Massively Multiplexed Zinc Finger Protein Engineering Harvard iGEM - - PowerPoint PPT Presentation

• K. Barclay, J. Chew, S. Choudhury, W. Clerx, N. Genuth,
• B. Gerberich, M. Kopelman, M. Lunati, N. Naushad

Harvard iGEM 2011

Massively Multiplexed Zinc Finger Protein Engineering

Foundational Advance A novel integrated system to make and test biological parts

Engineering Biological Parts Designing new interactions is difficult No set rules, only guidelines

Traditional: Two Extremes

• Small number of highly educated guesses,

using structural and biochemical information

– Higher probability of success – Fewer interactions tested

• Vast number of random guesses

– Lower probability of success – More interactions tested Higher probability of success More interactions tested

Our Method

• We reduced-to-practice a middle approach

– Test many interactions – Higher probability of success

• 1. Design
• 2. Synthesize
• 3. Test
• Applicable to many biological interactions and

future iGEM teams

Novel integration of technologies

Introduction to Zinc Finger Proteins

• Naturally evolved

DNA-binding protein

• Can be

customized to target arbitrary DNA sequences

Structure

Helix: Responsible for binding to a DNA triplet. Helices are made up of 7 amino acids. Backbone: Gives protein its structure Finger: contains a backbone and a helix, each binds to a single 3-base DNA triplet Zinc finger protein: array of three fingers that binds to 9 bases (3 triplets) of DNA.

Example finger sequence: FQCRICMRNFSRSDHLTTHIRTH

Backbone Helix

Why Zinc Finger Proteins?

• Binds directly to DNA

with high specificity

• Promising applications

for gene therapy

• Relatively small protein
• Found naturally in many
• rganisms

Zinc finger protein array bound to DNA

Our Project

• 1. Design: use a bioinformatics approach to predict

55,000 zinc finger sequences

– Targeted against six DNA sequences for three diseases

• 2. Synthesize: use chip-based DNA synthesis to make all

55,000 sequences in one tube

• 3. Test: use a genomic metabolic selection system to

test which zinc finger sequences successfully bind DNA

Result: 15 novel zinc fingers

Step 1: Design Determine the most suitable amino acid sequences for binding specific target nucleotide sequences of our choosing.

Design Synthesize Test Human Practices

Problem: With 7 amino acids per binding helix, there are 207= 1,300,000,000 possible helix sequences. How do we know which

• nes are likely to bind?

Design Synthesize Test

R S D H L T T C T R S D C P Q J A T C V G W I E S Q P O D C P N L A W R T R S D C P A Q S F K L P

Design Synthesize Test Human Practices

Plan:

Create an algorithm that generates zinc fingers with high probability of binding target sequences:

• 1. Analyze data from previous studies of zinc fingers
• 2. Make predictions using known models of zinc finger-DNA

binding

• 3. Expand the pool of zinc fingers by including homologous

backbones

• 4. Add randomness to discover even more possible

solutions

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CTG

Data Analysis: Novel Helices

CTG Helices

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Results Verifying Our Generator

To make sure our program is creating valid results, we compared our database’s helices for the DNA triplets ANN to ones we generated:

Database Frequencies Our Generated Frequencies

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Step 2: Synthesize We generated 55,000 predictions, but how do we synthesize that many

• ligos?

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Chip Synthesis

• New technology that synthesizes

DNA sequences on a microarray chip

• Cost is 1000x cheaper than

traditional methods

• 55,000 200-mer sequences per

chip – Allows us to test a large library to find zinc finger binders

Kosuri et al. 2011

DNA microchip Zinc Finger Library

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Finger 1

DNA Pool to Zinc Finger Library

Zinc Finger Expression Plasmid with Finger 1 Insert

Each prediction is a DNA sequence. Each DNA sequence enters one cell. These cells become a living library.

• 3. Transformation
• 1. qPCR
• 2. Digestion and ligation

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Chip Synthesis: Sequencing Results

Perfect sequence 1 point mutation 2.6% 2+ point muations 18.2% Frameshift 22.1% 57.1%

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mutations

Step 3: Test Now we have a library of 55,000 variants, but how do we test which

• nes work?

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One-Hybrid Selection System

Meng et al. Nature Biotechnology 2005.

Design Synthesize Test

• His3: positive

metabolic selection

• URA3: negative

selection

• 3-AT and 5-FOA to

fine-tune

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Plasmids

Advantages of genome-based parts:

• Stability
• One copy per cell
• Easy!

– Protocols available on Harvard iGEM 2011 wiki – Strains submitted to the Registry

Vs. Genome

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MAGE:

Multiplex Automated Genome Engineering

How it works:

• Lagging strand incorporation
• Make small alterations to existing genes
• Perform multiple changes

simultaneously and screen

Wang et al, Nature 2009

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Lambda Red

• Homologous recombination
• Introduce new sequences into genome
• Antibiotic resistance selection

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Building the Selection Strain

• HisB: endogenous E. coli version of His3, histidine production
• PyrF: endogenous E. coli version of URA3
• rpoZ: omega subunit of RNA polymerase

Design Synthesize Test

Selection construct

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Results Growth Phenotype: Incomplete Media

0.1 0.2 0.3 0.4 0.5 0.6 0:00:06 10:00:07 20:00:06

OD 600nm Time (hours)

EcNR2 selection strain EcNR2 selection strain + Zif268

Selection Strain Selection Strain + Zinc Fingers

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Results

Growth Phenotype in Incomplete Media Supplemented with Histidine

0.1 0.2 0.3 0.4 0.5 0.6 0:00:06 10:00:07 20:00:06

OD 600nm Time (hours)

EcNR2 selection strain EcNR2 selection strain + Zif268

Selection Strain Selection Strain + Zinc Fingers

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Results Fine-tuning selection

3-AT increases stringency of selection

0.05 0.1 0.15 0.2 0.25 0.3 0:00:07 10:00:07

OD 600nm Time (hours)

1mM 3-AT 2.5mM 3-AT 5mM 3-AT 10mM 3-AT 25mM 3-AT 50mM 3-AT

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Results Sensitivity

Recognizes control zinc fingers diluted one in one million with negative control zinc fingers

0.05 0.1 0.15 0.2 0.25 0.3 0.35 0:00 4:00:00 8:00:00 12:00:00

OD 600nm Time (hours)

1 in 1 1 in 10 1 in 100 1 in 1000 1 in 10000 1 in 100000 1 in 1000000 negative control plasmid no plasmid

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Results Novel Zinc Fingers

• Transformed zinc fingers

for the colorblindness target into selection strain and grew in minimal media

• Colonies grew in various

3-AT concentrations

• So far 15 novel zinc

fingers have been sequenced

0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0:00 2:30:00 5:00:00 7:30:00 10:00:00 12:30:00 15:00:00

OD 600nm

Time (hours)

Neg ctrl D1 D2 B1 B3 B10

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Human Practices

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• iGEM Goal: make a difference in the world
• How do we bring technology to the world?

– Commercialization – iGEM Entrepreneurial Division

“The iGEM Foundation is dedicated to…the development of open community and collaboration”

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iGEM: Open-Source and Commercialization

Conflict:

Commercialization: bring technology to the world, for a profit vs. Open Source Model: move technology ahead

What happens when iGEM technologies enter the commercial world?

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Case Study: Zinc Finger Proteins

• We explored the impact of our project in the

existing open-source and commercial context

• Sangamo Biosciences

– Produce zinc finger proteins as commercial tools – $15,000 per zinc finger protein – Restricted usage and distribution – Control the patent landscape

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What are the implications?

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Open Source Technology Intellectual Property Rights

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• (+) Increases idea sharing
• (+) Lowers cost of using new

technologies

Open Source Technology Intellectual Property Rights

Design Synthesize Test Human Practices

• (+) Increases idea sharing
• (+) Lowers cost of using new

technologies

• (+) Incentivizes research
• (+) Provides path to

commercialization

Open Source Technology Intellectual Property Rights

Design Synthesize Test Human Practices

• (+) Increases idea sharing
• (+) Lowers cost of using new

technologies

• (-) Lack of incentives
• (-) Problems with

commercialization

Open Source Technology Intellectual Property Rights

• (+) Incentivizes research
• (+) Provides path to

commercialization

Design Synthesize Test Human Practices

• (+) Increases idea sharing
• (+) Lowers cost of using new

technologies

• (-) Lack of incentives
• (-) Problems with

commercialization

Open Source Technology Intellectual Property Rights

• (+) Incentivizes research
• (+) Provides path to

commercialization

• (-) Temporarily inhibits

spread of technology

• (-) Increases costs of access

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Proposal: Research Exemption

Allows academic research without high licensing costs. (+) Opens the field to research (+) Maintains incentive for research (+) Can be implemented by policy-makers and scientists.

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Conclusion

Accomplishments

Design

• Programmed an algorithm to generate thousands of potential proteins to bind

to specific DNA triplets Synthesize

• Created a living library of our 55,000 sequences targeted to our six target DNA

sequences Test

• Built a metabolic genomic selection system sensitive enough to detect the

binding of 1:1,000,000 proteins Parts

• Submitted characterized chassis strains and Biobricks to the Registry
• All protocols available on wiki

Human Practices

• Initiated discussion on the balance between intellectual property and open

source technology

Overall

• Engineered 15 potential novel zinc fingers to bind the triplet TGG,

with more currently being characterized

Acknowledgments

Team Members Kristin Barclay Justin Chew William Clerx Sarah Choudhury Naomi Genuth Brandon Gerberich Mark Kopelman Matt Lunati Nida Naushad Teaching Fellows Dan Goodman Srivatsan Raman, PhD Joyce Yang Jun Li Noah Taylor Jamie Rodgers Special Thanks to: Jagesh Shah, PhD Alain Viel, PhD George Church, PhD Sri Kosuri, PhD

References

• Sriram Kosuri, Nikolai Eroshenko, Emily M LeProust, Michael Super, Jeffrey Way, Jin

Billy Li, George M Church. (2010). Scalable gene synthesis by selective amplification of DNA pools from high-fidelity microchips. Nature Biotechnology, 28(12):1295-9.

• Xiangdong Meng, Michael H Brodsky, Scot A Wolfe. A bacterial one-hybrid system

for determining the DNA-binding specificity of transcription factors. (2005). Nature Biotechnology, 23(8): 988-994.

• Harris H. Wang, Farren J. Isaacs, Peter A. Carr, Zachary Z. Sun, George Xu, Craig R.

Forest, George M. Church. Programming cells by multiplex genome engineering and accelerated evolution. (2009). Nature, 460(7257):894-8.

• Yu, D., H. M. Ellis, et al. (2000). "An efficient recombination system for

chromosome engineering in Escherichia coli." Proceedings of the National Academy of Sciences of the United States of America 97(11): 5978-5983.

Accomplishments

Design

• Programmed an algorithm to generate thousands of potential proteins to bind

to specific DNA triplets Synthesize

• Created a living library of our 55,000 sequences targeted to our six target DNA

sequences Test

• Built a metabolic genomic selection system sensitive enough to detect the

binding of 1:1,000,000 proteins Parts

• Submitted characterized chassis strains and Biobricks to the Registry
• All protocols available on wiki

Human Practices

• Initiated discussion on the balance between intellectual property and open

source technology

Overall

• Engineered 15 potential novel zinc fingers to bind the triplet TGG,

with more currently being characterized

Current Binding Models

Design Synthesize Test

Relate the change of a letter in a DNA triplet to a change in an amino acid on the helix

C T G R S T R L D V

1 2 3

1 2 3 4 5 6

7 DNA Triplet Zinc Finger Helix Persikov, 2011

Supplementary slides Lambda Red Recombination

downstream homology upstream homology rpoZ Rest of the ECNR2 genome Zeocin cassette being swapped into place for rpoZ by lambda red recombination

How Lambda Red works

• Lambda bacteriophage P1 transduction
• Lambda red machinery, exo, beta, gam

proteins

ECNR2: designed for lambda red recombination

• Has the lambda red machinery
• Is temperature inducible, 42°C for 15 min
• MutS knockout- reduction in DNA mismatch

repair activity, insert less likely to be excised.