
ry w
hile
fit
ting
the
mod
el in
den
sity
. The
se g
eom
etry
rest
rain
ts re
duce
the
degr
ees
of fr
eedo
m (s
ampl
ing
spac
e).
Mod
el re
finem
ent
34
-Th
e fit
of t
he p
robe
stru
ctur
e is
opt
imis
ed s
imul
tane
ousl
y w
ith th
e st
ereo
-che
mic
al
prop
ertie
s by
the
min
imis
atio
n of
a s
corin
g fu
nctio
n, s
uch
as:
-O
ptim
isat
ion
is p
erfo
rmed
on
“rig
id b
odie
s” b
y en
ergy
min
imis
atio
n an
d m
olec
ular
dyn
amic
s. Che
n &
Cha
pman
, JS
B 2
003;
To
pf e
t al.,
Str
uctu
re, 2
008;
Jo
seph
et a
l., M
etho
ds 2
016
;Tra
buco
et a
l. S
truc
ture
200
8;
E =
w1∗ECC
(P) +
w2∗ESC
(P) +w
3∗ENB(P
)
Rea
l-spa
ce re
finem
ent
Pros
: Fle
xibl
e (fi
ner f
ragm
enta
tion)
; Diff
eren
t op
timis
atio
n m
etho
ds c
an b
e ap
plie
d; e
asy
to a
dd
mor
e re
stra
ints
.
Con
s: O
nly
loca
l sea
rch;
slo
w; d
ange
r of o
ver-
fittin
g, s
ubje
ctiv
e rig
id b
odie
s/co
nstra
ints
.
Pro
gram
s:M
DFF
, Fle
x-E
M
Rea
l-Spa
ce R
efin
emen
t with
Fle
x-E
M
36
F-ac
tin c
ompl
ex (6
.6 Å
)
MK
LP2-
tubu
lin (5
.5 Å
)
Athe
rton e
t al. e
Life.
2017
E. c
oli E
F4/7
0S c
ompl
ex (1
1 Å
)
Conn
ell, T
opf e
t al. N
at S
truct
Mol
Biol.
2008
Hum
an a
popt
osom
e/C
AR
D c
ompl
ex (9
.5 Å
)
Yuan
, Yu e
t al. S
tructu
re 20
10
AA
A+
ATP
ase
Rav
A &
Ldc
I com
plex
(11
Å)
Malet
, Liu
et al.
eLife
2014
Fujii,
Iwan
e et a
l. Nat
ure 2
010
Gro
EL
(3.3
Å)
Jose
ph et
al. M
etho
ds 20
16
37
Flex
-EM
exa
mpl
es
Bef
ore
refin
emen
tA
fter r
efin
emen
t - c
lust
ered
Afte
r ref
inem
ent -
non
-clu
ster
ed
Ove
rfitti
ng
α
Flex
ible
fitti
ng o
f an
act
in s
ubun
itat
15
Å re
solu
tion
A cl
uste
r of
ato
ms
that
for
m a
com
pact
stru
ctur
al s
egm
ent
thro
ugh
a ne
twor
k of
con
tact
s ca
n be
rest
rain
ed :
-w
hen
the
reso
lutio
n of
den
sity
map
is in
suffi
cien
t to
fit
smal
ler
entit
ies
like
indi
vidu
al re
sidu
es o
r ato
ms.
-
to a
llow
fast
er la
rge
body
mov
emen
ts in
the
initi
al s
tage
s or
refin
emen
t
Flex
-EM
: use
RIB
FIN
D c
lust
er s
egm
ents
bas
ed o
n se
cond
ary
stru
ctur
e co
ntac
ts.
Long
rang
e di
stan
ce re
stra
ints
can
be
also
add
ed u
sing
MO
DE
LLE
R
Oth
er e
xam
ples
:
- Ref
mac
Jel
ly b
ody
rest
rain
ts: a
tom
pai
rs w
ithin
4.2
Å re
stra
ined
- Dire
x D
EN
rest
rain
ts :
harm
onic
rest
rain
ts a
re d
efin
ed b
etw
een
rand
omly
cho
sen
pairs
of a
tom
s th
at a
re w
ithin
a d
ista
nce
rang
e of
typi
cally
3 to
15
Å.
Pan
dura
ngan
& T
opf,
J S
truct
Bio
l 201
2; B
row
n et
al.
Act
a D
201
5; S
chro
der e
t al.
stru
ctur
e 20
07 &
Act
a D
201
4
Rig
id-b
ody
rest
rain
tsC
oars
e gr
aini
ng w
ith R
IBFI
ND
Pan
dura
ngan
& T
opf,
J S
truct
Bio
l 201
2
Initi
alU
n-cl
uste
red
Clu
ster
ed
Cα
RM
SD:
http
://rib
find.
ism
b.lo
n.ac
.uk/
Bot
tom
ring
cou
ld b
e fit
ted
usin
g rig
id fi
tting
alo
ne (P
DB
: 1oe
l).
Top
ring
need
ed re
finem
ent u
sing
hie
rarc
hica
l fle
xibl
e fit
ting
Hie
rarc
hica
l ref
inem
ent
TRs1
con
form
atio
n
Cla
re e
t al.,
Cel
l 201
2
Oth
er re
finem
ent m
etho
ds
Pro
gram
s: N
MFF
, iM
OD
FIT,
NO
RM
A, D
irex,
Ros
etta
, Ref
mac
, Coo
t, P
heni
x
MD
FF:
Mol
ecul
ar D
ynam
ics
(Tra
buco
et a
l. 20
08; S
ingh
aroy
et a
l. 20
16)
Dire
x, N
MFF
, iM
OD
FIT:
Nor
mal
mod
es (W
ang
and
Sch
rode
r 201
2; T
ama
et a
l. 20
04; B
lanc
o an
d C
haco
n 20
13)
Ros
etta
, Dire
x: M
onte
-Car
lo/s
toch
astic
(Wan
g et
al 2
016;
DiM
aio
et a
l. 20
15;
Wan
g an
d S
chro
der 2
012)
Ref
mac
: Max
imum
like
lihoo
d (M
ursh
udov
201
1; B
row
n et
al.
2015
) C
oot:
Inte
ract
ive/
stoc
hast
ic/e
xhau
stiv
e/gr
adie
nts
(Em
sley
et a
l. 20
10; B
row
n et
al.
2015
) P
heni
x: G
radi
ent/S
imul
ated
ann
ealin
g M
D/e
xhau
stiv
e (A
foni
ne e
t al.
2012
)
42
Fit /
Mod
el a
sses
smen
t and
Val
idat
ion
44
EM V
alid
atio
n Ta
sk F
orce
: “W
e re
com
men
d co
ordi
nate
d de
velo
pmen
t of m
odel
ass
essm
ent c
riter
ia a
nd c
orre
spon
ding
sof
twar
e,
with
spe
cial
em
phas
is o
n cr
iteria
refle
ctin
g th
e su
itabi
lity
of m
odel
s fo
r spe
cific
end
-use
r app
licat
ions
.”
Hend
erso
n et a
l. Stru
cture
2012
.
5125
map
s in
EM
DB
. ~1
717
fits
in P
DB
.it
Mod
el a
sses
smen
t and
val
idat
ion
– G
eom
etry
: dev
iatio
n fro
m id
eal b
onds
and
ang
les,
pla
nes,
Ram
acha
ndra
n pl
ots,
at
om c
lash
es
– C
ross
-val
idat
ion
of o
verfi
tting
(Dim
aio
et a
l. 20
13, F
alkn
er &
Sch
röde
r 201
3; B
row
n et
al.
2015
)
– C
onse
nsus
met
hods
(Ahm
ed &
Tam
a 20
13, P
andu
rang
an e
t al.
2014
)
– M
ultip
le s
corin
g of
ens
embl
e m
odel
s (F
arab
ella
et a
l 201
5)
– P
artia
l sco
ring
(Pan
dura
ngan
et a
l. 20
14, F
arab
ella
et a
l 201
5, B
arad
et a
l 201
5
– V
alid
atio
n by
oth
er e
xper
imen
ts
45
Mod
el fi
tM
odel
geo
met
ry
Mol
prob
ity: h
ttp://
mol
prob
ity.b
ioch
em.d
uke.
edu/
Wha
t che
ck: h
ttp://
swift
.cm
bi.ru
.nl/g
v/w
hatc
heck
/P
RO
CH
EC
K: h
ttp://
ww
w.e
bi.a
c.uk
/thor
nton
-srv
/sof
twar
e/P
RO
CH
EC
K/
pept
ide
plan
arity
ba
ckbo
ne to
rsio
ns (R
amac
hand
ran)
bo
nd le
ngth
s bo
nd a
ngle
s si
de c
hain
rota
mer
s
46
Mod
el fi
tM
odel
geo
met
ry
TEM
Py:
http
://te
mpy
.ism
b.lo
n.ac
.uk/
: gl
obal
, loc
al fi
t, en
sem
ble
asse
ssm
ent
EM
ringe
r: ht
tp://
emrin
ger.c
om/s
ubm
it : s
ide
chai
n fit
and
geo
met
ry
(http
s://g
ithub
.com
/fras
er-la
b/E
MR
inge
r/tre
e/m
aste
r/Phe
nix_
Scr
ipts
)
Glo
bal d
ensi
ty fi
t sco
res:
C
CC
, MI
Loca
l sco
res:
S
CC
C, S
MO
C, L
MI
47
NN
cry
o-E
M d
ensi
ty fo
r Kin
esin
-3 m
otor
dom
ain
Hea
t map
sho
win
g th
e qu
ality
of t
he lo
cal
fit fo
r spe
cific
ele
men
ts o
f the
mot
or
dom
ain
in d
iffer
ent n
ucle
otid
e st
ates
Ath
erto
n et
al.
eLife
201
4;3:
e036
80
6.3
Å re
solu
tion
EM
D-2
765
PD
B-4
uxo
Loca
l ass
essm
ent
mod
elle
d se
cond
ary
stru
ctur
e co
rres
pond
s to
pre
dict
ed?
psip
red
(http
://bi
oinf
.cs.
ucl.a
c.uk
/psi
pred
/) pr
edic
tpro
tein
: http
s://w
ww
.pre
dict
prot
ein.
org/
hom
olog
targ
et s
eque
nce
exte
nd h
elix
?ad
d he
lix?
Ass
essm
ent o
f sec
onda
ry s
truct
ure
p
For C
-alp
ha o
nly
mod
els:
P
rogr
ams:
Ric
hard
s an
d K
undr
ot 1
988;
STI
CK
(Tay
lor 2
001)
; PC
AS
SO
(Law
et
al. 2
014)
Sec
onda
ry s
truct
ure
assi
gnm
ent f
or a
mod
el:
DS
SP
(http
://w
ww
.cm
bi.ru
.nl/x
ssp/
)
Bake
r et
al.
Bio
poly
mer
s 20
12; L
inde
rt e
t al.
2012
DiM
aio
et a
l 201
3; D
iMai
o an
d C
hiu
2016
; Bro
wn
et a
l. 20
15
Cro
ss-v
alid
atio
n
Pro
gram
s: R
efm
ac, R
oset
ta, P
heni
x
Test
aga
inst
equ
ival
ent b
ut in
depe
nden
t dat
a
50
Ens
embl
e of
fits
: Loc
al a
sses
smen
tC
CC Lo
cal r
egio
ns o
f the
mod
el c
an b
e re
pres
ente
d by
an
ense
mbl
e to
indi
cate
am
bigu
ity (f
lexi
bilit
y)
Top
20 fi
ts
EM
D-2
795
PD
B-4
v3m
Ple
urot
olys
in (P
lyA
-B)
Por
e-fo
rmin
g pr
otei
n
Luko
yano
va e
t al.
PLo
S B
iol.
2015
51
Goo
dnes
s of
fit
TEM
Py
Coo
t/Ref
mac
P
heni
x E
Mrin
ger
Mod
el g
eom
etry
Mol
prob
ity
Coo
t W
hat-c
heck
Valid
atio
nC
ross
-val
idat
ion:
H
alf m
ap (R
efm
ac, R
oset
ta)
Res
olut
ion
shel
ls (D
irex)
Ens
embl
e as
sess
men
t with
m
ultip
le s
core
s (T
EM
Py)
Exp
erim
enta
l val
idat
ion
mut
atio
ns, c
ross
-link
s,
Sec
onda
ry s
truct
ure
Mol
pro
bity
, Coo
t, Q
mea
n
Psi
pred
,
Terti
ary
stru
ctur
eVe
rify-
3D, P
roQ
2 (R
oset
ta),
P
rosa
, DO
PE
(Mod
elle
r), M
odFo
ld, .
.
52
http
://ch
alle
nges
.em
data
bank
.org
/?q=
mod
el_c
halle
nge
2017
EM
DB
Mod
el C
halle
nge
•E
stab
lish
a be
nchm
ark
set o
f 3D
EM
map
s in
the
3.0-
4.5
Å re
solu
tion
rang
e, w
here
si
gnifi
cant
gro
wth
in th
e nu
mbe
r of m
aps
is a
ntic
ipat
ed o
ver t
he n
ext f
ew y
ears
and
w
here
a n
umbe
r of t
echn
ical
cha
lleng
es e
xist
to m
ap in
terp
reta
tion
and
fittin
g
•E
ncou
rage
dev
elop
ers
of m
odel
ling
softw
are
pack
ages
and
bio
logi
cal e
nd u
sers
to
anal
yze
thes
e m
aps
and
pres
ent m
odel
ling
resu
lts w
ith th
e be
st p
ract
ice
•E
volv
e cr
iteria
for e
valu
atio
n an
d va
lidat
ion
of 3
DE
M m
ap-d
eriv
ed m
odel
s
•C
ompa
re a
nd c
ontra
st th
e va
rious
mod
ellin
g an
d an
alys
is a
ppro
ache
s (in
a p
ositi
ve
spiri
t!)
Than
k yo
u!53
