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Anthopoulos, Athanasios, Pasqualetto, Gaia, Grimstead, Ian John and Brancale, Andrea 2014.

Haptic-driven, interactive drug design: implementing a GPU-based approach to evaluate the

induced fit effect. Faraday Discussions 169 , pp. 323-342. 10.1039/C3FD00139C file

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Hapti -d ive , i te a tive d ug desig : i ple e ti g a GPU-

ased app oa h to evaluate the i du ed fit effe t.

Atha asios A thopoulosa, , Gaia Pas ualettoa, Ia G i stead a d A d ea B a alea*

a“ hool of Pha a a d Pha a euti al “ ie es, Ca diff U i e sit , Ca diff, CF NB, UK

“ hool of Co pute “ ie e, Ca diff U i e sit , Ca diff, UK, CF AA

* a alea@ a diff.a .uk

A st a t: I this pape , e des i e a h id eta-heu isti s ethod of e e g i i izatio a d

o fo atio al sa pli g a d its appli atio i to ou hapti -d i e ole ula odelli g si ulato .

The p oposed ethod has ee desig ed to suit eal-ti e ole ula do ki g si ulatio s, he e

the ti e-lapse et ee t o su essi e liga d poses is elati el s all. I these situatio s the

e e g i i izatio p o le e o es i easi gl o ple a d haoti . The algo ith is tu ed to

take ad a tage of e e t ad a es i GPU o puti g ith as h o ous ke el e e utio , hi h

has allo ed us to i lude full p otei fle i ilit i the eal-ti e i te a ti e, hapti -d i e

si ulatio s. Fi all , i this pape e ill also dis uss the i ple e tatio of su h high-

pe fo a e o puti g app oa h i ou soft a e, dis ussi g the esults of ou i itial alidatio

studies, highlighti g the ad a tages a d li itatio s of su h i te a ti e ethodolog .

. I t odu tio

The assess e t of p otei fle i ilit upo liga d i di g e ai s o e of the ost halle gi g

aspe ts of o pute -aided d ug desig . I deed, i the last de ade, se e al ethodologies that

atte pt to i i the i du ed-fit effe t ha e ee de eloped a d i ple e ted i se e al soft a e

pa kages, f o the si ple e plo atio of ota e s of p edefi ed a i o a id side hai s to the

p edi tio of a k o e fle i ilit usi g sho t ole ula d a i s u s. I all ases, ole ula

o fo atio s a e updated usi g the app op iate algo ith , depe di g o the deg ee of fle i ilit

that the appli atio offe s. The o je ti e of fi di g sta le o p efe ed o fo atio s fo the

s ste e ui es lo ati g those o fo atio s that lie o i i u poi ts o the e e g su fa e.

Ho e e , this is a o ple ulti-di e sio al p o le . Fo a s ste ith N ato s, its pote tial

e e g is a fu tio of N Ca tesia o-o di ates. The efo e, the ole ula e e g su fa e is athe

haoti a d sea hi g fo the glo al i i u a ot al a s e feasi le. Faili g that, e plo i g the

pote tial e e g su fa e is the a to fi d a set of opti al solutio s lo al i i u s he e the

est solutio a e hose . The sea h fo opti al solutio s a e fa ilitated ith the use of

eta-heu isti s.

Meta-heu isti ethods o e a ide spe t u of algo ith i te h i ues. E olutio a algo ith s

EA a d ge eti algo ith s GA a e ethods a le to ite ati el ge e ate a set of solutio s.

E olutio a algo ith s a e a o gst the ost popula eta-heu isti s a d atte pt to sol e the

opti izatio p o le ge e ati g a populatio of possi le solutio s f o a pa e t utatio .

F o the ge e ated populatio the fittest is hose to ge e ate the e pool of solutio s a d so

fo th. Ge eti algo ith s adopt a si ila app oa h, ith the additio of the o ept of osso e

et ee t o solutio s to eate the e ge e atio . These solutio ge e atio s ill e g aduall

i p o i g ith espe t to the st ateg adopted to o e a oss the e e g la ds ape.

The e plo atio of pote tial e e g su fa es e ui es the adoptio of uphill o es i o de to

e o e f o lo al i i a. “u h st ategies fo uphill o e sele tio i lude si ulated a eali g

“A , Mo te-Ca lo MC , ite ated lo al sea h IL“ , guided lo al sea h GL“ a d a o e.

“i ulated a eali g uses a te pe atu e a ia le to ge e ate a p o a ilit fu tio fo a epti g

solutio s that go uphill o the e e g su fa e. Mo te Ca lo ethods adopt a si ila app oa h to

es ape lo al i i a a epti g uphill o es ased o a p o a ilit fu tio su h as the

Met opolis ite io . IL“ ea hes opti al solutio s ite ati el pe fo i g lo al sea h to ea h

a lo al i i u ; the a it es apes a lo al i i u is pe tu i g the dis o e ed solutio , i

hope that the pe tu atio e ha is ill i g the s ste i to a ell ith a lo e i i u

tha the pa e t o e. GL“ es apes f o lo al i i a g aduall i easi g the glo al i i u

e e g alue. This a , the s ste ill es ape f o a ell if it is t apped, taki g a uphill o e

lose to the i i u , hi h ill esult i to a e la ds ape ith the hope that a ette i i u

solutio ill e dis o e ed.

Va ious eta-heu isti s ha e ee used a e e t ole ula do ki g appli atio s. Autodo k-

Vi a used a h id IL“ st ateg usi g the Met opolis ite io to a ept opti al solutio s a d

p edi t i di g affi it of p otei -liga d s ste s. Va iatio s of the ge eti algo ith ha e ee

i ple e ted i a ious pu lished o ks to p edi t ole ula st u tu es. , “oa es used a

e olutio a st ateg to sol e this p o le a d Cai a d “hao used a h id ethod of EA

o i ed ith “A fo st u tu e p edi tio . Pa adisEO app oa hed the do ki g p o le ith a

pa allel GA i o de to le e age additio al p o essi g po e a d i p o e its pe fo a e a d

effi ie .

I ou g oup e a e de elopi g a i te a ti e ole ula do ki g p og a , hi h allo s the use to

pla e, i eal ti e, a s all ole ule i a iologi al ta get, hile feeli g the ole ula i te a tio s

it esta lishes th ough a hapti de i e. , I ou o igi al s ste the ta get st u tu e as kept igid,

hile the liga d as kept fle i le a d the s ste u de e t o ti uous e e g i i isatio , ia a

steepest des e t algo ith . Re e tl e ha e e plo ed the possi ilit of i ludi g a deg ee of

p otei fle i ilit i the i te a ti e si ulatio , taki g ad a tage of the o putatio al po e of

GPGPUs Ge e al-Pu pose G aphi s P o essi g U it . The ai halle ge i ou app oa h is that

if e a t to a hie e a le el of i te a ti it that p o ides the use ith a atu al e pe ie e, ou

algo ith should e a le to o tai a sta le, lo e e g , p otei /liga d o fo atio ithi a

est i ted ti ef a e. I ou soft a e, the liga d e ei es a sig al to o e at fi ed ti e i te als t

= s, hi h at hes the e de i g ate o s ee . Whe the sig al is e ei ed the liga d ato s

a e t a slated i to thei e positio . O the o pletio of liga d t a slatio , the ai is to

p o ide the s ste liga d a d p otei ith the est ato i st u tu e he the ti e-lapse t

e pi es a d efo e the e hapti poi te sig als to t a slate the liga d to its e t positio

i i izatio le .

I this pape , e p ese t a h id eta-heu isti ethod of e plo i g ole ula e e g su fa es

ith a fo us of sol i g this p o le as a set of i depe de t p otei liga d poses ge e ated i a

est i ted ti ef a e.

. Results a d Dis ussio

2.1 CUDA Streams

The recent generation of Kepler cards (GK110) has introduced new features in GPU computing,

including the ability to run up to 32 kernels asynchronously achieving execution overlaps very

close to that of a parallel execution. This introduces an additional dimension of parallelism in

CUDA and initiates the need of new optimization techniques in CUDA-parallel programming. To be

more specific, considering this new feature, an algorithm designer should not treat each kernel

individually any more, but as part of a wider context able to utilize the graphics card more

efficiently. However, there are some rules and constraints on the way that GPUs parallelize kernel

execution: at the present time, NVIDIA GPUs are able to run 16 scheduled blocks per streaming

multiprocessor (SM). In the case of a K20 card, that gives a total of 208 blocks able to run

concurrently at any time in its 13 SMs. From a resources perspective, the shared memory is

limited at 48kb, so asynchronously scheduled kernels in each stream must not exceed the this

limit. If it does, then the parallel execution is restricted to the number of streams whose total

shared memory usage is below the 48kb limit. Regarding register usage, similar restrictions apply

and if the total number of registers used per thread exceeds the a d’s limit (255 for GK110s) then

some register overspill might take place and the slower performing local memory might be used.

Finally, overlapping streams can only write on independent memory addresses; this is not a major

problem as arrays can be copied in multiple buffers for each stream to operate on independently.

. Pote tial E e g a d Fo e Cal ulatio s

The e e g a d fo e g adie ts o putatio s to fa ilitate the i i a sea h a d

o fo atio updates fo ou p oposed ethod e e pe fo ed usi g the MMFF s fo e-field

a d i ple e ted i CUDA.

E = ∑ E o ded + ∑ E o − o ded (1)

Fi⃗⃗ = -∇Ei (2)

Rega di g the o - o ded i te a tio s, ato s e e geo et i all apped i ed i to a D

load- ala ed i egula g id. , Cal ulatio s the took pla e ith ea h CUDA lo k espo si le fo

p o essi g the ato i i te a tio s et ee ea h ho e ell a d its eigh ou s.

O e the fo e e to is a aila le fo ea h ato i, a s ala alue step- alue eeds to e fou d i

o de to update the s ste i to the e o fo atio . The o fo atio al update is pe fo ed

fo ea h ato i the s ste i e to spa e as follo s:

C′⃗⃗⃗ i = Ci⃗⃗ + λF i (3)

Where C′i⃗⃗⃗⃗ is the update position of an atom i and Ci⃗⃗ is its position before the update.

. The G eed Algo ith I ple e tatio

Gi e that ea h i i izatio le ust e pe fo ed ithi a s ti ef a e, the i itial pla

as to fi d a lo al i i u a d te i ate sea hi g i the hope that the i i u ould

o espo d to a sta le ole ula o fo atio . Fo this easo e used a si ple steepest des e t

“D step i i izatio app oa h. Its ha a te isti is that it takes o l do hill o es the g eed

app oa h , hi h esults i o e gi g i to the fi st lo al i i u it fi ds. The o fo atio

sa pli g that e o tai ed f o this ethod as slo a d so eti es e o eous; it o l helped us

u de sta d so e additio al aspe ts of the p o le e e e t i g to sol e.

A e a ple o fo atio p odu ed the algo ith is p ese ted i Figu e a d it is o ious that

the ato geo et is o g. The ato s i the histidi e a o ati i g should e o-pla a , as the

u ea g oup o the liga d. The test s ste as elati el s all < , ato s a d the e e e o

pe fo a e est i tio s. The “D algo ith as o e gi g efo e t e pi ed a d the s ste ould

ui kl opti ize its o figu atio , ithi a fe i i izatio les, to ea h ofte a e o eous

geo et as de o st ated i Figu e . Fu the i p o e e ts o the s ste ’s o figu atio ould

e eithe eall slo o ot e ide t a d the o fo atio i the po ket a ea ould al a s e o -

ideal. As a fi st thought o e ould suspe t that this ould e a a u a p o le o the fo e-

field al ulatio s. Ho e e , p o i g the liga d i to a diffe e t a ea, i du i g fu the

o fo atio al updates, ould ui kl esto e the p e iousl disto ted a ea a d ause the sa e

p o le s i the a ea of latest i te a tio .

F o the a o e it is o ious that fo the t pe of p otei liga d si ulatio s pe fo ed ou

appli atio , fi di g the fi st pote tial e e g i i u o the ole ula e e g la ds ape, ould

ot gua a tee a easo a le o fo atio a ou d the a ea of i te a tio et ee the t o

ole ules. That ight e e ause the est of the p otei ould e ell i i ized a d o s ala

alue fo E uatio ould e ist, a le to o e all ato s i e to spa e su h that the s ste

ould ea h a lo e e e g alue. What is also o th oti g is that keepi g the liga d stalled i its

positio i o de to allo the s ste to slo l e o e ould ot al a s o k eithe . That ga e the

i p essio that e e e i i izatio le ould pi k up f o al ost he e it left off a d a

e i i u ould ot e fou d. As a esult fu the o fo atio al updates ould eithe ot e

pe fo ed o the ould e too fe ith a e s all la da alue i.e. λ = − hi h ould

ha e a uite i sig ifi a t i pa t o the s ste ’s o figu atio .

I ou atte pts to desig a effi ie t e e g su fa e algo ith e also o side ed Mo te Ca lo

a d si ulated a eali g ethods. Ho e e , gi e the st i t ti e o st ai ts a d the fi ite

a ou t of steps as a esult, pe tu atio s el i g o a p o a ilit ould e pose ou solutio to

the isk of asti g o es i side a si gle alle . This ea s that du i g a s le these

te h i ues ould u de -sa ple the pote tial e e g su fa e, o pa ed to faste sa pli g

te h i ues like ite ati e lo al sea h. Both ethods ould e good a didates to the p o le of

fi di g the est o fo atio fo a p otei liga d pose o a o -i te a ti e appli atio ithout

st i t ti e o st ai ts, ut p o a l this is ot the ase i ou s e a io.

The fi st atte pt to sol e this p o le as to pe fo a shuffle te h i ue. This ea s that at

the egi i g of ea h i i izatio le a d efo e sea hi g fo a lo al i i u , all ato s i

the s ste ould o e alo g thei fo e e to t aje to ies usi g a e tai alue. E pe i e ti g

a d fi e-tu i g ith diffe e t λ alues sho ed that a good alue ould e fou d i the − ag itude. I o de to a oid ig ato o es, a o st ai t as pla ed fo the a i u alue

of displa e e t. This te h i ue i p o ed the situatio , ut aised o e s fo the o e all ualit

of the s ste ’s o fo atio as a esult of the f e ue t shuffli g.

I a se o d atte pt, e de ided to i t odu e a uphill o e app oa h, a epti g a a do

s all uphill step as a t a sie t state to e o e the s ste f o a lo al i i u . I this a e

ould e plo e the pote tial e e g la ds apes fo othe alle s i the hope that a lo e e e g

alue a e fou d. This te h i ue i p o ed the lo al esto atio defe t e ide t i p e ious

e pe i e ts. The pote tial e e g su fa e PE“ e plo atio ould ot al a s esult i a lo e

e e g o figu atio , ut the o figu atio s isualized ithi the i te a tio a ea et ee the

t o ole ules i p o ed sig ifi a tl . This is due to the fa t that e plo i g the e e g la ds ape i

this a t igge ed a highe u e of o fo atio updates. This the aused the ato s ithi

the i te a tio a ea to o e alo g thei fo e e to s a u e of ti es a d e e tuall fo a

sta le o fo atio , ith o e t o d le gths a d a gles o oth ole ules.

These e pe i e ts led us to efo ulate ou o je ti es to seek fo a ethod a le to sea h the

pote tial e e g la ds ape fo a good i i u a d also esto e the a ea of i te a tio et ee

the t o ole ules i a si gle i i izatio le. The e algo ith should e a le to e aluate

a didate solutio s i effi ie t a e . It should also e su e that ithi ea h i i izatio le

the e ould e e ough o fo atio al updates to esto e the a ea of i te a tio .

The esulted i ple e tatio uses a e olutio a algo ith to ge e ate a set of a didate

solutio s to the p o le s ste o fo atio s . The a didates a e e aluated al ulati g thei

pote tial e e g as h o ousl usi g CUDA st ea s, hi h allo s fo a deg ee of pe fo a e

gai . The pote tial e e g alue of ea h a didate is used as a i di ato of fit ess. The fit

a didate is the o e hose pote tial e e g alue is lo e to the latest e o d of pote tial e e g

i i u old i i u . If o hild solutio has a alue lo e tha the old i i u , the the

fittest hild is hose ith ite ia set ou uphill o e st ateg . The fittest hild e o es the

pa e t of the e solutio s ge e atio a d the s ste ’s o figu atio is updated adopti g its o-

o di ates. Fi all afte the o fo atio al update, the fo e e to s fo the e l updated s ste

a e al ulated.

. Fast Su fa e E plo atio a d I du ed Lo al Resto atio

I itiall ou algo ith ould fi d its fi st alle a d ai do hill u til it ea hes a i i u . O

ea hi g a i i u , the pa e t solutio ould e u a le to ge e ate a populatio ith a

a didate solutio hose e e g alue is less tha the old i i u . At this poi t a st ateg fo

es api g the lo al i i a is eeded, hi h ould e to i ediatel t a diffe e t alle i hope

of fi di g a ette i i u a d i du e sta le o fo atio al updates at the sa e ti e. The

pe tu atio st ateg of IL“ Ite ated Lo al “ea h is ideal fo ui k ju pi g f o o e alle to

a othe a epti g a uphill o e he the s ste lies at a lo al i i u . This is a hie ed

hoosi g a s ala λ alue that is a le to pe fo a shuffle ope atio i a si ila a as e plai ed

ea lie .

IL“ i itiall ga e p o isi g esults, ho e e , the e e e t o ai p o le s ith it. The fi st is that

it ould o asio all sho a isual pulsati g effe t, he e ato s ould see to pe fo a fast

os illatio . That as e ause the ti e-lapse of the algo ith ould e pi e i ediatel afte a

pe tu atio a d the s ste o figu atio o e iti g the algo ith ould ot e opti al. This

p o le ould e easil sol ed ith a ghost pa ti les uffe holdi g ato o-o di ates afte a

pe tu atio o e. The post-pe tu atio ge e atio s ould i he it a d update o-o di ates o

the ghost uffe up u til a e i i u is ea hed lo e tha the o e asso iated ith the eal

o-o di ates a a . This sol ed the pulsati g isual effe t, ut i itiated the se o d p o le that

e des i ed, he e IL“ ould ofte fi d a good i i u ithi the fi st fe o es, a d the

a kg ou d sea h follo i g the last isited i i u ould ot e a le to fi d a ette solutio .

This ould lead i to the afo e e tio ed p o le s of too fe o fo atio al updates a d he e

i effi ie t i i izatio les.

Guided lo al sea h is ideal fo i du i g updates o the p i iple that the old i i u is ei g

aised a s all pe e tage at e e solutio e aluatio pe alt fu tio . E e tuall , the

algo ith a es ape f o the ell, ut ill spe d so e ti e i it aiti g fo its pe alt fu tio

to ea h the app op iate le els. E pe i e ts sho ed that this ethod u de pe fo ed o pa ed

to IL“ fo ou si ulatio s as sho i the esults se tio .

Ou fi al solutio a e as a a alga atio of the t o app oa hes. We i t odu ed a pe alt alue

p a d a e e g fu tio E, hi h is updated at e e step i: E i + = E i ∙ + p . The

pe alt alue is usuall i the ag itude a ge of . – . , hi h ea s that o e e

ite atio the old i i u a is slightl aised so that it a allo fo a update of the s ste ’s

eal o-o di ates. This odifi atio ai s to keep the fast t a k of alle e plo atio that IL“ a

offe a d i t odu e e ha ed o fo atio al update a ilities to ou app oa h. We ill efe to

this algo ith as IGL“ Ite ated Guided Lo al “ea h .

I esse e Ite ated Guided Lo al “ea h a hie es the follo i g: it a epts updates fo e e g

alues lose to a e e tl isited lo est e e g o figu atio , fo su se ue t solutio populatio s.

Faili g this, he su se ue t solutio ge e atio s do ot p odu e a update, it p e e ts the

algo ith f o asti g too a ge e atio s ithout pe fo i g a update, hi h i tu

e efits the isual effe t of ou appli atio as ell as the i te a ti g ole ules o figu atio ,

espe iall ithi thei i te a tio zo e.

The i ple e tatio of the a o e te h i ue is pe fo ed usi g t o e e g a ia les:

u e t_lo est a d the glo al_lo est. E e ti e a e i i u is ea hed, oth the

u e t_lo est a d the glo al_lo est take its alue. Whe a lo al i i u is ea hed a d the

e ge e atio a ot p o ide a a didate ith a lo e e e g alue, pe tu atio takes pla e fo

a e ell to e e plo ed a d the u e t_lo est takes the o espo di g e e g alue. While i

the e alle , the glo al_lo est alue is aised at e e e e g e aluatio a s all fa to su h

as . % , hile the u e t_lo est keeps ea hi g lo e alues, u til e ea h the e lo al

i i u . No at this poi t the e a e t o possi le out o es: the fi st is that the e lo al

i i u u e t_lo est , is lo e tha the glo al_lo est a d a o fo atio update is ei g

pe fo ed. Othe ise, a e pe tu atio o e is ei g pe fo ed. Ho e e , the p o a ilit of

fi di g a e i i u this a is di e tl p opo tio al to the u e of e e g e aluatio s

pe fo ed. This a , a g eate atio of o t i uti g o es o pa ed to the total u e of o es

is a hie ed, hi h i tu assists i to sol i g the lo al esto atio p o le .

This algo ith allo s fo ui k e plo atio of ole ula e e g su fa es, e ou agi g o e

o fo atio al updates tha a pu el IL“ st ateg ould pe fo . The isual esult is sig ifi a tl

i p o ed, as the e is apid lo al esto atio o the i pa t a ea.

. As h o ous E e utio

The performance of the above algorithm is important for our application as the potential energy

landscape exploration is constrained to run for a small timeframe t. The more moves we are able

to perform in the landscape, the better the quality of the resulting solution will be. In addition, the

more parent solutions the algorithm generates, the higher the probability of discovering lower

energy minimums. From the above, we understand that performance is vital for our simulations.

Initially, our force and energy kernels as well as a binning method to an irregular grid approach

were designed to minimize execution time on GK104 chipsets. After the introduction of GK110,

concurrent streaming capabilities were enhanced, giving the option of running up to 32 kernels

asynchronously, when the criteria listed in the background section were met.

For this reason, we designed an algorithm to take advantage of asynchronous kernel execution

using CUDA-streams (Hyper-Q according to NVIDIA terminology) and improve the performance of

the existing CUDA functions. In order to achieve peak performance, all the aforementioned

kernels (energy, force, binning) were refactored in order to meet the criteria for concurrent

execution and without sacrificing their individual performance. On the current generation of

graphics cards (GK110), we can now evaluate fit of a whole generation of solutions asynchronously

and almost in parallel for smaller protein-ligand systems. This means that the first energy

evaluation kernel hides the latency of the remaining s-1 evaluation kernels scheduled to run

asynchronously in s streams within a single move (a population evaluation series, followed by a

force calculation for the fittest child), where s<=16. However, if the conditions mentioned in the

background section are not met, then the number of asynchronous streams running concurrently

at any time drops as demonstrated in Figure 2.

I additio , e a hide the late of i i g the ato s i to a D i egula g id fo the ell-list

ge e atio i the fo e g adie ts ke el. This ea s that i i g o e heads a e o lo ge a issue

a d i i g a e safel pe fo ed at e e si gle o e, ith a s all ski alue�′ that ate s fo

the iggest displa e e t that e a ti ipate du i g ea h o e utofflist = ut-off + �′

. Pe fo a e of the Algo ith

The pe fo a e of ou CUDA-st ea s i ple e ted h id app oa h has ee e h a ked

agai st a s h o ous e e utio e sio i CUDA of the a o e algo ith . I o de to pe fo a fai

o pa iso , e h a ks e e s heduled su h that oth e sio s ould pe fo the e a t sa e

a ou t of e e g a d fo e g adie t ke el alls. The esults a e listed i Figu e fo —

o u e t st ea s. F o the figu es e a o se e that the e a e pe fo a e gai s flu tuati g

f o . X - X depe di g solel o the s ste size a d u e of st ea s. Rega di g sha ed

e o usage, oth ou o - o ded e e g a d fo e g adie t ke els a e usi g kB he eas the

o ded e e g a d fo es ke els do ot use sha ed e o . That ea s that ou o u e t

e e utio o e e g e aluatio s at a ti e is est i ted to fi e ke els as a si th ke el ould

o e take the kB sha ed e o pe “M li it.

Ou algo ith a hie es fi e o u e t e e g e aluatio s fo s ste sizes up to , ato s.

This is e ause the e e g ke els di ide e e utio i to lo ks of th eads, he e ea h th ead

is asso iated ith o e ato . This e uates i to a a i u of lo ks pe st ea , hi h is good

e ough to satu ate the li it of fi e o u e t st ea s gi e f o sha ed e o est i tio s

< o u e t lo ks li it . Be o d that li it, the u e of lo ks eeded to p o ess the

e e g ke els ises a o e a d that ea s that CUDA a ot ha dle the total u e of

lo ks s heduled fo as h o ous e e utio s, he e o u e effi ie sta ts dete io ati g.

Fi all , fo s ste s a o e , ato s, o u e al ost a ishes o the e e g ke els as the

u e of lo ks eeded to p o ess o e s ste alo e is lose to the a hite tu e’s li it. Ho e e ,

o u e i the fo es- i i g st ea pai still holds as ou i i g ethod has ee desig ed

usi g a se ies of fast e e uti g ke els a le to u usi g o l a fe lo ks. I additio , o ded

fo es ke els e ui e fe e th ead lo ks a d he e a o t i ute to a ds so e as h o ous

e e utio too. This is h e e i the ase of la ge s ste s , – , ato s the e is still a

s all pe fo a e gai .

Ta les a- de o st ate a o pa iso of the elapsed ti e of e e utio fo the al ulatio s

de o st ated i Figu e et ee CUDA-s h o ous a d CUDA-as h o ous e e utio s. I

additio , it appe ds e e utio ti es fo the se ial i ple e tatio o a o kstatio e uipped ith

a Tesla K- GPGPU a d a d I tel Xeo E - CPUs u i g at . GHz. The se ial e e utio

uses the Ope Ba el li a fo the MMFF s al ulatio s.

. Heu isti A ilit of the Algo ith

Figu es a-d p o ides a s he ati o e ie of the fou algo ith s e aluated i this epo t. The

g aphs depi t the steps take ea h algo ith i o de to o plete a s o putatio al le.

I o de to de o st ate ho IGL“ sol es ou p o le , the sta ti g o figu atio as hose afte

a u of , steps of the g eed algo ith . This a , the sta ti g o fo atio lies at o e

ea a lo al i i u a d Figu es a- de o st ate ho ea h of the th ee p oposed te h i ues

es ape it a d e plo e the la ds ape. Fo these al ulatio s, the pa a ete s used a e o

des i ed.

Eight e uall spa ed alues a e hose i the a ge . - . a d allo ated to ea h

o putatio al st ea ; pe tu atio fo oth GL“ a d ILG“ is happe i g hoosi g the hild solutio

ith i de fou . The e has ee so e e pe i e tatio ith diffe e t pa a ete s fo the

pe tu atio step, ut esults a e si ila . The esse e of pe tu atio is to es ape the u e t

alle a d e plo e the est of the la ds ape a d e ha e o k o ledge he e ea h hild solutio

ill i g us. The pe alt alue as set to . fo oth GL“ a d IGL“ . I IGL“ it is applied e e

ti e a pe tu atio takes pla e a d i GL“ e e ti e a e glo al i i u is ot ea hed. A

good e a ple of the e efits of ou app oa h is de o st ated i Figu es a a d he e IL“ a d

IGL“ a e o pa ed head to head. I steps th ee a d fou oth app oa hes a e pe tu ed a d i

step fi e the p oposed app oa h as a le to a ept a e good solutio slightl ela i g the

eGlo o st ai t glo al_lo est ith the aid of the pe alt fu tio . The easo h GL“ as

dis a ded is e ause it a spe d too u h ti e i the sa e ell aiti g fo the pe alt fu tio

to aise o e the latest dis o e ed e e g le els. We elie e that the p oposed ethod i he its the

e its of oth IL“ a d GL“ fo the solutio of ou p o le . Fi all , the g eed algo ith had

o e ged efo e step as e al ead e plai ed, he e e see a st aight li e depi ti g the est

hild solutio epeatedl fou d o ea h i le p o a l fo λ= . .

A othe set of e pe i e ts as desig ed to sho the flu tuatio of e e g alues usi g the fou

algo ith s i o pa iso . I o de to pe fo this e aluatio , e a t o sets of e pe i e ts. The

fi st set listed i Figu es a-d de o st ates the heu isti a ilit of ou app oa h fo liga ds

positio ed lose e ough to a p otei i o de to t igge i te a tio s. The se o d set of

e pe i e ts listed i Figu es a-d is si ila to the p e ious set, ith the o l diffe e e that it

pe fo s the sa e u e of i i izatio steps fo p otei s i thei i itial pd file st u tu e.

I the si ulatio esults p ese ted i Figu es a a d e a see that the h id ethod a d the

IL“ lea l outpe fo the GL“ a d the g eed app oa h. It is also i te esti g to see that i all fou

figu es the u es of the h id a d IL“ ethods ha i g a si ila t e d, ith the h id ethod

follo i g a o e tu ule t t aje to due to its pe alt fu tio . Figu e sho s a i te esti g

ase as e a see that IL“ is t apped at a i i u , hi h it a ot es ape f o . F o Ta le e

a see that fo the e pe i e t ith F E.pd st u tu e, IL“ ould o l ea h fou i i a a d

he e pe fo fou updates. This e plai s h this ethod a ot pe fo ell o ou

si ulatio s, as this is a o o s e a io. Fou updates a e usuall ot e ough to i g the s ste

a k to a sta le o fo atio . Ou ethod o the othe ha d, usi g the pe alt fu tio a

es ape su h a i i u a d a ies o e plo i g diffe e t e e g la ds apes, fi di g e

i i u s a d updati g the s ste i to ette o fo atio s. Rega di g GL“ o its o , as e

a see it al a s u de pe fo s o pa ed to the h id ethod a d it o l pe fo s ette tha

the IL“ at the F E e pe i e t, he e IL“ gets stu k i a ell.

The fou g aphs i Figu e p ese t si ila eha io . Ou h id ethod, togethe ith IL“ a e the

est pe fo i g o es as the al a s ha e the a ilit to dis o e deepe alle s i o pa iso to

the othe t o ethods.

F o these esults, it is e ide t that the h id app oa h is the ost suita le fo the t pe of

si ulatio s that ou soft a e pe fo s fo its a ilit to fi d a e good pote tial e e g i i u

i du i g o e o fo atio al updates tha it ould follo i g a pu e IL“ st ateg . Also, the GL“

ele e t i it does ot eall ea that the esulti g glo al i i u ould e lo e tha the

o espo di g IL“, o is the opposite t ue. The h id ethod see s p o isi g i sol i g othe

opti izatio p o le s too due to its a ilit to ha ge o e diffe e t e e g la ds apes a d

e aluate se e al diffe e t i i a at a s all te po a pote tial e e g ost.

O e all, the i p o ed h id algo ith as fast a d espo si e i ou si ulatio s. The e e e o

e ide t disto tio s i a of the ole ules st u tu e a d i te a tio s et ee liga d a d p otei

e e odelled a u atel i eal ti e. This algo ith is apa le of si ulati g s ste s of up to

, ato s, ith the i te a ti it ei g i e sel p opo tio al to the s ste size. Fo la ge

s ste s of , – , ato s, the use has to stall the liga d i side a po ket fo the

i i izatio algo ith to take effe t. The o e all o fo atio ualit a i p o e ith the

additio of a lo g a ge ele t ostati s outi e. Fi all , as the algo ith elies al ost solel o

floati g poi t ope atio s a d futu e GPGPU ge e atio s p o ise e e ette FLOP apa ilities, e

ould e pe t ou app oa h to pe fo e e ette o fo th o i g a ds.

. Algo ith P a ti al Appli atio s

To fu the alidate a d assess ou s ste e ha e also a ied out a set of si ulatio s that ai ed

to i i the a tual use of the soft a e. To e aluate the i du ed fit effe t, e ha e sele ted a

se ies of k o iologi al ta get fo hi h a se ies of stal st u tu es a e a aila le Ta le as

apo fo a d o- stallised ith o e o o e liga ds.

Ou app oa h as to e t a t a liga d f o a spe ifi o ple , the pla e it i the apo fo of the

o espo di g p otei usi g the hapti -d i e si ulato . The esults o tai ed f o the do ki g

e e the o pa ed ith the st u tu e of the o ple f o hi h the liga d as e t a ted,

easu i g oth the oot ea s ua e de iatio RM“D of the liga d a d the p otei esidues of

the i di g po ket supe positio fo the diffe e t p otei s as pe fo ed o the hole st u tu e

usi g the a k o e CA as efe e e . Fu the o e, the po ket esidues e e also o pa ed ith

the o espo di g a i o a ids i the apo fo a d thei RM“D al ulated. I this a e , e

ould esti ate of ho u h the p otei had ea ted to the liga d i di g a d if the i du ed-fit

effe t as o pa a le to the a tual stallog aphi st u tu e of the o ple .

The p otei e ha e hose p ese ts th ee s e a ios: CDK p ese ts a fai l igid i di g po ket

a d o l a fe i o side hai o e e ts a e isi le et ee the apo fo a d the

liga d/p otei o ple es. The t o hose alloste i po kets of the HCV N“ p ese t a o e

e ide t i du ed-fit effe t a d, i so e ases, the e is also a lea p otei a k o e o e e t;

the HIV e e se t a s iptase p ese ts o e of the ost d a ati effe ts of i du ed-fit, as the

i di g po ket of the o u leoside e e se t a s iptase i hi ito s NNRTIs is ot e e isi le i

the apo fo . All the si ulatio s e e pe fo ed o a uad o e Ma P o, u i g U u tu

. , e uipped ith a NVIDIA Gefo e a d a Pha to O i hapti de i e. As e o side

the idea of de elopi g a atu al a d i tuiti e i te fa e at the o e of ou soft a e de elop e t

effo ts, e ha e asked a u de g aduate Medi i al Che ist stude t to pe fo the si ulatio s.

. . CDK esults.

Fo CDK e ha e u si ulatio s, o e fo ea h liga d epo ted i Ta le . I te s of liga d

pla e e t, the hapti -d i e si ulatio pe fo ed elati el ell; the liga d RM“D alues a ged

et ee . Å a d . Å. The i di g site esidues RM“D also p odu ed so e e i te esti g

esults: Figu e sho s t o e a ples of the esults o tai ed a d it is possi le to see ho the

p otei has o ed i the si ulatio . I te esti gl , the iggest o e e ts a e see o the

esidues that a e i deed the ost fle i le he the apo a d the o ple st u tu es a e o pa ed.

Figu e sho s ho the diffe e t esidues ha e ha ged a d if thei o e e t is to a d the

o fo atio p ese t i the o ple st u tu e. I this ase, it is possi le to see a o e o ple

s e a io, he e so e esidues o e to a ds the o espo di g positio i the o ple st u tu e

i di ated i g ee a d othe s that o e a a f o that ideal positio i di ated i ed .

Ho e e , e should poi t out that the i te als pa a ete s ep ese ted i Figu e a e ot

a solute alues ut the ep ese t ho fa a a the hapti ge e ated st u tu e is to the

stallised st u tu e, elati e to the RM“D of the stallised st u tu e s the apo st u tu e. As

ost of the esidues i tuall etai the sa e o fo atio i all the diffe e t st u tu es, thei

a tual a solute RM“D-apo/ ryst alues a e e s all. He e, a i o , fa ou a le o

u fa ou a le, o e e t ge e ated f o the hapti -d i e si ulatio ill e flagged as

sig ifi a t. A e eptio is ep ese ted L s , hi h o es o side a l f o the apo

st u tu e to the JVP stal st u tu e RM“D of . Å . I the hapti d i e si ulatio , the

esidue o e to a d the positio p ese t i the stal gi i g a RM“D-hapti / st of . Å

Figu e . I this ase it is lea l e ide t the i du ed-fit effe t ge e ated the hapti -d i e

liga d pla e e t.

. . HCV NS esults.

The t o po kets sele ted fo this ta get Pal a d Thu - p ese t a o e o ious i du ed-fit

effe t, o pa ed to hat has ee o se ed ith CDK . I deed, the esults o tai ed a e o e

i fo ati e Figu e as sig ifi a t o fo atio al ha ges f o the apo st u tu e to oth the

hapti ge e ated st u tu e a d the stal o ple a e e ide t fo a good u e of esidues.

The data p ese ted i Figu e suggests that the e is a ge e al te de of the i di g site

esidues to o e i the di e tio of thei espe ti e o ple o fo atio . I o e spe ifi ase,

e e a ele a t a k o e o e e t is p ese t Figu e . I te esti gl , so e esidues o e

a a f o the ideal fi al positio a d this see s to e happe i g fo t o easo s: the pla e e t

of spe ifi liga ds is ot a u ate the liga ds RM“D a ge et ee . Å a d . Å ; so e

spe ifi esidues should u de go a o side a le o fo atio al ha ge a d the algo ith is ot

et a le to e plo e the full ota e spa e a aila le to the esidue.

. . HIV RT esults.

The HIV e e se t a s iptase p ese ts a e diffe e t s e a io: the NNRTIs po ket is ot

st u tu ed o isi le i the apo fo . The e te t of the i du ed fit effe t, i this ase, is eall

e a ka le. The hapti -d i e pla e e t esults a e sho i Figu e a d the lea l

de o st ate the o ple it of these si ulatio s. The use a push the liga d i side the po ket

a d all the RM“D alues sho s a positi e o e.

To a e tai e te t, this is ot su p isi g, o side i g ho diffe e t the apo st u tu e a d the

stal o ple a e. I deed, e a see that although the hapti -d i e si ulatio is a le to

i du e a su sta tial ea a ge e t i the p otei to a o odate the liga d i a easo a le

a e , the e te t of the p otei st u tu al ha ge upo liga d i di g i ealit is so e te si e

that, o e agai , the algo ith a ot e plo e effi ie tl su h o fo atio al spa e.

. Co lusio s

I this pape e ha e epo ted a h id e olutio a st ateg fo e plo i g ole ula e e g

la ds apes i a s all elapsed ti e pe iod. Ou algo ith as tu ed to take ad a tage of the e t a

pa allelis that CUDA st ea s a p o ide as the fo efield used as also oded i CUDA. The

p oposed ethod o ks ell fo o fo atio al sa pli g i situatio s he e the e it ite io fo

the eta-heu isti is the e pi atio of a e s all ti ef a e. We ha e also tested the algo ith

i a possi le a tual usage s e a io, i ple e ti g it i ou hapti -d i e ole ula odelli g

si ulato . Usi g a se ies of stal st u tu es, e ha e e a i ed ho the full fle i le hapti -

d i e si ulatio as a le to i i a i du ed-fit effe t.

O e all, e elie e the esults o tai ed a e positi e a d e e ou agi g. The hapti -d i e

si ulatio s o u i a e atu al a d i tuiti e fashio . Fu the o e, ou esults sho s that e

a i du e ea i gful a d app op iate o fo atio al ha ges i to a p otei pla i g a liga d

usi g a i te a ti e, eal ti e app oa h. The data o tai ed also sho s so e li itatio s of this

ethod. Although the algo ith pe fo s e t e el ell, it is possi l too o se ati e he

sa pli g the o fo atio al spa e of the p otei esidues of ou se, a o e po e ful GPGPU

a d ould e plo e o e o fo atio al states . This should ot e o side ed al a s a egati e

aspe t, as, fo e a ple, this o e autious e plo atio ould p ese e so e i po ta t st u tu al

i fo atio i a i di g po ket i those ases he the use applies a st o g fo e to the liga d

th ough the hapti de i e. Ho e e , as i the ase of HIV RT, so eti e the ha ges a e i deed

d a ati a d the ould e ui e a algo ith a le to sa ple a o side a l igge o fo atio al

spa e to o tai a a u ate p edi tio .

I o pa iso ith a lassi al a ual ole ula do ki g ethodolog , hi h ofte is pe fo ed

i th ee sepa ate stages pla e e t, e e g i i izatio a d e aluatio , the i teg ated a d

i te a ti e atu e of ou ethodolog allo s the esea he also to e plo e othe aspe ts of the

i di g p o ess. Fo e a ple, the use ould i estigate a d e aluate, to a e tai e te t, the

esults ge e ated app oa hi g the desi ed a ti e site f o diffe e t di e tio s, as the a tual

path take the liga d ould affe t the p otei o fo atio al ha ges. Clea l , a a u ate

esti ate of this p o ess ould e ui e a sig ifi a tl highe o putatio al po e tha the o e

a aila le to ou s ste , hi h u e tl a o l p o ide a s all i sight i this pa t of the i di g

e e tissue, as the esults o tai ed f o the HIV RT suggests.

Ho e e , e e if e elie e ou app oa h ep ese ts a step fo a d i add essi g se e al

i po ta t aspe t of p otei /liga d i di g, the e a e still se e al issues to e add essed. I

pa ti ula , effi ie tl e plo i g the liga d o fo atio al spa e ithi a e sho t ti ef a e a d

i a i te a ti e e i o e t e ai s a halle ge. I ou s ste , the esults fo fle i le liga ds >

ota le o ds ould e e diffe e t ased o the sta ti g o fo atio of the liga d. “ol atio ,

as i a othe do ki g algo ith s, is also diffi ult to e aluate. It is t ue that a i pli it odel

ould e used, ut ofte the i fo atio p o ided e pli it ate ole ules ould e esse tial

to u de sta d the a u ate i di g of a liga d.

I o lusio , e elie e the esults p ese ted he e a e e p o isi g a d e ou agi g. We a e

o taki g the e t step i the alidatio ou s ste a d the i te a ti e app oa h, o pa i g

the hapti -d i e si ulato to othe o e ial a d o - o e ial fle i le do ki g pa kages.

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P. L. Beaulieu, Bioorg. Med. Che . Lett., , , .

28 P. L. Beaulieu, R. Coulo e, J. Dua , G. Fazal, C. God out, O. Hu ke, A. Jakalia , M. A. Jol ,

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.

30 J. Re , R. Es ouf, E. Ga a , D. “o e s, C. Ross, I. Ki , J. Keeli g, G. Da , Y. Jo es a d

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31 Mole ula Ope ati g E i o e t . . Che i al Co puti g G oup, Mo t eal,

Ca ada. http:// . he o p. o

-“t ea s a

Proteins K atoms synchronous hyper-Q Serial

1UGM.pdb 2.103 1.8 0.87 127.5

3RDD.pdb 2.786 1.976 1.115 235

3F9E.pdb 4.74 2.508 1.378 647.5

3FAU.pdb 5.917 3.126 1.677 1017.5

2O0O.pdb 7.165 3.074 1.662 1447.5

3O05.pdb 13.053 4.216 2.368 7430

2EAR.pdb 15.528 4.14 2.826 7450

1TBG.pdb 26.927 5.46 4.74 32277.5

-“t ea s

Protein K atoms synchronous hyper-Q serial

1UGM.pdb 2.103 1.272 0.393 89.16667

3RDD.pdb 2.786 1.502 0.511 165

3F9E.pdb 4.74 1.862 0.74 455.8333

3FAU.pdb 5.917 2.307 0.868 719.1667

2O0O.pdb 7.165 2.248 0.941 1015.833

3O05.pdb 13.053 3.022 1.486 5870

2EAR.pdb 15.528 2.999 1.8 5423.333

1TBG.pdb 26.927 3.559 3.062 25385.83

Ta le a- Results fo CUDA-s h o ous, CUDA-as h o ous a d se ial e e utio s.

Ta le a. Nu e of o fo atio al updates fo ea h p otei fo the fou diffe e t algo ith i

app oa hes i Figu es a-d.

Hybrid ILS GLS Greedy

1UGM.pdb 27 24 20 15

3VB3.pdb 26 20 22 11

3F9E.pdb 18 4 17 5

3OOP.pdb 26 20 18 10

Table 2b. Nu er of o for atio al updates for ea h protei for the four differe t algorith i

approa hes i Figures 6a-d.

Hybrid ILS GLS Greedy

3RDD 23 20 25 13

3FAU 25 20 25 15

2O0O 29 22 19 18

3O0P 27 20 25 12

Ta le . C stal st u tu es used i the hapti -d i e si ulatio s

P otei PDB Liga d Na e

CDK

HCL -

G) MBP

JVP LIG

R F “C

R H “CE

R I “CF

R R “C

EK CK

FKL CK

HCV N“ B

NB -

UPH C

U O E

GNW XNC

J JE

J JG

JJU MB

P otei PDB Liga d Na e

CDK

HCL -

G) MBP

JVP LIG

R F “C

HIV RT

RTJ -

VRT NVP F ag e t

Figu e . A s apshot of liga d app oa hi g a p otei usi g a g eed lo al sea h app oa h. The

algo ith is o ti uousl i p o i g afte the i itial i pa t, ut settli g at e o eous

o figu atio s e ide t geo et i disto tio o the p otei a d the liga d

Figu e . Visual p ofili g data fo a o e. As h o ous e e utio of s st ea s he e the total

lo k a d sha ed e o o ditio s a e ot et. O e lappi g is di ided i to st ea g oups that

eet the ha d a e’s lo k a d sha ed e o est i tio s. The kE lo ks ep ese t the o -

o ded e e g ke els, a d the o espo di g o es u de the kF lo k a e the o ded fo es a d

the D g id i i g ke els.

Figu e a-d. Pe fo a e e h a ks et ee CUDA-as h o ous a d CUDA-s h o ous

st ea e sio s. The sa e a ou t of e e g ke els , a d Fo e g adie t ke els

, / st ea s a e e aluated f o oth app oa hes i ea h g aph.

0

5

10

0 20 40 60 80

Pe

rfo

rma

nce

(se

c)

Number of atoms (x1000)

Hyper-Q vs Synchronous

4-Streams (a)

synchronous hyper-Q

0

4

8

0 20 40 60 80

Pe

rfo

rma

nce

Number of atoms (x1000)

Hyper-Q vs Synchronous

8-Streams (b)

synchronous hyper-Q

0

3

6

0 20 40 60 80

Pe

rfo

rma

nce

Number of atoms (x1000)

Hyper-Q vs Synchronous

12-Streams (c)

synchronous hyper-Q

0

3

6

0 20 40 60 80

Pe

rfo

rma

nce

Number of atoms (x1000)

Hyper-Q vs Synchronous

16-Streams (d)

synchronous hyper-Q

Figures 4 a-d. A schematic overview figure of the four algorithms compared in this report,

depicting how a calculation circle of t=33ms is subdivided into 8 steps of a group of energy kernel

evaluation streams for the 1UGM.pdb protein file. The system is already minimized with a local

search method prior to running the experiment.

-2000

-1500

-1000

-500

0

0 2 4 6 8 10

IGLS (a)

eold eglob

-2000

-1500

-1000

-500

0

0 2 4 6 8 10

ILS (b)

eold eglob

-1550

-1500

-1450

-1400

-1350

0 2 4 6 8 10

GLS

eold eglob

-2000

-1500

-1000

-500

0

0 5 10

Greedy (d)

eold eglob

Figu es a-d. Co pa iso of the diffe e t app oa hes ith the liga d pla ed i to a po ket.

-2000

-1500

-1000

-500

0

0 10 20 30 40

De

lta

En

erg

y

Moves

Meta-Heuristics Comparison

1UGM.pdb (a)

Hybrid

ILS

-1500

-1000

-500

0

500

1000

1500

0 10 20 30 40

De

lta

En

erg

y

Moves

Meta-Heuristics Comparison

3F9E.pdb (b)

Hybrid

ILS

-8000

-6000

-4000

-2000

0

0 10 20 30 40

De

lta

En

erg

y

Moves

Meta-Heuristics Comparison

3VB3.pdb (c)

Hybrid ILS GLS Greedy -16000

-12000

-8000

-4000

0

0 10 20 30 40

De

lta

En

erg

y

Moves

Meta-Heuristics Comparison

3OOP.pdb (d)

HYBRID

ILS

Figu es a-d. Co pa iso of the diffe e t app oa hes ith fou u - i i ized p otei s.

-6000

-4000

-2000

0

2000

4000

0 10 20 30 40

3RDD

IGLS ILS GLS Greedy

-6000

-4000

-2000

0

2000

4000

6000

0 10 20 30 40

3FAU

IGLS ILS GLS Greedy

-2000

0

2000

4000

6000

8000

10000

0 10 20 30 40

2O0O

IGLS ILS GLS Greedy

-16000

-14000

-12000

-10000

-8000

-6000

-4000

-2000

0

0 10 20 30 40

3O05

IGLS ILS GLS Greedy

Figu e . “ apshot the “FC liga d a uall do ked i the CDK as e a ples of esidue

o e e ts. The apo st u tu e ep ese ted i g e ; the stallised o ple ep ese ted i a ;

the st u tu e o tai ed f o the si ulatio is i di ated i o a ge.

Figu e . Visual ep ese tatio of ho CDK esidues had o ed du i g the si ulatio . The

pe e tage of st u tu es st u tu es – % hose esidue RM“D- ryst/hapti efe ed as

RM“D /h i the lege d is lo e tha o espo de t esidue RM“D-apo/ ryst efe ed as RM“D

a/ a e sho i g ee hile highe o es a e sho i ed.

Figu e : “ apshot of liga d LIG i the CDK i di g site. L s o es to a d the positio

p ese t i the stallized st u tu e. The apo st u tu e ep ese ted i g e ; the stallised

o ple ep ese ted i a ; the st u tu e o tai ed f o the si ulatio is i di ated i o a ge.

Figu e . Visual ep ese tatio of ho HCV N“ B esidues had o ed du i g the si ulatio . The

pe e tage of st u tu es hose esidue RM“D- ryst/hapti efe ed as RM“D /h i the lege d is

lo e tha o espo de t esidue RM“D-apo/ ryst efe ed as RM“D a/ a e sho i g ee

hile highe o es a e sho i ed.

Figu e . “ apshot of JE i the HCV N“ i di g po ket. His a k o e o e e t is

highlighted. The apo st u tu e ep ese ted i g e ; the stallised o ple ep ese ted i a ;

the st u tu e o tai ed f o the si ulatio is i di ated i pu ple.

Figu e . “ apshot of liga d NVP i the HIV RT. The apo st u tu e ep ese ted i g e ; the

stallised o ple ep ese ted i g ee ; the st u tu e o tai ed f o the si ulatio is i di ated

i lue.