PERSPECTIVE
published: 20 Janua y 2017
doi: 10.3389/ nmol.2017.00007
Ta ge ing Neu oblas oma Cell
Su ace P o eins: Recommenda ions
o Homology Modeling o hNET,
ALK, and T kB
Yazan Haddad1,2,Zbynˇek Hege 1,2 and Voj ech Adam1,2*
1Depa men o Chemis y and Biochemis y, Mendel Uni e si y in B no, B no, Czechia, 2Cen al Eu opean Ins i u e o
Technology, B no Uni e si y o Technology, B no, Czechia
Edi ed by:
De le Boison,
Legacy Heal h, USA
Re iewed by:
Hong Qing,
Beijing Ins i u e o Technology, China
Nikki Ka he ine Ly le,
Uni e si y o Cali o nia, San Diego,
USA
Da ide Comole i,
Ru ge s Uni e si y, USA
*Co espondence:
Voj ech Adam
[email p o ec ed]
Recei ed: 26 Sep embe 2016
Accep ed: 06 Janua y 2017
Published: 20 Janua y 2017
Ci a ion:
Haddad Y, Hege Z and Adam V
(2017) Ta ge ing Neu oblas oma Cell
Su ace P o eins: Recommenda ions
o Homology Modeling o hNET,
ALK, and T kB.
F on . Mol. Neu osci. 10:7.
doi: 10.3389/ nmol.2017.00007
Ta ge ed he apy is a p omising app oach o ea men o neu oblas oma as e iden
om he la ge numbe o a ge ing agen s employed in clinical p ac ice oday. In
he absence o known c ys al s uc u es, esea che s ely on homology modeling o
cons uc empla e-based heo e ical s uc u es o d ug design and es ing. He e,
we discuss h ee candida e cell su ace p o eins ha a e sui able o homology
modeling: human no epineph ine anspo e (hNET), anaplas ic lymphoma kinase
(ALK), and neu o ophic y osine kinase ecep o 2 (NTRK2 o T kB). When choosing
empla es, bo h sequence iden i y and s uc u e quali y a e impo an o homology
modeling and pose he i s o many challenges in he modeling p ocess. Homology
modeling o hNET can be imp o ed using empla e models o dopamine and
se o onin anspo e s ins ead o he leucine anspo e (LeuT). The ex acellula
domains o ALK and T kB a e ye o be exploi ed by homology modeling. The e
a e se e al idiosync asies ha equi e di ec a en ion h oughou he p ocess o
model cons uc ion, e alua ion and e inemen . Shi s/gaps in he alignmen be ween
he empla e and a ge , backbone ou lie s and side-chain o ame ou lie s a e
among he main sou ces o physical e o s in he s uc u es. Low-conse ed egions
can be e ined wi h loop modeling me hod. Residue hyd ophobici y, accessibili y
o bound me als o glycosyla ion can aid in model e inemen . We ecommend
esol ing hese idiosync asies as pa o “good modeling p ac ice” o ob ain highes
quali y model. Dec easing physical e o s in p o ein s uc u es plays majo ole in he
de elopmen o a ge ing agen s and unde s anding o chemical in e ac ions a he
molecula le el.
Keywo ds: neu oblas oma, a ge ed he apy, homology modeling, no epineph ine anspo e , anaplas ic
lymphoma kinase, neu o ophic y osine kinase ecep o
INTRODUCTION
The la ge numbe o expe imen ally de e mined and deposi ed p o ein s uc u es in he da abases
is a aluable sou ce o explo e uncha ed e i o ies o o he p o eins, which ha e simila sequence.
Homology modeling, also known as compa a i e modeling, is used o cons uc ing p o ein models
based on empla e s uc u es o homolog p o eins. In his pe spec i e a icle, we ha e highligh ed
he main s uc u al issues in homology modeling o h ee candida e cell su ace p o eins o
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Haddad e al. Homology Modeling o Ta ge ing Neu oblas oma
neu oblas oma a ge ed he apy. In he nex sec ion, we
emphasize he challenges aced in each s ep in he homology
modeling p ocess. A b ie sec ion on a ge ing neu oblas oma
desc ibes he ole o cell su ace p o ein a ge s in de eloping
he apeu ics. A de ailed e alua ion o homology models
expands on h ee chosen cell su ace p o ein candida es: human
no epineph ine anspo e (hNET), anaplas ic lymphoma
kinase (ALK), and neu o ophic y osine kinase ecep o ype 2
(NTRK2, commonly known as T kB). While he echnical
aspec s o his manusc ip a o an audience o specialis s
wo king on d ug disco e y and homology modeling o hese
h ee a ge s, he same p inciples o model e alua ion and
challenging issues can be applied o o he homology models.
HOMOLOGY MODELING
P o ein s uc u es a e he inal on ie s in unde s anding
he human biology a he molecula le el. Gold s anda ds o
c ys al s uc u e cha ac e iza ion a a omic esolu ion, such as
X- ay c ys allog aphy and nuclea magne ic esonance (NMR)
spec oscopy, can be now sa is ac o ily complemen ed wi h
compu a ional me hods (Kund o as e al., 2012). Homology
modeling is a sys ema ic compu a ional p ocess whe e he
mos simila p o ein sequence, o a known c ys al s uc u e,
is used o cons uc ion o a new model by eplacing he
equi alen amino acids on an equi alen backbone. K iege
e al. (2003) desc ibed se en s eps in homology modeling:
(1) The choice o empla e(s) and ini ial alignmen a e he i s
challenges in homology modeling. The accu acy o a homology
model is co ela ed wi h he numbe o ma ching esidues in
alignmen . A minimum 25% sequence iden i y has been he
s anda d o homology modeling so a . Below 25% iden i y i
is ecommended o use mul iple empla es o modeling. The
quali y o empla e s uc u e is di ec ly inhe i ed in he homology
model. Polishing o empla e s uc u e is a good p ac ice be o e
use. Missing a oms should be ixed using o ame lib a y and
he hanging e mini can be immed. (2) Alignmen co ec ion
educes e o s caused by alse sequence iden i ies. Mul iple
alignmen s and s uc u al alignmen s (e.g., posi ion-speci ic
sco ing ma ices) a e ecommended al e na i es o con en ional
alignmen s. This s ep is he mos c i ical in homology modeling
be o e (3) backbone gene a ion, as i de e mines he o sional
angles o he backbone in he model. I has been shown ha
p oblems in he backbone can d as ically al e he co ec
olding o he side-chains as well (Al-Lazikani e al., 2001).
Issues ega ding empla e ecogni ion and alignmen will be
highligh ed he e. F om ou expe ience, many o he spo adic
e o s in modeling backbones a ise in p oline o adjacen o
p oline esidues. (4) Loop modeling is impo an o co ec ing
he olding o low-conse ed egions (i.e., loops) o p o ein.
I is now possible o make accu a e models using da abase
sea ch o ab ini io me hods o up o 8–13 esidues long loops
(To o , 2012). Loop modeling employs po en ial ene gy sco es
o e alua ion o he quali y o cons uc ed loop(s). I is also
possible o use al e na i e a ionales o e alua ion based on
biological unc ions. We show he e some examples o biological
unc ions ha can include, exclude o guide he modeling
p ocess such as hyd ophobici y, accessibili y o loop esidues
o glycosyla ion o o binding o ions. (5) Side-chain modeling
is di ec ly a ec ed by empla e sequence iden i y, alignmen
and backbone. Iden ical esidues in wo homolog p o eins ha e
nea ly iden ical o ame s. The SWISS-MODEL se e (Bo doli
e al., 2009) and Modelle (Webb and Sali, 2014) a e among
he mos commonly used pla o ms o backbone/side-chain
homology modeling. Homology models a e subjec ed o inal
op imiza ion and alida ion be o e conduc ing a ious kinds o
compu a ional s udies such as ene ge ics (molecula mechanics),
p o ein-d ug/p o ein-p o ein in e ac ions (molecula docking
and p o ein ne wo ks), mu a ional analysis, and simula ion in
physiological en i onmen (molecula dynamics). (6) Model
op imiza ion, also known as ene gy minimiza ion, is applica ion
o ene gy unc ions o compu e a global minimum ha ep esen s
he mos na i e olding (Bo dne , 2012). Minimiza ion aims
o adjus he geome ies o p o ein s uc u es o he ‘‘ o ce
ield’’ pa ame e s used in compu a ional s udies. This me hod
some imes e e ed o as ‘‘ elaxing he s uc u e’’ is su icien
o esol e a omic clashes in he model. Howe e , he s o y is
no inished he e. Se e al physical and s uc u al e o s should
be esol ed. (7) Valida ion o he inal model(s) checks i he
model complies wi h s anda d pa ame e s o p o ein s uc u e.
These pa ame e s include: bond leng hs, bond angles, o sions,
backbone ou lie s, o ame ou lie s and all a omic con ac s
(Chen e al., 2010). E alua ion p ocedu e includes physics-based,
knowledge-based and expe imen al-based me hods (Haddad
e al., 2016). The same igo ous ules o e alua ion ha a e
applied in c ys allog aphy mus also be applied in homology
modeling.
TARGETING NEUROBLASTOMA
Neu oblas omas a e one o he mos common and a al solid
umo s in child en below 2 yea s o age. Whe eas he su i al
a es o mos ypes o cance in child en ha e imp o ed in
he pas ew decades, neu oblas oma is s ill below 75% 5-yea
su i al (Siegel e al., 2016). Ta ge ed he apy is a p omising
app oach o de eloping ea men s o neu oblas oma. Ma hay
e al. (2012) desc ibed h ee success ul he apeu ic a ge s o
neu oblas oma ha a e in p ac ice oday: (1) hNET a ge ed by
adio he apy ia 131I-me aiodobenzylguanidine (MIBG) (2) he
GD2 ganglioside a ge ed by monoclonal an ibodies, and
(3) ALK a ge ed by kinase inhibi o s. An ex ended lis o a ge s
was p e iously compiled by Ve issimo e al. (2011), including
hose in clinical ials. The as majo i y o FDA-app o ed
medicines, pa icula ly hose disco e ed by a ional d ug design
on molecula a ge s, wo k by pe u bing he unc ion o a ge s
o p e en o e e se symp oms/disease (Kinch e al., 2015).
I is impo an o dis inguish he e m a ge ed he apy, which
occasionally indica es pe u bing he molecula a ge , om he
gene al concep o a ge ing which also includes d ug deli e y
by ecognizing cance cells. Hence, a ge ing also desc ibes
he apeu ics able o deli e and elease ca go o he umo
mic oen i onmen by binding o a ge s on he cance cell
su ace (Shin e al., 2014). Such he apeu ics (e.g., pep ides and
an ibodies) a e cha ac e ized by hei selec i i y o a ge s. They
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Haddad e al. Homology Modeling o Ta ge ing Neu oblas oma
can be de eloped ei he blindly ia sc eening me hods o by
molecula modeling. The ideal a ge should be ‘‘abundan and
accessible and should be exp essed homogeneously, consis en ly
and exclusi ely on he su ace o cance cells’’ (Sco e al., 2012).
Homology modeling has been success ully used in s uc u e-
based design o many he apeu ics a ge ing di e en ypes o
diseases including cance (Bu le e al., 2010; Schlessinge e al.,
2011; DeVo e and Sco , 2012). Howe e , i is wo h men ioning
wo ca ego ies o excellen p o ein a ge s in neu oblas oma ha
a e beyond he scope o homology modeling: i s , p o eins
which ha e known c ys al s uc u es such as CD147, which is
associa ed wi h dec eased neu oblas oma di e en ia ion (Ga cia
e al., 2009; W igh e al., 2014), and CD57, which has
also been implica ed in agg ession o neu oblas oma (Kakuda
e al., 2004; Schli e e al., 2012). Second, hose candida e
a ge s which lack any empla e s uc u e. One o hese
cases is a glycop o ein called CD133, and i s exp ession is
associa ed wi h poo p ognosis o neu oblas oma (Sa ele e al.,
2012).
In compa a i e modeling o de elopmen o d ugs, he
lesson lea ned is ha simila s uc u es o en exhibi simila
unc ions. This is p oblema ic pa icula ly when d ugs a ge he
p o ein and i s homologs simul aneously, esul ing in a ious
side e ec s. The h ee a ge s hNET, ALK and T kB a e
good examples o c oss in e ac ions esul ing in side e ec s.
While hNET neu o ansmi e anspo o e laps wi h o he
monoamine anspo e s, he kinase inhibi o s o ALK and
T kB can a ge hei homologs, espec i ely. Bo h homology
modeling and de elopmen o selec i e d ugs should ocus on he
low-conse ed egions o a ge ed p o eins.
hNET
The hNET egula es he up ake and ecycle o no epineph ine
in he neu ons. hNET is one o he highly exp essed p o eins
in neu oblas oma (∼90% o cases) and mos commonly used
in MIBG-based diagnosis/ he apy (B odeu e al., 1993; Ma hay
e al., 2012) and de elopmen o new he apeu ics (Mo ensen
and Ko age e, 2015). hNET p o ein has h ee iso o ms. The
canonical iso o m is 617 esidues in leng h ye i is second
o he longes iso o m which is 628 in leng h and di e s in
he C- e minus. P e ious homology models o hNET elied on
he c ys al s uc u e o p oka yo ic leucine anspo e (LeuT;
Yamashi a e al., 2005) and d osophila dopamine anspo e
(dDAT) c ys al s uc u es as empla es (Penma sa e al., 2013;
Wang e al., 2015;Table 1). Two ecen ly published mu an
se o onin anspo e (SERT) c ys al s uc u es (Coleman e al.,
2016) designa ed s2 and s3 can also aid in homology modeling
o hNET. The homology models cons uc ed by Schlessinge e al.
(2011) and Koldsø e al. (2013) we e based on LeuT empla e. In
ligh o newly published dDAT and SERT c ys al s uc u es, we
ha e ecen ly add essed he de elopmen s ega ding homology
modeling o hNET based on hese new empla es (Haddad e al.,
2016). B ie ly, he e a e ou majo alignmen gaps cha ac e izing
he sequences o dDAT, hNET and SERT; including he
glycosyla ed ex acellula loop 2 (EL2), ex acellula loop 4
(EL4) and wo in acellula loops (Figu e 1A). Loop modeling
is mos ly equi ed a he EL2 loop; pa icula ly esidues
189–207. A e cons uc ion o a numbe o loops, one should
exclude hose wi h non-embedded leucines/ aline esidues due
o hei hyd ophobici y. A i s glance, he accessibili y o he
glycosyla ed esidues can be an indica o o non-embedded
esidues in he loop (i.e., aspa agines). A leas wo glycosyla ed
o ms o hNET a e known wi h molecula weigh s o 80 kD and
54 kD co esponding o a co e 46k D hNET p o ein (Melikian
e al., 1994). I is nei he clea whe he all h ee aspa agines
a e accessible o glycosyla ion no i indeed he e is a single
con o ma ion o his loop. Mu an hNET (K189H) exhibi s a
zinc binding si e in he EL2 loop (No egaa d e al., 1998),
which migh sugges di ec p oximi y and accessibili y be ween
K189 and nea es his idine H372. Howe e , no in o ma ion
is a ailable on he p oximi y o H199 in he EL2 o hese
esidues. Simila ly, se e al sodium and chlo ide ion binding
si es a e epo ed in LeuT, dDAT and human SERT (hSERT;
Yamashi a e al., 2005; Penma sa e al., 2013; Wang e al., 2015;
Coleman e al., 2016). Ligands binding si es include he cen al
binding si e (S1) and seconda y binding si e (S2) ha o e laps
he ex acellula loop (EL4; Figu e 1A). Conse ed esidues
in binding si es desc ibed by Koldsø e al. (2015) ha e nea ly
he same o ame s excep o F317. Low-conse ed esidues in
binding si es desc ibed by Ande sen e al. (2015) con ol he
selec i eness o hNET and side-chain con o ma ions o hese
esidues can a ec he quali y o he model (shown in Figu e 1A).
As men ioned ea lie , he side-chains o iden ical aligned esidues
exhibi nea ly iden ical o ame s. As many as 53 amino acid
esidues in hNET ha ha e iden i y wi h hSERT and no dDAT
can be used om he hSERT s uc u e o complemen he
o ame s and imp o e accu acy o s uc u e (Haddad e al.,
2016). We ecommend ha he cons uc ed homology model is
alida ed o selec i i y by docking o p e iously known ligands
such as neu o ansmi e s and inhibi o s.
ALK
ALK is a ecep o y osine kinase o iginally disco e ed by
ch omosomal ea angemen associa ed wi h anaplas ic la ge
cell lymphoma. ALK was epo ed o be cons i u i ely ac i a ed
by gene ampli ica ion in se e al neu oblas oma cell lines
(Osajima-Hakomo i e al., 2005), while neu oblas oma-speci ic
mu a ions in ALK we e sui able a ge s o de elopmen o
se e al inhibi o he apeu ics (Ba one e al., 2013). The h ee
classes o ALK mu a ions include a cons i u i ely ac i e gain-
o - unc ion ecep o (ligand-independen ), kinase dead mu an s,
and ligand-dependen mu an s (Chand e al., 2013). Fusion
p o eins in ol ing he in acellula kinase domain o ALK
cause se e al ypes o cance and has been a ge ed by kinase
inhibi o s. In addi ion, se e al mu a ions in ALK kinase domain
esul in d ug esis ance (Roskoski, 2013). The ad an ages
o homology modeling o he ex acellula domains include
accessibili y on cell su ace and pe haps less c oss in e ac ion
han kinase inhibi o s. ALK gene codes o a p o ein o
app oxima ely 1620 esidues (Table 1). The c ys al s uc u e
o he kinase domain shows he in e ac ions o selec i e d ugs
wi h mu an ALK (Sakamo o e al., 2011; Eps ein e al., 2012;
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Haddad e al. Homology Modeling o Ta ge ing Neu oblas oma
FIGURE 1 | Rep esen a ions o he human no epineph ine anspo e (hNET), anaplas ic lymphoma kinase (ALK) and opomyosin ecep o kinase B
(T kB) s uc u es ea u ing he mos di e gen egions which equi e p ecise alignmen and possibly loop modeling. Di e gen loops a e shown in
ep esen a i e alignmen s wi h closes known homolog s uc u es. (A) hNET. Two di e gen ex acellula loops and wo in acellula loops a e shown. A op iew o
hNET is shown on he le . Low-conse ed esidues in binding si es as desc ibed by Ande sen e al. (2015) a e highligh ed in colo s: cen al binding si e (S1),
seconda y binding si e (S2) and ex acellula loop 4 (EL4). Red and blue ci cles highligh S1 and S2 si es, espec i ely. (B) ALK ex acellula domains. Conse ed
yp ophan in he MAM domains mus be embedded in he cen e o s uc u e (i.e., W288 and W501). Since he alignmen o MAM2 ( egion o W501) was co ec ed
manually, u he loop modeling o he egion is equi ed (pa icula ly o P496 and P499). Highligh ed conse ed esidues ha a e binding he calcium ion in he
Low-densi y lipop o ein ecep o class A (LDLa) domain a e impo an o alignmen . (C) T kB ex acellula domains. Th ee majo domains o he T kB s uc u e can
be used in analysis. The leucine ich epea (LRR) and Ig-like C2 Type 2 egions can be cons uc ed by homology modeling. Two shi s a e shown, ha migh equi e
loop modeling. I is possible ha di e gen loops be ween T k p o eins migh play ole in he selec i i y and c oss-in e ac ions wi h neu o ophins. The Ig-like C2 ype
1 domain, di ec ly connec ed o he ansmemb ane domain, has been illus a ed by X- ay c ys allog aphy s udies o he h ee T ks. Fi ing o he h ee T ks showed
a di e gen loop a T kB 334–341 egion; possibly playing ole in he selec i i y o hese T ks o di e en neu o ophins.
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Haddad e al. Homology Modeling o Ta ge ing Neu oblas oma
TABLE 1 | Templa es o homology modeling o human no epineph ine anspo e (hNET) and ex acellula domains o anaplas ic lymphoma kinase
(ALK) and opomyosin ecep o kinase B (T kB).
Ta ge (ID) Leng h Templa e (PDB IDs) Iden i y Co e age Main domains Re e ence
hNET (P23975) 617 LeuT (2A65) 150/541 (28%)56−578 All Yamashi a e al. (2005)
dDAT (4M48) 320/548 (58%)56−601 All Penma sa e al. (2013)
dDAT (4XPA) 322/547 (59%)56−601 All Wang e al. (2015)
SERT- s2 (5I6Z) 291/548 (53%)51−595 All Coleman e al. (2016)
ALK (Q9UM73) 1620 PTPRM (2C9A and 2V5Y) 25/164 (15%)∗264−427 MAM1 A icescu e al. (2006, 2007)
21/159 (13%)∗481−636 MAM2
MEP1B (4GWM) 19/164 (12%)∗264−427 MAM1 A olas e al. (2012)
17/159 (11%)∗481−636 MAM2
LDLR (2KRI) 15/37 (41%)437−473 LDLa Lee e al. (2010)
T kB (Q16620) 822 T kA (2IFG) 89/256 (35%)32−281 Ig-like C2 Type2, LRR Weh man e al. (2007)
∗Many iden i ies we e los due o posi ion-speci ic mul iple alignmen .
Huang e al., 2014). All known c ys al models o ALK co e he
amino acid ange 1058–1411 and un o una ely he ex acellula
s uc u e has no been well s udied. The egion spanning he
ex acellula pa o he ecep o ∼266–636 comp ises wo
mep in/A5/mu domains also known as he MAM domains
(Figu e 1). MAM1 spans 264–427 and MAM2 spans 478–636.
App oxima ely 16 N-linked glycosyla ed si es a e eco ded
spo adically in he ex acellula egion. C ys al s uc u es o
he MAM domain o ecep o p o ein y osine phospha ase
MU, also known as PTPRM (A icescu e al., 2006, 2007), and
Mep in A be a, also known as MEP1B (A olas e al., 2012)
can se e as mul iple empla es o he ex acellula domain
o ALK. Al hough many sequence iden i ies a e los due o
posi ional alignmen (Table 1), he dis inc MAM domains o
PTPRM and MEP1B a e highly simila wi h ∼1.35 Å oo -mean-
squa e de ia ion (RMSD) o Cαa oms and ∼1.43 Å RMSD
o all a oms. Two loop egions in MAM1 domain equi e
loop modeling (Figu e 1B). The o me loop egion con ains
conse ed yp ophan (W288) e y well s acked in he cen e o
he domain. The equi alen yp ophan (W501) in MAM2 was
misaligned in he posi ion-speci ic alignmen and mus be
co ec ed o p ope modeling (Figu e 1B). Simila o hNET,
glycosyla ed esidues o he MAM domains can be used o check
he o ien a ion o side chains as hey should be acing solu ion.
A low-densi y lipop o ein ecep o class A (LDLa) domain spans
he egion 437–473 be ween he wo MAM domains. The LDLR
s uc u e (Lee e al., 2010) is he mos ela ed homolog (Table 1).
A leas h ee conse ed aspa ic acid esidues and one glu amic
acid coo dina e a calcium ion in he LDLa domain (Figu e 1B).
T kB
The neu o ophic T ks, assis ed by p75 neu o ophin ecep o ,
play an essen ial ole in biology o neu ons by media ing
neu o ophin-ac i a ed signaling. Neu o ophins include
ne e g ow h ac o (NGF), b ain-de i ed neu o ophic
ac o (BDNF), neu o ophin-3 (NT-3), neu o ophin-4/5
(NT-4/5), neu o ophin-6 and neu o ophin-7 (Ul sch e al.,
1999). The exp ession o ype 1 (T kA) and ype 2 (T kB)
ecep o s is associa ed wi h a o able and un a o able p ima y
neu oblas oma pa ien ou come, espec i ely (Thiele and
Reynolds, 2005). The e a e ou p o ein iso o ms o T kA
epo ed in he Unip o da abase (ID: P04629). The longes
is 796 esidues in leng h. C ys al s uc u es co e wo la ge
segmen s o he ex acellula ; (Robe son e al., 2001; Weh man
e al., 2007), and in acellula T kA p o ein (Wang e al., 2012).
On he o he hand, ou o se en al e na i e iso o ms, he
canonical iso o m o T kB is 822 esidues in leng h (Table 1).
The c ys al s uc u es o T kB co e he in acellula kinase
domain (Be and e al., 2012) and Ig-like C2 Type 1 domain
o he ex acellula egion (PDB IDs: 1WWB, 1HCF; Ul sch
e al., 1999; Ban ield e al., 2001). The es o ex acellula
segmen o T kB ( esidues 32–281) can be econs uc ed
by homology modeling o T kA empla e which has ∼35%
iden i y (Table 1). The empla e allows o modeling o wo
majo ex acellula domains o T kB; namely, he leucine
ich epea (LRR) spanning egion 92–137, and he Ig-like
C2 Type 2 domain spanning he esidues 197–281 (Figu e 1C).
A leas wo alignmen shi s in he LRR and Ig-like C2 ype 2
domains equi e loop modeling. By supe posing he h ee
known s uc u es o Ig-like C2 ype 1 domains o T kA,
T kB and T kC (PDB IDs: 1WWA, 1WWB, and 1WWC,
espec i ely), a di e gen loop spanning T kB 334–341 egion
is highligh ed (Figu e 1C). Along wi h his loop, se e al
di e gen esidues migh also play ole in he selec i i y o
hese T ks o di e en neu o ophins. Howe e , u he wo k
is equi ed o iden i y he exac binding si es o di e en
neu o ophins (Ul sch e al., 1999). In ac , he ne wo k o
T k-neu o ophin is mo e complex i we assume ha ei he
Ig-like C2 Type 2 and LRR domains would be in ol ed in
neu o ophins in e ac ions. In ensi e wo k in homology
modeling, molecula docking and molecula dynamics is
equi ed o shed he ligh on his ne wo k. Simila o he
si ua ion in hNET and ALK, inhibi o s a ge ing se e al T k
ecep o s a he same ime esul in a ge ing o se e al pa hways
and lead o se e al d ug side e ec s. Unde s anding he selec i i y
o T ks ecep o s will play signi ican ole in de elopmen o
he apeu ics.
CONCLUSIONS AND PERSPECTIVES
Homology modeling is one o he i s s eps in de eloping
he apeu ics o new a ge s. Howe e , as we showed he e he
i s s eps a e o en c ucial and de imen al in cons uc ing
F on ie s in Molecula Neu oscience | www. on ie sin.o g 5Janua y 2017 | Volume 10 | A icle 7

Haddad e al. Homology Modeling o Ta ge ing Neu oblas oma
new homology s uc u es, no o men ion in de eloping new
d ugs. Al hough he ules o ‘‘good modeling p ac ice’’ a e no
w i en ye , many lessons can be lea ned by e alua ion o he
model and co ec ing/a oiding e o s a ea ly s ages o modeling.
On he o he hand, a bad quali y empla e will no gi e a
good quali y homology model. The quali y and iden i y o he
empla e(s) a e e y impo an issues. Low-conse ed egions in
he p o ein a ge o en play a signi ican biological ole. They
equi e mo e ocus in homology modeling and in a design o new
he apeu ics.
Se e al s a egies o neu oblas oma he apy ha e been
ad ancing in pa allel in he pas ew decades. Ta ge ing he cell
su ace molecules is a s a egy ha allows o dis ibu ion o
e o s. The dis ibu ion o e o s is de ined by e ec i e esea ch
managemen whe e asks a e dis ibu ed among esea che s
o p oduce mo e e icien he apeu ics. Indeed, no el cance
he apeu ics comp ise complexes o se e al agen s ha ca y
ou se e al unc ions. The selec i e a ge ing agen (e.g., pep ide
o an ibody) deli e s he ca go (i.e., oxic agen ) o he cance
cell, which also equi es a ca ie (soluble/ eleasing agen ) o
a memb ane pene a ing agen . In his pe spec i e a icle, he
modele has a mo e ocused objec i e, which is o de elop a
‘‘selec i e’’ a ge ing molecule. We hope ha hese s a egies will
p oduce mo e adap able, e icien and pe sonalized he apeu ics
in he u u e.
AUTHOR CONTRIBUTIONS
All au ho s con ibu ed o he design o wo k. YH w o e he
manusc ip . ZH e iewed he manusc ip , and VA was p inciple
in es iga o and con ibu o o scheme and o ganiza ion o wo k.
FUNDING
We g a e ully acknowledge he Czech Agency o Heal hca e
Resea ch, AZV (15-28334A) and Minis y o Educa ion, You h
and Spo s o he Czech Republic unde he p ojec CEITEC 2020
(LQ1601) o inancial suppo o his wo k. The compu a ional
esou ces we e p o ided by he CESNET LM2015042 and
he CERIT Scien i ic Cloud LM2015085, p o ided unde he
p og amme ‘‘P ojec s o La ge Resea ch, De elopmen and
Inno a ions In as uc u es’’.
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