computational design of peptide ligands

Computational design of peptideligandsPeter Vanhee1,2, Almer M. van der Sloot3, Erik Verschueren3, Luis Serrano3,4,Frederic Rousseau1,2 and Joost Schymkowitz1,2

1 VIB SWITCH Laboratory, Flanders Institute of Biotechnology (VIB), Pleinlaan 2, 1050 Brussels, Belgium2 Free University of Brussels (VUB), Pleinlaan 2, 1050 Brussels, Belgium3 EMBL/CRG Systems Biology Research Unit, Centre for Genomic Regulation (CRG), UPF, Dr. Aiguader 88, 08003 Barcelona, Spain4 ICREA Professor, Centre for Genomic Regulation (CRG), UPF, Barcelona, Spain

Review

Peptides possess several attractive features when com-pared to small molecule and protein therapeutics, suchas high structural compatibility with target proteins, theability to disrupt protein–protein interfaces, and smallsize. Efficient design of high-affinity peptide ligands viarational methods has been a major obstacle to thedevelopment of this potential drug class. However,structural insights into the architecture of protein–pep-tide interfaces have recently culminated in severalcomputational approaches for the rational design ofpeptides that target proteins. These methods providea valuable alternative to experimental high-resolutionstructures of target protein–peptide complexes, bring-ing closer the dream of in silico designed peptides fortherapeutic applications.

IntroductionThe majority of therapeutic compounds achieve theireffects by binding to and altering the function of targetprotein molecules. Traditionally, the main source of suc-cessful therapeutics has been small organic molecules,which usually bind in small cavities of the target proteinand inhibit specific catalytic centers or the binding sites ofnatural substrate analogs [1]. The recent focus on protein–

protein interaction networks has shifted the goal of drugtargeting increasingly towards disruption of protein–pro-tein interactions; a feat for which classical small moleculesare not always ideally suited [2,3]. The newest additions tothe pharmaceutical arsenal are protein-based therapeu-tics, which are generally improved recombinant replace-ments of endogenous proteins or monoclonal antibodiesthat are directed against a wide variety of targets [4].Although the introduction of protein therapeutics – inparticular monoclonal antibodies – has been very success-ful, their use is mainly limited to extracellular targets,such as membrane-bound receptors and secreted proteins,because uptake of these large molecules into intracellularcompartments remains cumbersome [5] (Figure 1a).

Given the current success of recombinant-protein-basedtherapeutics, we are already witnessing the erosion of thelong-standing and relatively narrow definition of what

Corresponding authors: Serrano, L. ([email protected]);Rousseau, F. ([email protected]);Schymkowitz, J. ([email protected]).

0167-7799/$ – see front matter � 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.tibtech.20

constitutes a ‘druggable target’ [6] (i.e. a protein thatcan be modulated by an orally administered active smallmolecule, adhering to Lipinski’s rule of five [7]). The defi-nition of ‘druggability’ has widened to include targetswhose activity can be modulated by larger molecules, suchas proteins and peptides (Box 1). Peptide therapeutics andpeptidomimetics could theoretically offer a powerful exten-sion to the available small molecule and protein therapeu-tics toolkit, because their chemical structures are highlycompatible with those of the target proteins (Figure 1b).Despite successful examples of peptide or peptide-likedrugs that are already available on the market, peptidesare usually considered poor drugs owing to insufficientbioavailability, poor pharmacokinetics, low in vivo stabili-ty, and parenteral-only delivery. However, recent techno-logical advances in formulation, delivery and chemistry [8–

12] have sparked much interest in peptide therapeutics.Moreover, owing to technological advances in membranepenetrability, peptides are now amenable to intracellulardelivery [5] (Figure 1a). Presently, >50 peptide-basedproducts are approved for clinical use in the United Statesand other countries [e.g. Fuzeon1 (Roche); Byetta1 (Amy-lin/Eli Lilly); Sandostatin1 (Novartis); Zoladex1 (Astra-Zeneca); Copaxone1 (Teva)] [13], which underlines thegreat market potential of peptidic drugs. This has spurreda lot of interest in technologies that are capable of provid-ing newpeptide sequenceswith high affinity and specificitytowards therapeutically relevant targets. Many differentmethods currently exist to engineer peptide ligands, rang-ing from organic chemistry to molecular biology, and fromhigh-throughput screening or directed evolution-basedmethods to rational approaches [14–16]. In this review,we focus on the rational design of peptides that targetproteins, based on recent advances in our understanding ofprotein–peptide interactions (Figure 2).

Better understanding of protein–peptide interactionsWith the increase of high-resolution structures of protein–

peptide complexes in the Protein Data Bank (PDB:www.pdb.org) [17], and in complementary databases, suchas the database of 3D interacting domains (3did: http://3did.irbbarcelona.org) [18] and thenon-redundantdatabaseof protein–peptide complexes (PepX: http://pepx.switchlab.org) [19], largestructural studieshaveattempted todescribe

11.01.004 Trends in Biotechnology, May 2011, Vol. 29, No. 5 231

http://www.pdb.org/

http://3did.irbbarcelona.org/

http://3did.irbbarcelona.org/

http://pepx.switchlab.org/

http://pepx.switchlab.org/

mailto:[email protected]



http://dx.doi.org/10.1016/j.tibtech.2011.01.004

[()TD$FIG]

x

Antibody

Receptor

Y-Kinase

Hormone

Smallmolecule

PeptideEffector

(i) (ii) (iii) (iv)

nNOS (1/1)

Grip1(6/7)

Erbin(1/1)

PSD-95(1/3)

Shank3(1/1)

PDZ-RGS3 (1/1)

1.5

1.0

0.5

0

-0.5

-1.0

-1.0-0.5 0

0.51.0

1.5-1.0

-0.50

0.51.0

Thi

rd p

rinci

pal a

xis

First principal axis Second principal axis

Hydrophobic

HydrophilicHydrogen bonds PDZ families’ optimized specificity

(a)

(b)

β2

(i) (ii) (iii)

TRENDS in Biotechnology

Figure 1. Targeting the cell with different molecules. (a) Overview of different drug strategies targeting protein signaling pathways: (i) normal scenario of a generic

pathway; (ii) therapeutic antibodies; (iii) small molecules and (iv) peptides. (b) Typical protein–peptide interactions: the PDZ domain of Erbin (PDB 1N7T) binds peptides in

an elongated way, with multiple residues contributing to the interaction. (i) The peptide extends one edge of the b-sandwich through several hydrogen bonds with the b2-

strand of the PDZ domain; (ii) the carboxy terminus of the peptide binds tightly in a hydrophobic pocket of the PDZ domain; and (iii) the contributions of different positions

to the binding allow PDZ domains to optimize their specificity and avoid cross-reactivity. Reprinted with permission from [24].

Review Trends in Biotechnology May 2011, Vol. 29, No. 5

the key properties of peptide binding [20,21]. For example,505 unique structural peptide-mediated interactions havebeen identified fromaset of 1431high-resolution structures,with a high over-representation of well-studied peptide

Box 1. Peptides and redefining druggability

Current small-molecule drugs target only a fraction of all proteins

inside and outside the cell (Figure 1a). Typical targets include G

protein-coupled receptors, enzymes, nuclear hormone receptors, and

ion channels; all of which have natural small-molecule substrates [60].

Most of these drugs target the binding pocket of the substrate

directly, but also other allosteric cavities can be targeted. On average,

the contact surface between a small-molecule ligand and its protein

receptor is 300–1000 A2 [61]. By contrast, the contact surface between

two interacting proteins is generally much flatter, larger (1200–

3000 A2) [62,63], and discontinuous in sequence. Most of the free

energy of binding is contributed by a limited number of amino acids

in the interface (hotspot residues) [22]. Interaction networks are

distributed and constructed with a modular architecture, showing

tight cooperative interactions within a module and additive interac-

tions between the modules [64] (Figure 1b). Conversely, protein–

peptide interfaces display a smaller contact surface and a more

continuous architecture, and often target well-outlined, large hydro-

phobic pockets on a protein [21]. These pockets are larger than the

typical clefts targeted by small molecules, but smaller than large

protein–protein interfaces.

Given the large, shallow and distributed nature and lack of pockets

and cavities in protein–protein interfaces, these interfaces are often

232

interactions, such as MHC-peptide complexes, thrombin-bound peptides, or peptides bound to the a-ligand bindingdomain of the estrogen receptor [19]. In a set of 103 peptidecomplexes, it has been noted that many interfaces exhibit

considered to be hard to target with small-molecule drugs. Although

progress has been made in the development of small-molecule drugs

that target protein–protein interactions [65], disruption of protein–

protein interfaces with classical small-molecule compounds remains

a difficult task [3]. Peptide-like drugs are likely to be more suitable

candidates to act as competitive inhibitors of protein–protein interac-

tions, considering their similar binding mode.

Alternatively, peptide-like ligands could target protein–protein

interactions in a non-competitive manner by acting as an allosteric

modulator. This concept has received a lot of attention owing to the

success of small-molecule allosteric modulators [66,67], but is also

well-established in protein-mediated interactions [68]. One limitation

to this allosteric approach is the need to identify the pressure points in

a target protein structure that should be hit to affect its function.

Several methods to map the dynamics of the amino acid interaction

network that constitutes the protein structure have been developed,

and these have, at a minimum, the potential to reveal sites on the

protein surface with allosteric modulatory power [69–72].

In short, targeting protein–protein interactions with peptide-based

competitive inhibitors or – albeit more challenging – peptide-based

allosteric modulators extends the definition of druggability by

expanding the potential classes of druggable targets.

[()TD$FIG]

(a) Structure-free peptide design

Phage-display library screening

Quantitave peptide assays

Sequence motif scanning

(b) Structure-based peptide design

Peptides derived fromprotein complex structures

De novo peptide design with structural scaffolds

Experimental

Experimental/Computational

Experimental/Computational

Computational

Computational

Optimizing binding affinity

Peptide docking andde novo design

1 2

(i) (ii)

(i) (ii)

(iii)

1 2 3 4 5 6 7 8

Selection

Sequencing

Direct read-out

...SEQUENCESEQUENCE...

Consensus motif1 2 3 4 5 6 7 8

1 2 3 4 5

de novo designo

1 2 3 4 5 6 7 81 2 3 4 5 6 7 8

......

...... ... ... ... ...

1 2 3 4 5 6 7 8ACDEFGHI..

.

...

Best binding motifPWM heatmap

Am

ino

acid

s

Position-specific mutagenesis


Figure 2. Example workflows for peptide design. (a) Structure-free peptide design using (i) large phage-display libraries (�108 peptides) or quantitative binding assays to

arrive at a sequence; or (ii) artificial neural networks – amongst other learning algorithms – that are able to learn complex, non-linear sequence motifs. (b) Structure-based

peptide design. (i) Peptide fragments derived from the crystallographic interface of a protein–protein interaction are the major source for rational design. (ii) Structural

information of a target can generally be used to construct or dock peptides along a chosen path on the target surface. (iii) Experimental or computational site-directed

mutagenesis can be applied to optimize the specificity of the peptide for the target.


tighter packing and more main chain hydrogen bonds thannormally found in protein–protein interfaces [21]. This dif-ference is logical: peptides in isolation cannot be too hydro-phobic because they would aggregate; therefore, part of thebinding energy to compensate for the loss of entropy uponbinding has to be derived from main-chain/main-chain andmain-chain/side-chain hydrogen bonds.

In silicomutagenesis of these interaction interfaces hasrevealed that peptide interfaces contain ‘hotspot’ residues,reminiscent of those found in protein–protein interfaces[22]. Peptides that are 6–8 residues long typically contain

two hotspot residues, whereas three hotspots are typicalfor peptides that comprise 9–11 residues [21]. In general,peptides often exhibit an elongated structure upon binding[23], and do not appear to induce any large conformationalchanges in their binding partners, to reduce the entropiccost of complex formation [21]. By contrast, many of thepeptide motifs are located in structurally disorderedregions of proteins and only adopt a stable fold uponbinding to their protein partner (‘fold-on-binding’).

Despite their limited size, peptide interactions can behighly specific. For example, many C-terminal peptides

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exhibit high specificity in vivo for certain PDZ domains,while avoiding cross-reactivity [24]. Contrary to the cur-rent belief, peptide specificity across 157 mouse PDZdomains (and matched with 217 peptide ligands) cannotbe captured in discrete classes, but instead shows a moreeven distribution in selectivity space (Figure 1b). Specifici-ty in peptide interactions can also be introduced by engi-neering approaches even when not observed in nature[25,26]. One such example is the basic-region leucine zip-per (bZIP) family of transcription factors that share a highdegree of structural and sequence similarity, and bindsDNA upon homo- and/or heterodimerization with an iden-tical or related bZIP monomer subunit. By replacing one ofthe wild-type (WT) monomer subunits with a variant thathas the basic region substituted by an acidic region, DNAbinding is prevented and, in turn, the activity of thetranscription factor is inhibited. These acidic variantsinherit the dimerization properties from theWT; therefore,it is difficult to inhibit one specific bZIP family member,owing to intrinsic heterodimerization properties. It hasrecently been shown that it is feasible to design anti-bZIPpeptide variants computationally that bind specifically toonly a single member of the human bZIP family [26]. Analgorithm has been employed that explicitly considers bothtarget and non-target interactions by selecting sequencesthat minimize the loss of affinity for the target, whilemaximizing differences in affinity between any non-targetmembers. Out of the 20 targeted bZIP families, 10 designedpeptides bind to their representative member of the familywith higher affinity than any other non-target competitors,which demonstrates peptide specificity. This study andrelated, albeit smaller computational design studies[25,27] have demonstrated that specific binding partnerscan be designed even in situations in which there is a highdegree of sequence and structural similarity between tar-get and non-target molecules.

Peptide design based on sequence motifsIf structural information is present for a drug target, eitherfrom the single structure or from the target in complex withits ligand, this information can beused in the drug discoveryprocess to speed up lead identification [28]. Unfortunately,structural information is available for only an estimated50% of all drug targets [29], with a significant under-repre-sentation of targets of high therapeutic importance, such asmembrane proteins. As a result, many research groups useexisting information compiled in databases of protein–pep-tide interactions to derive sequence-binding motifs thatcould be used to design peptides. The most obvious casesare the well-studied SH2, SH3, PDZ and WW domains, forwhich, using simple sequence-based rules, one can designpeptide templates (i.e. for SH3 domains, the well-knownPxxPmotif, with x as any amino acid; or, for PDZ class I, theT/S-x-I/V/L-COOH), although the discrete classification inmotifs has been disputed [24]. These templates can berandomized at the non-key positions and, using differentscreening methods such as yeast two-hybrid or phage dis-play, specific peptides can be discovered [15,30].

In cases in which enough information on peptide bind-ing is available, other, more sophisticated approaches canbe used. For example, an artificial neural network, capable

234

of learning to recognize non-linearity in complex datasets,has been trained on 650 peptides derived from T-cellepitopes that are known to bind MHC class II molecules[31]. The neural network has been used to speed up epitopescreening by reducing the experimental T-cell assay from68 to 22 peptides, with only a potential loss of five out of 17epitopes. In more recent work, prediction has been com-bined with genetic algorithms, hidden Markov models orother motif discovery algorithms [32]. Prediction fromsequence alone is often difficult because of permissivebinding modes (e.g. MHC class II accommodates 9–18residues, although longer peptides have also been ob-served), multiple binding cores and insufficient high-qual-ity binding data; all of which lead to noisy and ofteninaccurate predictions. Addition of structural informationto the prediction process (e.g. approximately 169 X-raystructures of MHC in complex with an antigenic peptideare available in PepX [19]) could increase prediction accu-racy, yet structure-based methods are still too slow forgenome-wide screening (for a review, see [32]). Althoughthese motif-scanning methodologies can lead to novel pep-tide discoveries, it is unclear whether they could lead tomore general approaches when little information is knownabout the target protein.

Protein complexes as a source of active peptidesPeptide fragments derived from the crystallographic inter-face of a protein–protein interaction are the major sourcesfor rational drug design [33]. In 2003, the anti-HIV peptideenfuvirtide (Fuzeon1) was the first peptide (36 aminoacids) to be derived from an extracellular protein interfaceto receive US Food andDrug Administration approval, andis therefore considered a landmark achievement in thefield of peptide therapeutics [34]. Intracellular targetsassociated with HIV infection have been targeted withpeptides as well.

Transcription factors, which are regarded as ‘undrug-gable’ by classical small-molecule drugs owing to theirlarge protein–protein interfaces, have now been targetedwith peptides. A landmark study has reported the directinhibition of a ternary complex that is required to initiatetranscription downstream of the NOTCH signalling path-way (Box 2) [35]. An inhibitory a-helical peptide wasdesigned from a cofactor and stabilized with a techniquecalled ‘peptide stapling’ [36]. Stapling has stabilized apeptide binding to MCL-1, a member of the BCL-2 familyof anti-apoptotic proteins that is typically overexpressed incancer, and that secures cell immortality [12]. The peptideis originally derived from the pro-apoptotic BH3 helix, andhas only a small number of residues involved in binding, asshown by mutagenesis. A single point mutation, V220F,abolishes binding to MCL-1 altogether. The binding profilethus reveals high selectivity for specific members of theBCL-2 family, which could be harnessed for selective pep-tide-drug design.

Leveraging nature’s use of helical peptides in protein–

protein interfaces provides exciting opportunities for pep-tide therapeutics [26,35,37]. Scanning the entire PDB forinterfaces involving helical segments has revealed manypotentially interesting interfaces in which a-helical inter-actions play an important role, such as nuclear hormone

Box 2. Stapled a-helical peptides as potent therapeutic peptides

Transcription factors (TFs) are amongst the most difficult targets

for therapeutic drug design, largely because of their large, flat

protein–protein interfaces that lack any hydrophobic pockets.

Although small-molecule drugs have been identified that target

proteases involved upstream in the NOTCH signaling pathway, the

inhibition of TFs downstream could directly regulate gene expres-

sion in a specific manner. Antibodies that have the potential to

inhibit the NOTCH TF interface and block expression do not

possess the capacity to penetrate the cell membrane, unless fused

with certain types of cell-penetrating peptides, as shown recently

for targeted tumor delivery [73]. An a-helical peptide has been

designed that is able to penetrate the cell membrane and bind to a

shallow groove formed by the intracellular domain of NOTCH

(ICN1) and a DNA-bound TF (CSL), thereby blocking the interaction

with a co-activator factor of the Mastermind-like-1 family (MAML-

1), which is required for recruiting the transcription machinery

(Figure Ia) [35].

The original discovery of a 59mer peptide fragment from co-

activator MAML-1 required for NOTCH signaling marked the start for

structure-based inhibitor design [74]. The ternary complex bound to

DNA has been resolved recently by two independent groups, who

have shown that the Mastermind peptide binds as a twisted helix in

the shallow protein–protein groove [75]. Using a technique termed

peptide stapling, a 16-residue peptide has been designed in which

two residues are stapled together using a hydrocarbon bond; this acts

to constrain the helix functionality of the peptide, while improving

binding affinity. The peptide, SAHM1, has been shown to halt

NOTCH1 signaling and stop the proliferation of T-cell acute lympho-

blastic leukemia cells.

In an entirely different class of proteins, stapled a-helical

peptides have been shown to be effective as well, by inhibiting

members of the anti-apoptotic BCL2-family [37]. These anti-

apoptotic proteins contain a hydrophobic groove that engages the

death-promoting BH3 helix. Molecular mimicry of that helix with a

stapled peptide has led to selective inhibition of the apoptotic

protein (Figure Ib).

Both examples of successful helical peptide design suggest that

helical fragments in protein–protein interfaces are specific and

interesting as peptide scaffolds [38]. The acquisition of Aileron’s

peptide stapling technology by Roche in August 2010 only confirms

the potential of these stabilized a-helix peptides as a new class of

powerful peptide therapeutics [76].[()TD$FIG]

(a) (b)

Hydrocarbon staple

Hydrophobic

Key:

Positive charge

Negative charge

Hydrophilic


Figure I. Therapeutic peptides derived from protein–protein interfaces. (a) Design of MAML-1-derived peptides by taking different portions of the MAML-1 helix and

turning them into peptides (sliding window of orange–red–pink–orange indicates different peptides used for stabilization; PDB: 2F8X). The 16-amino acid stretch of

MAML-1 targeting ICN1 and CSL (red) was used to design the stapled peptide. Adapted with permission from [35]. (b) Crystal structure of the stapled helix MCL-1

complex (PDB: 3MK8). The stapled helix engages in binding in the canonical binding groove. Hydrophobic interactions at the binding interface are reinforced by a

complementary polar interaction network. The side chains of hydrophobic (yellow), positively charged (blue), negatively charged (red), and hydrophilic (green) residues

are shown. Adapted with permission from [37].


receptors or other transcription factor–cofactor interfaces[19,38]. Generally, protein-interface-derived peptides con-stitute the major source of rational design [39] (Figure 2);yet so far, successful peptide designs seem to be largelylimited to the a-helical scaffold. One reason for this mightbe the large entropy cost associated with structuring apeptide upon binding, which is easier to achieve using a-helical peptides. For example, a leucine-zipper scaffold canbe used to fix the helix bundle [26] or chemical stapling ofselected side chains to fix a single helix scaffold [36]. Forhairpin structures, cyclization has also been employed [40].Yet another way to extend the structural stability of pep-tides is to incorporate them in a highly stable miniprotein,such as knottins or other scaffolds [41].

Peptide design using protein–peptide complexesOften structural information on the protein–peptide bind-ing interface can be used to the advantage of modeling theprotein–peptide interaction. Most approaches can be divid-ed in three main scenarios. (A) Use a structure with a

peptide ligand as template and model by homology adomain-related sequence, and then mutate in silico witha protein design algorithm the amino acid side chains ofthe peptide to change the specificity while keeping thepeptide backbone coordinates fixed [25,27]. (B) Use astructure with a peptide ligand to model by homology adomain-related sequence, while allowing peptide backboneflexibility. The crudest approach uses different domain–

peptide complexes of the same family to generate differentligand backbone structures that can be superimposed onthe target structure [42]. This has been probed recently forthe PDZ domain, for which peptide specificity has beencomputationally redesigned using all available structuresfrom the PDZ domain, and compared with large phagedisplay experiments [43]. Another approach introducesbackbone flexibility in the peptide starting from a seriesof perturbed X-ray protein–peptide complexes [44]. Thisprotocol has been validated on a set of 89 peptide com-plexes, and it has produced models that show sub-angstrom deviation from the native structure. (C) Use a

235


structure while only knowing the approximate binding siteof the peptide ligand, for example, based on evidence fromrelated domains. The PepSpec algorithm does not rely on astructural model of the peptide [45]. Instead, it only needsa single anchor residue positioned in the binding pocket,and introduces implicit backbone movements in the recep-tor through ensemble modeling. Evaluation has been car-ried out on a series of peptide-binding domain families,such as PDZ, SH2 and SH3. In the absence of an experi-mentally obtained structural model of the domain, andrelying on a model based on a homologous domain, thealgorithm captured some of the peptide specificities thatwere matched with experimental phage display libraries.However, long simulation times that scale unfavorablywith peptide length have been reported (100–300 h/pep-tide). In this field, we have achieved some progress as well:peptides have been designed for the PDZ domain, the a-ligand binding domain, and the SH2 domain with sub-angstrom accuracy, using structural data from BriX inter-action patterns in combination with the FoldX force fieldfor side-chain placement and energy evaluation (Figure 3)[20].

To summarize, fixed backbone peptide design (scenarioA) can be successfully used in situations when a highdegree of sequence and structural similarity exists – orcan be assumed – between template complex structure andthe target complex structure, while minimizing computa-tional cost. When changes in backbone conformation areexpected to play a greater role (e.g. in cases of decreasingsequence and structural similarity, or when insertions/
[()TD$FIG]
Target without ligand Monomeric int

Helix-loop mHelix-helix motif

Binding

site

(a)

(b)

(i) (ii)

Figure 3. Innovative structural approaches in peptide design. (a) Examples of monome

motif (PDB 153L); a helix-loop interaction motif (PDB 153L); a cation–p interaction mo

structures: (i) structure of a PDZ domain (PDB 2I1N) without its ligand and with the hel

helix–strand–strand motif (red) from an unrelated structure (PDB 1GSA); and (iii) co

intramolecular scaffold (red) and the original ligand (gold).

236

deletions relative to the template structure have to bemodeled), one of the approaches mentioned under scenarioB can be employed. When the exact binding site of thepeptide is not known, one of the approaches mentionedunder scenario C should be employed. For now, the compu-tational cost of these methods limits the use to selected(design) case studies rather than proteome-wide screening.Peptide (protein) docking methods can be considered alter-natives for scenario C.

Protein docking and fragment-based docking as toolsfor peptide designAmore general approach in peptide design uses structuresor homology models of the target in combination withdocking algorithms to construct peptides along a chosenpath on the target surface. Several tools can be used todetect putative binding sites structurally, for example,using geometric amino-acid-dependent preferences de-rived from a set of structural bindingmodes [46]. Autodock,a popular small-molecule docking algorithm, has been usedin combination with a genetic algorithm to design tetra-peptides against a selected hydrophobic region of a-synu-clein, a protein associated with aggregation diseases [47].Upon experimental validation, several binding peptideswith micromolar dissociation constants have been identi-fied that could be used as leads for further screening.Another approach uses a Gaussian Network Model toidentify the binding site, and Autodock is used to dock aseries of dipeptides in a pairwise fashion on the grid along aflexible binding path, which results in an optimal peptide

eraction motif Designed ligand

otif Cation-π motif

WT Design

(iii)


ric interaction motif mining in structures (shown in red): a helix–helix interaction

tif (PDB 1GAI). (b) Sub-angstrom design of peptide interactions using monomeric

ix and (2 strand forming the interface (blue); (ii) identification of an intramolecular

mparison between the structures of the WT sequence (EETSV) designed on the

Box 3. Case study: peptides targeting transmembrane helices derived from naturally occurring helix–helix interaction pairs

Integrins are important receptor proteins in mammalian cells, with a

flexible domain of transmembrane (TM) helices in the phospholipid

bilayer. Integrins process extracellular signals and transmit them to the

interior of the cell, thus making them attractive targets for tumor

therapy [77]. Peptides selectively targeting integrins aIIbb3 and aVb3

have been computationally designed with a new approach for rational

peptide design [78]. Although typical peptide designs are derived from

the crystal structure of the target protein–protein complex, the design

task mainly consists in stabilizing the hot-spot interactions with a

peptide. However, because in most cases, no crystal structure of the

interface is available, in this study the authors relied on a repertoire of

over 400 naturally occurring TM-helix interactions with recognizable

sequence signatures [49]. The computational design was divided in two

steps: (i) the helix–helix interaction motifs served as realistic backbone

templates (Figure IIa,b) – as opposed to idealized helix pairs often used

in protein design – and were selected based on sequence compatibility

with the target TMs; (ii) the authors threaded the sequence of the target

TM helix on one helix of the helix pair, and then selected a compatible

side chain for the peptide, using a side-chain-repacking algorithm for

the other helix (Figure IIc). The computationally designed peptides were

subsequently validated in micelles, bacterial membranes and mamma-

lian cells, where they inhibited the binding between the TMs of the a-

and b-subunits, thus activating the integrin [73].

Multiple advances reported in this study are noteworthy. First, the

authors have shown that peptides possess the capacity to integrate

within the lipid bilayer and selectively interact with and activate

integrins in mammalian cells; this had previously been difficult to

accomplish owing to the lack of a solvent-exposed binding site.

Second, by using a library of naturally constrained helix–helix

interaction motifs, they have circumvented the need to model

computationally expensive inter-helical hydrogen bonding patterns

and deviations from idealized helical geometry. Finally, this study

provides exciting opportunities for designing peptide inhibitors, and

avoids the need for high-resolution structures of the interface.[()TD$FIG]

Anti-αIIb scaffold (PDB 1JB0) TM helix-pairs clusterαIIb Peptide threaded

on scaffold and repacked

1 2

(a) (b) (c)


Figure II. Design of helices targeting TM proteins, using: (a) a library of TM helix pairs from (b) unrelated structures (e.g. PDB 1JB0) to (c) design novel peptide ligands.

Reprinted with permission from [78].


sequence for a given surface [48]. Although these methodswork well for peptides comparable in size to small mole-cules (typically no longer than 3–4 amino acids), the designof longer peptides still presents major combinatorial pro-blems.

Remedying the lack of structural informationA recently reported method addresses the lack of structur-al information on membrane proteins, by employing adatabase of helix–helix interaction scaffolds to initiatepeptide design [16,49] (Box 3). Although full-atom model-ing remains a challenging task in computational peptidedesign because of a prohibitive combinatorial problem, theuse of naturally occurring interaction motifs instead of abinitio models allows one to focus computer time on side-chain packing to improve overall results.

We have taken a radically different approach towardremedying the lack of structural data on protein–peptideinteractions [20]. Peptide binding motifs often resembleintramolecular packing motifs, which suggests that thewealth of data on single-chain proteins could be used tomodel peptide interactions (Figure 3). Through the analy-sis of a non-redundant representative set of 301 protein–

peptide binding interfaces, we have shown that more than

half of all peptide interaction motifs could be reliablymodeled from sets of interacting fragments from the BriXdatabase of protein fragments (http://brix.crg.es) [50,51].As a result, the number of structural peptide interactionmotifs increased from a few hundreds to over 100 000fragment interactions. In another study, the whole PDBand not just complex structures, was scanned for peptideinteraction motifs using a structural description of pep-tides, which resulted in the detection of >6000 interactionpatterns [23]. The approaches taken by these studieseffectively extend the structural space of peptide-basedinteractions by mining the existing structural databasesfor peptide or peptide-like interactions, and could be usedfor the development of peptide prediction algorithms.

The use of intramolecular fragment interaction motifsthat have pre-optimized packing represents an importantconceptual breakthrough because it transforms the wholedatabase of protein structures into learning data for com-puter algorithms that design peptide substrates de novo.Modeling accuracy is highly dependent on the amount ofsecondary structure exhibited by the protein–peptide in-terface because secondary structure packing motifs are themain building blocks within monomeric protein folds. Inthe near future, we expect that such algorithms will start

237

http://brix.crg.es/


to appear so that large-scale virtual peptide screening willbecome a valid opportunity.

The future: targeting oligomerization and allosteryA recent study has described the use of helix–helix inter-action motifs as important regulators of allosteric activity[52]. In the absence of high-resolution structures of thetarget protein, correlated mutational data combined withthe intrinsic helical periodicity (one turn every 3.6 resi-dues) has been used to predict helix interactions. Employ-ing this method, peptides have been synthesized from thepredicted helices and shown to block the allosteric move-ment of two viral envelope proteins in vitro. In anotherexample, peptides derived from the cellular-binding pro-tein LEDGF/p75 of HIV-1 integrase have been shown toshift the oligomerization equilibrium of HIV-1 integrasefrom the active dimeric state to the inactive tetramericstate, thereby preventing integrase activity. The smallpeptides (10 residues) can easily penetrate cells and, al-though these peptides only show micromolar affinity to-wards HIV-1 integrase, they are able to inhibit viralreplication. By targeting the oligomerization equilibriumof HIV-1, and not directly its nanomolar DNA bindingactivity via a non-competitive mechanism, the micromolaraffinity of the peptide is sufficient to inhibit viral replica-tion. Such an approach of stabilizing an inactive state overthe active state shows the power of allosteric inhibitors orrelated approaches, such as these ‘shiftide’ peptides [53].

OutlookThe main advantage of developing peptide or peptide-likedrugs is that they can help expand the ‘druggable genome’[6] by targeting specifically protein–protein interactions,which are less suitable for small-molecule-based therapies(Box 1). Peptide drugs can offer an increase in targetselectivity and have the potential to act on and showreduced toxicity in comparison with small molecules. Ra-tional peptide designs that have shown potent inhibition invitro or in vivo are still rare (for examples, see Boxes 2 and3), and often are directly derived from a crystallized pro-tein–protein interface.

From a design perspective, there are also clear advan-tages to develop peptide therapeutics: a large body ofexisting structural and other experimental data on pro-tein–protein and protein–peptide interactions is alreadyavailable. Moreover, the closely related protein design fieldis already relatively well-developed (reviewed in [54]).Peptides are constructed from amino acids, therefore,the large body of energy potentials developed in the fieldsof protein folding, docking and dynamics can be applied topeptide structure prediction [55,56]. To harness the fullpotential of such approaches requires, for example, theintroduction of non-natural amino acids to extend thechemical repertoire [57]. Existing methods for peptidedesign are currently being developed, but unfortunately,their evaluation is often based on the calculated root meansquare deviation – or similar measures – from existingstructural data, or by using calculated binding scores. Onlyin a few cases are other experimental data used, such asspecificity assays from phage display or peptide arrayexperiments (Figure 2). When designing peptides, the

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optimization process is often carried out using the interac-tion energy estimates as the scoring function, disregardingother relevant factors of successful peptide designs, such asmetabolic stability or specificity. Multi-objective peptidedesign algorithms are thus expected to improve over cur-rent designs. Finally, for the algorithm developer and user,few ready-to-use benchmark sets exist, which makes algo-rithm comparison time-consuming and often impossible.The organization of a dedicated peptide prediction compe-tition in the lines of CASP (Critical Assessment for ProteinStructure Prediction) [58] or CAPRI (Critical Assessmentof PRediction of Interactions) [59] could help advance thepeptide design field.

AcknowledgmentsThis work was partially supported by the EU program TRIDENT (grantnumber LSHC-CT-2006-037686); PhD scholarship from the Institute forScience and Innovation Flanders (IWT) (P.V.); long-term exchangefellowship from the Research Foundation Flanders (P.V.); PhDscholarship from the Initial Training Network ‘‘Penelope’’ (ProjectReference: 036076), which is funded by the European CommissionFramework Programme 6 (E.V.), and Juan de la Cierva fellowship ofthe Spanish Ministry of Education and Science (A.M.S).

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