DRUG DEVELOPMENT RESEARCH 72 : 26–35 (2011)

Research Overview

Role of Protein Flexibility in the Discovery of New DrugsGloria Fuentes,� Shubhra Ghosh Dastidar, Arumugam Madhumalar,

and Chandra S. Verma

Bioinformatics Institute (A*STAR), 30 Biopolis Street, 07-01 Matrix, Singapore 138671, Singapore

Strategy, Management and Health Policy

Enabling

Technology,

Genomics,

Proteomics

Preclinical

Research

Preclinical Development

Toxicology, Formulation

Drug Delivery,

Pharmacokinetics

Clinical Development

Phases I-III

Regulatory, Quality,

Manufacturing

Postmarketing

Phase IV

ABSTRACT Proteins have inherent flexibility, and this plays a critical role in a vast array of biologicalfunctions, including ligand binding. Structure-based drug design (SBDD) strategies incorporatebiomolecular structures with computational methods to predict and optimize ligand–receptor complexes.However, these strategies largely involve using static protein snapshots derived by classical X-raycrystallography, and thus critical and valuable information on flexibility is completely absent. With ahistorical perspective, we highlight relevant fundamental aspects of the character and importance of thetapestry of flexibility in molecular recognition events, especially when a ligand binds to a protein.Knowledge of methods that can provide details of the full spectrum of flexibility in proteins is a requisite tolaying the foundations for linking these concepts with the current algorithms employed in SBDD. Finally,we underline a number of examples that should urge the incorporation of protein flexibility in theindustrial drug design pipeline. Drug Dev Res 72:26–35, 2011. r 2010 Wiley-Liss, Inc.

Key words: protein flexibility; drug design; local motions; allostery; MD simulations

INTRODUCTION

Structure-based drug design (SBDD) integratesbiomolecular structure determination with computa-tional algorithms to predict and optimize ligand–receptor complexes. The traditional role in drugdiscovery programs of structural biology [Blundellet al., 2006; Abraham, 2007] in elucidating protein–ligand interactions and in the discovery of ‘‘hits’’(potential binders from a large collection of smallmolecules to proteins/nucleic acids) has more recentlybeen buoyed by successes in the form of fragment-based approaches [Murray and Rees, 2009]. Herelibraries of low-molecular-weight compounds arescreened by soaking them into crystals of proteintargets of interest. The ‘‘-omics’’ era, and morespecifically structural genomics, has boosted theimplementation of different ideas and protocols inthe drug discovery process. However, despite theexponential growth in the amount of informationavailable on structures of macromolecules, there has

not been the anticipated success of SBDD approaches;this stems in part from the erroneous emphasis placedon treating macromolecules as rigid structures that inreality behave as ‘‘soft’’ condensed matter [Nienhauset al., 1994; Frauenfelder and McMahon, 2001;Frauenfelder et al., 2001, 2003].

The softness of these molecules is encoded by theintrinsic property of molecular flexibility, which in turnhas a critical role in several biological functions[Hammes, 1964; Fersht, 1999; Karplus and Kuriyan,2005; Bhalla et al., 2006]. The landmark example thatdemonstrated a direct link between flexibility and

DDR

Published online in Wiley Online Library (wileyonlinelibrary.com).DOI: 10.1002/ddr.20399

Grant sponsor: Biomedical Research Council (Agency forScience, Technology and Research), Singapore.

�Correspondence to: Gloria Fuentes, Bioinformatics Institute(A*STAR), 30 Biopolis Street, 07-01 Matrix, Singapore 138671,Singapore. E-mail: gfuentes@bii.a-star.edu.sg

�c 2010 Wiley-Liss, Inc.

function was the crystallographic analysis of thestructure of hemoglobin. This yielded the location ofa bound ligand in a protein interior cavity, occludedfrom the solvent, and led to the realization that therewas no obvious route for a ligand access to the cavitywithout conformational changes [Perutz, 1965; Perutzand Mathews, 1966]. This marked the beginning ofideas that had embedded such open–close mechanismsof protein structures without losing overall structuralintegrity. Hence it was deemed necessary to developknowledge of macromolecular structural ‘‘dynamics’’and their accompanying intrinsic flexibilities.

This immediately suggests that a proper under-standing of the flexibility of biomolecules (from thispoint we will use proteins as a paradigm forbiomolecules) is essential for the design of molecules(drugs) that will replace natural ligands within bindingsites. Currently the effort suffers from limitationsimposed largely by the use of static structures. It iswell accepted that upon complexation, protein con-formations usually change (often substantially). How-ever, for many years the field of structural biology hasbeen limited in its ability to describe these changesaccurately, with limited success in cases where thesingle conformations of the proteins in their ligand-bound and apo states have been available. The gradualemergence of insights into molecular recognitionprocesses has necessitated the development of complexparadigms in order to explain the rich experimentaldata; this in turn has revealed a very complex tapestryof the dynamic behavior of proteins [Frauenfelderet al., 1991; Gerstein and Echols, 2004; Henzler-Wildman and Kern, 2007]. Historically, molecularrecognition has been classified under two distinct textbook paradigms: (1) the lock and key model [Fischer,1894]: proteins interact purely via surface shape andphysicochemical complementarity of their rigid un-bound structures, without invoking any conformationalchange; and (2) the induced-fit mechanism [Koshland,1958]: two proteins (or a protein and a ligand)recognize each other to form an encounter complexand then mutually and collectively alter their structuresto form the intricate surface complementary observedin bound structures.

However, these theories have matured, amalga-mating ideas from diverse fields, such as proteinallostery and folding, and polymer physics [Csermelyet al., 2010] into (1) the conformational selection; and(2) the population shift model [Monod et al., 1965;Kumar et al., 2000]. Before the binding interaction, theunliganded protein exists as an ensemble of low-energyconformations in dynamic equilibrium. Upon binding,the conformers whose shapes correspond to the boundstates are selected over the other conformers in the

ensemble (as a result of their favorable bindingenergies) resulting in subsequent population shiftstoward these conformers.

The flexible protein recognition model (FPRM)[Grunberg et al., 2004] is a three-step recognitionmodel characterized by the following sequence ofevents: diffusion, conformer selection, and induced fit.This finding suggests that once the ligand is in theproximity of the receptor, an initial binding encounterwill occur through a certain conformational selectionprocess, leading to an encounter complex that will befollowed by further changes to the receptor. This modelcomprehensively integrates the essential characteristicsand concepts of all the models; it also outlines thesystem dependency of the balance between induced fitand conformational selection.

With the realization of the importance offlexibility, the SBDD field has evolved from thedocking of rigid ligands into rigid receptors towardthe incorporation of ligand flexibility, and morerecently the inclusion of receptor flexibility [Owens,2004]. Ligand flexibility is addressed by systematicmethods, stochastic methods, and/or deterministicsearch methods. Receptor flexibility has included onlyactive site residues because the large size of thereceptors makes the problem computationally intract-able. More recently, this has been circumvented byincluding conformationally flexible ensembles of thecomplete receptors. These observations call for a shiftaway from the idea of one ligand perfectly adapted toone static protein ‘‘conformation.’’ Indeed, in thisregard, more insights were gained when the focus indrug development shifted toward the rational design ofmechanism-based, molecularly targeted agents. Thedevelopment and clinical successes of several small-molecule cancer drugs, including imatinib, gefitinib,and lapatinib, gave rise to a spectrum of ensembles ofconformations of the same drugs and active sites. Thefield is poised for promising developments even asbiological data drive innovative approaches, in terms ofboth target selection and technology (see later). In thisregard, the discovery that protein–protein interactions,traditionally ignored due to being large and featurelessand hence undruggable, can actually present very goodtargets, has opened up a totally new era of drugdevelopment [Arkin et al., 2003; Arkin and Wells, 2004;Wells and McClendon, 2007]. This area holds con-siderable promise because it may enable the discoveryof molecules that could allosterically modulate inter-actions and drive biological outcomes [DeDecker,2000]. The caveat here is that unlike the morepronounced pockets of enzymes, these regions arecharacterized by complex plasticity [Dastidar et al.,2008]. Clearly the immediate future is the era of

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flexibility and its imminent inclusion as an integral partof computational drug discovery [Cozzini et al., 2008].A deeper understanding of all these concepts will fosterthe comprehensive design of drugs that target the ‘‘invivo’’ receptors.

DETERMINATION OF FLEXIBILITY OF PROTEINS:EXPERIMENTAL AND COMPUTATIONAL APPROACHES

A wide range of experimental and computationaltechniques are currently used to investigate thedynamics and hence flexibility of proteins.

Experimental Methods

After the seminal hypotheses put forward byPerutz concerning the dynamic nature of proteins, itwas a while before experimental probes were success-ful. The most notable effort was that of Frauenfelderet al., who made extensive use of the technique of flashphotolysis coupled to IR spectroscopy and Mossbauerspectroscopy, using myoglobin as a testbed [Honget al., 1990]. These techniques revealed small-scaledynamic information but combination with mutagen-esis lead to elegant developments in the understandingof the links of local and global dynamics to functionaldynamics. Applications of various theories derived fromother disciplines [Frauenfelder et al., 1991] furthercemented the growing realization of the rich tapestry ofthe complex length and time scales that characterizeproteins, as well as how these various motions lead tothe emergence of the functionally important motions.Indeed, all these ideas led Frauenfelder et al. [1991] tocomment that proteins are where the physics ofcomplexity and the physics of simplicity meet. A majorimpetus was provided by the developments in time-resolved X-ray diffraction [Bourgeois and Royant, 2005;Schmidt et al., 2005a; Cho et al., 2010], NMRspectroscopy [Palmer, 2001; Mittermaier and Kay,2006], and more recently single molecule studies[Moerner and Orrit, 1999; Weiss, 1999, 2000]. Thisenabled a major departure from classical X-ray views ofan ‘‘average’’ conformation (each atom is described byits mean position, occupancy and a B-factor or atomicdisplacement parameter, the latter reflecting a certaindegree of flexibility). This problem is alleviated in partwhen the structures of the same protein are available indifferent crystallization conditions or as differentstructural intermediates in a catalytic cycle, or invarious bound states. Together these data can becombined into models of the dynamic nature of thatprotein [van Aalten et al., 1997; James and Tawfik,2003; van Westen et al., 2010]. In parallel with thesedevelopments, as sophisticated NMR techniques be-came applicable to large molecules, a new era ofpowerful insights emerged that began providing

incisive structural details of the conformational hetero-geneity inherent to the proteins [Clore et al., 2007;Lange et al., 2008; Baldwin and Kay, 2009].

In Silico Methods for Inferring Flexibility

In parallel with the above experimental techni-ques, the most significant impact in our understandingof conformational heterogeneity in proteins hasemerged from applications of physics-based atomisticsimulation methods. Although these methods arefraught with limitations, nevertheless, their extensivebenchmarking against available experimental data[Schmidt et al., 2005b] and predictive power to alimited extent has been quite revealing [Bahar et al.,2010]. The two major approaches have been moleculardynamics (MD) simulations and normal mode analysis(NMA).

Molecular dynamics (MD) simulationEach atom in a protein is subject to stochastic

influences from the surrounding atoms of the proteinas well as from the solvent atoms. The interactions ofthe atoms are modeled based on simple functions(termed force fields) that have been developed fromextensive experimental and quantum mechanical cal-culations on small systems. The time-dependentchanges in the positions of these atoms are computedby numerically solving Newton’s equations of motionsfor each atom constrained by the influence of the otheratoms; the positions are recomputed after a small timeinterval that is of the order of femtoseconds. Thecollection of these atomic positions over a period oftime (typically of the order of nanoseconds) yields anensemble of structures; the variance in their conforma-tions provides insights into the flexibilities of thevarious parts of the protein.

Normal Mode Analysis (NMA)Normal modes (NM) are the independent modes

of vibrations of an assembly of atoms (molecule),characterizing their collective motions. The indepen-dent modes are calculated by a suitable combinationof individual atomic displacements that are obtainedby the diagonalization of the Hessian matrix (composedof the second derivatives of potential energies).Each mode represents the inherent flexibility inthe system at a different resolution. Although themethod is limited in that it accounts for motionsin a single minimum energy conformation rather thanthe anisotropy imbibed in MD simulations, thisnevertheless is a relatively quick method to estimatethe flexibility patterns across a protein of interest.In addition, it is widely used to extract estimates

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of thermodynamic parameters, notably entropies[McCammon and Harvey, 1988].

Quantifying Different Degrees of Flexibility

Protein flexibility can vary across various scales ofspace and time [Henzler-Wildman et al., 2007; Henzler-Wildman and Kern, 2007], from small and local motions(vibrations of side chain), to large motions that includerearrangements of secondary structural elements/wholedomains [Daniel et al., 2003; Gerstein and Echols,2004]. Depending on the kinetics of the process ofinterest, the motion can be classified as (1) localizedflexibility, exemplified by enzymes, protein–peptide/small molecule interactions; and (2) global flexibility,exemplified by allosteric effects.

Proteins have the capacity to ‘‘jiggle’’ and mayhave multiple conformers in both bound and unboundstates [Mobley and Dill, 2009], but their preciseresolution is a challenge [Arora and Brooks, 2007; Xuet al., 2008; Boehr et al., 2009; Wlodarski and Zagrovic,2009; Joseph et al., 2010]. Studies of adenylate kinases[Daily et al., 2010] and kinase inhibitors [Aleksandrovand Simonson, 2010] have shown that the conforma-tions of the proteins in their ligand bound states arepart of the conformational spectrum that is accessed bythe proteins in their uncomplexed states. And yet, asthe two partners approach each other, small-scalemodulations of the conformations of each other isinevitable as has been demonstrated for the interac-tions of peptides derived from the transactivationdomain of p53 with the ligase, MDM2. Here, small-scale side-chain reorganizations couple the local foldingof p53 and its binding [Chen and Luo, 2007; Okazakiand Takada, 2008; Dastidar et al., 2009], and even thesesmall changes can have associated with them, signifi-cant effects on the eventual thermodynamics ofassociations [Dastidar et al., 2008]. Experimentscombined with simulations have demonstrated thatthe binding of small ligands to hydrophobic andhydrophilic cavities of the major urinary protein andlipocalin, respectively, appears to have very smalleffects on the atomic motions, and yet these changesare distributed over the whole protein [Syme et al.,2010], resulting in substantial net entropic changes;this situation is further complicated with the presenceof solvent molecules in the binding cavity of thehydrophilic cavity of lipocalin. The experience with p53and MDM2 [Dastidar et al., 2008; Joseph et al., 2010]has been exploited to design better peptide inhibitors[Madhumalar et al., 2009] and promises hope, yetseveral challenges remain to be overcome before acomprehensive theory of flexibility changes is devel-oped and can routinely be applied to drug discoveryprograms.

At the level of larger-amplitude motions, thetransitions between active and inactive conformations inkinases are a very good example of systems whereintimate knowledge of the flexibility is critical, as theseare commonly sought targets for drug discovery.Traditionally, the active state of kinases had beentargeted by ATP mimics. However, the discovery ofinhibitors that could specifically target the conforma-tions of these kinases that correspond to the inactivestates or mutant forms (especially implicated in disease)has opened up a whole new avenue for drugging theseenzymes. Detailed structural studies on the receptortyrosine kinase EGFR family shed light for the first timeon the presence of a novel allosteric site in the ATPbinding pocket where a major conformational changeresulted from the implicit flexibility of the system andpresented a new druggable site.

The structures of all these kinases have aconserved DFG motif with the phenylalanine residueburied in the hydrophobic pocket in the groovebetween the two lobes of the kinase (DFG-inconformation). In p38 MAP kinase, this region isintrinsically flexible and can adopt a very differentconformation whereby the phenylalanine side chainmoves by �10 A (DFG-out conformation), exposing alarge hydrophobic pocket [Pargellis et al., 2002]. TheDFG-in conformation is amenable to targeting by ATPmimics; in contrast, the DFG-out conformation canaccommodate very different ligands and their locationdoes not overlap with the ATP mimics. This develop-ment has great appeal, as evidenced by developmentsin inhibitors of the EGFR family of receptor tyrosinekinases that have been successfully targeted foroncology (the small molecule lapatinib accesses theinactive state of Her2, whereas erlotinib accesses theactive state [Zhou et al., 2009]).

In antibody design strategies, conformationaladaptation between antigen and antibody modulatesthe antibody specificity. The presumed ‘‘induced fit’’ inboth the antigen and the antibody upon binding hasbeen demonstrated for the association between asingle-chain antibody fragment and a random coilvariant of the leucine zipper domain of transcriptionfactor GCN4 [Berger et al., 1999].

One of the more elegant demonstrations of thecomplexity underlying the process of recognition whichsuggests that each process is really an amalgamation ofboth viewpoints (induced fit and conformationalselection) is a study of the interaction between adisordered phosphorylated kinase inducible activation(pKID) domain of the transcription factor CREB andthe KIX domain of the CREB binding protein. Thisstudy shows how the former interacts nonspecificallywith the latter and exists in multiple conformations in

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the transition state which then evolve into thestructured helical conformation as the two surfacesmodulate each other through specific interactions[Sugase et al., 2007].

INCORPORATING PROTEIN FLEXIBILITYIN STRUCTURE-BASED DRUG DESIGN: METHODS

AND ADVANTAGES

The term virtual screening (VS) was coined in thelate 1990s to describe the use of computationalmethods to predict binding modes and affinities oflarge numbers of compounds by means of high-throughput docking into the X-ray or modeledstructure of a target receptor. It is a major complementto widely used high-throughput screening. The rapidcomputational screens would then facilitate the identi-fication of novel molecules that could readily besynthesized and tested and also provide insights atthe mechanistic level into the putative binding modes.

Although the number of algorithms and proce-dures and the underlying philosophies in VS variesextensively [McInnes, 2007], the most general schemeemployed in high-throughput docking protocols is ahybrid multi-step process [Steinbrecher et al., 2006]:

1. The ligands are posed in different conformations andorientations within the active site (geometric problem).This requires the scanning of many conformationaldegrees of freedom, and has to be performed withhigh accuracy, yet with rapidity, to evaluate thousandsof compounds from the databases.

2. For a determined set of compounds, all thegenerated poses need to be evaluated and rankedusing energy-based scoring algorithms to be able todiscriminate between binders and nonbinders.

3. Finally, some of the poses resulting from the scoringphase are reevaluated in an attempt to estimate thefree energy of binding accurately to yield the mostaccurate ranking.

In steps 2 and 3, the complexity of the scoringfunctions varies. Step 2 requires simple scoringfunctions, based on simple calculations of shape andelectrostatic complementarity, to scan a large databaseto obtain a reasonable number of hits. However, toachieve the accuracy necessary to predict binding ofheterogeneous receptors and molecules, more complexschemes are required. Although this remains a majorunmet challenge [Kastritis and Bonvin, 2010], effortsare constantly ongoing to refine and develop theexisting functions. Some of the most successfulfunctions rely on detailed treatment of electrostaticsand van der Waals interactions, as well as descriptorsaccounting for desolvation, polarization, conforma-tional entropy [Gilson and Zhou, 2007].

So how does flexibility affect these protocols? Ithas been estimated that exclusion of protein flexibilityby using single rigid receptors predicts incorrectbinding poses for 50–70% of all ligands [Totrov andAbagyan, 2008]. Even when the binding site structureis relatively unperturbed upon binding, small changesin the energetics can substantially affect the results ofdocking [Kitchen et al., 2004]. Before reviewing theattempts to deal with these, we introduce a distinctionbetween two related terms: inherent protein flexibility(‘‘wiggling’’) and protein adaptability. The first onerefers to the dynamic modes and potential conforma-tions that the protein might adapt independently ofexternal factors; while the latter describes the ability ofthe protein to adapt itself to changes in its environ-ment, more specifically to the ligand.

According to the four biophysical models ofprotein binding (described in the introduction) distinctconformational sampling strategies in flexible proteindocking can be adopted. The available computationalmethods range in the degree of freedom involved inincorporating protein flexibility/adaptability during thedocking and how the associated conformational changesare taken into account [Carlson and McCammon, 2000;Alonso et al., 2006; B-Rao et al., 2009].

The inclusion of protein adaptability (allowingside chains and/or backbone flexibility) during thecalculations can be considered both implicitly andexplicitly. Soft docking is the simplest approachaccounting for flexibility; it involves scaling down therepulsive term of the Lennard-Jones potential in thescoring function [Jiang and Kim, 1991; Claussen et al.,2001]. This allows for a partial penetration of partners,thus accounting for some receptor plasticity. Forexplicit inclusion of flexibility, two approaches areused. The first approach is sampling of differentconformational states of side chains by means ofrotamer libraries to simulate the most favorableconformational spaces [Leach, 1994; Kallblad andDean, 2003]. This can be done at different stages inthe docking process: before the docking to mimicalternative receptor conformations, during the dockingto explore the dynamic binding interface, and/or afterdocking for a final optimization of the contacts. Thesecond approach involves using an ensemble [Knegtelet al., 1997; Totrov and Abagyan, 2008], with thegeneration of an ensemble of structures to be used fordocking that are either experimentally or computation-ally derived [Knegtel et al., 1997; Damm and Carlson,2007; B-Rao et al., 2009]. The most straightforwardmethod is to carry out sequential docking for eachconformer; however, in order to reduce the computa-tional cost, it is possible to combine the contributingconformations to generate an average representation of

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the receptor, using grid-based docking methods[Osterberg et al., 2002]. An additional implementationis the so-called ‘‘in situ cross docking,’’ where multipleprotein structures can be addressed simultaneously in asingle run [Sotriffer and Dramburg, 2005; Zentgrafet al., 2006]. In another alternative approach, instead ofcombining different conformations, a united proteindescription of receptors is created [Claussen et al.,2001]. In this case, a rigid average structure isconstructed from the most conserved structuralfeatures, while for the variable regions, differentconformations are explicitly considered and retainedas an ensemble.

The inclusion of flexibility in the recognitionprocess (through conformational ensembles) has two-fold added implications: (1) inclusion of conformationalensembles could lead to differences in binding pockets/cavities that are significant enough to generateconformation specific ligands [Lee and Craik, 2009];and (2) generation of allosteric inhibitors [Lindsleyand Emmitte, 2009], which holds considerablepromise after the recent demonstration of the experi-mental detection of a novel allosteric binding modeof a small molecule that stabilizes the tyrosinekinase cSrc in its inactive conformation [Simardet al., 2009].

These methods together with some others,reviewed by Alonso et al. [2006, 2007], have theirown merits and shortcomings. However, the mostlogical approach is to use a judicious combination toaccount for all types of conformational changes in bothligand and receptor, thus improving the reliability ofdocking methods [Cavasotto and Abagyan, 2004;Lill et al., 2005; Niv and Weinstein, 2005; Shermanet al., 2006].

The last step in most VS protocols is the accurateprediction of the free binding energy for a ligand andits receptor. There are different MD-based methodsthat can compute the thermodynamics of associations.Among them, thermodynamic integration and freeenergy perturbation are the most rigorous methods,making them extremely expensive computationally.This is increasingly being alleviated by MMPBSA[Kuhn et al., 2005; Gilson and Zhou, 2007], a methodthat is approximate yet that has become quite popularowing to its success in ranking affinities of differentligands/peptides to the same receptor. It is based oncomputing the molecular mechanical (MM) energy andsolvation energy whose polar part is estimated fromPoisson–Boltzmann (PB) models [or a much fasteralgorithm using the generalized Born (GB) approxima-tion of PB] and the nonpolar part is based on thechange of surface area (SA) that occurs uponcomplexation. This method has the advantage that it

can rapidly be averaged over an ensemble of structuresthat in turn can be obtained from MD simulations.

EXAMPLES OF SUCCESSFUL CASES WHERETHE FLEXIBILITY OF THE RECEPTOR HAS LED

TO NEW DISCOVERIES

So how good have these methods been? We nowselect a few examples that pertain to the various issuesdiscussed above. There have been several successstories [Klebe, 2006], for example, for the minimalrotation hypothesis, whereby side chains undergo onlysmall motions during ligand binding [Zavodszky andKuhn, 2005]. We complement this by selectingexamples dealing with historical targets and some tohighlight advances or twists in methodology.

The importance of using an ensemble of con-formations was highlighted in a pioneering study thatused MD simulations to generate a conformationalensemble of the hitherto undruggable HIV integrase.This ensemble was then subject to docking and led tothe discovery of a ‘‘novel binding trench’’ that wasadjacent to the protein’s active site but only appearedtransiently during the simulations [Schames et al., 2004].The availability of this ‘‘unseen’’ pocket led to anincrease in the array of ligands with greater selectiveaffinity and was the origin of the development of the firstclinically approved integrase inhibitor for the treatmentof HIV-infected patients, Raltegravir. Further explora-tion of this raltegravir–HIV complex for wild-type anddrug-resistant enzyme using a restrained MD protocolpermitted the dissection of drug-resistance mutationaleffects in the context of the dynamic flexibility andconformational preferences of the protein [Perrymanet al., 2010], opening the possibility of designing drugsthat would take resistance into account.

The same group similarly addressed the design ofmatrix metalloproteinase (MMP) inhibitors, a challen-ging test, as most MMP inhibitors have failed in clinicaltrials [Durrant et al., 2010]. The inclusion of con-formational selection and induced-fit effects led to theidentification of fragments that bind in the enzyme’sactive site S10 and in alternative binding pockets; theseawait the developments that would combined the twosites to generate a compound with improved potency.

Another example is the characterization of theadaptability and structural plasticity inherent to a cavitythat was found to be created in the structure of the DNAbinding domain of the oncogenic p53-Y220C mutantusing MD simulations [Basse et al., 2010]. Theseillustrated the dynamic nature of this cavity and werecombined with fragment screening to assess the drugg-ability of the protein. These studies are aimed at targetingthese misfolded mutant proteins with small molecules

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that will access such cavities and (allosterically) restabilizethe folded functional form [Wiman, 2010].

Simultaneously, several groups have tried toincorporate novel algorithms or introduce modifica-tions to established methods. Guvench and MacKerell[2009] developed an MD method termed site identi-fication by ligand competitive saturation (SILCS) thatmaps the affinity of hydrophobic and aromaticmolecules on the protein surface, including the effectof explicit water and protein flexibility. This protocolwas applied toprotein BCL-6 and, using the three-dimensional (3D) probability maps of fragment binding,the authors were able to predict the binding modeof two peptide substrates accurately, which weresubsequently confirmed by X-ray crystallography.

In Gmodel, a protocol to build 3D models ofG-protein-coupled receptors (GPCRs), low-frequencyvibrational modes are used to sample large-scalereceptor conformational changes to generate a con-formational ensemble that was subsequently validatedagainst experimental data for ensembles of GPCRs andhistamine 3 (H3) antagonist receptors [Rai et al., 2010].

The conformations defining the binding specifi-city of PDZ domains, homologues and mutants havebeen described using replica exchange moleculardynamics (REMD), together with normal modesobtained by the Elastic Network Model (ENM)incorporated as dihedral restraints to speed up thesearch [Gerek and Ozkan, 2010]. The authors correctlypredicted the pose and binding affinity for a largenumber of peptides, as well as proposed mutations onthe PDZ domains that altered the binding selectivity.

Finally, in a related method using existingstructural knowledge, B-Rao and colleagues haveproposed a knowledge-based method to statisticallymodel local flexibility in proteins [Subramanian et al.,2008]. They have been able to predict induced fit side-chain rearrangements and even more complex move-ments such as backbone flips and loop flexibility in thestructurally well-characterized p38 MAPK.

CONCLUSION

In spite of the bottlenecks still hampering thefield of the structure-based drug design and virtualscreening, there are ongoing efforts in academia,reflected in a boost in the credibility of such methods[Schneider, 2010]. Only with a deep understanding ofthe physics behind protein flexibility and the molecularrecognition events in all these endeavors delineatedhere and elsewhere in the literature, will we be able totranslationally integrate the amazing tapestry of proteinflexibility within the standard protocols for drugdiscovery employed in industrial settings.

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