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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

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slide1

Molecular Control Engineering

From Enzyme Design to Quantum Control

Raj Chakrabarti

School of Chemical Engineering

Purdue University

what is molecular control engineering

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 microscopic
  • inputs 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
Approaches to Molecular Optimization and Control

Static Optimization

Dynamic Control

Control of Biochemical

Reaction Networks

milliseconds,

micrometers

Molecular Structure/Function

Optimization: Enzyme Design

ms

picoseconds,

nanometers

Coherent Control of

Chemical Reaction Dynamics

[protein pic]

femtoseconds,

angstroms

how enzymes work
How enzymes work

How to design them?

What makes them optimal for catalysis, and how to improve?

Problem: hyperastronomical sequence space

slide5

Catalytic Mechanisms of Enzymes

General acid/base

Y159

Electrostatic stabilizer

Lys65

Catalytic nucleophile

Glu-299

Catalytic

Nucleophile Ser62

General acid/base

Glu-200

DD-peptidase

b-gal

slide6

A model fitness measure for enzyme sequence optimization

slack variable

Catalytic constraint: interatomic

distances rij < hbond dist

Enzyme-substrate

binding 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
slide7

The physics in the model: sequence optimization requires accurate

energy functions and solvation models

S-GB continuum solvation

10o resolution rotamer library (297 proteins)

Ghosh, A., Rapp, C.S. & Friesner, R.A. (1998)

J. Phys Chem. B102, 10983-10990.

Xiang, Z. and Honig, B. (2001) J. Mol. Biol.311: 421-430.

OPLS-AA molecular mechanics force field + Glidescore semiempirical binding affinity scoring function

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. B106, 11673-11680.

slide8

Computational sequence optimization correctly predicts most residues in

ligand-binding sites and enzyme active sites

StreptavidinNative –10.04 kcal/mol

CO2- is covalent attachment site

for biomolecules

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.

slide9

Computed amino acid distributions contain

detailed evolutionary information

Observed

(sequence alignment)

Glucose-binding proteinNative –8.81 kcal/mol

Epimeric

promiscuity

Anomeric promiscuity

Computed

OH

OH

  • Computed residue frequencies often mirror
  • natural frequencies

Chakrabarti, R., Klibanov, A.M. and Friesner, R.A. PNAS, 2005.

slide10

Catalytic constraints shift sequence distributions

and are associated with “evolutionary temperatures”

+2 kcal/mol

+ 1 kcal/mol

Constrained

Number of residues correctly predicted

Max entropy distributions

~ single moment, evolutionary T

multiple moments, evolutionary T’s

DD-peptidase

b-gal

slide11

High-resolution sequence optimization is robust across diverse functional families

Peptide

Nucleotide

Sugar

slide12

Computational active site optimization is structurally accurate

to near-crystallographic resolution

slide13

Reviews on Computational Sequence Optimization and Designability of Enzymes

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.
slide14

Sirtuin enzymes and regulation of age-related physiology

2008: GSK acquires

Sirtris Pharmaceuticals for

US $700 M

2010: Pfizer contests efficacy of drug leads from Sirtris experimental screening

2010: GSK terminates drug

development of several sirtuin activators

Sinclair DA. (2005) Mech. Ageing Dev. 126:987–1002

Brooks CL, Gu W. (2008) Cancer Cell 13:377–78

Brooks CL, Gu W. (2009) Nat. Rev. Cancer 9:123–28

Luo J, Nikolaev AY, Imai S, Chen D, Su F, et al. (2001) Cell 107:137–48

Vaziri H, Dessain SK, Eaton EN, Imai SI, Frye RA, et al. (2001) Cell 107:149–59

sirtuin enzymatic activities
Sirtuin enzymatic activities
  • Sirtuins control metabolic
  • pathways and aging through
  • amino acid deacetylation
  • Feedback regulated by their
  • reaction byproducts

Michan S and Sinclair D (2007) Biochem J 404, 1-13.

slide16

Computational sequence optimization and experimental mutagenesis of Sirtuins

Example of screening focused library of sequence variants

3 permissible mutations identified by modeling at a target position

3 positions subject to mutagenesis

43 mutation combinations

= 64 sequence variations

Synthetic gene assembly and variant library construction via DNA synthesis

Biological selection of variant library

New enzymes -

Improved catalytic turnover

Altered substrate selectivity

Chakrabarti, R., De Jong, R., Cornish, V.C. and Friesner, R.A., unpublished results

predicted mutations around nad
Predicted mutations around NAD+

`

Green: mutant; Orange: native; Yellow surface: acetyl-lysine Center: NAD+

E. Knoll, B. Ridder, and R. Chakrabarti, in preparation

from enzyme design to dynamic bionetwork control
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 activitiesin real time to achieve maximal fitness or selectivity of chemical products
  • Lessons for control of metabolic networks via drug dosage (e.g. sirtuin inhibitors)
slide19

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

2007: Celera Licenses and Roche negotiates for Chemical PCR

patents

slide20

DNA Melting

Again

Single Strand – Primer Duplex

Extension

DNA Melting

Primer

Annealing

9/18/2014

School of Chemical Engineering, Purdue University

20

pcr and disease diagnostics
PCR and Disease Diagnostics

Trinucleotide Repeat Diseases

  • Huntington’s Disease
  • Muscular Dystrophy
  • Fragile X (Autism’s leading cause)

Race for Diagnostic Methods: Standard PCR generally fails due to nonspecific amplification. First FDA-approved 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, Nucleic Acids Res., 2001

R. Chakrabarti and C.E. Schutt, Gene 2002

R. Chakrabarti, in PCR Technology: Current Innovations, 2003

slide22

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.

slide23

Parallel Parking and Bionetwork Control

  • 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
  • Tight spots: Move perpendicular to curb through sequences composed

of Left, Forward + Left, Reverse + Right, Forward + Right, Reverse

slide24

The DNA Amplification Control Problem and Cancer Diagnostics

Mutated DNA

Wild Type DNA

  • 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
slide25

Optimal Control of DNA Amplification

For N nucleotide template – 2N + 4 state equations

Typically N ~ 103

R. Chakrabarti et al. Optimal Control of Evolutionary Dynamics, Phys. Rev. Lett., 2008

K. Marimuthu and R. Chakrabarti, Optimally Controlled DNA amplification, in preparation

slide26

From bionetwork control to coherent control of chemical processes

  • 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?
slide28

Coherent Control versus Catalysis

  • 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

probability

density

time

interatomic

distance

slide29

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.

slide30

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

slide32

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.

slide33

Few-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

slide34

Optimal 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

slide35

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

slide36

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, in press, 2011.

.

summary
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
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, 2012.