Sta$s$cal(learning(and(docking(recover((the(reac$on(coordinates(of(a(GPCR(

Evan(N.(Feinberg,(M

.M.(Sultan,(R.T(M

cGibbon,(M.P.(Harrigan,(C.X.(Hernandez,(W

.R.(Fletcher(and(V.S.(Pande(

Abstract(GPCRs(com

prise(oneIthird(of(targets(of(all(FDAIapproved(drugs.(Molecular(dynam

ics((MD)(sim

ula$ons(of(GPCRs(can(contain(over(60,000(atom

s,(coun$ng(for(over(180,000(degrees(of(freedom.(The(

technique(described(here(reduces(the(dimensionality(of(GPCR(M

D(sim

ula$ons(through(a(combina$on(of(unsupervised(and(supervised(

learning.(In(par$cular,($meIstructure(Independent(Com

ponent(Analysis((tICA)([2,3](and(m

olecular(docking(are(used(complem

entarily(to(determ

ine(reac$on(coordinates(relevant(to(agonist(binding(and(receptor(ac$va$on.(Step(1:(Featurize(and(tICA(

Step(2:(Cluster(tICA(Coordinates(Input:(Projec'on)of)trajectories)onto)tICA)coordinates(Learning(class:(Unsupervised)For(illustra$on(purposes,(w

e(project(our(K=1000(clusters(onto(the(a)priori(reac$on(coordinates(described(in([1](by(Dror,)et)al.(and(color(by(tIC(value(of(each(cluster.(

Step(3:(Dock(Agonists(to(Conformers(

Take(s(sample(conform

a$ons(from(each(of(the(K((clusters,(and(dock(

each(of(a)several(agonists(with(know

n(pharmacology([4](to(the(

binding(site(of(each(conformer.(In(this(case,(this(am

ounts(to(70,000(docking(calcula$ons.(

0.00

0.25

0.50

0.75

1.00

0.000.25

0.500.75

1.00False Positive Rate

True Positive Rate

classTPR

Aggregate Docking Score, Five Agonists, Two Inverse Agonists

Docking(score(is(a(reliable(metric(of(how

(ac$ve(–(as(defined(by([1](II(a(given(conform

a$on(is,(as(displayed(by(the(RO

C(curve(above.((AU

C$=$0.84$

Step(4:(Docking(Score(as(a(Response(Variable(to(Choose(tICA(Coordinates(

−9

−8

−7

−6

−5

−4

−3

−0.02 0.00 0.02 0.04

Log L

am

bda

Coefficients

25

25

23

21

13

61

12 34 5 6 78 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

Perform(logis$c(

regression(of(docking(score(on(tICA(coordinates(for(each(conform

a$on.(Use(the(LASSO

(to(choose(from

(the(candidate(tICA(coordinates(for(further(analysis.(

Step(5:(Analyze(Candidate(tICs(Feature(im

portance(in(each(tIC(is(displayed(by(color,(with(

blue(being(least(and(red(being(most(im

portant.(

tIC9:(Connector(Region(

TM7,(

Tyr326(

tIC7:(NPxxY(Interconnec$vity(

TM7,(

Ile325(

TM6,(

Phe282(

Blue:(Inac$ve(Green:(Ac$ve(Orange:(M

D(Conform

er((

Ile325(

Asn318(Ile121(Phe328(

tIC7%high%tIC9%high%

tIC7%and%tIC9%9%high%

Ile121(

References:(1. 

Dror,(Ron(O.,(et(al.("Ac$va$on(m

echanism(of(the(β2Iadrenergic(receptor."(Proceedings)of)the)

Na'onal)Academy)of)Sciences(108.46((2011):(18684I18689.(

2. Schw

antes,(Chris$an(R.,(and(Vijay(S.(Pande.("Improvem

ents(in(Markov(state(m

odel(construc$on(reveal(m

any(nonIna$ve(interac$ons(in(the(folding(of(NTL9."(Journal)of)chem

ical)theory)and)com

puta'on(9.4((2013):(2000I2009.(3. 

PérezIHernández,(Guillermo,(et(al.("Iden$fica$on(of(slow

(molecular(order(param

eters(for(Markov(

model(construc$on."(The)Journal)of)chem

ical)physics(139.1((2013):(015102.(4. 

De(Graaf,(Chris,(and(Didier(Rognan.("Selec$ve(structureIbased(virtual(screening(for(full(and(par$al(agonists(of(the(β2(adrenergic(receptor."(Journal)of)m

edicinal)chemistry(51.16((2008):(4978I4985.(

Inac$ve(crystal(structure(

Ac$ve(crystal(structure(

Ac$ve(crystal(structure(

Inac$ve(crystal(structure(

Ac$ve(crystal(structure(

tIC2(tIC7(

tIC9(

tIC2:(Helix(6(Mo$on(

TM6(

TM5(

TM1(

tIC3:(Binding(Pocket(Affinity(

Agonist(

Phe290(

●

●0.00

0.25

0.50

0.75

1.00

1.25

7.510.0

12.515.0

TM6−TM

3 Distance

RMSD of NPxxY to Active

types

●C

rystalM

D

sizes●●●●●

3.03.54.04.55.0

tIC.2

●●●●●●●●●

(−2.62,−1.87](−1.87,−1.32](−1.32,−0.841](−0.841,−0.513](−0.513,−0.225](−0.225,0.0482](0.0482,0.627](0.627,1.33](1.33,1.89]

●

●0.00

0.25

0.50

0.75

1.00

1.25

7.510.0

12.515.0

TM6−TM

3 Distance

RMSD of NPxxY to Active

types

●C

rystalM

D

sizes●●●●●

3.03.54.04.55.0

tIC.7

●●●●●●●●●

(−3.62,−2.15](−2.15,−1.25](−1.25,−0.652](−0.652,−0.139](−0.139,0.342](0.342,0.846](0.846,1.47](1.47,2.18](2.18,3.6]

Aggregate$docking$score$accurately$predicts$if$a$given$conform

er$is$“ac>ve.”$

Clusters$colored$by$tIC$2$Clusters$colored$by$tIC$7$

Input:(tICA)coordinates)and)docking)scores)per)cluster(Learning(class:(Supervised)

Input:(Trajectories)(Learning(class:(U

nsupervised(a. 

Features:(Distances(between(all(pairs(of(residues(w

ith(an(ini$al(heavyIatom

(distance(≤(10(A(!(3,365(features.((

b. Com

pute(first(25(tICA(coordinates.(c. 

Note:$This$m

ethod$never$“sees”$a%priori$data$on$the$B2AR,$e.g.$the$reac>on$coordinates$described$in$[1]$

tIC3(

True$Posi>ve$Rate$

False$Posi>ve$Rate$

Agonist(

Phe290(

Phe208(Trp286(

Ile121(

Asn318(

Ile325(