proteinsSTRUCTURE O FUNCTION O BIOINFORMATICS

The binding affinities of proteins interactingwith the PDZ domain of PICK1Ashini Bolia,1 Z. Nevin Gerek,1 Ozlem Keskin,2 Sefika Banu Ozkan,1* and Kumlesh K. Dev3*

1Department of Physics, Center for Biological Physics, Arizona State University Tempe, Arizona

2 Center for Computational Biology and Bioinformatics, Department of Chemical and Biological Engineering, Koc University,

Istanbul, Turkey

3Department of Physiology, Molecular Neuropharmacology, School of Medicine, Trinity College Institute of Neuroscience,

Trinity College Dublin, Ireland

INTRODUCTION

PDZ (PSD95/DlgA/Zo-1) domains are common to over 150 proteins

that are otherwise unrelated.1 These domains are �90 residues long and

consist of six b-strands (bA to bF) and two a-helices (aA and aB). Incanonical PDZ domains, the PDZ domain binds the C-terminus-located

PDZ motif of its interacting partner in an elongated groove between the

bB sheet and the aB helix of the PDZ domain that is termed the PDZ

binding groove.2 The amino acids in PDZ domains are numbered

according to their topographical location, for example, aB1 is the first

residue on the aB helix.3 The selectivity of a PDZ domain for its ligands

is dictated by the first residue of the a-helix B (aB1) of the PDZ

domain. The residue in the aB1 position of a Type I PDZ domain is

histidine, in Type II is generally hydrophobic and in Type III is usually

tyrosine.3 The amino acids of PDZ motifs (or ligands) are numbered in

relation to the residue at the extreme C-terminus, which is referred to as

position 0 (P0), with the subsequent residue from the C-terminus

occupying the -1 position (P-1). Type I PDZ domains bind PDZ motifs

consisting of X-Thr/Ser-X-hydrophobic residues, Type II recognize a

X-hydrophobic-X-hydrophobic residue motif, while Type III interact with

a X-Asp-Glu-X-hydrophobic residue motif; where X denotes any amino

acid.3 The relatively loose consensus of PDZ motifs allows PDZ domains

to bind to a range of ligands.

Protein interacting with C kinase (PICK1) is 416 residues in length con-

taining a single N-terminal PDZ domain that interacts with the PDZ motifs

of several proteins, which have roles in synaptic plasticity,4–12 neuronal cell

morphology,13 and mitochondrial-dependent apoptosis.14 The functions of

PICK1 are largely fulfilled by controlling the trafficking of its binding partners

and by facilitating their phosphorylation by recruiting protein kinase C-a(PKCa).15 The proteins interacting with PICK1 have roles in epilepsy,16,17

Abbreviations: PICK1, protein interacting with C kinase; PRS, perturbation response scanning; PDZ

(PSD95/DlgA/Zo-1)

Grant sponsors: Enterprise Ireland and Science Foundation Ireland, Ireland, National Science Foundation

through TeraGrid resources (NCSA)

*Correspondence to: Kumlesh K. Dev, Department of Physiology, Molecular Neuropharmacology, School of

Medicine, Trinity College Institute of Neuroscience, Trinity College Dublin, Ireland. E-mail: devk@tcd.ie and

Sefika Banu Ozkan, Department of Physics, Center for Biological Physics, Arizona State University Tempe,

Arizona. E-mail: banu.ozkan@asu.edu.

Received 22 May 2011; Revised 30 December 2011; Accepted 3 January 2012

Published online 17 January 2012 in Wiley Online Library (wileyonlinelibrary.com).

DOI: 10.1002/prot.24034

ABSTRACT

Protein interacting with C kinase (PICK1)

is well conserved throughout evolution

and plays a critical role in synaptic plastic-

ity by regulating the trafficking and post-

translational modification of its interact-

ing proteins. PICK1 contains a single

PSD95/DlgA/Zo-1 (PDZ) protein–protein

interaction domain, which is promiscuous

and shown to interact with over 60 pro-

teins, most of which play roles in neuronal

function. Several reports have suggested

the role of PICK1 in disorders such as epi-

lepsy, pain, brain trauma and stroke, drug

abuse and dependence, schizophrenia and

psychosis. Importantly, lead compounds

that block PICK1 interactions are also

now becoming available. Here, a new

modeling approach was developed to

investigate binding affinities of PDZ inter-

actions. Using these methods, the binding

affinities of all major PICK1 interacting

proteins are reported and the effects of

PICK1 mutations on these interactions are

described. These modeling methods have

important implications in defining the

binding properties of proteins interacting

with PICK1 as well as the general struc-

tural requirements of PDZ interactions.

The study also provides modeling methods

to support in the drug design of ligands

for PDZ domains, which may further aid

in development of the family of PDZ

domains as a drug target.

Proteins 2012; 80:1393–1408.VVC 2012 Wiley Periodicals, Inc.

Key words: PICK1; molecular docking;

modeling; binding affinity.

VVC 2012 WILEY PERIODICALS, INC. PROTEINS 1393

pain,18,19 and also in brain trauma, stroke, excitotoxicity,

and cell death.20–22 Recent evidence from genetic associa-

tion studies and characterization of PICK1 knockout ani-

mals suggests a role for PICK1 in drug abuse and depend-

ence, schizophrenia, and psychosis.23–31 PICK1 has also

been reported to be expressed in insulin-producing pan-

creatic beta-cells where it may play a role in the neuroen-

docrine system.32

The PICK1 residues that interact with P-2 and P0 resi-

dues of PDZ motifs have been best studied. Interestingly,

for the ‘‘P-2 binding pocket,’’ the Lys83 residue (usually a

histidine in other PDZ domains) in PICK1 creates a

hydrogen bond with P-2 residues to preserve class I motif

binding and a hydrophobic interaction to satisfy Class II

motif binding.33–35 Additional residues involved in

interaction with P-2 residues of PDZ motifs include

Thr82, Val84, and Ala87 in the aB helix of PICK1.33,34

There are also a number of residues in PICK1 that create

a ‘‘P0 binding pocket’’ where P0 residues of PDZ motifs

interact. Firstly, the PDZ domain of PICK1 contains an

8–9 amino acid carboxylate (��COO��) binding domain

(CBD) containing the residues Lys27 and Asp28 (‘‘KD

motif ’’) commonly found in Type II PDZ domain inter-

acting with P0 residues.4,15 Second, the Ile37 residue in

the bB strand of PICK1 plays a critical role in interaction

with P-2 and P0 residues. Third, the carboxylate binding

motif comprises GLGF motif, which in PICK1 are resi-

dues Leu32-Ile33-Gly34-Ile35 (LIGI motif) in the bB sheet

play a role in PDZ motif interaction. Specifically, the res-

idues Ile33 and Ile35 of the LIGI motif and the Val86,

Ala87, Ile90 residues of the aB helix also interact with P0

residues of PDZ motifs.34 Residues at P0 and P-2 of

PDZ motifs also seem to influence each other, for exam-

ple a small residue at P0 (valine) would allow Ile37 in

PICK1 to adopt multiple conformations allowing versatil-

ity of the P-2 binding pocket.33

There are over 150 proteins that contain PDZ domains,

making this a large family of potential drug targets. How-

ever, to date, PDZ interactions represent a drug target that

still remains largely untapped.36 Recently, drugability of

PICK1 has been suggested by discovery of a lead com-

pound, FSC231, which inhibits PICK1 interactions.37,38

Recent studies have also helped characterize the structure-

activity relationship of the various domains found in

PICK1 giving insight into the binding properties of its

PDZ domain. Here, a new modeling approach was devel-

oped in order to further characterize the binding affinities

of all the major PICK1 interacting proteins. In this study,

the binding affinities of all PDZ motifs known to interact

with PICK1 are reported. These results further elucidate

the determinants of PICK1 binding and may aid in the de-

velopment of therapeutic agents aimed at regulating spe-

cific PICK1 interactions. The study also provides model-

ing methods to support in the drug design of ligands for

PDZ domains, which may further aid in development of

the family of PDZ domains as a drug target.

MATERIALS AND METHODS

Crystal structures applied

All PICK1 PDZ domain structures interacting with

peptides listed in Table I were analyzed in this study. The

atomic coordinates for the proteins used in this study

were obtained from RCSB Protein Data Bank

(www.rcsb.org), with PDB Ids 2GZV, 2PKU, and 1GZV.

The crystal structure of PICK1 protein bound with the

PKCa peptide (PDB code: 2GZV) was retrieved from the

PDB (http://www.rcsb.org/pdb).39 This structure was

used as a template and the following procedure was car-

ried out in our docking calculations: first, the original

PKCa peptide was redocked into a known PDZ domain

of the bound conformation (self-docking); then, the

computational point mutations were introduced into the

X-ray structure of the PKCa peptide via Swiss PDB

Viewer40 to obtain other peptides listed in Table I.

Generating multiple receptor conformations

An ensemble of the wild type (WT) and mutant ver-

sions (K27A, K27E, K83H, and K83V) of PICK1 were gen-

erated through Perturbation Response Scanning (PRS).

PRS couples elastic network models with linear response

theory.41 This PRS computes the fluctuation profile of a

protein upon an external force or a perturbation on a

single residue when a ligand approaches a receptor and

starts exerting external forces on the receptor structure

inducing conformational changes. To mimic this process,

a random force was applied in every direction to the a-carbon atom of each residue in the protein, the residue

displacement responses were recorded as a linear function

and were used to generate low-resolution receptor con-

formations (based on Ca atom). These responses upon

perturbation of each single residue capture the shift in

conformational change between the unbound and bound

conformations as previously shown.41 Therefore, the

multiple receptor conformation (MRC) ensemble gener-

ated from the perturbations include the conformations

that are sampled through the binding process. The

deformed/perturbed structures of the PICK1 crystal

structure were then clustered to discard the similar con-

formations generated from the perturbations on different

residues. After clustering, all atom minimization of the

clustered structures were performed to account for the

fluctuation in the side chains upon perturbation on the

backbone of the protein and to relieve strain in the sys-

tem. Thus, each perturbed structure was subjected to an

energy minimization of 50 steepest descent iterations fol-

lowed by 1000 conjugate gradient iterations using

AMBER force field along with a Generalized Born solva-

tion model.42 Finally, an ensemble docking for all these

different peptides using all the structures were generated

by PRS for the PICK1. The outline of this method is

depicted in Figure 1. For the mutant proteins, an initial

A. Bolia et al.

1394 PROTEINS

TableI

PICK1BindingAffinities

Interactor

Binding

site

PDZmotif

type

E bind

(kca

l/mol)

Family

mem

ber

Func

tion

Reference

Wild

type

PICK

1K27E

PICK

1K27A

PICK

1K83H

PICK

1K83V

PICK

1

Ephrin

B1

EphRe

ceptor

ligand

Torres

etal.,1998

YYKV

II2338.85

2320.45

2304.43

2301.76

2301.91

Parkin

E3ub

iquitin

ligase

Joch

etal.,2007

WFD

VII

2297.37

2285.42

2271.51

2272.62

2288.87

DAT

Dop

aminetran

sporter

Torres

etal.,2001

WLKV

II2291.93

2274.00

2301.13

2297.99

2292.42

JAM2A,B

,CJu

nctionad

hesion

molec

ule

Reym

ondet

al.,2007

SFVL

II2289.97

2278.34

2282.91

2283.15

2269.94

Nec

tin1a

,2a,2

d,3a

,3b,4

Cell-ad

hesion

molec

ules

Reym

ondet

al.,2007

EWYV

II2289.93

2270.72

2278.20

2285.37

2280.40

Syntenin

PDZprotein

Torres

etal.,1998

IPEV

II2280.73

2269.23

2269.87

2264.24

2265.12

NET

Norad

renalinetran

sporter

Torres

etal.,2001

WLA

III

2278.25

2267.13

2276.93

2285.33

2273.70

PrRP

Prolactin

releasingpe

ptiderece

ptor

Linet

al.,2001

SVVI

II2270.14

2234.65

2242.45

2255.71

2249.70

Anion

exch

ange

r21,22

Chlorid

e/bica

rbonateexch

ange

rsCo

wan

etal.,2000

AMPV

II2265.25

2259.05

2249.38

2246.14

2248.84

ARF1,ARF3

ADP2

ribosylationfactors

Takeya

etal.,2000

RNQK

X2262.11

2257.45

2230.85

2249.81

2230.45

ErbB

2/HER

2Ep

idermal

grow

thfactor

rece

ptor

Julin-Bastard

etal.,2001

DVP

VII

2260.81

2246.30

2260.07

2252.42

2256.86

Neu

roligin

1,2

Synaptic

cell-adhe

sion

molec

ules

Meyer

etal.,2004

TTRV

I2247.76

2263.31

2245.44

2252.19

2248.31

EphB

2,Ep

hA7

Ephrin

Rece

ptor

tyrosine

kinase

Torres

etal.,1998

SVEV

II2246.30

2214.34

2225.32

2227.38

2228.25

GLT1b

Glutamatetran

sporter

Bassanet

al.,2008

ETCI

I2246.09

2270.72

2258.22

2254.00

2258.20

MuS

KMusclespec

ifictyrosine

kina

seXiaet

al.,1999;Torreset

al.,1998

TVSV

II2242.86

2201.42

2227.86

2205.02

2215.32

Serin

erace

mace

Synthe

sisof

L-serin

eFujiiet

al.,2006

SVSV

II2242.25

2208.70

2217.03

2229.20

2229.92

Kalirin-7

Gua

nine

-nucleotide-exch

ange

Factor

Penzes

etal.,2001

STYV

I2240.14

2258.17

2242.46

2240.24

2256.37

GluR2,3

,4AMPA

rece

ptors

Dev

etal.,1999;X

iaet

al.,1999

SVKI

II2236.79

2213.58

2233.20

2222.56

2236.57

Aqu

aporin-1,2,9

Mem

branewater

chan

nels

Cowan

etal.,2000

SVIM

II2236.60

2205.88

2241.84

2230.91

2221.42

mGluR3,4

a,7,8

Metab

otropicglutam

aterece

ptors

Bou

dinet

al.,2000;D

evet

al.,2000;

ElFarOet

al.,2000;H

irbec

etal.,2002

NLVI

II2234.51

2230.76

2226.53

2222.23

2219.57

SERT

Serotonintran

sporter

Torres

etal.,2001

LNAV

II2233.98

2205.26

2232.99

2229.09

2211.48

ASIC1a,

2a,b

/BNaC

1Acid-sensingionch

anne

lHruska-Hag

eman

etal.,2002;

Dug

ganet

al.,2002

EIAC

II2221.09

2205.11

2219.56

2224.44

2217.72

CAR

CoxsackieB&

adenoviru

srece

ptor

Exco

ffonet

al.,2004

GSIV

I2217.38

2221.33

2220.35

2227.85

2228.99

PKCa

ProteinKina

seCa

Stau

ding

eret

al.,1995;1997

QSA

VI

2217.18

2232.90

2223.56

2220.36

2225.46

Calcineurin

Proteinph

osphatase(2B)

Iidaet

al.,2008

VDV

II2215.91

2202.02

2206.73

2196.11

2203.61

GluR5(2b,c),R

6Ka

inaterece

ptors(KAR)

Hirb

ecet

al.,2003

ETVA

I2215.78

2220.31

2201.31

2206.15

2207.75

LRP1B

LDLrece

ptor-related

protein

Shiro

shimaet

al.,2009

ETVA

I2215.78

2220.31

2201.31

2206.15

2207.75

UNC5H1

Netrin

rece

ptor

Williamset

al.,2003

EAEC

X2211.88

2200.28

2202.17

2196.33

2195.11

a7nA

ChRs

Nicotinic

Ace

tylcho

linerece

ptors

Bae

ret

al.,2007

c-term

atypical

TIS21

TPAIndu

cibleSe

quence

Linet

al.,2001

Internal

Arp2/3

Actin-Related

Proteins

Rocc

aet

al.,2008

c-term

acidic

NCS

-1NeuronalC

a21

sensor

(NCS

)Jo

etal.,2008

BAR

ICA69

IsletCe

llAutoa

ntigen

(diabetes)

Caoet

al.,2007

BAR

GRIP

PDZprotein

Luet

al.,2005

BAR

F-ac

tinMicrofilam

entG-actin

polymers

Rocc

aet

al.,2008

BAR

a-SNAP,b-SNAP

SolubleNSF

attachmen

tproteins

Han

leyet

al.,2002

BAR

CK2a

Catalytic

subu

nitproteinkina

se2

Xiao

etal.,2009

n.d.

GPO

CGolgi-assoc

iatedprotein

Xiao

etal.,2009

n.d.

KRIP6

KARinteractingproteinGluR6

Laezza

etal.,2008

n.d.

BindingaffinitiesforwildtypeandmutatedversionsofPICK1areshown.Higher

negativevalues

indicateahigher

bindingaffinityprediction.PDZinteractionsareindicated

withPDZbindingmotifresidues

andtypeofbinding

motifas

IandII.Alternatively,Xforatypical;BAR,BARdomaininteraction;n.d.notdetermined

areindicated

asinteractionsiteswithPICK1.

Modeling PICK1 Binding Affinities

PROTEINS 1395

receptor structure was generated by performing a point

mutation by Swiss PDB Viewer and following an all atom

minimization through AMBER package. Next, PRS was

applied to these initial structures in order to generate new

receptor ensemble for each mutant. Overall backbone

root-mean-square deviations (RMSDs) of the mutants

were observed, to some extent, different than WT PICK1.

Moreover, some side chain rotamers were significantly

changed in the mutant structures due to perturbation on

the backbone upon mutation through the PRS analysis,

which leads to changes in binding affinities.

ROSETTALIGAND docking analysis

All docking analysis for each of the structure in the en-

semble in this study was performed using ROSETTALI-

GAND43,44 protocol in the ROSETTA package. The

ligand flexibility was established by changing torsional

angles, and the backbone of the ligand and the whole

protein were held fixed throughout the docking simula-

tion. ROSETTALIGAND is a method specifically devel-

oped for docking ligands into protein binding sites. The

method uses a Monte Carlo minimization protocol to

optimize the rigid body position and orientation of the

ligand and the protein side-chain conformations. The

energy function includes van der Waals interactions, an

implicit solvation model, an electrostatics model, explicit

orientation hydrogen bonding potential, and empirically

derived torsional potentials. The ROSETTALIGAND pro-

tocol applied was substantially the same as described pre-

viously.44 The coordinates of the peptides were taken

from the crystallographic complexes and were treated as

a single residue. The peptide flexibility was introduced by

changing torsional angles and the backbone was held

fixed throughout the docking simulation. In this study,

the ligand position and orientation were perturbed ran-

domly with the translation of mean 0.1 A and rotations

of mean 38, respectively. Computation of 10,000 trajecto-

ries was performed to generate a comprehensive ensem-

ble of conformations of the receptor-ligand complex for

each peptide. The formation of a distinct binding funnel

in binding energy/RMSD plots was taken as an indication

of successful docking and the final docked conformations

were selected based on the lowest free energy pose in the

protein-binding site.

DrugScore binding affinity

After selecting the pose corresponding to the lowest

free energy of binding at the end of Rosetta docking, the

binding energy score of this complex using DrugScore45

was reassessed. Thus, the receptor domain of the docked

pose in PDB format and the ligand coordinates in mol2

format were submitted into DrugScore online (http://

pc1664.pharmazie.uni-marburg.de/drugscore/). Drug-

Score is a knowledge-based scoring function for protein–

ligand interactions that employs statistically derived pair

potentials using the distance-dependent occurrence fre-

quencies by which a particular ligand atom type is found

in contact with a protein atom type. Higher negative val-

ues indicate a higher binding affinity prediction. With

DrugScore, the binding selectivity preferences for various

peptides of PICK1 PDZ domain proteins were found to

be more significant than others.

Figure 1Flow chart of flexible docking method. In our flexible docking approach,

we generated an ensemble of receptor conformations through several

steps: (i) sequentially exerting random external force on each single-

residue, (ii) calculating the response fluctuation vector using Perturbation

Response Scanning (PRS) method, (iii) constructing the low resolution

deformed structures (i.e., backbone) using the response vectors after each

single residue perturbation, (iv) clustering the perturbed conformations

using k-clustering method, and (v) all-atom minimization of each

clustered conformation. Once the multiple receptor conformations

(MRCs) ensemble was completed, we performed a docking simulation

using the ROSETTALIGAND option in the Rosetta package for each

minimized structure in the ensemble. [Color figure can be viewed in the

online issue, which is available at wileyonlinelibrary.com.]

A. Bolia et al.

1396 PROTEINS

Figure 2Sequence analysis of PICK1. A: Alignment of PICK1 PDZ domain. Alignments were performed using Vector NTI software and the following

accession numbers were used: Human PICK1 Q9NRD5; Human PSD-95 P78352; NHERF O14745; Disks large homolog 1 Human Q1295; CSKP

O14936; Syntenin1 O00560; NOS1 P29475. Bovine PICK1 Q2T9M1; Macaque PICK1 Q4R7Q5; Murine PICK1 Q62083; Orangutan PICK1

Q5REH1; Rat PICK1 Q9EP80, Drosophila NP609582, C. Elegans NP502796. The residues important for interaction with PDZ motifs are

highlighted in bold. The residues underlined appear to be well conserved in PDZ domains Types I, II, and III. B: Domain structure of PICK1. ThePDZ and BAR domains, an N-terminal 18 residue acidic region, a 40 residue a-helical linker region, and the C-terminal acidic region are shown.

The residues in PICK1 that create the ‘‘P0 binding pocket’’ include (i) the carboxylate binding domain (CBD) residues Lys27 and Asp28 (‘‘KD

motif ’’), (ii) the Ile37 residue, and (iii) the GLGF-like motif (LIGI) residues Lue32, Ile33, Gly34, Ile35. The residues in PICK1 that create the ‘‘P-2

binding pocket’’ include (i) the Lys83 at position aB1 and (ii) the residues Thr82, Val84, and Ala87. Three lysine residues in the PICK1 BAR domain

are needed for PICK1 clustering are shown. Positively charged residues Arg76, Lys79, Lys81 (‘‘RKK motif ’’) and hydrophobic residues Cys44-Pro45-

Cys46 (CPC motif) essential for membrane, lipid, and zinc binding52,53 are also shown.

PROTEINS 1397

Modeling PICK1 Binding Affinities

Computational hotspots

The computational hotspots of the PICK1 complexes

were found using the HotPoint Server (http://

prism.ccbb.ku.edu.tr/hotpoint). This model uses an intui-

tive efficient method to determine computational hot

spots based on conservation, solvent accessibility and sta-

tistical pairwise residue potentials (PP) of the interface

residues as we have recently described.46,47 The PICK1-

peptide complexes in Figure 3 are modeled using PRISM

as described previously48,49 and Chimera.50

RESULTS

Categorization of PICK1 residues intobinding pockets

To categorize PICK1 residues into binding pockets for

P-4 to P0 residues of PDZ motifs, sequence alignments of

several PDZ domains were performed (Fig. 2) and all

PDZ motifs that interact with PICK1 were analyzed (Table

I). For the ‘‘P-2 binding pocket,’’ alignment data showed

Lys83 is uniquely found in PICK1 compared to histidine

and is well-conserved suggesting the importance of

PICK1’s diverse binding ability throughout evolution (Fig.

2). For the other three residues suggested to play a role in

P-2 residue binding, namely Thr82, Val84, and Ala87 (Ref.33), the alignment data showed that Thr82 (or Ser) is well

conserved in Types I, II, and III PDZ domains, Val84 dis-

played little homology, and the small sized Ala87 (normally

Val in Type I PDZ domains) occurred in PICK1 that may

allow additional ‘‘space’’ to accept a diverse set of P-2 resi-

dues (Fig. 2). For the ‘‘P0 binding pocket,’’ alignment data

showed the residues Lys27 and Asp28 (‘‘KD motif ’’) were

commonly found in Type II PDZ domains, that the Ile37

is reasonably well conserved between PDZ domains and

that PICK1 contains and GLGF-like motif (LIGI) (Fig. 1).

In support of a preference for valine at P0,33–35 this resi-

due was found in a majority of PICK1 interacting Type II

PDZ motifs (Table I). Taken together, we classify (i) the

‘‘P-2 binding pocket’’ as containing Lys83 together with

Thr82, Val84, and Ala87; and (ii) ‘‘P0 binding pocket’’ as

comprising the ‘‘KD motif ’’ (Lys27 and Asp28), the Ile37

residue, and the LIGI motif (Ile33, Ile35). Notably, neither

the ‘‘RKK motif ’’ (Arg76, Lys79, Lys81) nor the CPC motif

(Cys44-Pro45-Cys46), which are both important for mem-

brane binding51–53 were found to be conserved in PDZ

domains, indicating a unique role for PICK1’s PDZ do-

main in membrane association (Fig. 2).

Modeling of PICK1 interaction residues intohotspots

Next, four of the best-studied PDZ motifs of PICK1

namely, GluR2 (-SVKI), PKCa (-QSAV), DAT (-WLKV),

and Ephrin B1 (-YYKV) were modeled. The data showed

PICK1 residues Lys83 and Ala87 (P-2 binding pocket) and

the ‘‘KD motif ’’ (Lys27, Asp28) and the Ile37 residue (P0

binding pocket) interacted with the GluR2 PDZ motif (-

SVKI). In addition, the PICK1 residues Gly39 and Val84

made contact with Ephrin B1 (-YYKV) and PKCa (-

QSAV) peptides. For the PKCa peptide (-QSAC), the

Lys83 of PICK1 also made contact with P-3 (Gln) (Fig. 3).

To investigate the binding hot spots54 of the four PICK1

complexes, alanine mutations in PICK1 were analyzed and

the change in binding affinity with the HotPoint Server47

was examined. In the GluR2 peptide (-SVKI) the residues

P-2 (Val) and P0 (Ile) made the major binding contribu-

tions, as did the residues P-2 (Ser) and P0 (Val) for the

PKCa peptide (-QSAV). In the PICK1-GluR2 complex

(PDB ID: 2pku) the residues Lys83 and Ala87 of the P-2

binding pocket and the KD motif (Lys27), the Ile37 residue,

the LIGI motif (Ile33, Ile35), as well as the Ile90 residue of

the P0 binding pocket were all found to represent hot

spots. The Ala87 residue of PICK1 made hydrophobic

interactions with P-2 (Val) of GluR2 (-SVKI) making a

‘‘P-2 binding pocket’’ as expected. In the PICK1-PKCacomplex (modeled structure), the residue Lys83 of the P-2

binding pocket, and the Ile37 residue, the LIGI motif

(Leu32, Ile33, Ile35), as well as Ile90 of the P0 binding

pocket represented hot spots. Interestingly, the side chain

atoms of Ser36 of PICK1 also made strong H-bonds with

the backbone atoms of P-2 (Ser) of PKCa (-QSAV) and

were involved in creating a ‘‘P-2 binding pocket’’ (Fig. 3).

These results are in agreement with previous studies.33–35

Binding affinities of PICK1 interactingproteins

To date almost 60 proteins have been shown to inter-

act with PICK1, which were sorted according to their

PDZ binding affinities (Table I). Using a flexible docking

approach (see Fig. 1 and methods for details), the bind-

ing affinities of all the PDZ motifs known to interact

with PICK1 were computed (Fig. 4). This data displays

the poses of PICK1 complexes corresponding to the low-

est binding energies and their rescored binding affinity

values. Importantly, the predicted binding energy scores

agree qualitatively with experiments where data is avail-

able. For example, the dopamine transporter (DAT) pep-

tide (-WLKV) binds to the PDZ domain of PICK1 with

higher affinity than the PKCa peptide (-QSAV).34 In

agreement the predicted binding affinity of DAT

(2291.93 kcal/mol) was found to be higher than PKCa(2217.18 kcal/mol). Furthermore, experimental analysis

indicates that the affinity of GLT1b (-ETCI) for PICK1 is

significantly better than that of PKCa55 and slightly bet-

ter than that of GluR2,55 on the other hand it is slightly

lower than that of the DAT.34 Likewise, our predicted af-

finity value of GLT1b (2246.09 kcal/mol) is in between

the affinities of DAT (2291.93 kcal/mol) and GluR2

(2236.79 kcal/mol) peptides. On the other hand, binding

energy scores obtained by rigid docking did not correlate

A. Bolia et al.

1398 PROTEINS

Figure 3Specific interaction sites in PICK1’s PDZ domain. A: Structures of PICK1 PDZ domain with different peptides. The PDZ motif is in blue and the

PICK1 residues making contact with the peptides are in red. Upper left panel shows the crystal structure of PICK1 with the C-terminal tails of

GluR2 (SVKI) (PDB ID: 2pku). Upper right panel is the complex structure with the ‘‘YYKV’’ PDZ motif peptide. This structure is a model

obtained by using the PICK1 PDZ domain and YYKI PDZ motif peptide complex (PDB ID: 2gzv) as the template. Lower left panel shows the

structure of PICK1 PDZ domain with the C-terminal tail of PKCa (QSAV). This is a model structure obtained by superimposing the QSAV peptide

with YYKI peptide in 2gzv and energy minimizing. K83 makes contact with P-3(Q). Lower right panel, the PICK1 and DAT (WLKV) complex is

also modeled by taking the 2gzv (YYKI) peptide as the template and minimizing. The GluR2 structure was gained from PDB, while the other three

are models were obtained in this present study. B: General interaction sites PICK1’s PDZ domain. The cartoon shows residues identified as

important in binding PDZ motifs. In general the ‘‘P-2 binding pocket’’ is composed of Lys83 together Thr82, Val84, and Ala87. The ‘‘P0 bindingpocket’’ is composed of the ‘‘KD motif ’’ (Lys27 and Asp28), the Ile37 residue, and the GLGF-like motif (LIGI) (Ile33, Ile35). [Color figure can be

viewed in the online issue, which is available at wileyonlinelibrary.com.]

Modeling PICK1 Binding Affinities

PROTEINS 1399

with the order of experimental binding affinities. Specifi-

cally, the rigid docking predicted that GluR2 and DAT

peptides have the same affinity and higher than PKCa.

The K27 and K83 binding hotspots in PICK1

Previous studies show that mutation of K27A (in the

‘‘KD motif ’’) in PICK1 prevents binding to both Class I

Figure 4Binding affinities of PDZ motifs interacting with PICK1. The sorted binding affinity values of C-terminal peptides interacting with the PDZ

domain of PICK1 and the ribbon diagrams of the complex structure with the best score. Higher negative values indicate a higher binding affinity

prediction. The key residues near the binding pocket making side interactions with the peptide are also shown.

A. Bolia et al.

1400 PROTEINS

(PKCa, -QSAV) and Class II (GluR2, -SVKI)

motifs.4,11,15 In contrast, a K27E mutation in PICK1

reduces binding to P0 residues and disrupts interactions

with Class II motifs (GluR2, -SVKI) while maintaining

binding with Class I motifs (PKCa, -QSAV).33,36 This

may explain why the ‘‘KD motif ’’ is conserved in Type II

PDZ domains but not found in Type I PDZ domains

(Fig. 2). Importantly, the replacement of Lys83 with His83

(K83H) in PICK1 enhances the affinity for Class I motifs

(PKCa, -QSAV), and decreases the affinity for Class II

motifs (DAT, -WLKV).34 In contrast, mutation of Lys83

to Val83 (K83V) in PICK1 enhances affinities for both

class I (PKCa, -QSAV) and Class II (DAT, -WLKV) PDZ

motifs.33,34,52 On the basis of these experimental stud-

ies, four mutations K27E, K27A, K83H, and K83V of

PICK1 were investigated (Table I). In agreement with ex-

perimental observations36 the binding energy score of

GluR2 (-SVKI) was found to significantly decrease,

whereas that of PKCa (-QSAV) was enhanced with K27E

PICK1 (Table I). Moreover, the data showed a disruption

in binding affinity of mGluR7 (-NLVI) upon K27E muta-

tion as observed experimentally.4 In most cases, the K27E

mutation and the K27A mutation increased the binding

affinity for Class I peptides whereas it decreased those

for Class II peptides (Table I). However, there were out-

liers; for example, binding energy of GluR5-2b (Class I

peptide) decreased, while binding of DAT and NET

(Class II peptides) increased upon K27A mutation. Bind-

ing energy of PKCa (-QSAV), but not DAT (-WLKV)

was also higher with K83H PICK1, as previously

reported.34 In general, mutations at position Lys83 (K83V

and K83H) did not perturb the Class I type interactions,

and in some cases caused an increase in binding energies,

while they disturbed the interactions with Class II pep-

tides (Table I). As in the case of K27A, the DAT, and

NET (Class II) peptides were not affected from K83V and

K83H mutations. All mutations also disturbed the bind-

ing of PICK1 to atypical peptides. In order to compare

the accuracy of flexible docking in predicting binding

affinities, the binding scores without incorporating the

flexibility of PICK1 was also computed. Specifically, resi-

dues were mutated in the wild type crystal structure for

cases where experimental data is available and an energy

minimization was performed to obtain a reasonable mu-

tant structure. These energy minimized mutant structures

were used for ROSETTA docking. The flexible docking

results with PRS showed better agreement with the ex-

perimental affinity data15,34,36 compared with the rigid

docking results (Table II). For PKCa, the flexible docking

method captured the increase in binding affinity upon

K83V and K83H mutants that was comparable with the

experimental affinity results,4,15,34,36 yet rigid docking

failed to predict these. Moreover, rigid docking was not

able to predict the change in binding affinity of DAT in

K83H or K83V mutation. Overall, these results suggest

that the conformational dynamics of PICK1 is critical in

binding affinities, and besides affecting critical interac-

tions, mutations lead to change in conformational dy-

namics, which affects binding affinities.61,62,63

GluR2 interaction with wild typeand mutant PICK1

In GluR2, the carboxyl group of P0 Ile (-ESVKI) forms

a hydrogen bond in the LIGI motif of PICK1, whereas

the side chain of P-1 Lys (-ESVKI) makes minimal con-

tact with PICK1.52 In addition, the P-2 Val (-ESVKI)

interacts with the side chain of Lys83 and the methyl

group of Ala87 of PICK1. The negatively charged P-3

phosphorylated Ser (-ESVKI) also interacts with the posi-

tively charged Lys83 in PICK1. Finally, a stabilizing

hydrogen bond and side chain interactions are also found

between the amino group of Lys83 on PICK1 and the car-

bonyl of P-4 Glu (-ESVKI).52 In agreement with these

observations, the docking method used in this study

indicated that P-3 Ser (-SVKI) interacts with the Lys83 at

the aB1 position. Furthermore, it was observed that P-2

Val (-SVKI) forms a hydrophobic association with the

aliphatic side chain of Lys83 of PICK1 and also interacts

with the Ile37 of PICK1. This strong hydrophobic interac-

tion appeared to increase the binding affinity (-236.79

kcal/mol) of GluR2 for WT PICK1 as proposed earlier.34

Table IIComparison of Rigid and Flexible Docking with PRS

Peptide Mutation

Rigid docking Flexible docking with PRS

Experimental affinityDDE# Agreement with experiments DDE# Agreement with experiments

GluR2 (-SVKI) K27E 230 Yes 223.20 Yes DecreasePKCa (-QSAV) K27A 0 Yes 6.38 Yes Still interacts

K27E 1 Yes 15.72 Yes Still interactsK83H 25 No 3.18 Yes IncreaseK83V 26 No 8.28 Yes Increase

DAT (-WLKV) K83H 0 No 6.06 No DecreaseK83V 23 No 0.47 Yes Increase

Flexible docking results show better agreement with the experimental affinity data as compared to the rigid docking results. #DDE 5 DEWT 2 DEMut.

Modeling PICK1 Binding Affinities

PROTEINS 1401

The K27E PICK1 interaction with GluR2 showed destabi-

lization between P-3 Ser and Ile37 and a loss of interac-

tion between P-2 Val and Lys83, which could have

accounted for the decreased binding affinity (2213.58

kcal/mol). The K27A PICK1 also showed a similar loss of

interaction between P-2 Val and Lys83 and decreased

binding (2233.20 kcal/mol). The data showed that the

interaction between P-3 Ser and Lys83 were lost in K83H

PICK1. Furthermore, the phosphorylated P-3 Ser did not

interact with the His83 in PICK1, which explains the

decrease in binding affinity of K83H PICK1 (2222.56

kcal/mol). For K83V PICK1, the interactions between P-3

Ser and Val83, and P-2 Val and Ala87, which were not

observed between WT PICK1 and GluR2, might have

compensated for the loss of interaction between P-3 Ser

and Lys83 and explain the similar binding affinities of

K83V PICK1 (2236.57 kcal/mol) and WT PICK1.

PKCa interaction with wild type andmutant PICK1

Further analysis of the PKCa-PICK1 interactionshowed that P-2 Ser and P0 Val of the PKCa motif(-QSAV) interacts with Ala87 (P-2 binding pocket) ofPICK1. It was also observed that P-2 Ser interacts withside chains of Ile37 (P-2 binding pocket) and P0 Valinteracts with Ile33, Ile35, and Ile90 (P0 binding pocket)of PICK1, in agreement with previous observations33,34

Figure 5Ribbon diagrams of PKCa and DAT with PICK1. Interactions shown between PKCa (-QSAV) for (A) WT-PICK1 and (B) mutant K83H PICK. Also

shown are interactions between DAT (-WLKV) for (C) WT-PICK1 and (D) mutant K83V-PICK1. PKCa and DAT peptides are show with side-chains. The critical residues interacting with PKCa and DAT peptide are shown and labelled. The mutated residues Lys83, Val83, and His83 are

shown in dark grey. The residues whose interactions with PKCa are lost after K83H mutation are Ser36 and Ile35. [Color figure can be viewed in

the online issue, which is available at wileyonlinelibrary.com.]

A. Bolia et al.

1402 PROTEINS

[Fig. 5(A)]. Analysis on the lowest energy docked pose ofK27E PICK1 with PKCa showed an interaction betweenP-2 Ser and Lys83 in PICK1. As proposed earlier, thisinteraction increases the binding affinity of PKCa forK27E PICK1 (2232.90 kcal/mol) as compared with WT

PICK1 (2217.18 kcal/mol). The data showed that the

interactions of P0, P-1, P-2, and P-3 residues of PKCa(-QSAV) were more stable with K27A PICK1 (2223.56

kcal/mol). Interestingly, the P-2 Ser interacts with His83

in the PDZ domain of K83H PICK1 [Fig. 5(B)], which is

not observed at the P-2 residue in Class II peptides. As

proposed in previous studies,33–36 the K83H mutation

allowed a hydrogen bond between P-2 Ser in PKCa(-QSAV) and His83 in PICK1, which might have compen-

sated for the loss of interaction between P-2 Ser and

Lys83 in PICK1. Thus, the interaction between P-2 Ser

and His83 might account for the slight increase in the

binding affinity of PKCa for K83H PICK1 (2220.36 kcal/

mol). Likewise, interaction between P-2 Ser and Val83

might have compensated for the loss of interaction

between P-2 Ser and Lys83 in K83V PICK1. This might

explain the increase in binding affinity for K83V PICK1

(-225.46 kcal/mol).

DAT interaction with wild type andmutant PICK1

The data generated also showed that P-2 Leu in DAT

(-WLKV) interacts with Val84 and Ala87 (P-2 binding

pocket) in PICK1 and also with Ile37 of the bB strand of

PICK1 [Fig. 5(C)]. These interactions might contribute

towards the high binding affinity of DAT for PICK1

(2291.93 kcal/mol). Further analysis indicated that a

hydrophobic interaction between aliphatic side chain of

P-1 Lys in DAT (-WLKV) and Phe53 of PICK1 is stabi-

lized significantly in K27A PICK1. In contrast, the inter-

action between P-2 Leu of DAT (-WLKV) and Ser36 of

PICK1 is destabilized in K27A PICK1, whereas it is stabi-

lized in K27E PICK1. Therefore, we propose that the P-2

Leu and Ser36 might form an unfavourable interaction

and the destabilization of this interaction along with the

stabilization of a hydrophobic interaction of the aliphatic

side chain of P-1 Lys of DAT and Phe53 in K27A PICK1

could increase the binding affinity (2301.13 kcal/mol)

compared to K27E PICK1 (2274.00 kcal/mol). Analysis

also showed that interactions between P-2 Leu of DAT (-

WLKV) with His83, Val84, and Ile37 in PICK1 became

more stabilized in K83H PICK1. Thus, these strong inter-

actions between P-2 Leu of DAT (-WLKV) and the cru-

cial residues of PICK1 increases the binding affinity of

DAT for K83H PICK1 (2297.99 kcal/mol). Likewise, it

was observed that the interactions between P-2 Leu with

Val83 and Val84 became more stabilized in K83V PICK1

[Fig. 5(D)]. Although data showed increased binding

energies for K83V and K83H PICK1, the experimental

analysis suggests an increase in affinity of DAT for K83V

PICK1, while a decrease in binding affinity for K83H

PICK1. To investigate this further, we found that some

hydrogen bond interactions were lost between DAT pep-

tide and K83H PICK1 mutant suggesting a possible rea-

son for decrease in binding affinity for K83H PICK1 as

seen in experimental results. However, our scoring func-

tion was not sensitive enough to capture this effect in its

binding score. Overall, the analysis suggests that the K83V

mutation is not perturbed due to a more stabilized inter-

action of P-2 Leu of DAT (-WLKV) with the mutant res-

idue at Val83 as proposed earlier,34 yet it is not sensitive

to small changes in binding affinity detected by experi-

mental methods.

Computational docking analysis of thePICK1 compound FSC231

The first small-molecule inhibitor (FSC231) of the

PDZ domain in PICK1 has been identified among

�44,000 compounds with a fluorescent polarization

assay.37,38 The fluorescent polarization binding experi-

ments showed that the FSC231 compound has higher af-

finity for the K83H mutant than for the wild type of

PICK1.37 The affinity for PICK1 compound FSC231 was

also analyzed and predicted using the docking

approaches explained above. The chemical structure of

the compound was generated with the PRODGR server

(http://davac1.bioch.dundee.ac.uk/programs/prodrg/

prodrg.html) and then used a geometric-based conforma-

tional sampling technique called FRODA (Framework Ri-

gidity Optimized Dynamics Algorithm)56 to generate a

variety of different topological structures of the com-

pound. These structures were docked into the wild type

and K83H mutant of PICK1 structures. The predicted

binding energy values agree with fluorescent polarization

binding experiments showing a higher affinity of FSC231

for the K83H mutant (299.50 kcal/mol) than for the

wild type (295.69 kcal/mol) (Fig. 6).

Correlation of flexible versus rigid dockingwith experimental data

Previous studies have shown that incorporating back-

bone flexibility of PDZ domains increases the prediction

accuracy in determining their peptide binding specific-

ity57–63 and therefore in their binding selectivity.55 As

indicated above, in this study, a PDZ flexibility

approach was introduced into the docking simulations

through the generation of an ensemble of multiple re-

ceptor conformations (MRCs) together with a newly

developed coarse grain approach, where an elastic net-

work method (ENM) was coupled with linear response

theory (LRT).64 The LRT states that the response behav-

ior is related to the equilibrium fluctuations in the

unperturbed state (i.e., unbound state). Therefore, after

constructing the structural information of the unbound

form using ENM (i.e., a purely mechanical model that

Modeling PICK1 Binding Affinities

PROTEINS 1403

views a protein structure as a network of harmonic resi-

due–residue interactions), the LRT was used to formu-

late the response of the protein structure to ligand bind-

ing and to obtain structural changes of the given struc-

ture. These computed structural changes, upon binding,

were integrated into the docking simulations through

generation of multiple conformations of the receptor

(PDZ domain). To further determine if this flexible

modeling approach increased the prediction accuracy

compared with rigid modeling (Table II), both flexible

and rigid modeled binding values were compared with

experimental data and correlation plots were generated.

In these comparisons the binding energy scores were

compared with binding energy of experimental results

using Ki values. Specifically, a quantitative comparison

was made between the predicted and experimental affin-

ities34 of wild-type and single point mutations of DAT

and PKCa peptides bound to wild-type and mutated

PICK1 (K83H). The binding energy was computed for

all cases with both rigid and flexible docking by PRS

(Table III). Simultaneously, the experimental binding

free energy (DG) values were also calculated from the

experimental Ki values. The data showed some disagree-

ment in both flexible and rigid values compared to ex-

perimental data, likely due to the stringent test of pre-

dicting the change of affinity per single mutations.

Nevertheless, the correlation between the experimental

affinity and predicted affinities showed a value of 0.87

for flexible docking as compared with 0.51 for rigid

docking for the DAT peptide (Fig. 7). Similarly for the

PKCa peptide, a correlation of 0.75 for flexible docking

and 0.53 for rigid docking was obtained (Fig. 7). This

analysis demonstrated further that the accuracy and pre-

diction of the flexible PRS docking approach provided

better correlation with the experimental data compared

with rigid docking methods.

Table IIICorrelation of Docking Models with Experimental Data

PDZ Motif Experimental Theoretical

PICK1 DAT Ki DG DG Rigiddocking

DG FlexibleDocking

WT 2LKV 2.3 27.790 2282.62 2291.932 SKV 42 26.047 2287.04 2270.262 LKI 9.5 26.939 2272.03 2282.622 LKL 37 26.123 2188.12 2277.322 LKA 49 25.954 2235.15 2254.80

K83H 2LKV 21 26.463 2282.63 2297.992 SKV 1.1 28.232 2291.24 2321.712 LKI 24 26.382 2294.11 2280.202 LKL 64 25.794 2207.99 2246.632 LKA 90 25.590 2260.10 2247.96

Correlation 0.51 0.87

PICK1 PKCa Ki DGDG Rigiddocking

DG flexibledocking

WT 2SAV 33 26.191 2241.74 2217.182LAV 1.7 27.971 2242.70 2283.652SAI 77 25.683 2175.64 2219.652SAL 166 25.222 2209.55 2192.982SAA 40 26.076 2213.93 2220.22

K83H 2SAV 0.54 28.659 2236.75 2220.362LAV 10.4 26.884 2238.89 2224.602SAI 1.46 28.062 2192.79 2263.462SAL 4.6 27.374 2253.94 2251.652SAA 0.34 28.937 2261.94 2275.84

Correlation 0.53 0.75

Docking results show comparison between experimental binding free energy (DG)with rigid and flexible docking methods.

Figure 6Binding affinity of FSC231. The lowest binding energy scores and their corresponding structures of docking FSC231 compound to (A) the wild type

and (B) the mutant (K83H) of PICK1s are displayed as ribbon diagrams. The key residues of the PICK1 domain making side interactions with

FSC231 are also shown. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

A. Bolia et al.

1404 PROTEINS

DISCUSSION

This study used bioinformatics along with biophysical

docking simulations to categorize the binding properties

of PICK1. The sequence alignments of several PDZ

domains and analysis of all PDZ motifs that interact

with PICK1 revealed the regions of PICK1 critical in

ligand recognition. Data showed that the PICK1 residues

Lys83 together with Thr82, Val84, and Ala87 create a P-2

binding pocket that interact with P-2 residues of PDZ

ligands, whereas the ‘‘KD motif ’’ (Lys27 and Asp28), the

Ile37 residue and the GLGF-like motif (LIGI) (Ile33,

Ile35) create a P0 binding pocket that bind P0 residues

of PDZ ligands. Using structural database, four of the

best studied PDZ motifs of PICK1 were also modeled

and their hot spots (i.e., critical residues in recognition

of peptide motifs) were examined using the method of

alanine mutational drop in binding affinity through the

HotPoint Server.47 We observed that the Lys83 residue,

the ‘‘KD motif (Lys27), the Ile37 residue, the GLGF-like

motif (LIGI) (Ile33, Ile35) and the I90 residue represented

hotspots in recognition of PDZ motifs. In addition,

PICK1 wildtype and mutant complexes (K27E, K27A,

K83H, and K83V) were modeled with various peptides

using novel docking methods. Our computational analy-

sis revealed that, in most cases, K27E (and K27A) muta-

tion enhanced the binding affinity for all Class I pep-

tides whereas it decreased those for Class II peptides.

On the other hand, mutations at position Lys83 (K83V

and K83A) did not alter Class I type interactions signifi-

cantly, while the affinity to Class II peptides decreased.

One another interesting observation was that all the

mutations also disturbed the binding interactions of

PICK1 with the atypical peptides.

Our modeling approach comes with two additional

advantages: (i) first, we can generate a set of conforma-

tions that includes the conformations sampled during

binding process and (ii) second, we can, based on the

rapid computational speed, apply this approach to a vast

majority of ligands (PDZ motifs) or different mutants of

a receptor (PDZ domain) and obtain a virtual screening

library for a given receptor (PDZ domain) in a short pe-

riod of time. Moreover, perturbation on the residues

applied by PRS also allowed us to create a different set of

ensemble for mutant versions of PICK1 compared with

that of the wild type PICK1 receptor (PDZ domain).

This increased the prediction accuracy of our peptide

affinities by docking as determine by correlation with ex-

perimental data (Table III). With this approach, our pre-

dicted binding energy scores agreed qualitatively with the

Figure 7Accuracy of flexible and rigid docking models. Correlations plots of predicted and experimental DG values for (A) rigid and (B) flexible dockings

for DAT peptide and PKCa peptide. Flexible docking shows a better correlation with experimental affinity values. [Color figure can be viewed in

the online issue, which is available at wileyonlinelibrary.com.]

Modeling PICK1 Binding Affinities

PROTEINS 1405

available experimental data. For example, our analysis

shows that Ephrin B1, Parkin and DAT (Type II sequen-

ces) have strong affinities while the atypical peptide of

UNCH5 has the weakest affinity. With the same

approach, we also examined the two crucial binding hot-

spots of PICK1, namely Lys83 and Lys27. Using the single

mutation of K27E in our flexible docking, we observed

that the binding energy score of GluR2 significantly

decreased, whereas that of PKCa enhanced with this mu-

tant version of PICK1 (Table I). Likewise, we found

increased binding energy of PKCa, but not of DAT, whenwe mutated Lys83 to His, which is also observed in muta-

tional binding analysis.34 Moreover, our docking analysis

showed a disruption in binding affinity of GluR7 upon

K27E mutation as observed experimentally.4 Notably,

these binding affinity changes were unlikely to be

obtained if a single crystal structure of PICK1 had been

used in our computational analysis. Thus, the accuracy

in our flexible docking method lies in incorporating the

binding-induced conformational changes through PRS. It

is known that when a ligand (PDZ motif) approaches a

receptor (PDZ domain), it exerts force on the binding

site. Likewise, by exerting small random forces on the

binding residue site, we computed the displacement of

each residue and also incorporated those displacement

results into our docking analysis. This enabled us to inte-

grate both backbone and side chain conformational

changes of the receptor (PDZ domain) in the docking

analysis. These small conformational changes in the back-

bone can lead to a significant change in the side chain,

thus simulating the changes of how a ligand (PDZ motif)

interacts with the receptor (PDZ domain).

In closing, we suggest these new computational analy-

ses methods will advance our understanding of the struc-

ture and binding relationships of PICK1. We have previ-

ously highlighted the value of PDZ domains as novel

drug targets for disease.65 The study provides modeling

methods to aid in the drug design of ligands for PDZ

domains, which may further aid in development of the

family of PDZ domains as a drug target.

ACKNOWLEDGMENTS

The authors thanks the Fulton High Performance

Computing Initiative at ASU for computer time. The

authors would like to thank Dr. Graham Sheridan,

Yvonne Kuma, Ciara Twomey and Shane Lyons for help

with the manuscript.

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