Sequence Optimization For Synthetic Genes Using Genetic Algorithms

1
Sequence Optimization For Synthetic GenesUsing
Genetic Algorithms

• David Sigfredo Angulo1
• Rob Vogelbacher1, Benjamin R. Capraro2, Tobin
  Sosnick2,
• Shohei Koide2
• 1 School of Computer Science Telecommunications
  and
• Information Systems DePaul University
• 2 Department of Biochemistry and Molecular
  Biology
• The University of Chicago

2
Introduction

• Genetic Algorithms
• Using ideas based on the biology of genes
• Create software to use such a stochastic means to
  search through large searchspaces
• Resulting algorithm has nothing to do with genes
• Designing Genes
• This search space is huge
• REALLY NOVEL IDEA
• Use Genetic Algorithms based on genes to design
  genes!!

3
Outline

• Short biology Tutorial
• DNA Sequence Generation
• Why is the problem difficult?
• IBG Gene Designer
• Genetic Algorithm (GA) solution
• Heuristics and Fitness Evaluation

4
First

• Before the problem can be described
• Must give some background biochemistry principles
• Tutorial outline
• DNA
• Codons
• Protein
• Synthetic genes
• What are they and what are they used for?
• Restriction Enzymes
• Expressing Proteins using Vectors

5
Transcription/Translation

• Transcription
  Translation
• DNA RNA
  Protein
• RNA Polymerase Ribosomes

6
DNA

• Deoxyribonucleic acid
• Strand backbone is made of sugar phosphate
  molecules
• Strands connected by nitrogen containing
  nucleotide bases
• Two strands join making a double helix
• Each strand is made of nucleotides joined together

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2 nm 11 nm
30 nm 300 nm
700 nm 1100 nm

• Short region of DNA 2bl helix
• "beads on a string" form of Chromatin
• 30 nm chromatin fiber of packed nucleosomes
• Section of chromosome in an extended form
• Condensed section of chromosome
• Entire mitotic chromosome

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DNA
Four Nucleotides AGTC
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DNA Base Pairing
10
Short Biology Tutorial

• Tutorial outline
• DNA
• Codons
• Protein
• Restriction Enzymes
• Expressing Proteins using Vectors

11
DNA Sequence GenerationCodon to Amino Acid
Translation

• http//campus.queens.edu/faculty/jannr/Genetics/im
  ages/codon.jpg

12
Short Biology Tutorial

• Tutorial outline
• DNA
• Codons
• Protein
• Restriction Enzymes
• Expressing Proteins using Vectors

13
Proteins AA Chains
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Proteins

• Amino Acid Chains Fold Into complex 3D Structures
• Functional properties depend on3D structure
• Usefulness depends onfunctional properties
• E.g. designing drugs

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Designed/Expressed Proteins Extremely Useful

• Designed Proteins
• Can be used to study protein structure
• Can be used to study effects of otther proteins
• Can be designed to knock out other proteins
• Can be designed to block the acgtion of other
  proteins
• Expressed proteins
• Expressed in cows milk or chicken eggs
• Can manufacture drugs on large scales in this way
• E.g. insulin

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Synthetic Genes

• DNA sequences
• backtranslated from a novel Protein or Amino
  Acid sequence
• 
  Transcription Translation
• DNA
  RNA Protein
• RNA Polymerase
  Ribosomes
• Well put the DNA for our designed protein into
  an organism (a vector)
• Then that vector will make (express) our protein
• But, how do we get the DNA into an organism???

17
Short Biology Tutorial

• Tutorial outline
• DNA
• Codons
• Protein
• Restriction Enzymes
• Expressing Proteins using Vectors

18
Restriction Enzyme Digests

• Watson Crick 1953
• Took 20 years to be able to do anything with DNA
• H. Smith (and others) made a discovery that
  allowed manipulation and deciphering of DNA
• Discovery was that bacteria produced enzymes that
  introduce breaks in double stranded DNA molecules
  whenever they encountered a specific string of
  nucleotides
• These enzymes are called Restriction Enzymes
• Restriction Enzymes can be used as precise
  scissors
• They let biologists cut (and paste) portions of
  DNA

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EcoRI

• EcoRI was the very first Restriction Enzyme
  discovered
• "Eco" because it was isolated from E. Coli
  (Escherichia Coli)
• "R" because it is a Restriction Enzyme
• "I" because it was the first Restriction Enzyme
  from E. Coli
• Now over 300 Restriction Enzymes known
• EcoRI cleaves (restricts, digests) DNA
• Between the G and A nucleotides
• Only when it encounters them in the string
  5'-GAATTC-3'
• This is called therestriction site

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Sticky Ends

• Many restriction enzymes in such a way that some
  single stranded DNA is left at both ends
• These nucleotide sequences
• Are complimentary to each other
• Are 5'-AATT-3' in the case of EcoRI
• Can base pair with other nucleotides in a
  sequence
• Thus, are called "sticky ends"
• Can temporarily hold twoDNA strands together
• The enzyme ligasewill permanently jointhose
  strands
• This is calledligation

21
Short Biology Tutorial

• Tutorial outline
• DNA
• Codons
• Protein
• Restriction Enzymes
• Expressing Proteins using Vectors

22
Gene SynthesisOn the Lab Bench

• Initial Sequence Construction
• Oligonucleotides (short strands of DNA) are
  defined with complementary overlapping sites
• The sticky ends
• Assembly PCR
• Oligonucleotides and polymerase are mixed and
  placed in a thermocycler
• Creates contiguous DNA sequence from component
  oligos

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Gene SynthesisOn the Lab Bench (cont)?

• After PCR, generated DNA sequence cut with
  restriction enzymes
• Expression hosts's plasmid cut with restriction
  enzymes
• Synthetic gene inserted into plasmid and plasmid
  repaired
• Expression Vectors
• Host organisms used to express the synthetic
  genes (make the protein)
• Typically E. Coli
• Possibly Chickens or Cows
• Expression vector can now express protein coded
  for by synthetic gene
• A bit more complicated than described above!!!

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DNA Sequence GenerationGene Insertion
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Outline

• Short biology Tutorial
• DNA Sequence Generation
• Why is the problem difficult?
• IBG Gene Designer
• Genetic Algorithm (GA) solution
• Heuristics and Fitness Evaluation

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DNA Sequence GenerationThe Computational Problem

• Why is the problem difficult?
• Conflicting goals
• Avoid restriction sites
• Maximizing Codon Preference
• Thus, cannot use deterministic algorithm
• Degeneracy (redundancy) of the DNA code 64
  codons, 20 (21) amino acids (see next slide)
• Several synonymous codons are translated into the
  same amino acid
• Synonymous codons per AA vary from one to six
  (average is four codons per AA)?
• Huge number of possible DNA Sequences
• Average 2N for protein of amino acid length n
• Codon Preference
• Varying levels of tRNA assembly components in
  organisms
• Codon usage for a particular AA greatly influence
  protein expression
• (continued)

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DNA Sequence GenerationCodon to Amino Acid
Translation

• http//campus.queens.edu/faculty/jannr/Genetics/im
  ages/codon.jpg

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DNA Sequence GenerationThe Computational
Problem (cont)?

• Why is the problem difficult?
• (continued)
• Restriction Enzymes
• The vector will contain many restriction enzymes
• If these cut up our DNA, we wont express our
  proteins
• We must design the DNA string using synonymous
  codons so that there are no restriction sites
• Helpful to include some other restriction sites
• We must design the DNA string using synonymous
  codons so that these are included
• (continued)

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DNA Sequence GenerationThe Computational
Problem (cont)?

• Why is the problem difficult?
• (continued)
• mRNA Secondary Structure
• In prokaryotes, mRNA can fold into complex shapes
• This inhibits protein creation
• Oligonucleotide generation
• Want a specific melting temperature so that the
  complex folding doesnt take place
• The sticky ends must have the same melting
  temperature so that they will bind together.

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Outline

• Short biology Tutorial
• DNA Sequence Generation
• Why is the problem difficult?
• IBG Gene Designer
• Genetic Algorithm (GA) solution
• Heuristics and Fitness Evaluation

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IBG GeneDesignerOur Solution

• IBG GeneDesigner

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IBG GeneDesignerGenetic Algorithm

• Uses a Genetic Algorithm for sequence
  optimization
• Tournament selection model
• Uniform and single-point crossover (behind the
  scenes not user selectable at present.)?
• Mutation causes codon wobbling
• Sequence fitness determined by heuristic
  evaluation

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IBG GeneDesignerFitness Evaluation

• GeneDesigner heuristics
• Manipulation of nucleotide percentages/ratios to
  reduce mRNA secondary structure formation
• Inclusion and Exclusion of restriction sites
• Restriction sites requested for inclusion should
  only occur once
• Matching of codon preference
• Oligonucleotide generation
• Fitness determined by melting points, start and
  end nucleotide

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IBG GeneDesignerFuture Work

• Algorithm parameters
• Systematically manipulate GA parameters to
  identify default values for sequence optimization
• Population size
• Number of generations
• Mutation rate
• Convergence criteria
• Modify heuristic weighting scheme
• Selection models
• Experiment with alternative selection models
  (Roulette wheel, elitism, limit population
  replacement)?

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IBG GeneDesignerFuture Work

• Move algorithm to ECJ architecture
• Use the Strength-Pareto multi-objective
  optimization algorithm
• Create web-based version of application
• Explore island model effects on optimization

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Results

• IBG GeneDesigner utilized to generate a
  nucleotide sequence for the SH3 domain of
  a-spectrin1.
• The codon optimization option was set for
  expression in E. coli with a 40 G/C bias
• We also used the application to generate four
  assembly PCR template oligonucleotide sequences
  to produce the protein coding sequence flanked by
  desired restriction enzyme recognition sites.
• The calculated Tm values of the three overlapping
  regions were within 1.6oC
• Promoting similar annealing behavior between
  strands.
• Success of the reaction was confirmed by DNA
  sequencing of a pUC19 expression vector
  containing the PCR product cloned between
  restriction sites included in the gene design.
• Summary Protein Made!!!

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Input Protein Sequnce, Vector, Restriction
Enzymes
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Input Flanking Sequences
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Input Algorithm Parameters and Fitness Scores
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Output Generation of Oligonucleotides
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Acknowledgements

• Graduate student who did much of the coding
• Rob Vogelbacher
• University of Chicago undergraduate who used it
  to build a protein
• Benjamin R. Capraro
• His advisor
• Tobin Sosnick
• Our collaborator at University of chicago
• Shohei Koide