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Nuclear M a gnetic Resonance Structure Based Assignment

Nuclear M a gnetic Resonance Structure Based Assignment. Mehmet Çağrı Çalpur Gizem Çavuşlar Mehmet Serkan Apaydın Bülent Çatay. Agenda. Protein Structure Determination Nuclear Magnetic Resonance Spectroscopy Problem Definition and Formulation Metaheuristics Tabu Search

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Nuclear M a gnetic Resonance Structure Based Assignment

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  1. Nuclear Magnetic Resonance Structure Based Assignment MehmetÇağrı Çalpur Gizem Çavuşlar Mehmet Serkan Apaydın Bülent Çatay

  2. Agenda • Protein Structure Determination • Nuclear Magnetic Resonance Spectroscopy • Problem Definition and Formulation • Metaheuristics • Tabu Search • Ant Colony Optimization • Conclusion and Future Work

  3. Protein • Made of amino acids • Functions • Catalyzing biochemical reactions (enzymes) • Structural and mechanical roles (bones, muscles) • Immune system responses (antibodies) • Material transportation (hemoglobin) • Protein structure determination Function definition • Drug design • Enzyme design

  4. Nuclear Magnetic Resonance • Two main approaches for structure determination • XRC – 85% of solved structures • NMR - 15% of solved structures • Nuclear magnetic resonance is a phenomenon which occurs when the nuclei of certain atoms are immersed in a static magnetic field and exposed to a second oscillating magnetic field

  5. Problem Definition • The assignment problem Peaks Amino Acids xpeak1,aa1=0 Distance relations xpeak2,aa3=1 NOE relations

  6. Binary Integer Programming • Proposed by Apaydin et.al. (2009)

  7. Solution Methods • Exact solution method • Heuristic Methods • Ant Colony Optimization • Tabu Search

  8. Why Heuristics? • Solutions for large problems • Multiple solutions • Optimal may not be the most accurate • Common assignments in best k solutions

  9. Tabu Search • Solution • Neighbor • Evaluation • Tabu Check • Move • Tabu Update *nuweb2.neu.edu

  10. Quadratic Assignment Problem Formulation • BIP • QAP formulation

  11. Tabu Search Algorithm • Solution

  12. Tabu Search Algorithm • Solution • Neighbor generation • Swap P1 and P3

  13. Tabu Search Algorithm • Solution • Neighbor generation • Swap P1 and P3

  14. Tabu Search Algorithm • Solution • Neighbor generation • Swap P1 and P3

  15. Tabu Search Algorithm • Solution • Neighbor generation • Swap P1 and P3

  16. Tabu Search Algorithm • Solution • Neighbor generation • Swap P1 and P3 • Evaluation Objective function of QAP formulation

  17. Tabu Search Algorithm Tabu Structure 1 Tabu Structure 2 P1 P2 P3 P4 A1 A2 A3 A4 P1 P2 P3 P4 P1 P2 P3 P4 Iteration number until the attribute stays tabu

  18. Tabu Search Algorithm Tabu Structure 1 P1 P2 P3 P4 P1 P2 P3 P4 • Randomly picked from [smin, smax]

  19. Tabu Search Algorithm • Diversification • Probabilistic • Long term memory

  20. Results

  21. Ant Colony Optimization(ACO) • Swarm Intelligence • Behavioral, decentralized decision making • Robotics • Optimization • Proposed by Marco Dorigo in 1992.

  22. Ant’s Foraging Behavior

  23. The Algorithm • Main components • InitializeData • ConstructSolutions • Ant System DecisionRule • Backtrack • Pheromone Update • GlobalBest Update

  24. The Algorithm Procedure NVR-EAS InitializeData While (not terminated) do ConstructSolutions Update Tour Best Assignment Update Pheromone Trails End-while End-Procedure

  25. Assignment Selection • Probability of assigning peak i to aa j Peak i Peak i+1 Peak i+2 aa m aa n aa k

  26. Backtracking • Selected assignment become unavailable • Selection is done from the updated possibility list Peak i Peak i+1 Peak i+2 aa m aa n aa k

  27. Results

  28. Conclusion and Future Work • Exact solution methods • Metaheuristic implementation • Tabu search • Changing diversification mechanism • Implementing new operator • Ant colony • Extensive parameter testing • Adapting new scoring functions • Scoring matrix generated by machine learning performs better than NVR-BIP data

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