Chakrabarti Group

Overview of Research and Educational Initiatives

CAPD Meeting March 11, 2013

Approaches to Molecular Design 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

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

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

2,hbond

1

N N

bind ij ij ij iji j i

J seq G seq r r seq

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

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Phe120 Asn161 Trp233 Arg285 Thr299 Ser326 Ser62 Lys65 Tyr159

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

• 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

From Enzyme Design to Bionetwork Control

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?

04/21/23 School of Chemical Engineering, Purdue University 11

DNA Melting

PrimerAnnealing

Single Strand – Primer Duplex

Extension

DNA MeltingAgain21

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DNASS tt kk 12

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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.

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DNA f DNAT t

Tr

S S E D DNA

Min C t C

dxst f x u

dt

x C C C C

For N nucleotide template – 2N + 13 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

Optimal control of PCR

Cycle 1 Cycle 2

Geometric growth:after 15 cycles,DNA concentrationsare

red – 4×10-10 Mblue – 8×10-9 Mgreen – 2×10-8 M

• DecydEd 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

• 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.

Chakrabarti Group Educational Initiatives: DecydEd

•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

1. Systems Biology

2. Molecular Design

3. Financial Engineering

•Target applications include protein engineering, catalyst design, biochemical reaction engineering

•Funded by PMC Group, Inc

DecydEd (cont’d)

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.

DecydEd Courses

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

The DecydEd User Portal

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.

DecydEd Discussion Forum

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

Input information

Target chemical

Desired raw material

Existing synthetic pathways

Existing biocatalysts

Zymzyne™ Computational Design Process

System Output

~1000 potential candidatesexpected catalytic activity

Zymzyne™ Experimental Optimization

Optimized Biocatalyst

Design Computationally Refine Experimentally

1030 candidates screened 500 candidates screened

DecydEd Industry Application Example: Computational Enzyme Design

DOE Top Value Added Renewable Chemicals1,4 succinic, fumaric and

malic acids2,5 furan dicarboxylic acid3 hydroxy propionic acid

aspartic acidglucaric acidglutamic aciditaconic acidlevulinic acid

3-hydroxybutyrolactoneglycerolsorbitol

xylitol/arabinitol

Plant oils

Starches

Biomass

Specialty chemicals

Polymers

Computational Enzyme Design: Enabling renewable chemical manufacturing

Enzyme Design Models

Protein structure Substrate binding Reactive chemistry

Active site reshaping

Loop Sidechain Glidescore Pose sampling

ClassicalSequence Optimization(fixed ligand)

ClassicalSequence Optimization(free ligand)

Calculatingmutant enzyme reaction rates

• for QM/MM refinement of enzyme design• speeding up mutant TS searches

New algorithms for side chain optimization

• scores desired loop against other low-energy excitations

QM sequence refinement

• Hierarchical pose screening• Locates global seq/struct optima for a given active site/ligand comb • Estimates “designability” of active site (fixed backbone)

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D A F R S N E Y H I L K N G T W V

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DecydEd Molecular Design Decision-Making

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

/ expf r

Gk k K

RT

ΔG – From Nearest Neighbor Model

1 2

1

eq eqr f S Sk k C C

,

1 2f rk k

S S D

τ – Relaxation time(Theoretical/Experimental)

Solve above equations to obtain rate constants

Reaction

Equilibrium Information

Relaxation Time Similar to the Time constant in Process Control

DecydEd Systems Biology Models

K. Marimuthu and R. Chakrabarti, Sequence-Dependent Modeling of DNA Hybridization Kinetics: Deterministic and Stochastic Theory, in preparation

Wild Type DNA

Mutated DNA

DNA Amplification Control Problem and Cancer Diagnostics DNA Amplification Control Problem and Cancer Diagnostics

Feed the PCR State Equations

Objective Function(noncompetitive, competitive)

DecidEd Systems Biology Decision-Making Example

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

The DecydEd Backend Technology

•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

•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

“f”, linear objective function

Energyconstraint

Can only have 1 rotamer at each positionNo

“impossibles” allowed

Nonlinear constraint term

Possible collaborations to id the global optimum for fitness measure (w pairwise decomposability assumptions, reduced energy model)