DNA structure simulation based on sequence-structure relationship HaYoung Jang 2002. 5. 15.

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Presentation transcript:

DNA structure simulation based on sequence-structure relationship HaYoung Jang

© 2002, SNU BioIntelligence Lab, DNA computing – Is it true? Massive parallelism of DNA computing make it possible to solve the NP-problem. - On the weight of computations, Jadas Hartmanis, 1995 Adleman says “I solved Hamiltonial Path Problem with DNA in only seven days, even though it had just four node.” - Molecular computation: Adleman's experiment repeated, Kaplan et al., 1995

© 2002, SNU BioIntelligence Lab, What is sequence-structure relationship? Intrinsic curvature that can be induced by a variety of base sequences. A-tracts - reduced electrophoretic mobility on polyacrylamide gels - enhanced ring closure probabilities

© 2002, SNU BioIntelligence Lab, Why is sequence-structure relationship important? Biology - DNA contains many signals. These signals are contained in the base sequence, but they are expressed through local DNA structure and flexibility. DNA computing - more efficient sequence design - from tube to individual

© 2002, SNU BioIntelligence Lab, Is it Possible? Ion Interaction in the grooves of DNA - MD simulation shows it. DNA Bending - Subsequent experimental investigations found intrinsic curvature of the polyadenine stretch(A-tract) Environmental Dependence of Nucleic Acid Structure - MD simulation with Particle Mesh Ewald Method Varied DNA Structures - MD simulation suggests stable state

© 2002, SNU BioIntelligence Lab, Why don’t use MD simulation? It’s too expensive. It does not suggest any model. It have been designed for dynamics.

© 2002, SNU BioIntelligence Lab, It have to… Suggest approximate model for DNA structure. Consider the time step and choose best one which represents the structure of specific base sequence. Be able to use experimental result. Be able to compute in reasonable cose.

© 2002, SNU BioIntelligence Lab, How can we do? Periodicity Artifacts

© 2002, SNU BioIntelligence Lab, How can we do? (continued.) Minimal Solvation Models  Implicit solvent models - generalized Born methodologies for treating the implicit solvation electrostatic interactions in nucleic acid simulation - Poison-Boltzmann methods in MD simulation.  Minimally including explicit solvent in MD simulation.

© 2002, SNU BioIntelligence Lab, How can we do? (continued.) Genetic Algorithm. - construct model for sequence-structure relationship - use fitness function made from simple MD algorithm and experimental result. Machine Learning - make and improve fitness function using experimental data.

© 2002, SNU BioIntelligence Lab, Present Objectives Find an appropriate MD algorithms. Determine the range of parameters. - effect of solvent. (temperature, charged ion, etc.) - range of basepair How to encode the fitness function. What kind of learning algorithm is used.