Protein Structure Prediction With Evolutionary Algorithms

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Protein Structure Prediction With Evolutionary
Algorithms

• Natalio Krasnogor, U of the West of England
• William Hart, Sandia National
  Laboratories
• Jim Smith, U of the West of
  England
• David Pelta, Universidad de Granada

Presenter Elena Zheleva
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Introduction

• Problem Description
• Biology Background
• Protein Folding
• HP Protein Folding Model
• Genetic Algorithm (GA) Design Factors
• Encodings for Internal Coordinates
• Potential Energy Formulation
• Constraint Management
• Methods and Results
• Conclusion

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Problem Description

• Computational Biology open problem protein
  structure prediction
• Genetic algorithms have been used in the research
  literature
• Authors analyze 3 algorithm parameters that
  impact performance and behavior of GAs
• Goal make suggestions for future algorithm design

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Outline

• Problem Description
• Biology Background
• Protein Folding
• HP Protein Folding Model
• GA Design Factors
• Encodings for Internal Coordinates
• Potential Energy Formulation
• Constraint Management
• Methods and Results
• Conclusion

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Protein Folding

• Proteins driving force behind all of the
  biochemical reactions which make biology work
• Protein is an amino acid chain!
• Amino acid chain -gt Structure of a protein
• Structure of a protein -gt Function of a protein

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Protein Folding

• Protein Folding connection between the genome
  (sequence) and what the proteins actually do
  (their function).
• Currently, no reliable computational solution for
  protein folding (3D structure) problem.
• Chemistry, Physics, Biology, CS

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Outline

• Problem Description
• Biology Background
• Protein Folding
• HP Protein Folding Model
• GA Design Factors
• Encodings for Internal Coordinates
• Potential Energy Formulation
• Constraint Management
• Methods and Results
• Conclusion

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HP Protein Folding Model

• Amino acid chains (proteins) are represented as
  connected beads on a 2D or 3D lattice
• HP hydrophobic hydrophilic property
• Hydrophobic amino acids can form a hydrophobic
  core w/ energy potential

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HP Protein Folding Model

• Model adds energy value e to each pair of
  hydrophobics that are adjacent on lattice AND not
  consecutive in the sequence
• Goal of GA find low energy configurations!

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Outline

• Problem Description
• Biology Background
• Protein Folding
• HP Protein Folding Model
• GA Design Factors
• Encodings for Internal Coordinates
• Potential Energy Formulation
• Constraint Management
• Methods and Results
• Conclusion

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Encodings for Internal Coordinates

• Proteins are represented using internal
  coordinates (vs. Cartesian)
• Absolute vs. Relative encoding
• Absolute Encoding specifies an absolute
  direction
• cubic lattice U,D,L,R,F,B
• Relative Encoding specifies direction relative
  to the previous amino acid
• cubic lattice U,D,L,R,F

n-1
n-1
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Encodings for Internal Coordinates

• Encoding impacts global search behavior of GA
• Example One-point Mutations
• Relative Encoding
• FLLFRRLRLLR-gt
• FLLFRFLRLLR
• Absolute Encoding
• RULLURURULU-gt
• RULLUULULDL

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Outline

• Problem Description
• Biology Background
• Protein Folding
• HP Protein Folding Model
• GA Design Factors
• Encodings for Internal Coordinates
• Potential Energy Formulation
• Constraint Management
• Methods and Results
• Conclusion

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Potential Energy Formulation

• Problem same energy but different potential
• (Picture ?)
• Augment energy function to allow a
  distance-dependent hydrophobic-hydrophobic
  potential
• (Formula)

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Outline

• Problem Description
• Biology Background
• Protein Folding
• HP Protein Folding Model
• GA Design Factors
• Encodings for Internal Coordinates
• Potential Energy Formulation
• Constraint Management
• Methods and Results
• Conclusion

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Constraint Management

• Methods for penalizing infeasible conformations
• Method 1 Consider only feasible conformations
• Weakness shortest path from one feasible
  conformation to another may be very long
• Method 2 Fixed Penalty Approach
• Violations
• 2 amino acids lying on the same lattice point
• Lattice point at which there are 2 or more amino
  acids
• Penalty per violation 2number of hydrophobics
  2
• (any infeasible conformation has positive
  energy)

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Outline

• Problem Description
• Biology Background
• Protein Folding
• HP Protein Folding Model
• GA Design Factors
• Encodings for Internal Coordinates
• Potential Energy Formulation
• Constraint Management
• Methods and Results
• Conclusion

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Methods and Results

• 1-point and 2-point Mutation operators
• 1-point, 2-point and Uniform Crossover operators
• 5 polymer sequences (lt 50 amino acids)
• Each run of GA 200 generations

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Methods and Results

• Relative vs. Absolute Encoding
• (Diagram ?)
• Distribution of relative ranks on the 3 lattices

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Methods and Results

• Standard vs. Distant Energy
• Does the modified energy potential improve the
  search capabilities of the GA?
• No significant difference on test sequences
• A guess there might be on longer sequences

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Conclusion

• GAs applied to Protein Structure Prediction
  problem have 3 important factors to consider
• Relative encoding is at least as good as absolute
  encoding, in some cases much better
• Modified energy potential does not improve search
  capabilities of GA
• The proposed constraint/penalty method ensures
  feasibility of the optimal solution

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PE (Post Exhibitum)
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