sbdd 4 2016 pdf - mmsmms.dsfarm.unipd.it/files/lezioni/psf/pdf/sbdd_4_2016...molecular dynamics...
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Time…Time…Time…Time…
MM S Confidential and Property of ©2005 Molecular Modeling Section
Dept. Pharmaceutical and Pharmacological Sciences –University of Padova - Italy S. MORO – PSF – 2016/2017
“Life “Life wouldwould notnot exitexit withoutwithout motionmotion” ” byby Martin Martin KarplusKarplus
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Lead Lead toto
CandidateCandidate
Hit Hit to Leadto Lead
Screen Screen to Hitto Hit
Virtual LeadOptimization
MM S Confidential and Property of ©2005 Molecular Modeling Section
Dept. Pharmaceutical and Pharmacological Sciences –University of Padova - Italy S. MORO – PSF – 2016/2017
TargetStructure Determination
Ligand-based Optimization
PharmacophoreQSAR
Structure-based Optimization
Molecular DockingMolecular Dynamics
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Why we need Why we need timetime virtualization?virtualization?
1.1. Several molecular properties are timeSeveral molecular properties are time--dependentdependent
22.. ConformationalConformational spacespace isis naturallynaturally exploredexploredfollowingfollowing timetime coordinatecoordinate
3. Any recognition process is time3. Any recognition process is time--dependentdependent
MM S Confidential and Property of ©2005 Molecular Modeling Section
Dept. Pharmaceutical and Pharmacological Sciences –University of Padova - Italy S. MORO – PSF – 2016/2017
3. Any recognition process is time3. Any recognition process is time--dependentdependent
4. Dynamics controls equilibrium position4. Dynamics controls equilibrium position
5. …5. …
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Molecular energy also fall under these categories:
BackBack againagain toto stabilitystability conceptconcept::BackBack againagain toto stabilitystability conceptconcept::
POTENTIALPOTENTIALstored energystored energy
KINETICKINETICenergy of motionenergy of motion
MM S Confidential and Property of ©2005 Molecular Modeling Section
Dept. Pharmaceutical and Pharmacological Sciences –University of Padova - Italy S. MORO – PSF – 2016/2017
stored energystored energy energy of motionenergy of motion
Ep = Ep = ff (x, y, z)(x, y, z)
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Basic concepts:Basic concepts:
1. 1. Degrees of freedom of moleculesDegrees of freedom of molecules
Monatomic Linear molecules Non-linear molecules
Translation (x, y, and z) 3 3 3
Rotation (x, y, and z) 0 2 3
MM S Confidential and Property of ©2005 Molecular Modeling Section
Dept. Pharmaceutical and Pharmacological Sciences –University of Padova - Italy S. MORO – PSF – 2016/2017
Vibration 0 3N − 5 3N − 6
Total 3 3N 3N
x
y
z
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Basic concepts:Basic concepts:
2. 2. EquipartitionEquipartition theoremtheoremSince the degrees of freedom are independent, the internal energy of the systemis equal to the sum of the mean energy associated with each degree of freedom,which demonstrates the result:
kTE1=
MM S Confidential and Property of ©2005 Molecular Modeling Section
Dept. Pharmaceutical and Pharmacological Sciences –University of Padova - Italy S. MORO – PSF – 2016/2017
kTEi 2
1=
x
y
z
kT2
1
kT2
1kT
2
1kTEi 2
3=
Boltzmann constant, k = R/NA = 1.38 x 10-23 J/K
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3. 3. Kinetic energyKinetic energy
Basic concepts:Basic concepts:
x
y
zxv
yv
zv
MM S Confidential and Property of ©2005 Molecular Modeling Section
Dept. Pharmaceutical and Pharmacological Sciences –University of Padova - Italy S. MORO – PSF – 2016/2017
2
2
1mvEk =
z
Velocity is a vectorial propertyVelocity is a vectorial property
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4. Combining the 4. Combining the internal with kinetic energyinternal with kinetic energy
Basic concepts:Basic concepts:
x
y
zxv
yv
zvx
y
z
kT2
1
kT2
1kT
2
1
For a monoatomic gas (only 3 translation degrees of freedom) :
MM S Confidential and Property of ©2005 Molecular Modeling Section
Dept. Pharmaceutical and Pharmacological Sciences –University of Padova - Italy S. MORO – PSF – 2016/2017
For a monoatomic gas (only 3 translation degrees of freedom) :
kTmv2
3
2
1 2 =m
kTv
3=m
kT
dt
dx 3=
For a non-linear molecule (with 3N degrees of freedom) :
kTN
mv2
3
2
1 2 =m
NkTv
3=m
NkT
dt
dx 3=
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Molecular Dynamics basics:Molecular Dynamics basics:
A working definition of molecular dynamics (MD)simulation is technique by which one generates theatomic trajectories of a system of N particles bynumerical integration of Newton’s equation of
MM S Confidential and Property of ©2005 Molecular Modeling Section
Dept. Pharmaceutical and Pharmacological Sciences –University of Padova - Italy S. MORO – PSF – 2016/2017
numerical integration of Newton’s equation ofmotion, for a specific interatomic potential, withcertain initial condition (IC) and boundary condition(BC).
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Molecular Dynamics basics:Molecular Dynamics basics:1. 1. Physical systemPhysical systemWe can define as physical system a set of atomic coordinatesusing a vector notation:
x
y 1122
3344
NN
),,( iiii zyxr =r
MM S Confidential and Property of ©2005 Molecular Modeling Section
Dept. Pharmaceutical and Pharmacological Sciences –University of Padova - Italy S. MORO – PSF – 2016/2017
x
z
2. 2. TrajectoryTrajectoryA mapping from time to a point in the 3-dimensional space:
)0( =tri
r)( 1ttri =r
)0( =tvi
r
)( 1ttvi =r
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Molecular Dynamics basics:Molecular Dynamics basics:
forfor continuouscontinuous time,time, wewe considerconsider aa sequencesequence ofof statesstates::
( ) ( ) ( ))2(),2()(),( )0(),0( ∆∆∆∆ iiiiii vrvrvrrr
arr
arr
)3( ∆=trr
time steptime step
MM S Confidential and Property of ©2005 Molecular Modeling Section
Dept. Pharmaceutical and Pharmacological Sciences –University of Padova - Italy S. MORO – PSF – 2016/2017
)0( =tri
r
)( ∆=tri
r
)2( ∆=tri
r )3( ∆=tri
The question is: How to predict the next state from thecurrent state?
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Molecular Dynamics basics:Molecular Dynamics basics:
3. 3. VelocityVelocityShortShort timetime limitlimit ofof anan averageaverage speedspeed (how(how fastfast andand inin whichwhichdirectiondirection thethe particleparticle isis moving)moving)::
∆−∆+==
→∆
)()(lim)(
0
trtr
dt
rdtv iii
i
rrrr
MM S Confidential and Property of ©2005 Molecular Modeling Section
Dept. Pharmaceutical and Pharmacological Sciences –University of Padova - Italy S. MORO – PSF – 2016/2017
∆→∆ 0dtitime steptime step
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Molecular Dynamics basics:Molecular Dynamics basics:
4. 4. Random velocitiesRandom velocities
WeWe generategenerate randomrandom velocitiesvelocities ofof magnitudemagnitude::
m
kTv i3
0 = simulation temperaturesimulation temperature
MM S Confidential and Property of ©2005 Molecular Modeling Section
Dept. Pharmaceutical and Pharmacological Sciences –University of Padova - Italy S. MORO – PSF – 2016/2017
mFor each atom, the velocity vector is then given by:
ξξξξrr
000 ),,( vvv zyx ==where is a randomly oriented vector of unit length.
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∆−∆+==
→∆
)()(lim)(
0
trtr
dt
rdtv iii
i
rrrr
and finally:
MM S Confidential and Property of ©2005 Molecular Modeling Section
Dept. Pharmaceutical and Pharmacological Sciences –University of Padova - Italy S. MORO – PSF – 2016/2017
∆=∆+=∆+ ζm
NkTtvtrtr iii
3)()()(
rrr
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BackBack toto thethe definitiondefinition ofof trajectorytrajectory::
( ) ( ) ( ))2(),2()(),( )0(),0( ∆∆∆∆ iiiiii vrvrvrrr
arr
arr
)2( ∆=tri
r )3( ∆=tri
r
time steptime step
)3( ∆=tEp
MM S Confidential and Property of ©2005 Molecular Modeling Section
Dept. Pharmaceutical and Pharmacological Sciences –University of Padova - Italy S. MORO – PSF – 2016/2017
)0( =tri
r
)( ∆=tri
ri
using the appropriate force field is possible to calculate thepotential energy of the system during all MD step.
)0( =tEp
)( ∆=tE p
)2( ∆=tEp
)3( ∆=tE p
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How to select the appropriate How to select the appropriate ∆∆t t ::
ToTo ensureensure aa correctcorrect numericalnumerical integrationintegration ofof thethe equationsequations ofofmotion,motion, andand thereforetherefore reducereduce thethe errorerror inin thethe calculationcalculation ofof thetheenergyenergy ofof thethe system,system, itit isis NECESSARYNECESSARY thatthat thethe intervalinterval ofofintegrationintegration isis betweenbetween 11//100100 andand 11//2020 ofof thethe timetime associatedassociated withwiththethe fastestfastest motionmotion inin ourour molecularmolecular systemsystem..
InIn classicalclassical molecularmolecular dynamicsdynamics fasterfaster motionsmotions areare associatedassociated
MM S Confidential and Property of ©2005 Molecular Modeling Section
Dept. Pharmaceutical and Pharmacological Sciences –University of Padova - Italy S. MORO – PSF – 2016/2017
InIn classicalclassical molecularmolecular dynamicsdynamics fasterfaster motionsmotions areare associatedassociatedwithwith thethe bondbond vibrationsvibrations ((1010--100100 fs)fs).. InIn particular,particular, allall CC--H,H, NN--HHandand OO--HH stretchingstretching timestimes areare aroundaround 1010 fsfs.. AnAn acceptedacceptedcompromisecompromise isis toto setset asas valuevalue intervalinterval ofof integrationintegration isis equalequal toto11fsfs.. RememberRemember:: 11 fsfs == 1010--1515 ssThereThere areare algorithmsalgorithms thatthat provideprovide thethe freezingfreezing ofof vibrationalvibrationalmotionsmotions linkedlinked inin particularparticular toto thethe CC--HH bonds,bonds, NN--HH andand OO--HH((SHAKESHAKE ALGORITHMALGORITHM)).. ThisThis allowsallows youyou toto doubledouble thethe valuevalue ofof thetheintegrationintegration timetime toto 22fsfs..
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HowHow longlong wewe havehave followedfollowed MDMDsimulation?simulation?
Bond vibrations: 1 fsBond vibrations: 1 fsCollective vibrations: 1 psCollective vibrations: 1 psConformational transitions: ns or longerConformational transitions: ns or longer
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Conformational transitions: ns or longerConformational transitions: ns or longerEnzyme catalysis: microsecond/millisecondEnzyme catalysis: microsecond/millisecondLigand Binding: micro/millisecondLigand Binding: micro/millisecondProtein Folding: millisecond/secondProtein Folding: millisecond/second
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ToTo depictdepict realisticrealistic MDMD trajectories,trajectories, itit isiscrucialcrucial toto guaranteeguarantee realisticrealistic boundaryboundaryconditions!!!conditions!!!
1.1.Solvent (water)Solvent (water)2.2.pH and ionic strength pH and ionic strength
MM S Confidential and Property of ©2005 Molecular Modeling Section
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2.2.pH and ionic strength pH and ionic strength
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Here is what I mean:Here is what I mean:
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Number of atoms: 35523 (after solvation)
Number of atoms: 5526 (before solvation) 78 x 78 x 78 Å
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MDMD simulationssimulations:: wherewhere theorytheoryneedsneeds technologytechnology..
RememberRemember:: forfor thethe explorationexploration ofof aa veryverylittlelittle ““molecularmolecular”” timetime wewe stillstill needneed aa hugehuge
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Dept. Pharmaceutical and Pharmacological Sciences –University of Padova - Italy S. MORO – PSF – 2016/2017
amountamount ofof ““computationalcomputational”” time!time!
OurOur unitunit ofof measurementmeasurement isis stillstill...... nsns//dayday
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My favorite C/G mutation
TheThe GPUGPU revolution!!!revolution!!!
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in collaboration with:
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HereHere isis thethe bigbig emotionemotion::
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Molecular Dynamics Molecular Docking
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HowHow toto analyzeanalyze aa MDMD trajectorytrajectory::
MM S Confidential and Property of ©2005 Molecular Modeling Section
Dept. Pharmaceutical and Pharmacological Sciences –University of Padova - Italy S. MORO – PSF – 2016/2017
Molecular Dynamics
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Ligandhypothesis
consensusdocking & scoring
A possible workflow:A possible workflow:
MM S Confidential and Property of ©2005 Molecular Modeling Section
Dept. Pharmaceutical and Pharmacological Sciences –University of Padova - Italy S. MORO – PSF – 2016/2017
Rankingbest candidates
MD simulations
prioritize synthesis biological assay
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MMSMMS dynamicsdynamics::MMSMMS dynamicsdynamics::
A. Cuzzolin
MM S Confidential and Property of ©2005 Molecular Modeling Section
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LL
LL
what about ligand-receptor binding process?
MMSMMS dynamicsdynamics::
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Dept. Pharmaceutical and Pharmacological Sciences –University of Padova - Italy S. MORO – PSF – 2016/2017
L + R L-R ∆Eon
“A common application of protein–ligand simulations is to compute the binding affinity of a
ligand, often a drug candidate, to a known binding site. Unbiased MD simulations of ligand
binding are usually ill suited for this purpose, as precise estimation of ligand affinity would
typically require seconds to hours of simulated time in order to observe sufficiently many
binding and unbinding events.” David E. Shaw Annu. Rev. Biophys. 2012. 41:429–52
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ZM241385
MM S Confidential and Property of ©2005 Molecular Modeling Section
Dept. Pharmaceutical and Pharmacological Sciences –University of Padova - Italy S. MORO – PSF – 2016/2017
Unbiased Molecular Dynamics
D. SabbadinSabbadin D.; Moro S. J Chem Inf Mod 54, 372-376, 2014)
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MM S Confidential and Property of ©2005 Molecular Modeling Section
Dept. Pharmaceutical and Pharmacological Sciences –University of Padova - Italy S. MORO – PSF – 2016/2017
“I can run of protein–ligand simulations to compute the binding recognition in a nanosecond
time scale on my laptop!” Davide Sabbadin (J Chem Inf Mod 54, 372-376, 2014)
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Unbiased Molecular Dynamics
Supervised Molecular Dynamics
MM S Confidential and Property of ©2005 Molecular Modeling Section
Dept. Pharmaceutical and Pharmacological Sciences –University of Padova - Italy S. MORO – PSF – 2016/2017
Sabbadin D.; Moro S. J Chem Inf Mod 54, 372-376 (2014)
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Supervised Molecular Dynamics (SuMD)
ZM
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META BINDING SITES
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MM S Confidential and Property of ©2005 Molecular Modeling Section
Dept. Pharmaceutical and Pharmacological Sciences –University of Padova - Italy S. MORO – PSF – 2016/2017
Sabbadin D.; Moro S. J Chem Inf Mod 54, 372-376 (2014)
META BINDING SITES
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Supervised Molecular Dynamics (SuMD) – We love
MM S Confidential and Property of ©2005 Molecular Modeling Section
Dept. Pharmaceutical and Pharmacological Sciences –University of Padova - Italy S. MORO – PSF – 2016/2017
G. Deganutti A. CuzzolinCuzzolin A., Deganutti G., Moro S. (2016) manuscript in preparation
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GRAZIEPER LA PAZIENZA
MM S Confidential and Property of ©2005 Molecular Modeling Section
Dept. Pharmaceutical and Pharmacological Sciences –University of Padova - Italy S. MORO – PSF – 2016/2017
PER LA PAZIENZA
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“Progettazione e Sviluppodi un Farmaco”
MM S Confidential and Property of ©2005 Molecular Modeling Section
Dept. Pharmaceutical and Pharmacological Sciences –University of Padova - Italy S. MORO – PSF – 2016/2017
Stefano Moro
Chimica e Tecnologia Farmaceutiche