Upload
valmai
View
31
Download
0
Tags:
Embed Size (px)
DESCRIPTION
Molecular Control Engineering From Enzyme Design to Quantum Control. Raj Chakrabarti School of Chemical Engineering Purdue University. What is Molecular Control Engineering?. Control engineering : Manipulation of system dynamics through nonequilibrium - PowerPoint PPT Presentation
Citation preview
Molecular Control Engineering
From Enzyme Design to Quantum Control
Molecular Control Engineering
From Enzyme Design to Quantum Control
Raj Chakrabarti
School of Chemical EngineeringPurdue University
Raj Chakrabarti
School of Chemical EngineeringPurdue University
What is Molecular Control Engineering?
Control engineering: Manipulation of system dynamics through nonequilibrium modeling and optimization. Inputs and outputs are macroscopic variables.
Molecular control engineering: Control of chemical phenomena through microscopicinputs and chemical physics modeling. Adapts to changes in the laws of Nature at these length and time scales.
Aims
Reaching ultimate limits on product selectivity Reaching ultimate limits on sustainability Emulation of and improvement upon Nature’s strategies
Approaches to Molecular Optimization and Control
Static Optimization Dynamic Control
Molecular Structure/Function Optimization: Enzyme Design
Control of Biochemical Reaction Networks
[protein pic][protein pic]
msms
femtoseconds,angstroms
femtoseconds,angstroms
milliseconds, micrometersmilliseconds, micrometers
picoseconds,nanometerspicoseconds,nanometers
Coherent Control of Chemical Reaction Dynamics
How enzymes work
How to design them?
What makes them optimal for catalysis, and how to improve?
Problem: hyperastronomical sequence space
Catalytic Nucleophile Ser62
General acid/baseY159 Electrostatic stabilizer
Lys65
Catalytic nucleophileGlu-299
General acid/baseGlu-200
DD-peptidase -gal
Catalytic Mechanisms of EnzymesCatalytic Mechanisms of Enzymes
A model fitness measure for enzyme sequence optimizationA model fitness measure for enzyme sequence optimization
Catalytic constraint: interatomic distances rij < hbond dist
Catalytic constraint: interatomic distances rij < hbond dist Enzyme-substrate
binding affinity
Enzyme-substratebinding affinity
• Minimize J over sequence space
• Represent dynamical constraint with requirement that total energy of complex minimized for any sequence
• Omits selection pressure for product release
• Minimize J over sequence space
• Represent dynamical constraint with requirement that total energy of complex minimized for any sequence
• Omits selection pressure for product release
slack variableslack variable
1
1 1
2hbond,
N
i
N
jijijijijbind seqrrseqGseqJ
The physics in the model: sequence optimization requires accurate energy functions and solvation models
10o resolution rotamer library (297 proteins)
Ghosh, A., Rapp, C.S. & Friesner, R.A. (1998) J. Phys Chem. B 102, 10983-10990.
Xiang, Z. and Honig, B. (2001) J. Mol. Biol. 311: 421-430.
Friesner, R.A, Banks, J.L., Murphy, R.B., Halgren, T.A. et al. (2004) J. Med. Chem. 47, 1739-1749.Jacobson, M.P., Kaminski, G.A. Rapp, C.S. & Friesner, R.A. (2002) J. Phys. Chem. B 106, 11673-11680.
S-GB continuum solvation
OPLS-AA molecular mechanics force field + Glidescore semiempirical binding affinity scoring function
Streptavidin Native –10.04 kcal/mol
Computational sequence optimization correctly predicts most residues in ligand-binding sites and enzyme active sites
9 / 10 residues predicted correctly in top 0.5 kcal/mol of sequences
Chakrabarti, R., Klibanov, A.M. and Friesner, R.A. Computational prediction of native protein ligand-binding and enzyme active site sequences. PNAS, 2005.
CO2- is covalent attachment site
for biomolecules
Glucose-binding protein Native –8.81 kcal/mol
0
0.1
0.2
0.3
0.4
0.5
0.6
D A F R S Q E Y H I L K N G T W V M
Fre
qu
ency
0
0.1
0.2
0.3
0.4
0.5
0.6
D A F R S Q E Y H I L K N G T WV M
Fre
quency
Computed
Observed (sequence alignment)
Computed amino acid distributions contain detailed evolutionary information
• Computed residue frequencies often mirror natural frequencies
OH
OH
Anomeric promiscuityEpimeric promiscuity
Chakrabarti, R., Klibanov, A.M. and Friesner, R.A. PNAS, 2005.
Number of residues correctly predicted
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0 1 2 3 4 5 6 7
Fra
ctio
n o
f to
tal
seq
uen
ces
0 1 2 3 4 5 6 7 8 9 10
Catalytic constraints shift sequence distributions and are associated with “evolutionary temperatures”
DD-peptidase -gal
+ 1 kcal/mol
+2 kcal/mol
Constrained
Max entropy distributionsMax entropy distributions
multiple moments, evolutionary T’smultiple moments, evolutionary T’s
~ single moment, evolutionary T~ single moment, evolutionary T
,0
1ln bind bind optS Z seq G seq G
T
dseqGseqGT
Z optbindbind
,
0
1exp
1
, hbond1 10
1 1ln
N N
bind bind opt iji j ij
S Z seq G seq G r seq rT T
1
, hbond1 10
1 1exp
N N
bind bind opt iji j ij
Z G seq G r seq r dseqT T
High-resolution sequence optimization is robust across diverse functional families
Peptide
Nucleotide
Sugar
0
0.2
0.4
0.6
0.8
1
1.2
Phe120 Asn161 Trp233 Arg285 Thr299 Ser326 Ser62 Lys65 Tyr159
Rm
sd t
o n
ativ
e (A
)
Computational active site optimization is structurally accurate to near-crystallographic resolution
Nature Chemical Biology Volume 4 Number 5 May 2008:
“In a study by Chakrabarti et al. it was suggested that different enzyme active sites in natural proteins vary in their designability – that is, the number of sequences that are compatible with a specified structure and function.”
Chakrabarti R. Klibanov AM, Friesner RA. Sequence optimization and designability of enzyme active sites. Proc Natl Acad Sci USA 102:12035-12040, 2005
Current Opinion in Biotechnology Volume 18 2007:
•“ …Chakrabarti et al. found that they could recover the majority of wild type enzyme sequences by optimizing enzyme-substrate binding affinity while imposing geometric constraints on catalytic side-chain conformations.”
• “…Work by Chakrabarti et al. May also be useful for guiding the search for protein scaffolds suitable for introduction of de novo activity.”
“Of Outstanding Interest”: Chakrabarti R, Klibanov AM, Friesner RA. Computational prediction of native protein-ligand binding and enzyme active site sequences. Proc Natl Acad Sci USA 102: 10153-10158, 2005.
Reviews on Computational Sequence Optimization and Designability of EnzymesReviews on Computational Sequence Optimization and Designability of Enzymes
Sirtuin enzymes and regulation of age-related physiology
Sinclair DA. (2005) Mech. Ageing Dev. 126:987–1002Brooks CL, Gu W. (2008) Cancer Cell 13:377–78Brooks CL, Gu W. (2009) Nat. Rev. Cancer 9:123–28Luo J, Nikolaev AY, Imai S, Chen D, Su F, et al. (2001) Cell 107:137–48Vaziri H, Dessain SK, Eaton EN, Imai SI, Frye RA, et al. (2001) Cell 107:149–59
2008: GSK acquiresSirtris Pharmaceuticals for US $700 M
2010: Pfizer contests efficacy of drug leads from Sirtris experimental screening
2010: GSK terminates drugdevelopment of several sirtuin activators
Sirtuin enzymatic activities
Michan S and Sinclair D (2007) Biochem J 404, 1-13.
Sirtuins control metabolic pathways and aging through amino acid deacetylation
Feedback regulated by their reaction byproducts
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
D A F R S N E Y H I L K N G T W V
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
D A F R S N E Y H I L K N G T W V C
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
D A F R S N E Y H I L K N G T W V C
Computational sequence optimization and experimental mutagenesis of Sirtuins
New enzymes - Improved catalytic turnover Altered substrate selectivity
New enzymes - Improved catalytic turnover Altered substrate selectivity
3 permissible mutations identified by modeling at a target position
3 permissible mutations identified by modeling at a target position
43 mutation combinations = 64 sequence variations
43 mutation combinations = 64 sequence variations
Example of screening focused library of sequence variants
Example of screening focused library of sequence variants
3 positions subject to mutagenesis3 positions subject to mutagenesis
Synthetic gene assembly and variant library construction via DNA synthesis Synthetic gene assembly and variant library construction via DNA synthesis
Biological selection of variant library Biological selection of variant library
From Enzyme Design to Dynamic Bionetwork Control
• Maximizing kcat/Km of a given enzyme does not always maximize the fitness of a network of enzymes and substrates
• More generally, modulate enzyme activities in real time to achieve maximal fitness or selectivity of chemical products
• Lessons for control of metabolic networks via drug dosage (e.g. sirtuin inhibitors)
The Polymerase Chain Reaction: An example of bionetwork control
Nobel Prize in Chemistry 1994; one of the most cited papers in Science (12757 citations in Science alone)
Produce millions of DNA molecules starting from one
Used every day in every Biochemistry and Molecular Biology lab ( Diagnosis, Genome Sequencing, Gene Expression, etc.)
March 2005: Roche Molecular Diagnostics PCR patents expire
2008-2012: Celera and New England Biolabs License Chemical PCR Patents; Roche negotiates for Chemical PCR Patents
04/21/23 School of Chemical Engineering, Purdue University 19
DNA Melting
PrimerAnnealing
Single Strand – Primer Duplex
Extension
DNA MeltingAgain21
, 21 SSDmm kk
DNASS tt kk 12
11 ,
21
22,
22
22
21 PSPS kk
DNAEDE
DENDENDE
DENSPENSPE
SPEESP
kcatN
kcatkk
kcatkk
kk
nn
nn
ee
'
.
.
.]..[.
.]..[.
.
21,
1
1,
,
11,
11
12
11 PSPS kk
PCR and Disease Diagnostics
Trinucleotide Repeat Diseases
Huntington’s Disease Muscular Dystrophy Fragile X (Autism’s leading cause)
R. Chakrabarti and C.E. Schutt, Nucleic Acids Res., 2001R. Chakrabarti and C.E. Schutt, Gene 2002R. Chakrabarti, in PCR Technology: Current Innovations, 2003
Race for Diagnostic Methods: Standard PCR generally fails due to nonspecific amplification. First FDA-compliant Fragile X test based on Chemical PCR
Chemical PCR: uses solvent engineering of PCR reaction media, to alter kinetic parameters of the reaction network and enable sequencing of untractable genomic DNA
Race for Diagnostic Methods: Standard PCR generally fails due to nonspecific amplification. First FDA-compliant Fragile X test based on Chemical PCR
Chemical PCR: uses solvent engineering of PCR reaction media, to alter kinetic parameters of the reaction network and enable sequencing of untractable genomic DNA
R. Chakrabarti and C.E. Schutt, Chemical PCR: Compositions for enhancing polynucleotide amplification reactions. US Patent 7.772.383, issued 8-10-10. R. Chakrabarti and C.E. Schutt, Compositions and methods for improving polynucleotide amplification reactions using amides, sulfones and sulfoxides: II. US Patent 7.276,357, issued 10-2-07.
R.Chakrabarti and C.E. Schutt, US Patent 6,949,368, issued 9-27-05.
R. Chakrabarti and C.E. Schutt, Chemical PCR: Compositions for enhancing polynucleotide amplification reactions. US Patent 7.772.383, issued 8-10-10. R. Chakrabarti and C.E. Schutt, Compositions and methods for improving polynucleotide amplification reactions using amides, sulfones and sulfoxides: II. US Patent 7.276,357, issued 10-2-07.
R.Chakrabarti and C.E. Schutt, US Patent 6,949,368, issued 9-27-05.
Parallel Parking and Bionetwork Control
Tight spots: Move perpendicular to curb through sequences composed of Left, Forward + Left, Reverse + Right, Forward + Right, Reverse
Stepping on gas not enough: can’t move directly in direction of interest
Must change directions repeatedly
Left, Forward + Right, Reverse enough in most situations
Stepping on gas not enough: can’t move directly in direction of interest
Must change directions repeatedly
Left, Forward + Right, Reverse enough in most situations
Wild Type DNA
Mutated DNA
The DNA Amplification Control Problem and Cancer DiagnosticsThe DNA Amplification Control Problem and Cancer Diagnostics
Can’t maximize concentration of target DNA sequence by maximizing any individual kinetic parameter
Analogy between a) exiting a tight parking spot
b) maximizing the concentration of one DNA sequence in the presence of single nucleotide polymorphisms
Can’t maximize concentration of target DNA sequence by maximizing any individual kinetic parameter
Analogy between a) exiting a tight parking spot
b) maximizing the concentration of one DNA sequence in the presence of single nucleotide polymorphisms
TrDNADESS
DNAfDNAtT
CCCCx
Txfdt
dxst
CtCMin
.....,.....,
,
121 .
2max
)(
For N nucleotide template – 2N + 4 state equations
Typically N ~ 103
Optimal Control of DNA Amplification
R. Chakrabarti et al. Optimal Control of Evolutionary Dynamics, Phys. Rev. Lett., 2008K. Marimuthu and R. Chakrabarti, Optimally Controlled DNA amplification, in preparation
FMO photosynthetic protein complex transports solar energy with ~100% efficiency
Phase coherent oscillations in excitonic transport: exploit wave interference
Biology exploits changes in the laws of nature in control strategy: can we?
From bionetwork control to coherent control of chemical processes
Potential Energy Surface with two competing reaction channels
Saddle points separate products from reactants
Dynamically reshape the wavepacket traveling on the PES to maximize the probability of a transition into the desired product channel
Coherent Control versus Catalysis
probability densityprobability density
timetime interatomic distanceinteratomic distance
C. Brif, R. Chakrabarti and H. Rabitz, New J. Physics, 2010.
C. Brif, R. Chakrabarti and H. Rabitz, Control of Quantum Phenomena. Advances in Chemical Physics, 2011.
C. Brif, R. Chakrabarti and H. Rabitz, New J. Physics, 2010.
C. Brif, R. Chakrabarti and H. Rabitz, Control of Quantum Phenomena. Advances in Chemical Physics, 2011.
Femtosecond Quantum Control Laser Setup
2011: An NSF funded quantum control experiment collaboration between Purdue’s Andy Weiner (a founder of fs pulse shaping) and Chakrabarti Group
Coherent Control of State Transitions in Atomic Rubidium
R. Chakrabarti, R. Wu and H. Rabitz, Quantum Multiobservable Control. Phys. Rev. A, 2008.
R. Chakrabarti, R. Wu and H. Rabitz, Quantum Pareto Optimal Control. Phys. Rev. A, 2008.
R. Chakrabarti, R. Wu and H. Rabitz, Quantum Multiobservable Control. Phys. Rev. A, 2008.
R. Chakrabarti, R. Wu and H. Rabitz, Quantum Pareto Optimal Control. Phys. Rev. A, 2008.
Few-Parameter Control of Quantum DynamicsFew-Parameter Control of Quantum Dynamics
Conventional strategies based on excitation with resonant frequencies fails to achieve maximal population transfer to desired channels
Selectivity is poor; more directions of motion are needed to avoid undesired states
Conventional strategies based on excitation with resonant frequencies fails to achieve maximal population transfer to desired channels
Selectivity is poor; more directions of motion are needed to avoid undesired states
Optimal Control of Quantum DynamicsOptimal Control of Quantum Dynamics
Shaped laser pulse generates all directions necessary for steering system toward target state
Exploits wave-particle duality to achieve maximal selectivity, like coherent control of photosynthesis
Shaped laser pulse generates all directions necessary for steering system toward target state
Exploits wave-particle duality to achieve maximal selectivity, like coherent control of photosynthesis
A Foundation for Quantum System Control
K. Moore, R. Chakrabarti, G. Riviello and H. Rabitz, Search Complexity and Resource Scaling for Quantum Control of Unitary Transformations. Phys. Rev. A, 2010
R. Wu, R. Chakrabarti and H. Rabitz, Critical Topology for Optimization on the Symplectic Group. J Opt. Theory, 2009
R. Chakrabarti and H. Rabitz, Quantum Control Landscapes, Int. Rev. Phys. Chem., 2007
R. Chakrabarti, Notions of Local Controllability and Optimal Feedforward Control for Quantum Systems. J. Physics A: Mathematical and Theoretical, 2011.
R. Chakrabarti and A. Ghosh. Optimal State Estimation of Controllable Quantum Dynamical Systems. Phys. Rev. A, 2012.
.
Summary
• Can reach ultimate limits in sustainable and selective chemical engineering through advanced dynamical control strategies at the nanoscale
• Requires balance of systems strategies and chemical physics
• New approaches to the integration of computational and experimental design are being developed
Reviews of our work
Protein Design and Bionetwork Control
“Progress in Computational Protein Design”, Curr. Opin. Biotech., 2007
“Do-it-yourself-enzymes”, Nature Chem. Biol., 2008
R. Chakrabarti in PCR Technology: Current Innovations, CRC Press, 2003.
Media Coverage of Evolutionary Control Theory: The Scientist, 2008. Princeton U Press Releases
Quantum control
R. Chakrabarti and H. Rabitz, “Quantum Control Landscapes”, Int. Rev. Phys. Chem., 2007
C. Brif, R. Chakrabarti and H. Rabitz, “Control of Quantum Phenomena” New Journal of Physics, 2010; Advances in Chemical Physics, 2011
R. Chakrabarti and H. Rabitz, Quantum Control and Quantum Estimation Theory, Invited Book, Taylor and Francis, 2013.