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Genetic Algorithm in Protein Structure Comparison

Genetic Algorithm in Protein Structure Comparison. Chen Jinxiu,Feng yuan,Lan Man, Li Haiquan,Li Quan, Ren li'an Laboratories for Information Technology. Outline. Background: Protein Structure Comparison and Genetic Algorithm Overview of GA in structure comparison

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Genetic Algorithm in Protein Structure Comparison

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  1. Genetic Algorithm in Protein Structure Comparison Chen Jinxiu,Feng yuan,Lan Man, LiHaiquan,Li Quan, Ren li'an Laboratories for Information Technology

  2. Outline • Background: Protein Structure Comparison and Genetic Algorithm • Overview of GA in structure comparison • Case Study: Related Protein Comparison • Conclusion

  3. 1 Background – Intuition

  4. Background - Problem • Find the similarity of two structure or substructure, more conserved than sequence. • Three kinds of problems • Single Protein Comparison, find the invariant part • Related Protein comparison, find the essential substructure of a family • General comparison, find the structure for motif

  5. Protein Structure Comparison • Presentation of structure, usually a linear sequence. • Score function, intermolecular and intra-molecular distance. • Comparison algorithm: superposition based, graph based, GA based. • Post-processing: check

  6. Major issues in comparison • Huge Information • Huge Searching space, a lot of alignment method • So the algorithms are very important

  7. Genetic Algorithm • Basic Philosophy, excellent parents produce outstanding children. • Of course, it is not true. By checking, remove unexpected ones. • Steps: • Population: a set of hypothesis • Remove poor ones • Produce next generation by combine good hypothesis.

  8. 2 GA in Structure Comparison • Motivation Protein structure have huge information, comparison have huge searching space. GA can get the optimum very fast.

  9. Basic Issues in GA comparison • Design the hypothesis, must be biological significant • Design genetic operations, must be biological significant too. • Score function to evaluate hypothesis • Constraints and Stop criteria.

  10. Case: Related Protein Comparison • Biological Background A family of protein must have some crucial common structures, which is by revolution. • Main Idea Align the secondary structure first, which means a large region, then find the detail local alignment. GA is only used in SSE level.

  11. Cases: Intuition • Representation: A sequence of SSE. • Intuitive Goal

  12. Case: GA Steps • Hypothesis • GA Operations • Swap Exchange with the whole type • Crossover Exchange inside a type of SSE • Mutate Alignment changes inside a SSE. • Hop Inside a Hypothesis

  13. GA Operation-swap

  14. GA Operation -crossover

  15. Details alignment • Atom alignment after SSE alignment is fixed. • Still a lot of means.

  16. Score Function • Formula

  17. Flow Review • Start from a population • Evaluate each hypothesis • GA operations on good ones • Next generation

  18. Conclusion • GA is suitable for protein structure comparison • GA is an efficient approximate method to find the optimum alignment. • Hypothesis and GA operations are the key issues. • Current research in this area is limited.

  19. Open Problems • Apply GA into other comparison approach, such as graph based. • Apply GA into multiple structure Comparison • Theoretical Research on GA

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