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Molecular Control Engineering From Enzyme Design to Quantum Control

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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Molecular Control Engineering From Enzyme Design to Quantum Control

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  1. Molecular Control Engineering From Enzyme Design to Quantum Control Raj Chakrabarti School of Chemical Engineering Purdue University

  2. 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

  3. 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

  4. How enzymes work How to design them? What makes them optimal for catalysis, and how to improve? Problem: hyperastronomical sequence space

  5. 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

  6. 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

  7. 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.

  8. 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.

  9. 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.

  10. 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

  11. High-resolution sequence optimization is robust across diverse functional families Peptide Nucleotide Sugar

  12. Computational active site optimization is structurally accurate to near-crystallographic resolution

  13. 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.

  14. 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

  15. 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.

  16. 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

  17. Predicted mutations around NAD+ ` Green: mutant; Orange: native; Yellow surface: acetyl-lysine Center: NAD+ E. Knoll, B. Ridder, and R. Chakrabarti, in preparation

  18. 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)

  19. 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

  20. DNA Melting Again Single Strand – Primer Duplex Extension DNA Melting Primer Annealing 9/18/2014 School of Chemical Engineering, Purdue University 20

  21. 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

  22. 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.

  23. 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

  24. 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

  25. 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

  26. 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?

  27. 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

  28. 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.

  29. 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

  30. Coherent Control of State Transitions in Atomic Rubidium http://www.lamptech.co.uk

  31. 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.

  32. 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

  33. 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

  34. 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

  35. 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. .

  36. 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

  37. 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.

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