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

Approaches to Molecular Design and Control Static Optimization Dynamic Control milliseconds, micrometers Control of Approaches to Molecular Design and Control Static Optimization Dynamic Control milliseconds, micrometers Control of Biochemical Reaction Networks Molecular Structure/Function Optimization: Enzyme Design picoseconds, nanometers [protein pic] femtoseconds, angstroms ms Coherent Control of Chemical Reaction Dynamics

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

Catalytic Mechanisms of Enzymes General acid/base Y 159 Electrostatic stabilizer Lys 65 Catalytic nucleophile Catalytic Mechanisms of Enzymes General acid/base Y 159 Electrostatic stabilizer Lys 65 Catalytic nucleophile Glu-299 Catalytic Nucleophile Ser 62 DD-peptidase General acid/base Glu-200 b-gal

The physics in the model: sequence optimization requires accurate energy functions and solvation models The physics in the model: sequence optimization requires accurate energy functions and solvation models S-GB continuum solvation 10 o resolution rotamer library (297 proteins) Xiang, Z. and Honig, B. (2001) J. Mol. Biol. 311: 421 -430. Ghosh, A. , Rapp, C. S. & Friesner, R. A. (1998) J. Phys Chem. B 102, 10983 -10990. 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. B 106, 11673 -11680.

A model fitness measure for enzyme sequence optimization slack variable Enzyme-substrate binding affinity Catalytic A model fitness measure for enzyme sequence optimization slack variable Enzyme-substrate binding affinity Catalytic constraint: interatomic distances rij < hbond dist • 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

Computational sequence optimization correctly predicts most residues in ligand-binding sites and enzyme active sites Computational sequence optimization correctly predicts most residues in ligand-binding sites and enzyme active sites Streptavidin kcal/mol Native – 10. 04 CO 2 - 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.

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

From Enzyme Design to Bionetwork Control • Nature has also devised remarkable catalysts through 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 activities in real time to achieve maximal fitness or selectivity of chemical products

The Polymerase Chain Reaction: An example of bionetwork control Nobel Prize in Chemistry 1994; 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?

Single Strand – Primer Duplex Extension DNA Melting Again DNA Melting Primer Annealing 3/19/2018 Single Strand – Primer Duplex Extension DNA Melting Again DNA Melting Primer Annealing 3/19/2018 School of Chemical Engineering, Purdue University 11

R. Chakrabarti and C. E. Schutt, Chemical PCR: Compositions for enhancing polynucleotide amplification reactions. 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.

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

Optimal control of PCR 95 85 Temperature in Deg C Cycle 1 Cycle 2 Optimal control of PCR 95 85 Temperature in Deg C Cycle 1 Cycle 2 75 Geometric growth: after 15 cycles, DNA concentrations are 65 red – 4× 10 -10 M blue – 8× 10 -9 M green – 2× 10 -8 M 55 45 0 20 40 Annealing Time = 10 s 60 80 Time in Seconds Annealing time = 12 s 100 120 140 Annealing time = 15 s

Chakrabarti Group Educational Initiatives: Decyd. Ed • Decyd. Ed is an online course consortium Chakrabarti Group Educational Initiatives: Decyd. Ed • Decyd. Ed is an online course consortium with a two-prong objective: 1. Offer online education in systems engineering to a broader community of students, researchers, and practitioners around the world 2. Deliver fully automated real-time decision-making tools which build upon the course material taught, to users for the first time • Decyd. Ed 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.

Decyd. Ed (cont’d) • Decyd. Ed offers fully automated tools, based on the content Decyd. Ed (cont’d) • Decyd. Ed 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 1. Systems Biology 2. Molecular Design 3. Financial Engineering • Target applications include protein engineering, catalyst design, biochemical reaction engineering • Funded by PMC Group, Inc

PMC Group Global Operations Fully integrated group of companies involved in development, manufacture, marketing 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.

Decyd. Ed Courses Decyd. Ed Courses

The Decyd. Ed User Portal § The Decyd. Ed User portal provides a rich The Decyd. Ed User Portal § The Decyd. Ed 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

Decyd. Ed Discussion Forum § Decyd. Ed’s expert panel currently consists of professors from Decyd. Ed Discussion Forum § Decyd. Ed’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.

Decyd. Ed’s Decision Making Tools in Chemical and Biochemical Engineering • Molecular Design Example: Decyd. Ed’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. • Decyd. Ed’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). • Decyd. Ed’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

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

Computational Enzyme Design: Enabling renewable chemical manufacturing Starches Plant oils Biomass DOE Top Value Computational Enzyme Design: Enabling renewable chemical manufacturing Starches Plant oils Biomass DOE Top Value Added Renewable Chemicals 1, 4 succinic, fumaric and malic acids 2, 5 furan dicarboxylic acid 3 hydroxy propionic acid aspartic acid glucaric acid glutamic acid itaconic acid levulinic acid 3 -hydroxybutyrolactone glycerol sorbitol xylitol/arabinitol Specialty chemicals Polymers

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

Decyd. Ed Molecular Design Decision-Making Example of screening focused library of sequence variants 3 Decyd. Ed 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

Decyd. Ed Systems Biology Models Reaction Equilibrium Information ΔG – From Nearest Neighbor Model Decyd. Ed 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

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

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

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

The Decyd. Ed Backend Technology • The Decyd. Ed backend collects the latest simulation, The Decyd. Ed Backend Technology • The Decyd. Ed backend collects the latest simulation, optimization and estimation algorithms from the world’s top research centers • The Decyd. Ed Model API is an application Programming Interface (API) supports integration of continuous influx of models with optimization and estimation algorithms. • Instructors from both academia and industry can contribute models built using standard modeling packages (e. g. AIMMS, GAMS) for use by Decyd. Ed students • 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 Decyd. Ed backend

“f”, linear objective function Energy constraint Can only have 1 rotamer at each position “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)