Dimitris Papamichail

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Design tools for Synthetic Virology

• Dimitris Papamichail
• Assistant Professor
• Computer Science Department
• University of Miami

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Our group

• I have been working on design and synthesis of
  genomic sequences in collaboration with
• Professor Steven Skiena
• Computer Science Department
• Stony Brook University
• Professor Eckard Wimmer
• Department of Molecular Genetics and Microbiology
• Stony Brook University
• Other members Rob Coleman, Steffen Mueller,
  Bruce Futcher

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Initial motive Vaccine Design

• Question How can we rapidly create a vaccine for
  a new
• viral disease?
• Motivation Mutated lethal viruses,
  bio-terrorism, cheap
• synthesis technologies
• Input The genome of a virus
•      
• Output Our design of a "better" virus to serve
  as a vaccine
• candidate
• Aim Redesign life  (if you assume viruses are
  alive)

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Our designs

• So far, our group has designed, synthesized, and
  evaluated (or still evaluates)
• Four new variants of poliovirus
• - Two codon optimized designs
• - Two codon pair optimized designs
• Several optimized flu virus segments
• Recently a couple of bacterial genes
• A couple of artificial constructs for hypothesis
  testing.

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Novel sequence design
Our goal

• Debilitate virus genome translation /
  replication.
• Make a difficult to revert and genetically stable
  virus as the cumulative phenotype of many
  mutations each with a small effect.
• Optimization problem at hand
• Select one (or few) of the 7.9 x 10442 possible
  synonymous encodings for a poliovirus capsid gene
  of 881 amino acids (compared to an estimated 1.3
  x 1079 atoms in the known universe), that serves
  our design criteria.

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Novel sequence design
First two designs Codon (de-)optimized

• We tested the hypothesis that underrepresented
  codons reduce translation efficiency by creating
  a novel polio capsid design (PV-AB) which
• Encoded the same amino-acid sequence
• Used only the least frequent codon for each
  amino-acid in human brain specific genes (and in
  human tissues in general).
• Total number of silent mutations 680
• We also created another design (PV-SD) which
  maximized the hamming distance of the capsid
  encoding, while keeping the same codon frequency
  distribution.
• Total number of silent mutations 934
• Why alter only the capsid coding region?
• No cis-acting structural RNA elements

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Novel sequence design
Experiments

• The shuffled polio design translates relatively
  well and is as potent in killing mice as the
  wildtype.
• The brain-hostile design translates minimally,
  but use of smaller segments leads to attenuated
  strains.

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Novel sequence design
Methods

• To achieve maximum hamming distance without
  altering the codon distribution, we used maximum
  weight bipartite matching between codon positions
  and codons, using as weight the number of bases
  changed.
• Restriction sites were inserted uniquely
  (inserted in specific areas and then eliminated
  everywhere else).
• Certain regions were locked to preserve secondary
  structure.

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Novel sequence design
Codon pair bias

• According to Hatfield et al., another source of
  translation (in)efficiency is the codon pair
  bias.
• We quantified the bias with the following score

• Many viruses actually are using overrepresented
  codon pairs (in human) to encode their genes.

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Novel sequence design
Codon pair bias

• There also seems to be significant correlation
  between related eucaryotes and codon pair bias.

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Novel sequence design
Another two designs Codon pair (de-)optimized

• We designed two polio capsid sequences that
  optimize the usage of over-represented and
  under-represented codon pairs in human.

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Novel sequence design
Our designs

• The design consisted of the following steps
• Same codon frequency distribution
• Optimized codon pair score
• Restriction site uniqueness and elimination
• Local Secondary structure folding energy
  restriction
• Splice site elimination
• Goals achieved with simulated annealing,
  optimization passes and manual intervention.

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Novel sequence design
Codon pair optimization problem

• Our problem variant of TSP (Travelling
  Salesperson Problem)
• This variant is polynomially time solvable, but
  with O(n65) complexity, using dynamic
  programming.
• We have 20 countries (amino-acids), 64 cities
  (codons), each country has from 1 to 6 cities.
• We will make n visits to the countries
  (amino-acid chain).
• Each time we visit a country we can visit only
  one city (select the codon to code for an amino
  acid).
• The total number of times we visit each city is
  fixed (codon distribution).

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Novel sequence design
Results

• How do the new codon pair biased designs behave?
• maxP1 (using overrepresented codon pairs, 566
  mutations) translates as well as the wildtype.
• minP1 (using underrepresented codon pairs, 631
  mutations) translates poorly.
• Details on the results can be found in our June
  27, 2008
• Science publication
• Currently we are also investigating other
  signals, such as CpG dinucleotide content, which
  are inherent in such biased constructs.

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Sequence design tools

• Tools already exist in sequence design
• GeMS
• CAD-PAM
• Gene2oligo
• DNAWorks
• GeneDesign
• GenoCAD
• Most of these tools offer
• Oligo-design
• Restriction site creation/elimination
• Codon usage alteration
• Oligo-design is the process of breaking the
  sequence into oligos (short sequence fragments),
  which can be used to self-assemble through PCR
  cycles into synthons (or the whole sequence, if
  small enough).
• We consider this process as a black box provided
  by synthesis companies.

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Our goal

• Efficient Algorithm Design for coding sequence
  alterations.
• Techniques for multi-objective optimization in
  genomic sequence design.
• Expand the knowledge base of systematic sequence
  bias in genomic sequences
• Incorporation of pathways, transcriptional
  control, boolean logic and other complex criteria
  in novel genomic sequence design.
• We aim to create a system conceptually built
  around constraints instead of sequences.
• The gene/genome designer will work on the level
  of specifying characteristics of the desired
  gene/genome (amino acid sequences,
  codon/codon-pair distribution, distribution of
  restriction sites, RNA secondary structure
  constraints, incorporation/elimination of
  patterns, etc.) and the gene editor will
  algorithmically design a DNA sequence realizing
  these constraints.

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Sequence design tools
SeEd (Sequence Editor)
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• Thank you!

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• Questions