Molecular dynamics and protein functionM. Karplus*†§ and J. Kuriyan§¶�

*Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138; †Laboratoire de Chimie Biophysique,Institut de Science et d’Ingenierie Supramoleculaires, Universite Louis Pasteur, 67000 Strasbourg, France; ¶Howard Hughes MedicalInstitute and Departments of Molecular and Cell Biology and Chemistry, University of California, Berkeley, CA 94720-3202; and�Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720

Edited by Bruce J. Berne, Columbia University, New York, NY, and approved February 15, 2005 (received for review January 18, 2005)

A fundamental appreciation for how biological macromolecules work requires knowledge of structure and dynamics. Molecular dy-namics simulations provide powerful tools for the exploration of the conformational energy landscape accessible to these molecules,and the rapid increase in computational power coupled with improvements in methodology makes this an exciting time for the ap-plication of simulation to structural biology. In this Perspective we survey two areas, protein folding and enzymatic catalysis, inwhich simulations have contributed to a general understanding of mechanism. We also describe results for the F1 ATPase molecularmotor and the Src family of signaling proteins as examples of applications of simulations to specific biological systems.

The conformational dynamics ofprotein molecules is encoded intheir structures and is often acritical element of their func-

tion. A fundamental appreciation forhow proteins work therefore requires anunderstanding of the connection be-tween three-dimensional structure, ob-tained with increasing rapidity by x-raycrystallography and NMR, and dynam-ics, which is much more difficult toprobe experimentally. Molecular dynam-ics simulations provide links betweenstructure and dynamics by enabling theexploration of the conformational en-ergy landscape accessible to proteinmolecules (1–3). The first molecular dy-namics simulation of a protein was re-ported in 1977 and consisted of a 9.2-pstrajectory for a small protein in vacuum(4). Eleven years later, a 210-ps simula-tion of the same protein in water wasreported (5), and the phenomenal in-crease in computing power since thennow makes it routine to run simulationsof much larger proteins that are 1,000–10,000 times as long as the originalsimulation (�10–100 ns), in which theprotein is surrounded by water and salt(Fig. 1). Significant improvements in thepotential functions have also beenachieved, making the simulations muchmore stable and accurate (6).

The engineering principles underlyingprotein design are truly baroque, withevolutionary tinkering resulting in molecu-lar machines that may be effective andefficient but whose three-dimensionalstructures are often so complicated as toobscure the mechanism of action. Cluesto the essential dynamics of the mole-cule are quite often provided by theseparation of the protein into domainsconnected by hinges and the availabilityof structures corresponding to differentfunctional states, but it is not easy fromvisual inspection or simple calculationsto deduce the workings of these molecu-lar machines. Molecular dynamics simu-

lations can provide the ultimate detailconcerning individual atomic motions asa function of time; thus, they can beused to answer specific questions aboutthe properties of a model system oftenmore readily than experiments on theactual system. For many aspects of bi-omolecule function, these details are ofinterest (e.g., which of the many resi-dues surrounding ATP in a motor pro-tein are most important for couplingATP binding to molecular movement).

The combination of increased com-puter power and improved potentialfunctions has resulted in an ability togenerate simulations that approach thepoint at which they can survive criticalexamination by the experimentalists whodetermine the structures of the proteinsbeing simulated. For small proteins or

protein domains that are not expectedto undergo large conformational transi-tions (e.g., an SH2 domain bound to aphosphopeptide), a simulation lastingseveral nanoseconds in a fully solvatedenvironment and using all-atom poten-tials without truncation of the electro-static interactions will typically result inrms deviations from the crystal struc-ture of �1 Å for the backbone atoms ofthe core region of the protein (7). Suchsimulations engender a degree of confi-dence that the interesting structuralchanges that result from the simulationof larger assemblies [e.g., intact Src ki-nases containing SH2 domains (8)] aremeaningful, as we describe later.

Simulations are most effective whenanalyzed in close conjunction with ex-periments on protein function, whichplay an essential role in validating andimproving the simulations (see, for ex-ample, refs. 9–12). One outcome of theincrease in readily available computerpower and the standardization of simu-lation protocols is that it is now becomingfeasible for nonspecialists to reproduceand check some, if not all, of the con-clusions of simulation analyses publishedby others. This kind of cross-validationwill aid in the integration of simulationsinto the normal process of interpretingnewly emerging structural results. Aprescription for making the analysis ofsimulation results more rigorous anda list of possible errors has been pro-vided (13).

In this Perspective, we illustrate theapplication of molecular dynamics simu-lations to biology by describing severalselected examples, emphasizing our ownwork. We start with a broader survey oftwo areas, protein folding and enzymatic

This paper was submitted directly (Track II) to the PNASoffice.

§To whom correspondence may be addressed. E-mail:marci@tammy.harvard.edu or kuriyan@berkeley.edu.

© 2005 by The National Academy of Sciences of the USA

Fig. 1. Simulation of a solvated protein. This slicethrough a simulation system shows a Src kinaseprotein (green) surrounded by �15,000 water mol-ecules (oxygen atoms are red and hydrogen atomsare white). The simulation system consists of�50,000 atoms, including potassium and chlorideions (purple and orange spheres, respectively). A1-ns molecular dynamics trajectory for this systemcan be generated in 4 days by using a cluster of fourinexpensive Linux-based computers. (Courtesy ofOlga Kuchment.)

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catalysis, in which simulations combinedwith experiment have led to a generalunderstanding of mechanism. The ex-plosive growth of structural results foran increasingly comprehensive range ofbiological processes demands simula-tions that address issues that are specificto particular systems. As examples, wedescribe simulation results for the F1ATPase molecular motor and the Srcfamily of signaling proteins.

Protein FoldingAn understanding of how the newly syn-thesized polypeptide chain is able tofold to its native structure is fundamen-tal to the description of life at a molecu-lar level. This issue is made all the morecritical because misfolding is involved ina range of diseases (14). A synergy be-tween simulation and experiment hasled to a conceptual solution for one as-pect of the folding problem that is con-cerned with the mechanism by which theprotein chain finds the native conforma-tion despite the immensity of the poten-tial search space. This advance hasoccurred despite the fact that folding isslow on the simulation time scale. (Eventhe fastest proteins take microsecondsto fold, and the process cannot be simu-lated directly.) Most of the simulationsof folding have used simplified modelsto capture the essential aspects of theprocess (15–17).

Key insights as to how the nativestructure can be found by the polypep-tide chain in a reasonable time wereobtained by using Monte Carlo dynam-ics for the folding of simplified beadmodels on a cubic lattice, a process fastenough so that the hundreds of trajecto-ries needed to obtain statistically mean-ingful results could be calculated withthe available computers (18). These lat-tice Monte Carlo studies showed thatfolding to the native state occurred on atime scale many orders of magnitudeshorter than that required to sample allof the configurations (the so-calledLevinthal time); e.g., with time mea-sured in Monte Carlo steps, only �5 �107 steps were required for folding a27-bead polymer that had 1016 possibleconfigurations. Although each foldingtrajectory is very different, the bias tothe native state on the free energy sur-face is sufficient that the stochasticsearch samples only a small fraction ofthe total configurations in finding thenative state. This principle is now gener-ally accepted as the solution of thesearch problem in protein folding (17,19), and the objective of present studiesis concerned with the specifics of thefolding of individual proteins and theways that misfolding is avoided.

One such study of three-helix bundleproteins used a C� model to representthe protein chain and a square well po-tential for the interactions between pairsof nonbonded residues. These simplifica-tions made possible the use of discretemolecular dynamics algorithms forstudying the folding process (20). Thespeed of the latter is such that severalhundred folding trajectories could becalculated for different relative weightsof native and nonnative interactions inthe model potential function. Fig. 2shows typical trajectories for a modelwith the native state strongly favored(Go-like potential) and a model inwhich nonnative interactions make asignificant contribution along the foldingpathway. In the former model, the heli-ces form first and then diffuse to findthe native fold [a limiting case of thediffusion–collision model (21)], whereas,in the latter model, there first is a col-lapse to a relatively disordered globuleand the helices form simultaneouslywith the native tertiary structure. Themodel results correlate with recent fold-ing experiments and all-atom (unfold-ing) simulations in explicit solvent (22).Interestingly, an extension of the dis-crete dynamics methodology has re-cently been used to simulate fibril

formation from random coil peptides,illustrating a possible mechanism bywhich large, relatively well ordered�-sheet aggregates could appear in solu-tion (23).

It is not yet possible to do statisticallymeaningful folding simulations of pro-teins with all-atom models. However,peptides composed of 20 or so residueswith a well defined native structure arebeing simulated by molecular dynamicswith implicit (15, 24, 25) and explicit(16) solvent. Although the peptides aresmall, the complexity of the simulationscan be usefully interpreted in terms ofdisconnectivity graphical (24) and net-work (26) descriptions of the foldingprocess. Given the increase in the speedof computers, particularly through thedevelopment of massively parallel ma-chines, it is likely that within the next 10years ab initio folding will reach thestage at which it can be used not only todetermine the details of the foldingmechanism but also to aid in solving the‘‘other’’ folding problem, that of deter-mining the structure of the native statedirectly from the sequence by moleculardynamics simulations.

Enzyme CatalysisEnzyme catalysis can produce rate ac-celerations of a factor of 1019 (27). This

Fig. 2. Simulations of the folding of a three-helix bundle protein. (a and b) (Upper) Semilog plots of thetime dependence of the fractions of native helical and interhelical contacts and the inverse fractions ofnative volume (calculated from the inverse cube of the radius of gyration) for two different trajectories.(Lower) Structures of the protein molecule at selected times. [Reproduced with permission from ref. 20(Copyright 1999, Nature Publishing Group).]

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process involves ‘‘molecular recognition’’at the highest level; the catalysis of pro-ton-transfer reactions, for example, re-quires the recognition of a change in aCOH bond length of �0.5 Å in goingfrom the reactant to the transition state.In 1946, before structural informationwas available, Linus Pauling (28) pro-posed that enzymes can accelerate reac-tion rates because they bind the transitionstate better than the substrate andthereby lower the activation free energy,�G‡. The validity of this key concept inenzyme catalysis has been confirmed bymany studies (29, 30), although thedynamical contribution to the preexpo-nential factor, A(T), in the Arrheniusexpression k � A(T) exp(��G‡�RT),where k is the rate constant, has to beconsidered as well (30).

Computer simulations are essentialfor understanding the lowering of theactivation free energy in terms of thestructure of the enzyme and its f lexibil-ity. The structure provides a ‘‘preorga-nized environment’’ (31) that enhancescatalysis by a greater stabilization of thetransition state than of the reactantstate, with contributions from interac-tions with the bound substrate (32, 33)and from the enzyme itself. A certaindegree of enzyme flexibility is essentialfor catalysis because atomic motions ofthe enzyme are required in all reactions(34). Moreover, larger scale motionsalso can be involved directly in catalysisand in providing a protected catalyticsite for the reaction while permitting thesubstrate to enter and the product toescape. The changes in enzyme structureand vibrational modes associated withthe progress along the reaction coordi-nate have been shown to promote catal-ysis most efficiently by lowering �G‡.This effect is distinct from the role ofsuch motions in determining A(T).

One of the enzymes that has beenstudied in detail experimentally and bysimulations is triosephosphate isomerase(TIM), which catalyzes the conversionof dihydroxyacetone phosphate (DHAP)to (R)-glyceraldehyde 3-phosphate. Theapparent barrier for the reaction in theenzyme has been calculated to be 11–13kcal�mol (1 cal � 4.18 J) lower thanthat for the reaction in aqueous solution(35, 36). The rate-determining step isthe transfer of a proton from DHAP toGlu-165 and the contributions obtainedfrom a perturbation model (35) of indi-vidual residues to lowering the activa-tion energy of this step are shown inFig. 3A; the positions of important resi-dues in the active site are shown in Fig.3B. The charged residue Lys-12 makesthe most important contribution, but theneutral His-95 side chain as well as cer-tain main chain NH groups also contrib-

ute. This type of decomposition, whichcan be validated in part by experiment,also provides a basis for determining theevolutionary variation in �G‡ for thelarge number of available TIM se-quences (M.K., unpublished data). Be-cause water can lead to a side reaction,protection of the active site is achievedby a ‘‘lid’’ motion, which makes the activesite of TIM accessible to the substrate butcloses it off for catalysis (37, 38).

Another enzyme that has been stud-ied extensively is dihydrofolate reduc-tase (DHFR), which catalyzes hydridetransfer between nicotinamide adeninedinucleotide phosphate and 7,8-dihydro-folate. Experiments (39, 40) and simula-tions (41, 42) point to the fact that thereaction coordinate is complex, involv-ing motions of parts of the enzymedistant from the active site. Althoughenzyme motion is involved, the catalysis

is primarily due to the lowering of theactivation barriers.

Dynamic effects associated with thepreexponential factor, A(T), are nor-mally very difficult to isolate experimen-tally. One contribution concerns theEyring-type transmission coefficient,which can be less than unity because ofthe recrossing of the barrier. Simula-tions have shown that, although therecrossing factor is quantitatively inter-esting in terms of a full understandingof the reaction, the magnitude of itseffect is generally small (no more thana factor of 2 or 3) as compared withother contributions (30). By contrast,tunneling can be more important inenzymatic reactions, particularly thoseinvolving the transfer of hydrogen (hy-drogen atom, proton, or hydride ion)(43–45). Because inferences concerningthis effect from isotope studies are indi-rect (46, 47), simulations have been im-portant for a direct determination of themagnitude of the tunneling. At ambienttemperatures, the calculated rate en-hancements due to tunneling range froma factor of 1.5 for the intermolecularproton transfer in TIM (43) to 780 forsoybean lipoxygenase (48). These rateaccelerations are equivalent to thermo-dynamic free energy effects of 0.2–3.9kcal�mol, a significant contribution butone that is still small compared withthe lowering of the activation free en-ergy by 10 kcal�mol or more. Detailedcalculations of the tunneling contribu-tion in DHFR are providing additionalinsights concerning its role in enzymecatalysis (49).

Molecular Dynamics Analyses of F1ATPaseThe enzyme Fo�F1 ATP synthase isperhaps the most remarkable of the mo-lecular machines that have been studiedat atomic resolution (Fig. 4A). This en-zyme is central to life because it synthe-sizes ATP by harnessing the chemicalpotential of proton gradients across cellmembranes. One component of Fo�F1ATP synthase (Fo) is mainly within themembrane and is responsible for con-verting the chemical energy of the pro-ton gradient into rotatory motion of adrive shaft that is located within the sec-ond component, which is known as F1ATPase (50). The rotational motion ofthe drive shaft is converted by F1ATPase into the production of ATPfrom ADP and Pi (inorganic phosphate,H2PO4

�). The landmark crystal struc-tures of F1 ATPase (51, 52), along withthe results of single molecule studies(53) and more conventional biochemicalexperiments (54), have provided the ba-sis for a series of simulations aimed atunderstanding how F1 ATPase works.

Fig. 3. Mechanism of TIM. (a) Electrostatic con-tribution of individual residues (in kcal�mol on theordinate) to the lowering of the activation energybarrier (TS1) of the reaction of the DHAP substrateto form the enolate intermediate. This is the rate-determining step of the overall chemical reaction.The residues are plotted on the abscissa as a func-tion of the distance from the C� carbon of theresidue [or the oxygen of a water molecule (W)] toC1 of the substrate. Negative values correspond tothe lowering of the barrier. (b) Active-site structureat transition state showing important residues andwater molecules. [Reproduced with permissionfrom ref. 30 (Copyright 2004, AAAS).]

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Of particular interest is the demonstra-tion that F1 ATPase, which by itselfnormally hydrolyzes ATP, can alsosynthesize ATP if the central shaft isrotated by external forces (55). High-resolution structures for the Fo compo-nent are not available at present, butthis experiment demonstrates that it isreasonable to hope to understand howF1 ATPase synthesizes ATP without in-cluding the Fo component in simulations(but see ref. 56 for a simulation analysisof how Fo functions).

The major structural element of F1ATPase is a hexameric assembly ofthree �-subunits and three �-subunitssurrounding the �-subunit, which has aglobular base and an extended coiled-coil domain (51) (see Fig. 4A). All six ofthe �- and �- subunits bind nucleotides,but only the three �-subunits are cata-lytically active. The crystal structures ofF1 ATPase have proven to be enor-mously informative regarding the mech-anism of the motor because each crys-tallographic snapshot provides views ofthree distinct states of the catalytic�-subunits (51, 52, 57). The centrallylocated and asymmetric �-subunit formsa shaft, and its orientation determines

the conformation of the �-subunits. Theoriginal crystal structure of F1 ATPaseled to the identification of three confor-mations of the �-subunit: �E (empty),�TP (ATP bound), and �DP (ADPbound) (51). The open conformation ofthe �E-subunit is different from that ofthe �TP- and �DP-subunits, which areboth closed and very similar to eachother.

The prescient analysis of kinetic databy Paul Boyer (58) led to his proposal(in advance of structural information) ofa remarkable and unprecedented ‘‘bind-ing change mechanism’’ that describedthe action of F1 ATPase. In terms ofthis mechanism, ATP synthesis proceedsby the cyclical conversion of the �-subunit from an ‘‘open’’ state that bindsATP only weakly to a ‘‘loose’’ state thathas higher affinity for ATP to a ‘‘tight’’state that has the highest affinity forATP. The crystal structure (51), with itsthree distinct conformations of the�-subunits and asymmetric dispositionof the �-subunit shaft, clearly supportedthe binding change mechanism proposedby Boyer (58).

Molecular dynamics simulations havemade contributions to our understand-ing of two aspects of the mechanism ofF1 ATPase. As is usually the case incrystallography, the structures are silentabout the nature of the transitions fromone state to the other and the forcesinvolved. Simulations have been usefulin piecing together at least some of whatmust happen as the motor movesthrough its duty cycle, and we discussthese studies first (59–61). The secondissue concerns the identification of par-ticular conformations of the �-subunitwith the open, loose, and tight states ofthe binding change mechanism. It is ap-parent that the empty conformation(�E) is the low-affinity state of the�-subunit, but the �DP and �TP statesare very similar to each other, and it hasnot been possible on the basis of experi-mental data alone to identify which ofthe two is the high-affinity binding sitefor ATP. Free energy difference sim-ulations of nucleotide binding to the�-subunits have resolved these ambigu-ities (62), allowing the large body of ex-perimental data on the complex to beintegrated into a detailed kinetic schemethat models the catalysis of ATP hydro-lysis by F1 ATPase in the absence of theFo component (63).

Conformational Transitions in F1 ATPase.The time scale for one rotation of the �shaft is in the microsecond to millisec-ond range (53) and is therefore not ac-cessible to the submicrosecond timescales probed by standard molecular dy-namics simulations. Because the begin-

ning and end states corresponding to a120° rotation of the �-subunit areknown (51), the challenge is essentiallyone of mapping a low-energy path thatconnects one stable conformation of thesystem to another, a general problemthat is receiving considerable attentionat present (64). The earliest attempt tocharacterize the intermediate structuresin F1 ATPase used an interpolationmethod in a cyclindrical coordinate sys-tem defined by the rotation axis of the�-subunit (65). These interpolated struc-tures were recently used after localrelaxation by molecular dynamics to an-alyze the breaking of interactions be-tween the ATP and the protein as thesystem is driven from the �TP state tothe �E state (59). More details concern-ing the conformational transitions in F1ATPase were obtained by moleculardynamics simulations in the presence ofbiasing forces that were applied to the�-subunit alone (60, 61) or to the entirestructure in a procedure that drives thesystem from one state to the other with-out explicitly constraining the nature ofthe transition path (61). The spirit inwhich these simulations are done entails‘‘pushing’’ on the molecular assembly(e.g., by forcing the �-subunit to rotate)and analyzing how the rest of the struc-ture responds (Fig. 4B). Because thetime scale of the forced rotational tran-sition of the �-subunit is orders of mag-nitude faster than the actual rotationrate, the implicit assumption in suchstudies is that meaningful informationconcerning the mechanism can be stillbe obtained.

Both of these studies demonstratedthat the rotation of the �-shaft triggersthe opening of the nucleotide-bound�TP-subunit and the closure of the open�E-subunit (60, 61). The importance ofa track of ionic residues on the �-shaftand on the inward-facing surfaces of the�- and �-subunits has been highlighted(61). These ionic residues provide amechanism for smooth rotation of the�-subunit by enabling the sequentialhandover of ion pairing interactions.The functional difference between the�- and �-subunits is demonstrated bysmooth bypass of the �-subunits bythe rotating �-subunit. In contrast,interlocking elements from the �- and�-subunits generate responses in the�-subunits as the �-subunits move.

One interesting phenomenon thatemerged from both sets of simulations(60, 61) is the rapid relaxation of the�E- subunit once the steric block im-posed by the �-subunit is removed byrotation. Although solution NMR datafor the isolated �E-subunit indicate thatthe equilibrium is shifted toward theopen conformation until the nucleotide

Fig. 4. F1 ATPase and targeted molecular dynam-ics. (a) Structure of Fo�F1 ATP synthase based oncrystal structures of the F1 ATPase (51, 52). The Fo

component is indicated as a gray cylinder withinthe membrane (yellow), and the �-subunit shaft isshown in green. The three �-subunits are indicatedby blue backbone traces, and the molecular sur-faces of the three �-subunits are shown. The�-subunit rotates in a clockwise direction duringATP synthesis, as viewed from the membrane, andthe effects of a 120° rotation of the �-subunit aremanifested as a change in the conformation of the�-subunits. (b) Schematic representation of tar-geted molecular dynamics. The simulated structureis restrained to be at a specified rms distance fromthe target structure, and this distance is graduallydecreased during the simulation. Because the re-straint is applied in an overall rms sense, the inter-mediate structures are not specified explicitly anddifferent parts of the structure can relax towardthe final structures at different rates.

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binds, the rapid closure of the �E-subunit seen in the simulations with (61)and without (60) nucleotides is consis-tent with the results of normal modecalculations (66), which show that oneof the lowest frequency (i.e., mostreadily excitable) normal modes of the�-subunit accounts for a significant frac-tion of the conformational differencebetween the open and closed forms ofthis subunit.

Free Energy Simulations of F1 ATPase. Thesimulations described so far do not ad-dress the energetics of ATP binding tothe various states. The essential diffi-culty here arises from the striking simi-larity in structure between the �DP and�TP states (52, 57). A recently deter-mined high-resolution structure of F1ATPase has the ATP analog ADP.AlF4bound at the �DP and the �TP sites, withtight coordination of the nucleotide inboth cases (52). Which site featureshigh-affinity ATP binding and which sitemore closely resembles the structure ofthe subunit when the chemical catalysisstep occurs? Answers to these questionshave been provided by free energy dif-ference simulations in which ATP.H2Ois converted computationally to ADP.Pi(62). These calculations, which are oftenreferred to as ‘‘computational alchemy,’’provide estimates of the standard freeenergy change, �Go, for the conversionof ATP into ADP at each of the occu-pied binding sites. This free energychange is calculated to be �9 kcal�molin the �DP site (i.e., ADP is strongly fa-vored over ATP at this site) and 1.5kcal�mole at the �TP site (i.e., ATP andADP have almost the same chemicalpotential at this site).

Solution measurements have shownthat, under ‘‘unisite’’ hydrolysis conditions(i.e., when the ATP concentration is solow that only the strongest binding site isinvolved), the reaction free energy is nearzero; the experimental value is 0.4 kcal�mol for the mitochondrial enzyme, whosestructure has been determined, and �0.6kcal�mol for the Escherichia coli enzyme(63). Given the free energy simulationresults just cited, it is possible to identifythe �TP as the strong binding site for ATPand the �DP site as the intermediate bind-ing site. This ‘‘missing link’’ between thesolution measurements and the crystalstructure has made possible a completeassignment of the measured binding con-stants not only for ATP but also forADP.Pi (62). Interestingly, it was alsoshown that the �DP site is the strong bind-ing site for ADP.Pi (63). These resultsindicate that there are two major contri-butions to the driving force (actually abiasing free energy) that rotates the�-subunit when ATP is hydrolysed by F1

ATPase. The first contribution is the in-crease in the binding free energy of ATPin going from the �E to the �TP state; thisresult is in agreement with the originalmodel of Wang and Oster (65). However,the second contribution is a new result; itarises from the fact that once hydrolysishas taken place and ADP.Pi occupies thebinding site, the conformation is biasedtoward �DP. A detailed kinetic model forATP hydrolysis by F1 ATPase has beendeveloped with these results and solutionkinetic constants identified with the dif-ferent �-subunits (63).

Dynamics of the Src Tyrosine KinasesThe Src tyrosine kinases are a family ofclosely related proteins that transmit sig-nals initiated by growth factor receptors inhuman cells (67). A mutant and constitu-tively activated form of Src is encoded bythe first oncogene to be discovered (v-Src;so named for the sarcomas caused by theexpression of this retroviral gene). TheSrc proteins contain a tyrosine kinase do-main that catalyzes the transfer of phos-phate from ATP to tyrosine residues onsubstrate proteins. The inappropriateactivation of tyrosine kinases such as Srccan have deadly consequences in terms ofthe onset of cancers because phosphory-lated tyrosines serve as targeting signalsfor SH2 domains in signaling proteinsthat control cell growth and differentia-tion (68).

The Src kinases themselves contain anSH2 domain and another peptide-bindingmodule known as the SH3 domain, bothof which are important for the mainte-nance of the inactive state of these pro-teins. Crystal structures of inactive Srckinases unexpectedly revealed that theSH2 and SH3 domains bind to the distalface of the kinase domain, rather thannear the active site (69, 70). How do theSH2 and SH3 domains affect the catalyticactivity of the kinase domain without be-ing located near the active site? One pos-sibility is that these domains affect theglobal dynamics of the protein, making itmore difficult for the kinase domain toundergo the change in conformation fromthe observed inactive state (69, 70) to thestructurally different active form (71).

Molecular dynamics simulations of theSrc kinases c-Src and Hck have revealedan unexpected feature of the SH2 andSH3 domains that may be a key to theregulation of the Src kinases (8). Beforethe simulation analysis, the SH2 and SH3domains were considered to be flexiblylinked and independently functioningmodules (72, 73). Thus, it came as a sur-prise that the simulations showed theSH2–SH3 unit of the assembled Src pro-teins to be coupled together relatively rig-idly. Unbiased simulations extending forseveral nanoseconds revealed that the

SH2 and SH3 domains tended to movetogether as a unit, with the linker betweenthem playing a particularly important rolein clamping these domains to the kinasedomain. In contrast, NMR studies of iso-lated SH2–SH3 units of Src kinases hadshown that these domains are linked to-gether flexibly (73). Indeed, simulations ofisolated SH2–SH3 domains do revealthem to be extremely flexible in terms oftheir relative orientation (8), suggestingthat the linker between them functions asan ‘‘inducible snap lock,’’ a term intro-duced by Wright and coworkers (74) todescribe flexible connections between cer-tain zinc finger modules that snap intorigidity when the zinc fingers bind to theircognate DNA recognition elements. Inthe Src proteins, the linker between theSH2 and SH3 domains is stabilized in theassembled and inactive state by severalhydrogen bonds. When released from theinteractions with the kinase domain, simu-lations show that these hydrogen bondsare broken by water molecules and thelinker becomes flexible (8).

Targeted molecular dynamics simula-tions in which an ‘‘activation loop’’ that islocated near the active site of the kinasedomain is driven from the inactive confor-mation to the active conformation whileleaving the SH2 and SH3 domains unre-strained showed that movements at theactive site of the kinase reverberatethrough to the SH2 domain, even thoughit is located �40 Å away (8). The cou-pling between the SH2 and SH3 domainsis the key to this intersite communicationin Src and underlies the importance of thephosphotyrosine–SH2 linkage at the baseof the distal surface of the kinase domain.If this linkage is broken, as in the form ofthe protein produced by the v-Src onco-gene, the kinase activity of Src is turnedon constitutively (67). The molecular dy-namics simulations show that the residuesin the SH2 domain that interact with thephosphotyrosine residue in the tail movesignificantly when the activation loop inthe kinase domain is displaced. The SH2–phosphotyrosine linkage would be ex-pected to resist these movements of theactivation loop and, thereby, to impedethe activation process.

The importance of rigidity in theSH2–SH3 linker was tested experimen-tally and computationally (8). Simula-tions in which linker residues werereplaced by glycine caused the SH2 andSH3 domains to become less tightly cou-pled. Introduction of corresponding mu-tations into Src proteins in cell-basedassays led to their constitutive activa-tion, bypassing the inhibitory action ofthe distal SH2–phosphotyrosine linkage(8). We note that these mutations weredesigned only as a consequence of themolecular dynamics simulations, despite

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years of prior experimental investigationinto Src function.

The Abelson (Abl) Kinase and theSpecificity of Imatinib Mesylate(Gleevec)An interesting corroboration of these re-sults from molecular dynamics was pro-vided by a subsequent structural analysisof the Abl tyrosine kinase (75). A closerelative of the Src kinases, Abl containsSH2 and SH3 domains that are similar insequence to those of Src, and, although itlacks the tyrosine residue that anchors theSH2 domain to the kinase in Src whenphosphorylated, the overall structure ofautoinhibited Abl is similar to that of Src(75). Molecular dynamics simulations ofAbl carried out as for Src suggested thatthese domains form a similar snap lock inAbl (75). As in Src, mutations in theSH2–SH3 linker activate the Abl kinase(76, 77). The difference is that the SH2domain in Abl is anchored to the kinasedomain not by a phosphotyrosine-medi-ated linkage, which keeps the SH2 andkinase domains apart in Src, but throughan intimate and direct interface thatforms between the SH2 domain and thekinase domain.

The Abl kinase domain is the targetof the successful cancer drug Gleevec,which works by blocking the catalyticactivity of a form of the Abl proteinthat is activated in chronic myelogenousleukemia (78). Gleevec binds with highaffinity to the inactive Abl kinase do-main but not to that of Src, even thoughall of the residues that make contactwith the drug in Abl are conserved inSrc (79, 80). The striking feature of thestructures of the inactive forms of Abland Src is that it is the conformation ofthe SH2–SH3 unit and not that of thekinase domain that is conserved be-tween the two proteins (Fig. 5). It ap-pears that the rigidity of the SH2–SH3unit, which is a feature of the Src andAbl simulations (8, 75), is manifested asa conservation of the structure of theSH2–SH3 unit in the inactive states ofboth proteins. Because the SH2 domainin Abl is closer to the kinase domainthan in Src, the SH3 domain in the rigidSH2–SH3 unit swings away, and the ki-nase domain adjusts its conformation soas to preserve the interaction with theSH3 domain. As a result, the kinase do-main of Abl is more open, and it is this

difference in conformation to whichGleevec is sensitive.

Future ProspectsIt is now often the case that experimentalstructures are available for more than onefunctional state of a molecular assembly,so that the problem of finding transitionpaths between low-energy regions of acomplex conformational space is becom-ing increasingly important. Despite thedifficulties inherent in the difference be-tween the experimental and simulationtime scales, it is likely that creative solu-tions to the problem will be found, as il-lustrated by the recent application of atransition path sampling method (64) tothe problem of fidelity checking by DNApolymerases (81). It will be particularlyinteresting to be able to calculate the freeenergy barriers to conformational transi-tions, which has not as yet been widelydone for protein conformational changesbecause of the difficulties in specifying thereaction coordinate for the transition. Aprominent exception occurs in the studyof ion channels, for which the reactioncoordinate is essentially defined by thesystem (82). Alternatively, the generationof multiple targeted molecular dynamicstrajectories could be used to extract thefree energy directly (83).

It is to be hoped that experimentalstructural biologists, who know theirsystems better than anyone else, willmake increasing use of molecular dy-namics simulations for obtaining a

deeper understanding of particular bio-logical systems. When molecular dynam-ics simulations are a routine part ofstructural biology, it will become clearerwhat refinements and extensions of themethodology are most needed to im-prove the results and to perfect the con-structive interplay between the simula-tions and experiment. These conclusionswill, in turn, provide challenges for thesimulation experts and catalyze new de-velopments in the field. In addition toreaction path search methods, improvedtreatments of solvent by implicit meth-ods (84) are likely to play a role here.Although simulations with explicit watermolecules are the gold standard, implicitsolvent models make possible the testingof hypotheses by repeated simulations(e.g., with different mutants and�or withdifferent constraints) for larger systems.Given the availability of several molecu-lar dynamics programs, large amounts ofcomputer time, and examples for whichmolecular dynamics has really played arole in furthering our understanding ofprotein functions, we look forward to awide field of biological applications formolecular dynamics in the future.

We thank the National Energy Research Sci-entific Computing Center for supercomputingresources and Lore Leighton for preparingFig. 4. J.K. thanks Matthew Young for manystimulating discussions regarding moleculardynamics. M.K. thanks M. Viloca-Garcia, J.Gao, and D. G. Truhlar for helpful discus-sions and collaboration on ref. 30, whichforms the basis of the enzyme kinetics text.

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