Chakrabarti Group

1. Chakrabarti Group Overview of Research and Educational Initiatives CAPD Meeting March 11, 2013

2. Approaches to Molecular Design 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

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

4. 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

5. 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.

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. 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.

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

9. From Enzyme Design to Bionetwork Control • Nature has also devised remarkable catalysts through molecular design / evolution • 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

10. 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 through temperature cycling Used every day in every Biochemistry and Molecular Biology lab ( Diagnosis, Genome Sequencing, Gene Expression, etc.) How to automate choice of temperature cycling protocols?

11. DNA Melting Again Single Strand – Primer Duplex Extension DNA Melting Primer Annealing 9/18/2014 School of Chemical Engineering, Purdue University 11

12. 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.

13. Optimal Control of DNA Amplification For N nucleotide template – 2N + 13 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

14. Optimal control of PCR Cycle 1 Cycle 2 Geometric growth: after 15 cycles, DNA concentrations are red – 4×10-10 M blue – 8×10-9 M green – 2×10-8 M

15. Chakrabarti Group Educational Initiatives: DecydEd • DecydEdis an online course consortium with a two-prong objective: • Offer online education in systemsengineering to a broader community of students, researchers, and practitioners around the world • Deliver fully automated real-time decision-making tools which build upon the course material taught, to users for the first time • DecydEd envisions broadening awareness of the latest academic research • in systems engineering, educating users on how to apply PSE tools to • industrial applications that have traditionally not been addressed using such • methods.

16. DecydEd (cont’d) • DecydEd offers fully automated tools, based on the content covered in the courses, aimed at solving real-world engineering problems in a host of areas including • Systems Biology • Molecular Design • 3. Financial Engineering • Target applications include protein engineering, catalyst design, biochemical reaction engineering • Funded by PMC Group, Inc

17. PMC Group Global Operations Fully integrated group of companies involved in development, manufacture, marketing and sales of specialty, performance and fine chemicals. Among the world’s top chemical manufacturers in several of these areas.

18. DecydEd Courses

19. The DecydEd User Portal • The DecydEd User portal provides a rich experience to registered students, including simulations, the ability to network with other users (using leading social media platforms), collaborating on homeworks, viewing lectures, and solving automatically graded homework exercises

20. DecydEd Discussion Forum • DecydEd’s expert panel currently consists of professors from top universities including CMU, the University of Chicago, the University of Toronto and the London School of Economics • Students can ask questions and get advice from these experts on a wide range of topics while enrolled in the courses.

21. DecydEd’s Decision Making Tools in Chemical and Biochemical Engineering • Molecular Design Example: Protein Engineering involves a high-dimensional search over the space of possible functional groups in an active site. • DecydEd’s automated protein optimization software will enable any molecular biologist to apply computational protein engineering techniques • Systems Biology Example: DNA sequencing involves the control of a biochemical reaction network through the choice of temperature profiles in the polymerase chain reaction (PCR). • DecydEd’s automated PCR control software will enable molecular biologists to apply systems biology in lab experiments through the website • Most practicing molecular biologists are not trained in the above methods and often do not have access to the latest tools

22. Design Computationally Refine Experimentally Input information Target chemical Desired raw material Existing synthetic pathways Existing biocatalysts System Output ~1000 potential candidates expected catalytic activity Optimized Biocatalyst Zymzyne™ Computational Design Process Zymzyne™ Experimental Optimization 1030 candidates screened 500 candidates screened DecydEd Industry Application Example: Computational Enzyme Design

23. Computational Enzyme Design: Enabling renewable chemical manufacturing Starches Specialty chemicals Plant oils Polymers Biomass

24. Enzyme Design Models Protein structure Substrate binding Reactive chemistry Loop Sidechain Glidescore Pose sampling New algorithms for side chain optimization QM sequence refinement Classical Sequence Optimization (fixed ligand) • for QM/MM refinement • of enzyme design • speeding up mutant • TS searches Calculating mutant enzyme reaction rates Classical Sequence Optimization (free ligand) Active site reshaping • Hierarchical pose screening • Locates global seq/struct optima • for a given active site/ligand comb • Estimates “designability” of active site • (fixed backbone) • scores desired loop • against other low-energy • excitations

25. DecydEd Molecular Design Decision-Making 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 Ask for details from Zhen and/or NEB; limitations on how libraries are made, how many sequences can be screened? Chakrabarti, R., De Jong, R., Cornish, V.C. and Friesner, R.A., unpublished results

26. DecydEd Systems Biology Models Reaction Equilibrium Information ΔG – From Nearest Neighbor Model Relaxation Time Similar to the Time constant in Process Control τ – Relaxation time (Theoretical/Experimental) Solve above equations to obtain rate constants K. Marimuthu and R. Chakrabarti, Sequence-Dependent Modeling of DNA Hybridization Kinetics: Deterministic and Stochastic Theory, in preparation

27. DNA Amplification Control Problem and Cancer Diagnostics Mutated DNA Wild Type DNA

28. DecidEd Systems Biology Decision-Making Example Feed the PCR State Equations Objective Function (noncompetitive, competitive)

29. DecydEd launched its business platform, called The Academic Financial Trading Platform (AFTP) in November 2012, with engineering to follow in Summer 2013

32. The DecydEd Backend Technology • The DecydEd backend collects the latest simulation, optimization and estimation algorithms from the world’s top research centers • Instructors from both academia and industry can contribute models built using standard modeling packages (e.g. AIMMS, GAMS) for use by DecydEd students • The DecydEd Model API is an application Programming Interface (API) supports • integration of continuous influx of models with optimization and estimation algorithms. • The backend employs MPI-based parallel computing that is massively scalable • for large numbers of users with on-demand deployment of cloud instances • PMC Group plans to integrate open source mathematical programming and • dynamic optimization libraries/solvers such as IPOPT, GLPK with the DecydEd backend

34. “f”, linear objective function Energy constraint Can only have 1 rotamer at each position No “impossibles” allowed Nonlinear constraint term Possible collaborations to id the global optimum for fitness measure (w pairwise decomposability assumptions, reduced energy model)