Protein Structure Prediction With Evolutionary Algorithms

1. 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

2. 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

3. 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

4. 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

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

6. 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

7. 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

8. 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

9. 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!

10. 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

11. 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

12. Encodings for Internal Coordinates • Encoding impacts global search behavior of GA • Example: One-point Mutations • Relative Encoding: FLLFRRLRLLR-> FLLFRFLRLLR • Absolute Encoding: RULLURURULU-> RULLUULULDL

13. 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

14. Potential Energy Formulation • Problem: same energy but different potential (Picture ) • Augment energy function to allow a distance-dependent hydrophobic-hydrophobic potential (Formula)

15. 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

16. 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 = 2*number of hydrophobics + 2 (any infeasible conformation has positive energy)

17. 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

18. Methods and Results • 1-point and 2-point Mutation operators • 1-point, 2-point and Uniform Crossover operators • 5 polymer sequences (< 50 amino acids) • Each run of GA: 200 generations

19. Methods and Results • Relative vs. Absolute Encoding (Diagram ) Distribution of relative ranks on the 3 lattices

20. 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

21. 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

22. PE (Post Exhibitum)

23. PE

24. PE

25. PE

26. PE