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Biol. Chem. 2020; 401(1): 165–182
Review
Shiyu Chen, Joshua J. Yim and Matthew Bogyo*
Synthetic and biological approaches to map substrate specificities of proteaseshttps://doi.org/10.1515/hsz-2019-0332Received August 1, 2019; accepted October 11, 2019; previously published online October 22, 2019
Abstract: Proteases are regulators of diverse biological pathways including protein catabolism, antigen process-ing and inflammation, as well as various disease condi-tions, such as malignant metastasis, viral infection and parasite invasion. The identification of substrates of a given protease is essential to understand its function and this information can also aid in the design of specific inhibitors and active site probes. However, the diversity of putative protein and peptide substrates makes connecting a protease to its downstream substrates technically dif-ficult and time-consuming. To address this challenge in protease research, a range of methods have been devel-oped to identify natural protein substrates as well as map the overall substrate specificity patterns of proteases. In this review, we highlight recent examples of both synthetic and biological methods that are being used to define the substrate specificity of protease so that new protease-spe-cific tools and therapeutic agents can be developed.
Keywords: activity-based probe; combinatorial peptide library; protease reactive warhead; protease substrate; proteomics; substrate specificity.
IntroductionIt is estimated that over 600 proteases, roughly 2% of the human genome (Rawlings et al., 2006; Quesada et al., 2009), function together in diverse aspects of normal
cellular physiology. In addition, proteases are key regula-tors of numerous pathological processes such as tumor metastasis (Mason and Joyce, 2011; Russell et al., 2015), angiogenesis (Bauvois, 2004) and inflammation, and play critical roles in the life cycles of various pathogens. For example, HIV-1 protease is essential for the life cycle of HIV virus, which cleaves newly synthesized polypro-teins to create the mature protein components of an HIV virion (Brik and Wong, 2003). The Gram-positive human pathogen, Staphylococcus aureus, uses serine protease fluorophosphonate-binding hydrolase B (FphB) to manip-ulate host-pathogen interactions to establish infection in distinct sites in vivo (Lentz et al., 2018). Studying protease function is crucial for understanding the mechanisms of both healthy and diseased states. Therefore, proteases are promising therapeutic targets for a multitude of disease indications.
Proteases are enzymes that hydrolyze peptide bonds in a process that can result not only in the destruction of the protein target but also in the activation of signal-ing and other biological functions. The active site of pro-teases generally contains a substrate recognition motif and a catalytic triad or dyad where the chemical reaction to break the scissile amide bond of a substrate protein occurs. The primary catalytic mechanisms used by pro-teases have largely remained unchanged over evolution. However, substrate specificity has evolved to enable pro-cessing of diverse substrates for both protein turnover as well as functional activation of substrate proteins. Most serine proteases, for example, have active sites com-posed of two β-barrels, with the catalytic Ser195, His57 and Asp102 amino acids at the interface of the two domains for forming H-bonds with the P1–P3 residues of the substrate. The surface loops around the active site have evolved to enable highly divergent substrate recognition (Perona and Craik, 1997). An understanding of the substrate specifici-ties of proteases provides the potential to define protease function as they have evolved over long periods of time.
Functional characterization of proteases in a bio-logical system has traditionally involved determining the substrate specificity and generating a specific probe or inhibitor. Due to the coexistence of a large number
*Corresponding author: Matthew Bogyo, Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; and Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA, e-mail: [email protected]. https://orcid.org/0000-0003-3753-4412 Shiyu Chen: Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USAJoshua J. Yim: Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
166 S. Chen et al.: Mapping protease substrate specificity
Tabl
e 1:
Exa
mpl
e of
pro
teas
e su
bstra
te s
peci
ficiti
es m
appe
d us
ing
tech
nolo
gies
repo
rted
in th
is re
view
.
Prot
ease
nam
e
Prot
ease
type
M
etho
d/lib
rary
Di
scov
ered
sub
stra
te s
eque
nces
a
Refe
renc
e
Hepa
tocy
te g
row
th fa
ctor
act
ivat
or (H
GFA)
Tr
ansm
embr
ane
serin
e pr
otea
se
PS-S
CL
K(L/
M/n
)R | A
CCb
(D
amal
anka
et a
l., 2
019)
KLK8
/neu
rops
in
Serin
e pr
otea
se
PS-S
CL
(T/W
)(R/K
)(L/V
/I)R
| Acc
(D
ebel
a et
al.,
201
8)Ca
thep
sin
L
Cyst
eine
pro
teas
e
PS-S
CL
(Dab
/Dap
/Orn
/Agp
)(R/K
/Orn
/Dab
)FR
| ACC
(P
oreb
a et
al.,
201
8)Ca
spas
e-1
Cy
stei
ne p
rote
ase
PS
-SCL
W
XHD
| ACC
(R
amire
z et a
l., 2
018)
Casp
ase-
11
Cyst
eine
pro
teas
e
PS-S
CL
VXHD
| ACC
(R
amire
z et a
l., 2
018)
Fact
or V
II (F
VII)
activ
atin
g pr
otea
se (F
SAP)
Se
rine
prot
ease
PS
-SCL
X(
K/R)
Nle(
K/R)
| ACC
(K
ara
et a
l., 2
017)
Urok
inas
e-ty
pe p
lasm
inog
en a
ctiv
ator
(uPA
)
Serin
e pr
otea
se
PS-S
CL
AcGT
AR-p
NA
(Li e
t al.,
201
9)Ca
spas
e-3
Cy
stei
ne p
rote
ase
PS
-SCL
DE
(V/I
) | A
CC
(Por
eba
et a
l., 2
014b
)Hu
man
neu
troph
il se
rine
prot
ease
4
Serin
e pr
otea
se
PS-S
CL
Ac-h
Cha-
Phe(
guan
)-Oic
-Arg
-ACC
(K
aspe
rkie
wic
z et a
l., 2
015)
S. a
ureu
s Cl
pXP
M
ultip
le p
rote
ases
PS
-SCL
(E
/I/V
/P)(E
/K/I
/L)(A
/L/D
/G) |
(L/I
)
(Ger
sch
et a
l., 2
016)
Esch
eric
hia
coli
ClpX
P
Mul
tiple
pro
teas
es
PS-S
CL
(E/P
/V/I
)(K/L
/E/Y
)(L/A
/G/N
/M/G
) |(L
/I)
(G
ersc
h et
al.,
201
6)Ho
mo
sapi
ens
ClpX
P
Mul
tiple
pro
teas
es
PS-S
CL
(P/V
/L/E
)(L/F
/E/K
/V)(L
/A/G
/D/N
) |(K
/Q/L
/E)
(Ger
sch
et a
l., 2
016)
TcM
CP-1
Cy
stei
ne p
rote
ase
PS
-SCL
Tc
MCP
-1 A
bz-G
XX(K
/R/F
/Y)(R
/T/F
)K(D
np)-O
H (E
kino
et a
l., 2
018)
TbM
CP-1
Cy
stei
ne p
rote
ase
PS
-SCL
Tb
MCP
-1 A
bz-G
XX(K
)FK(
Dnp)
-OH
(E
kino
et a
l., 2
018)
Hydr
olas
e im
porta
nt fo
r pat
hoge
nesi
s 1
(Hip
1)
Serin
e pr
otea
se
PS-S
CL
(W/F
)(K/P
)(L/n
) | G
(F/n
)F(I/
F/n)
(L
entz
et a
l., 2
016)
Casp
ase-
1
Cyst
eine
pro
teas
e
HyCo
SuL
VX
HD-A
CC
(Ram
irez e
t al.,
201
8)Ca
spas
e-11
Cy
stei
ne p
rote
ase
Hy
CoSu
L
WXH
D-AC
C
(Ram
irez e
t al.,
201
8)Le
gum
ain
(AEP
)
Aspa
ragi
nyl p
rote
ase
Hy
CoSu
L
Ac-D
-Tyr
-L-T
ic-L
-Ser
-L-A
sp-A
CC
(Por
eba
et a
l., 2
017a
)Ca
thep
sin
L
Cyst
eine
pro
teas
e
HyCo
SuL
Ac
-Dap
-Orn
-Phe
(3-C
l)-Cy
s(OM
eBzl
)-ACC
(P
oreb
a et
al.,
201
8)Ca
spas
e-11
Cy
stei
ne p
rote
ase
Hy
CoSu
L
Ac-T
le-B
pa-H
is(B
zl)-A
sp-A
CC
(Ram
irez e
t al.,
201
8)Ca
spas
e-11
Cy
stei
ne p
rote
ase
Hy
CoSu
L
Ac-T
le-B
ip-H
is-A
sp-A
CC
(Ram
irez e
t al.,
201
8)Ca
spas
e-2
Cy
stei
ne p
rote
ase
Hy
CoSu
L
Ac-Id
c-hG
lu-T
hr(B
zl)-S
er-A
sp-A
CC
(Por
eba
et a
l., 2
019c
)Ca
spas
e-9
Cy
stei
ne p
rote
ase
Hy
CoSu
L
Oic-
Tle-
His-
Asp-
ACC
(P
oreb
a et
al.,
201
9a)
Casp
ase-
9
Cyst
eine
pro
teas
e
HyCo
SuL
Ly
s(tfa
)-Tle
-His
-Asp
-ACC
(P
oreb
a et
al.,
201
9a)
Casp
ase-
9
Cyst
eine
pro
teas
e
HyCo
SuL
Ly
s(Ac
)-Tle
-His
-Asp
-ACC
(P
oreb
a et
al.,
201
9a)
Cath
epsi
n B
Cy
stei
ne p
rote
ase
Hy
CoSu
L
Ac-C
ha-L
eu-h
Ser(B
zl)-A
rg-A
CC
(Por
eba
et a
l., 2
019b
)Fa
ctor
VII
activ
atin
g pr
otea
se
Serin
e pr
otea
se
HyCo
SuL
Ac
-Pro
-DTy
r-Lys
-Arg
-ACC
(R
ut e
t al.,
201
9)hS
ENP1
Cy
stei
ne p
rote
ase
Hy
CoSu
L
(Q/L
/n)(S
/T/F
/V)G
G | A
CC
(Pon
der e
t al.,
201
1)Ca
spas
e-6
Cy
stei
ne p
rote
ase
Co
SeSu
L
TETD
| ACC
(E
dgin
gton
et a
l., 2
012)
Muc
osa-
asso
ciat
ed ly
mph
oid
tissu
e ly
mph
oma
trans
loca
tion
prot
ein
1 (M
ALT1
)
Cyst
eine
pro
teas
e
CoSe
SuL
Ac
c(Ah
x)AL
VSRG
(nV)
K(Dn
p)G
(K
aspe
rkie
wic
z et a
l., 2
018)
DPP-
VII
Am
inop
eptid
ase
IS
FPL
H-
KP-A
MC
(L
eitin
g et
al.,
200
3)DP
P-II
Am
inop
eptid
ase
IS
FPL
H-
Nle-
Pro-
AMC
(L
eitin
g et
al.,
200
3)DP
P-IV
Am
inop
eptid
ase
IS
FPL
H-
Ala-
Pro-
AFC
(L
eitin
g et
al.,
200
3)Hu
man
cath
epsi
n C
Am
inop
eptid
ase
IS
FPL
M
et-N
le(O
-Bzl
)-ACC
(P
oreb
a et
al.,
201
4a)
Leuk
otrie
ne A
4 hy
drol
ase
Am
inop
eptid
ase
IS
FPL
As
pBzl
-ACC
(B
yzia
et a
l., 2
014)
Bleo
myc
in h
ydro
lase
Am
inop
eptid
ase
IS
FPL
H-
Lys(
2-Cl
-Cbz
)-ACC
(v
an d
er L
inde
n et
al.,
201
5)Ki
dney
cell
lysa
te
Amin
opep
tidas
e
ISFP
L
hPhe
-ACC
(B
yzia
et a
l., 2
016)
Mal
aria
l dip
eptid
yl a
min
opep
tidas
e 3
Am
inop
eptid
ase
IS
FPL
M
et-n
Leu(
o-Bz
l)-AC
C
(de
Vrie
s et
al.,
201
9)Tb
MCP
-1
Met
allo
carb
oxyp
eptid
ase
IQ
F
Abz-
LLKF
K(Dn
p)-O
H
(Fra
sch
et a
l., 2
018)
S. Chen et al.: Mapping protease substrate specificity 167
Prot
ease
nam
e
Prot
ease
type
M
etho
d/lib
rary
Di
scov
ered
sub
stra
te s
eque
nces
a
Refe
renc
e
TcbM
CP-1
M
etal
loca
rbox
ypep
tidas
e
IQF
Ab
z-RR
FFK(
Dnp)
-OH
(F
rasc
h et
al.,
201
8)NS
2B-N
S3 p
rote
ase
Se
rine
prot
ease
IQ
F
ABZ-
VK(K
/R)R
-ANB
-NH 2
(G
ruba
et a
l., 2
016)
KLK1
3
Serin
e pr
otea
se
IQF
AB
Z-VR
FR-A
NB-N
H 2
(Gru
ba e
t al.,
201
9)Ca
spas
e-3
Cy
stei
ne p
rote
ase
IQ
F
Abz-
GDEV
D | G
VY(N
O 2)D-O
H
(Ste
nnic
ke e
t al.,
200
0)NS
2B/N
S3
Serin
e pr
otea
se
IQF
Bz
-Nle
-Lys
-Arg
-Arg
-ACM
C
(Li e
t al.,
200
5)Ca
spas
e-3
Cy
stei
ne p
rote
ase
IQ
F
ACC-
GDEV
D | G
VK(D
NP)D
-NH 2
(P
oreb
a et
al.,
201
7b)
Casp
ase-
7
Cyst
eine
pro
teas
e
IQF
AC
C-GD
EVD
| GVK
(DNP
)D-N
H 2
(Por
eba
et a
l., 2
017b
)Ca
spas
e-8
Cy
stei
ne p
rote
ase
IQ
F
ACC-
GDEV
D | G
VK(D
NP)D
-NH 2
(P
oreb
a et
al.,
201
7b)
Legu
mai
n
Cyst
eine
pro
teas
e
IQF
AC
C-GP
TN | K
VK(D
NP)R
-NH 2
(P
oreb
a et
al.,
201
7b)
Elas
tase
Se
rine
prot
ease
IQ
F
ACC-
GAEP
V | S
LK(D
NP)L
-NH 2
(P
oreb
a et
al.,
201
7b)
MM
P-2
M
etal
lopr
otea
se
IQF
AC
C-GP
LG | L
K(DN
P)AR
-NH 2
(P
oreb
a et
al.,
201
7b)
MM
P-9
M
etal
lopr
otea
se
IQF
AC
C-GP
LG | L
K(DN
P)AR
-NH 2
(P
oreb
a et
al.,
201
7b)
MAL
T1
Cyst
eine
pro
teas
e
IQF
H 2N-
ACC-
Ahx-
ALVS
RGT-
K(Dn
p)G-
OH
(Kas
perk
iew
icz e
t al.,
201
8)Ca
thep
sin
G
Serin
e pr
otea
se
IQF
AC
C-Gl
y-Hi
s(Bz
l)-Tl
e-Pr
o-Ph
e-Se
r-Asp
-Met
(O)-
Gly-
Lys(
DNP)
-Gly
-NH 2
(G
robo
rz e
t al.,
201
9)
HiGl
pG
Serin
e pr
otea
se
Synt
hetic
libr
ary
(m
ca)R
PKPY
AvW
MK(
dnp)
(A
ruty
unov
a et
al.,
201
8)Pl
asm
odiu
m p
rote
asom
e
Prot
easo
me
Sy
nthe
tic li
brar
y
Mor
-Hfe
-Ser
(Me)
-Thi
-ACC
(D
ydio
et a
l., 2
017)
Thro
mbi
n
Serin
e pr
otea
se
Phag
e di
spla
y
(P/A
/V/L
)R |(
S/A)
(K
retz
et a
l., 2
018)
ADAM
TS13
M
etal
lopr
otea
se
Phag
e di
spla
y
(L/I
/M)X
Y |(Y
/L/M
/F)
(K
retz
et a
l., 2
018)
Hepa
titis
C vi
rus
(HCV
) NS3
/4A
prot
ease
Se
rine
prot
ease
Ye
ast d
ispl
ay
PSTV
FC | A
(P
ethe
et a
l., 2
019)
Atyp
ical
asp
artic
pro
teas
e in
root
s 1
(ASP
R1)
As
part
ic p
rote
ase
Tr
yptic
pro
teom
e lib
rary
G(
Y/E)
(E/V
/I)(L
) |(F
/Y/H
)(A/V
)(A/G
/N)(P
/N)
(S
oare
s et
al.,
201
9)At
ypic
al a
spar
tic p
rote
ase
in ro
ots
1 (A
SPR1
)
Aspa
rtic
pro
teas
e
GluC
pro
teom
e lib
rary
(N
/F)(F
/Y/N
)(K/I
/V)(L
/N/K
) |(F
/Y/V
)(V/A
/I)
(K/G
/A)(N
/P/T
)
(Soa
res
et a
l., 2
019)
Sirt
ilin-
a
Serin
e pr
otea
se
Legu
mai
n pr
oteo
me
libra
ry
V(G/
A)R
|(S/T
/V)(A
/G/F
)(F/E
/M)
(D
ahm
s et
al.,
201
9)Si
rtili
n-a
Se
rine
prot
ease
Gl
uC p
rote
ome
libra
ry
(L/Y
/V)(G
/A)R
|(V/
T)(A
/G/Y
)
(Dah
ms
et a
l., 2
019)
FIXa
Se
rine
prot
ease
Le
gum
ain
prot
eom
e lib
rary
(G
/V)R
|(T/
S/C/
R)(L
/I)
(D
ahm
s et
al.,
201
9)FI
Xa
Serin
e pr
otea
se
GluC
pro
teom
e lib
rary
L(
L/G)
R |(A
/S)(L
/I)
(D
ahm
s et
al.,
201
9)FX
a
Serin
e pr
otea
se
Legu
mai
n pr
oteo
me
libra
ry
(G/A
/E)R
|(A/
S/G)
(G/A
)
(Dah
ms
et a
l., 2
019)
FXa
Se
rine
prot
ease
Gl
uC p
rote
ome
libra
ry
(G/A
)R |(
A/S)
(L/G
)
(Dah
ms
et a
l., 2
019)
Tryp
sin-
3
Serin
e pr
otea
se
Prot
eom
e lib
rarie
s
(K/R
) | T(
D/E)
(S
chill
ing
et a
l., 2
018)
GluC
Se
rine
prot
ease
Ch
aFRA
tip
E | X
(N
guye
n et
al.,
201
8)Ca
spas
e-3
Cy
stei
ne p
rote
ase
Ch
aFRA
tip
DX(V
/P/L
)D |(
G/A)
(N
guye
n et
al.,
201
8)Ch
ymot
ryps
in
Serin
e pr
otea
se
ChaF
RAtip
A(
V/T)
(L/F
/W/Y
/M) |
(K/T
)
(Ngu
yen
et a
l., 2
018)
MM
P-1
M
etal
lopr
otea
se
ChaF
RAtip
(P
/A)Q
(A/N
/D) |
(L/I
/K)(T
/K/V
)(A/D
)
(Ngu
yen
et a
l., 2
018)
Cath
epsi
n G
Se
rine
prot
ease
Ch
aFRA
tip
E(P/
K)(L
/F/Y
/M/N
) |(K
/I/A
/S)(D
/E)
(N
guye
n et
al.,
201
8)Su
btili
gase
Cy
stei
ne p
rote
ase
PI
LS
|(A/
G/S/
M/R
)(F/W
/Y/I
/L/V
)
(Wee
ks a
nd W
ells
, 201
8)Ca
spas
e-3
Cy
stei
ne p
rote
ase
N-
term
inal
pep
tides
DE
(V/I
/P)D
|(G/
S)
(Mah
rus
et a
l., 2
008)
Casp
ase-
1
Cyst
eine
pro
teas
e
N-te
rmin
al p
eptid
es
(F/L
/Y/W
)(E/V
/L/D
)(S/P
/T/V
)D |(
G/S/
A)(V
/F/L
/Y)
(A
gard
et a
l., 2
010)
Tabl
e 1
(con
tinue
d)
168 S. Chen et al.: Mapping protease substrate specificity
Prot
ease
nam
e
Prot
ease
type
M
etho
d/lib
rary
Di
scov
ered
sub
stra
te s
eque
nces
a
Refe
renc
e
Casp
ase-
8
Cyst
eine
pro
teas
e
N-te
rmin
al p
eptid
es
EXD
|(G/S
)
(Aga
rd e
t al.,
201
2)Ca
spas
e
Mul
tiple
pro
teas
es
N-te
rmin
al p
eptid
es
DEVD
|(G/
S/A)
V
(Julie
n et
al.,
201
4)Ca
spas
e
Mul
tiple
pro
teas
es
N-te
rmin
al p
eptid
es
HtrA
2: A
| AVP
SPPP
ASPR
(W
iita
et a
l., 2
014a
)Ca
spas
e
Mul
tiple
pro
teas
es
N-te
rmin
al p
eptid
es
Vim
entin
: D | A
LKGT
NESL
ER
(Wiit
a et
al.,
201
4a)
Casp
ase-
2
Cyst
eine
pro
teas
e
N-te
rmin
al p
eptid
es
DE(V
/T/P
)D |(
G/S/
A)(V
/A/L
)
(Julie
n et
al.,
201
6)Ca
spas
e-6
Cy
stei
ne p
rote
ase
N-
term
inal
pep
tides
(V
/T)(E
/D)(V
/T)D
|(G/
S/A)
(V/A
)
(Julie
n et
al.,
201
6)Bl
ood
prot
ease
s
Mul
tiple
pro
teas
es
N-te
rmin
al p
eptid
es
(R/K
/N) |
(S/A
/G/V
)
(Wild
es a
nd W
ells
, 201
0)M
itoch
ondr
ial p
rote
ases
M
ultip
le p
rote
ases
TA
ILS
(R
/A/L
/V)(R
/A/S
/L/P
)(L/A
/R/K
) |(S/
A/L/
M)
(S/T
/A/E
)(S/G
/A/T
)
(Mar
shal
l et a
l., 2
018)
Neut
roph
il el
asta
se
Serin
e pr
otea
se
TAIL
S
P(V/
I) | A
LXL
(K
ing
et a
l., 2
018)
MM
P-9
M
etal
lopr
otea
se
TAIL
S
PXP
| C(R
/Q)
(K
ing
et a
l., 2
018)
Polio
viru
s 3C
pro
Cy
stei
ne p
rote
ase
TA
ILS
(A
/V)X
XQ |(
G/A/
Q/M
)
(Jagd
eo e
t al.,
201
8)CV
B3 3
Cpro
Cy
stei
ne p
rote
ase
TA
ILS
(A
/V/I
)X(P
/H)Q
|(G/
A)(G
/E)
(Ja
gdeo
et a
l., 2
018)
ADAM
10
Met
allo
prot
ease
TA
ILS
GH
IYG
| EEG
SF
(Jeffe
rson
et a
l., 2
013)
MM
P-1
M
etal
lopr
otea
se
TAIL
S
SFPA
T | LE
| | TQ
| EQD
(Je
ffers
on e
t al.,
201
3)M
MP-
7
Met
allo
prot
ease
TA
ILS
LP
LPQ
| E | A
GGM
S
(Jeffe
rson
et a
l., 2
013)
ADAM
9
Met
allo
prot
ease
TA
ILS
YV
IQA
| EGK
EH
(Jeffe
rson
et a
l., 2
013)
ADAM
TS-1
M
etal
lopr
otea
se
TAIL
S
SDAL
G | R
PSEE
| DEE
LV
(Jeffe
rson
et a
l., 2
013)
KLK7
Se
rine
prot
ease
TA
ILS
TA
GEE |
AQG
| DKI
ID
(Jeffe
rson
et a
l., 2
013)
MM
P-2
M
etal
lopr
otea
se
TAIL
S
(P/A
/V)(A
/S/R
)(A/G
/N) |
(L/I
)(K/A
/Y)(A
/S/G
)
(Pru
dova
et a
l., 2
010)
MM
P-9
M
etal
lopr
otea
se
TAIL
S
GPK(
G/P)
|(L/
I)K(G
/A)(A
/P/Y
)
(Pru
dova
et a
l., 2
010)
MM
P-2
M
etal
lopr
otea
se
TAIL
S
VIQH
| FQE
KVES
LEQE
AANE
R
(Kel
ler e
t al.,
201
0)M
T6-M
MP
M
etal
lopr
otei
nase
PI
CS
(A/P
/V)(A
/N/E
)(E/A
/N) |
(L/I
)(V/L
/T)Q
(S
tarr
et a
l., 2
012)
AtCa
thB2
Cy
stei
ne p
rote
inas
e
PICS
(P
/I/L
/V)(P
/V/D
)(G/A
/T) |
(V/L
I)(A/
T)
(Por
odko
et a
l., 2
018)
AtCa
thB3
Cy
stei
ne p
rote
inas
e
PICS
(P
/I/F
)(V/R
/P)(A
/G/R
/T) |
(V/I
/L)(D
/A)
(P
orod
ko e
t al.,
201
8)Sl
Phyt
1
Cyst
eine
pro
teas
e
PICS
(V
/I/L
)XP(
D/E)
|(K/
A)
(Rei
char
dt e
t al.,
201
8)Sl
PShy
t3
Cyst
eine
pro
teas
e
PICS
(A
/I)D
|(S/
G/H)
(V/I
)
(Rei
char
dt e
t al.,
201
8)Sl
Phyt
4
Cyst
eine
pro
teas
e
PICS
P(
D/M
) | H
T(E/
V)(E
/D/A
)
(Rei
char
dt e
t al.,
201
8)Sl
Phyt
5
Cyst
eine
pro
teas
e
PICS
AD
|(G/
E/H)
V
(Rei
char
dt e
t al.,
201
8)Sl
P69A
Cy
stei
ne p
rote
ase
PI
CS
(A/T
/I)D
|(G/
H)(Y
/I/A
)
(Rei
char
dt e
t al.,
201
8)Ps
eudo
gym
noas
cus
dest
ruct
ans
PdCP
1
Serin
e pr
otea
se
MSP
-MS
(n
/K/V
)(H/K
/R/W
)(R/P
)R |(
R/n)
(B
eekm
an e
t al.,
201
8)An
gios
trong
ylus
cost
arice
nsis
adu
lt wo
rm ly
sate
s M
ainl
y as
part
yl p
eptid
ase,
pH
3
MSP
-MS
(F
/L/n
) |(F
/Y/n
)(R/T
)
(Reb
ello
et a
l., 2
018)
A. co
star
icen
sis
adul
t wor
m ly
sate
s
Mai
nly
cyst
eine
pep
tidas
e, p
H 5
M
SP-M
S
(K/R
)X(V
/F)(K
/R) |
(n/F
)
(Reb
ello
et a
l., 2
018)
A. co
star
icen
sis
adul
t wor
m ly
sate
s
Mai
nly
cyst
eine
pep
tidas
e, p
H 8
M
SP-M
S
(D/Y
) | X
R
(Reb
ello
et a
l., 2
018)
A. co
star
icen
sis
L1 ly
sate
s
Mai
nly
aspa
rtyl
pep
tidas
e, p
H 3
M
SP-M
S
E(F/
Y) | n
XV
(Reb
ello
et a
l., 2
018)
A. co
star
icen
sis
L1 ly
sate
s
Mai
nly
cyst
eine
pep
tidas
e, p
H 8
M
SP-M
S
(R/I
)(R/A
)L(R
/K/H
/W) |
X
(Reb
ello
et a
l., 2
018)
FheC
L1
Cyst
eine
pro
teas
e
MSP
-MS
(V
/I/L
/M)(K
/R/Q
) | X
(C
orvo
et a
l., 2
013)
FheC
L3
Cyst
eine
pro
teas
e
MSP
-MS
GP
(K/R
/Q) |
(S/G
/A/M
)
(Cor
vo e
t al.,
201
3)Ne
utro
phil
extra
cellu
lar t
raps
(NET
s)
Mul
tiple
pro
teas
es
MSP
-MS
(R
/Y)(Q
/S)P
(I/V/
T) |(
S/R/
n)W
(O
’Don
oghu
e et
al.,
201
3)De
stru
ctin
-1
Serin
e pr
otea
se
MSP
-MS
(I/
n/F)
(R/W
/K)(n
/I)(Q
/Y/F
) |(K
/T)(I
/W/Y
)
(O’D
onog
hue
et a
l., 2
015)
Tabl
e 1
(con
tinue
d)
S. Chen et al.: Mapping protease substrate specificity 169
of proteases and their diverse roles in many biological pathways (Lopez-Otin and Bond, 2008), it is difficult to identify specific cleavage products in a pool of complex cellular components. Therefore, technological advances are required to fully define protease function. Further-more, a number of proteases belong to families that share highly related active sites, making conventional analyti-cal methods based on gel electrophoresis ineffective for distinguishing substrates for a single member within the family. Detailed knowledge of substrate specificities of individual proteases in complex biological systems affords new opportunities to understand their roles in homeostasis and disease. Information on substrate speci-ficity can also guide the development of chemical tools for protease detection or inhibition. Over the past decades, both synthetic and biological methods for generating combinatorial peptide libraries have greatly facilitated the process of mapping protease substrate specificity. There have also been a number of highly successful methods developed using gel-free proteomic methods to globally monitor proteolysis of native protein substrates using proteomic methods. Review papers focusing on broader topics (Poreba and Drag, 2010), such as protease hydrol-ysis mechanisms (Vizovisek et al., 2018), applications of protease probes and profiling approaches (Rut et al., 2015; Kasperkiewicz et al., 2017), have been reported. This review will focus on the synthetic and biological approaches to generate and screen diverse peptide librar-ies to map protease substrate specificities, followed by a summary of some recent examples of protease specificity profiles mapped using these methods (Table 1).
The search for natural substratesThe most simple and effective way to confirm hydrolysis of individual substrate proteins by a protease is to resolve the resulting hydrolyzed polypeptide chains using sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), followed by chromogenic staining or immunoblot-ting to visualize the breakdown products. However, these methods require prior knowledge of candidate substrates for a given protease. In a complex biological sample, protease substrates cannot be easily identified with the limited predictive value of SDS-PAGE staining. As an alter-native, methods such as PROTOMAP (Dix et al., 2008) have been developed to globally identify proteolytic events in complex proteomic samples that have been resolved by SDS-PAGE. This allows the direct identification of pro-teolytic fragments recovered from SDS-PAGE gels using Pr
otea
se n
ame
Pr
otea
se ty
pe
Met
hod/
libra
ry
Disc
over
ed s
ubst
rate
seq
uenc
esa
Re
fere
nce
Plas
mod
ium
falc
ipar
um 2
0S p
rote
asom
e
Prot
easo
me
M
SP-M
S
(F/I
/n)(W
/L/I
/V)(R
/Y/K
)(F/Y
/L/W
) |(R
/A)
(L
i et a
l., 2
016)
Cons
titut
ive
prot
easo
me
Pr
otea
som
e
MSP
-MS
(P
/n/F
/I/L
)(I/K
/L/V
)(K/S
/R/T
/Q)
(R/L
/F/H
) |(K
/R/A
/N)(n
/L/W
)
(Win
ter e
t al.,
201
7)
Imm
unop
rote
asom
e
Mul
tiple
pro
teas
es
MSP
-MS
(P
/F/I
)(V/L
/I/n
)(K/R
/N)(L
/F/n
/W
/Y) |
(R/H
/A/N
)
(Win
ter e
t al.,
201
7)
Pd_d
inas
e
Cyst
eine
Pro
teas
e
MSP
-MS
(G
/n)(N
/H/Q
/R) |
(n/S
/L)
(X
u et
al.,
201
8)Sc
hist
osom
a m
anso
ni se
rine
prot
ease
2 (S
mSP
2)
Serin
e pr
otea
se
MSP
-MS
(R
/K) |
S(A
/G)
(L
eont
ovyc
et a
l., 2
018)
PsAa
rA
Serin
e pr
otea
se
qMSP
-MS
FX
L(A/
V) |(
R/S)
F
(Lap
ek e
t al.,
201
9)Ha
emop
hilu
s in
fluen
zae
rhom
boid
pep
tidas
e (H
iglp
g)
Serin
e pr
otea
se
qMSP
-MS
M
ca-V
KLFR
FN | W
MK(
DNP)
-NH 2
(L
apek
et a
l., 2
019)
Aspe
rgill
us p
hoen
icis
mon
o-ca
rbox
ypep
tidas
e
Mul
tiple
pro
teas
es
qMSP
-MS
(A
/H)(R
/K/Y
/E)W
(P/R
) | V
nK
(Lap
ek e
t al.,
201
9)As
perg
illus
pho
enic
is f
endo
pept
idas
es
Mul
tiple
pro
teas
es
qMSP
-MS
(T
/L)(R
/K/H
)(I/T
/n)(R
/K/n
) |(n/
I/F)
(F/R
)(F/K
)W
(Lap
ek e
t al.,
201
9)Lu
ng ca
ncer
sec
retio
ns m
onoa
min
opep
tidas
es
Mul
tiple
pro
teas
es
qMSP
-MS
(A
/W/F
/Y) |
(n/A
)(n/Y
)(H/L
/S)(Y
/G)
(L
apek
et a
l., 2
019)
Lung
canc
er s
ecre
tions
di-a
min
opep
tidas
es
Mul
tiple
pro
teas
es
qMSP
-MS
(A
/F/G
)(S/N
/n) |
(Y/n
)(F/Y
)(W/K
/R/N
)(R/Y
/T)
(Lap
ek e
t al.,
201
9)
a ‘ | ’ i
ndic
ates
the
prot
ease
hyd
roly
sis
posi
tion.
b ‘n’ c
orre
spon
ds to
nor
leuc
ine
or n
ongr
ayed
resi
dues
.
Tabl
e 1
(con
tinue
d)
170 S. Chen et al.: Mapping protease substrate specificity
unbiased mass spectrometry (MS) detection. The benefits of this approach are the need for only small quantities of protein and the ability to monitor dynamic changes in proteolytic processing events en masse without any prior knowledge of the potential substrates. However, this method is limited because it is time-consuming, requires extensive MS analysis for each sample being tested and is limited to protein fragments that can be resolved by SDS-PAGE.
Alternative to PROTOMAP, a number of gel-free methods have been developed to enable simultaneous identification and quantification of substrate hydrolysis events in complex cellular environments (Van Damme et al., 2005; Enoksson et al., 2007; Schilling and Overall, 2008; Impens et al., 2010; Kleifeld et al., 2010; Wiita et al., 2014b). All of these methods depend on the enrichment of unique α-amino peptides or isotopically labeled frag-ments of native proteins produced by proteolysis. These approaches have enabled a better understanding of protease substrate networks and their roles in specific biological pathways. The identification of substrate rec-ognition motifs has also contributed to the categorization of proteases based on their specificities for substrate pro-cessing. An interesting example of the value of proteom-ics-based protease substrate discovery was the discovery of Dicer as one of the large number of substrates for the caspase family of cysteine proteases involved in apopto-sis (Pop and Salvesen, 2009; Crawford et al., 2013; Julien and Wells, 2017). Dicer, one of the central proteins of the microRNA (miRNA) processing machinery, was discovered as a target for caspases during apoptosis of HeLa cells, triggered by tumor necrosis factor α (TNFα) (Matskevich and Moelling, 2008). The level of miRNAs was also sub-stantially repressed during glucocorticoid-induced apop-tosis of primary rat thymocytes, due to Dicer depletion by caspases (Smith et al., 2010).
The disadvantage of using substrate cleavage to study protease activity is that it relies on the assumption that only a single protease is responsible for the cleavage of a given substrate protein. This assumption is often not true, especially for large, closely related protease families such as the matrix metalloproteases (MMPs) (Prudova et al., 2010), cysteine cathepsins (Turk et al., 2012) and cas-pases (Pop and Salvesen, 2009), in which multiple pro-teases share highly similar substrate specificity profiles. Furthermore, conditional or transient protease-substrate interactions may also lead to false-negative discovery of natural substrates (Seo and Rhee, 2018). Therefore, as a complement to proteomic methods that map processing of native protein cleavage events, a number of methods have been developed that allow direct screening of randomized
peptide sequences to identify global patterns of substrate specificity for a single protease of interest. These methods are the focus of this review and make use of both synthetic chemistry and biological expression systems to generate the necessary diversity of peptides to perform effective substrate specificity profiling studies.
Synthetic combinatorial peptide librarySolid-phase peptide synthesis (SPPS) with Fmoc chemis-try is the most widely used synthetic strategy to prepare peptide substrates of proteases (Merrifield, 1985; Behrendt et al., 2016). By attaching the C-terminus of the peptide chain to a solid support, the polypeptide chain can be synthesized in high yields by rounds of amide bond cou-plings. Due to the large chemical space of peptides that result from the combination of 20 natural amino acid building blocks, it is logistically difficult to synthesize and test a sufficiently diverse library of peptides individu-ally. Using mixtures of peptide substrates, libraries can be efficiently screened in a high throughput manner. Two methods have been developed and are commonly used for the rapid and efficient synthesis of peptide libraries using combinatorial chemistry techniques. In the first approach, amino acids are mixed according to their cou-pling efficiency to produce combinatorial libraries with equal distribution of each amino acid at designated posi-tions (Ostresh et al., 1994). Establishing the isokinetic mixture of amino acids is important to avoid over-popu-lation of highly reactive amino acids and to ensure equal amino acid distribution in the final product. Beyond amino acids, this technique has also been used to incor-porate carboxylic acids in mixture-based combinatorial libraries (Acharya et al., 2002). In the second approach, solid support beads are physically split into equal por-tions and individual amino acids are coupled separately, ensuring equimolar substitution (Furka et al., 1991). By splitting and combining beads over multiple rounds of amino acid couplings, millions of peptides can be syn-thesized to produce ‘one-bead, one-peptide’ libraries (Lam et al., 1991). However, these libraries are typically screened with the peptides attached to the beads and require some form of decoding to identify the sequences on beads that contain the optimal substrates.
To evaluate the peptide substrate specificity for a given protease, the cleavage of the peptide substrate must be detected. Fluorescent reporter groups such as 7-amino-4-methylcoumarin (AMC, Figure 1) are attached by an
S. Chen et al.: Mapping protease substrate specificity 171
amide bond to the carboxylic acid end of the peptide sub-strate to convert it into a fluorogenic substrate. While the peptide is intact, the amide form of the coumarin stays optically silent. Upon proteolytic hydrolysis of the peptide substrate at the P1 residue, the free amino form of the AMC is released and becomes 700-fold brighter, allowing for fluorescent detection of a cleavage event (Zimmerman et al., 1976). To allow for solid-phase compatible synthesis of fluorogenic substrates, AMC has been further modified to 7-amino-4-carbamoylmethylcoumarin (ACC), which contains additionally carboxylic acid that can be directly coupled to a solid support (Harris et al., 2000). This allows direct split and mix or isokinetic mixture synthesis of diverse peptide substrate libraries.
The development of positional scanning substrate combinatorial libraries (PS-SCL) made it possible to rapidly and exhaustively screen peptide substrates to determine primary amino acid specificity without any knowledge of natural substrates (Rano et al., 1997; Thornberry et al., 1997; Harris et al., 2000). The PS-SCL is composed of fluorogenic sub-libraries where each posi-tion of the peptide is fixed with one amino acid, while the remaining positions contain an equimolar mixture of amino acids (Figure 2). With the PS-SCL format, pro-teases can be assayed and the optimally preferred amino acid residue at each position of the peptide can be rapidly identified. Ultimately, optimal residues at each posi-tion in the peptide can be combined to generate sub-strate sequences that are highly specific to the protease of interest. Because substrate libraries are synthesized in an unbiased manner, it is possible to use the result-ing specificity profiles to uncover substrate specifici-ties other than those defined by the most abundant and efficiently cleaved native protein substrate. In the first example of applying PS-SCL to determine protease speci-ficity, optimal substrate sequences were discovered for
interleukin-1β converting enzyme (ICE; now known as caspase 1) that were divergent from its native substrates (Rano et al., 1997). Substrate specificities of cathepsins K and S were also profiled and showed sequence pref-erences that matched known physiological substrates (Choe et al., 2006). Using PS-SCL, human mitochondrial intermediate peptidase (hMIP) was discovered to prefer polar, uncharged residues at P1 and P1′ substrate posi-tions (Marcondes et al., 2015). Substrate specificity of hepatocyte growth factor activator (HGFA) was elucidated through PS-SCL screening (Damalanka et al., 2019), which was then used to design selective inhibitors of matriptase and hepsin (Damalanka et al., 2019). PS-SCL is one of the most widely applied method to map protease substrate specificity, and more examples are listed in Table 1.
However, due to overlapping substrate specificity of proteases from the same family, conventional PS-SCL approaches have often been insufficient to generate selec-tive substrates for a single family member. In addition, proteases from different but related families can also have a great deal of overlap in substrate specificities. To explore a larger chemical space of substrate peptides, hybrid combinatorial substrate libraries (HyCoSuL) using both
Figure 1: Fluorescent properties of 7-amido and 7-amino-4-methylcoumarin fluorophores.The excitation maxima (Ex) and emission maxima (Em) of 7-amido and 7-amino 4-methylcoumarin are distinct. The relative fluorescence (RF) intensity of 7-amino-4-methylcoumarin is approximately 700-fold greater than that of an equimolar amount of 7-amido-4-methylcoumarin when excited at 380 nm and emission detected at 460 nm.
Figure 2: Positional scanning-substrate combinatorial library (PS-SCL) for mapping protease substrate preferences.In each sub-library, a single position is fixed with a defined amino acid and the remaining positions mixed with equimolar concentrations of amino acids (minus cysteine and methionine to avoid oxidation). The 7-amino-4-carbamoylmethylcoumarin (ACC) reporter is conjugated to the C-terminus of the peptide library so that hydrolysis of each sub-library can be measured using a fluorescent plate reader. Ultimately, the optimal residues at each position (P1–P4) can be determined and then combined to make an optimal set of substrates for a given protease.
172 S. Chen et al.: Mapping protease substrate specificity
natural and non-natural amino acids have been devel-oped. This use of non-natural amino acids has led to the development of selective protease substrates, inhibitors and activity-based probes with increased selectivity over molecules that contain only natural amino acids (Poreba et al., 2017a). The HyCoSul approach has been success-fully applied to generate selective tools for proteases such as caspases (Poreba et al., 2014b; Ramirez et al., 2018), human neutrophil serine protease 4 (Kasperkiewicz et al., 2015), neutrophil elastase (Kasperkiewicz et al., 2014) and a protease expressed by Mycobacterium tuberculosis (Lentz et al., 2016) as well as others. It has also been used to develop selective active site probes and inhibitors for protease activities within multi-proteolytic protease com-plexes such as the proteasome (Rut et al., 2018; Yoo et al., 2018). The application of non-natural amino acids in indi-vidual substrate fluorogenic peptide library (ISFPL) has also proven valuable for profiling substrate preferences of mono-, di- and tri-aminopeptidases (Drag et al., 2010; Poreba et al., 2012).
PS-SCL strategies using a C-terminal reporter are valuable for mapping substrate specificities of proteases that derive the majority of their specific binding interac-tions from the non-prime residues on the N-terminal side of the scissile bond. For protease that derives specificity from sequences on both sides or from the prime, C-ter-minal side of the amide bond, internally quenched fluo-rescent (IQF) peptide substrate libraries can be used. In IQF peptide substrate libraries, a fluorescent reporter is attached to one end of the peptide while a quencher mole-cule is incorporated at the other end. While the peptide is intact, the proximal quencher molecule absorbs the fluo-rescence from the fluorophore, keeping the peptide sub-strate optically silent. Upon proteolytic hydrolysis of the substrate peptide anywhere in the sequence between the fluorophore and quencher, the reporter is released to emit fluorescence, identifying the specific peptide sequences that the protease prefers (Yaron et al., 1979). The signifi-cant limitation of this method is that cleavage events at multiple positions in the peptide sequences can result in a positive signal, making it difficult to map the exact cleavage site without further analytical studies of the optimized substrates. Overall, synthetic combinatorial libraries of both natural and hybrid natural/non-natural peptides have enabled the profiling of substrate specifici-ties for many proteases (see Table 1 for recent examples). This general approach has greatly promoted the study of peptide sequence preferences of many proteases, acceler-ated the understanding of their biological functions and facilitated the design and discovery of clinically relevant protease inhibitors.
Fragment-based discovery of small molecule substratesIn addition to using peptide substrate libraries to identify native substrate cleavage specificities, it is also possible to design and sequentially build small molecule fragment-based substrate libraries to identify non-peptidic building blocks that can be efficiently recognized by proteases. Sub-strate activity screening (SAS) is a fragment-based method for the rapid development of selective small molecule protease substrates and inhibitors (Wood et al., 2005). SAS can efficiently identify weak binding fragments and allow rapid optimization of the initial weak binding frag-ments into higher-affinity compounds (Figure 3). The SAS method consists of two sequential screening steps to dis-cover non-peptide small molecule substrate of proteases and a final step to convert them into potent inhibitors. Ini-tially, a fluorogenic coumarin derivative substrate library is synthesized with diverse, low-molecular-weight N-acyl fragments and screened against a given protease. In the first step, the protease substrates are identified through a high-throughput fluorescence-based assay, and generally only weak substrates are discovered. In the second step, the substrates with low activity are further elaborated using combinatorial chemistry. Key chemical structures are identified from the first fragment library screen and are incorporated to generate a new focused substrate library. After a second round of screening against the given pro-tease, specific substrates can be rapidly discovered. These substrates can then be converted into reversible or irrevers-ible inhibitors by directly replacing the aminocoumarin with known mechanism-based warheads. This method has been successfully applied to multiple protease targets (Rawls et al., 2009; Verdoes et al., 2012; Jamali et al., 2015).
Biological display methods to generate diverse protease substrate librariesWhile synthetic chemistry methods to generate diverse peptide libraries have the advantage of overall flexibility and potential to include both natural and non-natural amino acids, they suffer from an overall inability to access the complete diversity of peptide sequences for longer peptides and lack of rapid methods to screen and select for optimal sequences over multiple rounds of screening. To address these shortcomings of chemically synthesized libraries, biological tools have been developed to rapidly
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display and screen highly diverse pools of peptide sub-strates. Phage display is a technique in which degener-ated DNA sequences are fused to the pIII gene such that randomized polypeptide libraries are expressed on the phage surface. Using this approach, billions of proteins or peptides can be rapidly produced with their genetic information embedded in the connected phage particles. In this way, high diversity libraries have been used to dis-cover peptides, small proteins and single chain antibody fragment binders of target proteins (Smith, 1985; Ward et al., 1989; Scott and Smith, 1990; Clackson et al., 1991). When an extra affinity tag is placed at the N-terminus of the peptide substrate library, phage display can be used to iteratively screen for selective protease substrates (Figure 4). So far, various peptide libraries and tags have been employed to generate phage libraries for the dis-covery of protease substrates (Matthews and Wells, 1993; Deperthes, 2002; Capek et al., 2010; Caberoy et al., 2011). Compared with synthetic peptide libraries, phage-dis-played peptide libraries have much higher diversity and optimal substrate peptide sequences can be iteratively enriched over multiple rounds of selection by increasing the stringency of selection conditions. The identified pep-tides are then synthesized and tested in vitro to establish the exact site of hydrolysis.
As an alternative to phage screening, yeast endoplas-mic reticulum sequestration screening (YESS) coupled with next generation sequencing (NGS) has been reported as a method to survey protease substrate specificity
(Li et al., 2017). In this approach, a combinatorial sub-strate library conjugated with an HA tag and FLAG tag is targeted to the yeast endoplasmic reticulum (ER) and transported through the secretory pathway, allowing any proteases present in the ER to cleave the peptide substrate (Figure 5). Fluorescein isothiocyanate (FITC)-conjugated
Figure 3: The substrate activity screening (SAS) method.A library of N-acyl aminocoumarins with diverse, low-molecular-weight N-acyl fragments is prepared and screened to identify protease substrates that bind with low affinity (red colored). A focused library is synthesized based on the substrate identified from the initial screen, and this second library of closely related non-peptidic fragments is screened to identify potent protease substrates (orange + red colored). The most potent substrate can also be converted to an inhibitor or activity-based probe by replacing the coumarin reporter with a protease reactive pharmacophore (W).
Figure 4: Schematic illustration of a phage-based approach to discover protease substrate peptides.A random peptide library (X represents any canonical amino acid) and an affinity tag (for immobilizing phage on solid support) are fused to the N-terminus of phage pIII D1D2 domains. After absorbing phage onto a solid support and adding the protease of interest, phage containing peptides that are efficiently hydrolyzed by the protease are released, recovered and subjected to amplification. Iterative rounds of selection will identify specific peptide substrate sequences. Sequencing the phage pIII gene yields the identity of the corresponding optimal substrate peptides.
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anti-HA and phycoerythrin (PE)-conjugated anti-FLAG antibodies, combined with multicolor fluorescence-acti-vated cell sorting (FACS) screening, are used to detect and isolate cells that presented a cleaved peptide sequence. Cleavage is detected by monitoring the ratio of PE to FITC fluorescence. High amounts of both fluorescent signals indicate a lack of cleavage, whereas a high PE to FITC ratio indicates cleavage by an expressed protease. This method was used to profile the tobacco etch mosaic virus protease and confirmed the substrate preference reported previ-ously (Li et al., 2017).
A method for profiling protease substrates displayed between two affinity domains on the Gram-positive bac-terium Staphylococcus carnosus has also recently been reported (Sandersjoo et al., 2017). The reporter tag (RT) has affinity to both the reporter (R) and the blocking domain (BD), thereby BD can block RT from interacting with the reporter as long as the substrate peptide is intact. If the substrate peptide is hydrolyzed by the protease, the BD will diffuse away and the RT will be able to bind to the reporter (Figure 6). Proteolysis is therefore reflected by reporter binding. Incubation with fluorescently labeled reporter and human serum albumin (HSA) enables simul-taneous flow cytometric analysis of proteolysis and surface expression levels. When applied to screening MMP-1, peptides with PXXXHy consensus sequences were enriched, and the discovered peptides were shown to be effectively cleaved by the protease.
Microarray peptide librariesPeptide microarrays display a collection of peptides on a solid surface and are widely used to profile protein-protein
interactions, enzyme activity, as well as to map antibody epitopes. The advantages of using microarray peptide libraries include minimal sample and enzyme usage for analysis and ease of recording data through direct scanning of the microarray slide. Peptide microarrays have helped elucidate protease-substrate interactions to advance the understanding of proteases and have the potential for diagnostic applications (Salisbury et al., 2002; Gosalia and Diamond, 2003; Gosalia et al., 2005a,b). Fluorogenic peptide substrate microarrays provide a rapid way to identify substrate specificity and can help to design selective protease inhibitors. In one study, a 722-member library of fluorogenic protease substrates of the general format Ac-Ala-X-X-(Arg/Lys)-coumarin was synthesized and arrayed, providing maps of protease specificity for human thrombin, factor Xa, plasmin and urokinase plas-minogen activator (Gosalia et al., 2005b).
Microarray strategies can also be used to deconvo-lute proteolytic activity signals after peptide mixtures have been incubated with a small amount of a given protease. Peptide nucleic acid (PNA)-tagged rhodamine-based fluorogenic substrates have been employed to study protease hydrolysis activity (Winssinger et al., 2004) (Figure 7). Peptide libraries are synthesized and conjugated to the ends of the rhodamine which are PNA barcoded. These libraries are pooled and incubated with the protease of interest. Cleaved peptides generate a free amino-rhodamine, resulting in a 1000-fold increase in fluorescence. To deconvolute multiple signals from the peptide mixtures, the PNA-barcoded libraries are allowed to hybridize to a DNA microarray chip. Subsequently, the microarray chip can then be fluorescently scanned and the peptide sequence determined from the correspond-ing PNA barcode. PNA-tagged synthetic peptide libraries
Figure 5: Schematic illustration of yeast endoplasmic reticulum sequestration screening (YESS) system.The substrate peptide library cassette is fused to the C-terminus of the Aga2 protein and translocated to the ER secretory pathway. Interaction of the Aga2 with Aga1 protein displays the peptide on the yeast surface. When the peptide substrate is recognized and hydrolyzed by the protease, the HA tag is released. After staining with PE-labeled anti-FLAG and FITC-labeled anti-HA antibodies, FACS sorting isolates yeast cells containing only PE fluorescence. The identity of the peptide substrate can be determined by sequencing the peptide gene from the recovered yeast.
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Figure 6: Schematic illustration of bacteria surface display for discovering protease substrates.The substrate peptide library is inserted between a pair of interaction proteins consisting of a reporter tag (RT) and blocking domain (BD). When a peptide substrate is recognized and hydrolyzed by the protease, the BD domain diffuses away, allowing the PE fluorescently labeled reporter (R) domain to bind to the RT. The peptide expression level is quantified by measuring the expression of albumin binding protein (ABP), through its interaction with an Alexa647 fluorescently labeled affinity protein human serum albumin (HSA). After FACS sorting of bacteria carrying both PE and Alexa647 dyes, the substrate peptide sequences are determined by sequencing the peptide gene.
Figure 7: Schematic illustration of peptide nucleic acid (PNA)-tagged synthetic peptide libraries for discovering protease substrates.Rhodamine-based fluorogenic substrates encoded with PNA tags are chemically synthesized and treated with a protease of interest. Recognition by the protease results in the hydrolysis of the C-terminal amide bonds to generate free amino-rhodamine, which becomes fluorescent. After hybridization to the DNA microarray and fluorescent scanning, the sequence of the substrate peptide is deconvoluted by way of the DNA sequence information of the fluorescent array spots.
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have been used to quantify protease activity and sub-strate specificity of serine and cysteine proteases includ-ing caspase-3, thrombin and plasmin (Winssinger et al., 2004). While there have not been many recent examples of applications of nucleotide-encoded protease substrate libraries, the approach still has potential value as current sequencing methods have improved and there remains great potential for use of such encoded libraries for rapid screening of highly diverse substrate libraries.
Mass spectrometry-based approachesOne of the limitations of most synthetic PS-SCLs and peptide microarrays is the use of tags or fluorescent reporters for read out of proteolytic activity. These labels can alter the substrate structure and affect cleavage rates. As an alternative, MS has a significant advantage over other methods to detect substrate specificity as it does not require analytes to be labeled with reporters, offering greater flexibility in experiments. In one recent example of an MS-based approach, self-assembled monolayers for matrix-assisted laser-desorption-ionization mass spectro-metry (SAMDI-MS) are used to detect peptide substrates in their native states. Peptides from libraries are individu-ally treated with a protease in 384-well plates and then immobilized onto a monolayer array plate. The monolayer is then irradiated with a laser that releases the ionized peptide species from the surface, which can be analyzed by a mass spectrometer for characterization. In this way, a 76-peptide array for scanning the P2, P1, P1′ and P2′ sub-strate positions led to the identification of a tetrapeptide substrate exhibiting high activity for the bacterial outer-membrane protease (OmpT) (Wood et al., 2017).
In another recently developed MS method, multiplex substrate profiling by mass spectrometry (MSP-MS), sub-strate specificity of endo- or exopeptidases was determined using liquid chromatography-tandem mass spectrometry (LC-MS/MS) sequencing (O’Donoghue et al., 2012). This method is built around a synthetic library of 124 tetradeca-peptides, which were computationally designed to result in 1612 potential protease cleavage sites that comprehensively cover the substrate signatures of all protease families. Com-paring the LC-MS/MS traces of the library with and without peptidases added at multiple time intervals reveals cleav-age sites and amino acid residue preferences for a given protease of interest. Since its first development in 2012, this approach has been used to map the substrate specificity of a number of important protease targets including caspases,
rhomboids, matriptase and hepsin. It has also been used to map the substrate specificity of the malaria proteasome and other important pathogens (Corvo et al., 2013; Julien et al., 2016; Lentz et al., 2016; Li et al., 2016; Beekman et al., 2018; Leontovyc et al., 2018; Dahms et al., 2019).
Quantitative information of the peptide cleavage can be collected by further incorporating isobaric tandem mass tag (TMT) labels into the MSP-MS workflow. This method, called quantitative multiplex substrate profiling by mass spectrometry (qMSP-MS) minimizes experimental and instrument-derived variance while improving throughput of the assay. Furthermore, by labeling samples at multi-ple time intervals, it is possible to accurately quantify peptides and calculate turnover rates of each proteolytic event (Figure 8). To validate the workflow of qMSP-MS, substrate specificity of papain, HiGlpG, PsAarA and lung cancer secretions were characterized (Lapek et al., 2019).
Other related methods to search for protease substratesBesides the mentioned synthetic and biological approaches to generate peptide libraries for discovering protease substrates, peptide libraries can also be gener-ated by processing of native proteomes to produce frag-ments which can then be used to map substrate specificity by MS. Terminal amine isotopic labeling of substrates (TAILS) applies quantitative proteomic methods to iden-tify the difference of N-terminal fragments of proteins in a proteome sample after addition of target proteases to identify native cut sites (Kleifeld et al., 2010). Proteomic identification of cleavage sites (PICS) quantifies the prime side of protein sequences generated by protease hydroly-sis and searches the non-prime portion of the discov-ered protein fragment to simultaneously identify both the prime- and non-prime-side specificities of individual protease targets (Schilling et al., 2011). Some examples of protease substrates identified using proteomics-based methods are summarized in Table 1.
Conclusions and future perspectivesAdvances in protease substrate library design and syn-thesis, including chemical and biological approaches, have greatly aided in the development of selective pro-tease substrates and inhibitors. These peptides and small molecules have become powerful tools to study the roles of proteases in complex biological contexts including
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their mechanisms of action, regulation, processing and their association with specific human disease states. Fur-thermore, innovations in substrate library generation, screening and deconvolution techniques have accelerated the identification of native cellular protease substrates. However, it is still challenging to develop specific sub-strates to target single proteases without being recognized and cleaved by other proteases sharing similar substrate recognition preferences. In addition to the sequence of the peptide chains, the conformation that the substrate can adopt plays a crucial role in determining substrate speci-ficity. This is largely due to the intrinsic flexibility of linear polypeptide chains. The flexibility around C-α drives linear polypeptide chains to adopt conformations influ-enced by disulfide bonding and hydrophobic restraints. On the other hand, the flexibility of linear peptide chains enables these substrates to fit into defined specificity pockets of different proteases. Cyclizing polypeptide chains to generate rigid conformations has been shown to be a promising strategy to reduce flexibility and increase target binding affinity and specificity (Heinis et al., 2009; Angelini et al., 2012; Baeriswyl et al., 2012; Chen et al., 2013; Chen et al., 2014). Cyclic peptides have been demon-strated to be potent protease inhibitors that are less prone to unspecific protease degradation resulting in increased bio-availability and improved drug-like properties. Due to the difficulties of generating cyclic peptide libraries and sequencing cyclic peptides with tandem MS methods, there are currently no synthetic cyclic peptide libraries for the screening and discovery of protease substrates. However, current biological display methods are being
engineered to allow cyclic and bi-cyclic peptide display which should help to facilitate future screening of these potentially valuable scaffolds as substrates that can be converted into substrates and inhibitors with good phar-macological properties (Maola et al., 2019; Wang et al., 2019). This review has hopefully provided some insight into the current synthetic and biological methods used to generate highly diverse substrates for mapping protease substrate specificity, and also has highlighted the poten-tial application of cyclic peptide in generating potent and specific probes with improved bioavailability. It is likely that future advances in these methods will lead to a further expansion of the tool box of reagents for the study and therapeutic targeting of proteases.
Acknowledgments: This work was funded by NIH grant R01 EB026285 02 (Funder Id: http://dx.doi.org/10.13039/100000002) (to M.B.), Swiss National Science Foundation Postdoc. Mobility fellowship P2ELP3_155323 P300PB_164725 (to S.C.), Stanford ChEM-H Chemistry/Biology Interface Predoctoral Training Program and NSF Graduate Research Fellowship Grant DGE-114747 (to J.J.Y.). Stanford University is also acknowledged.
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