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Lab Presentation. Tianyin Zhou 3/14/2012. Brief Recap. Main points: transient bubble forms in the dsDNA spontaneously – DNA breathing MC simulations for compiling the DNA breathing profile The high-affinity DNA binding sites of Fis tends to have particular DNA Breathing Dynamics
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Lab Presentation Tianyin Zhou 3/14/2012
Brief Recap Main points: transient bubble forms in the dsDNA spontaneously – DNA breathing MC simulations for compiling the DNA breathing profile The high-affinity DNA binding sites of Fis tends to have particular DNA Breathing Dynamics Such feature could be used to distinguish between high and low affinity DNA binding site of Fis. • Title: Binding of Nucleoid-associated Protein Fis to DNA is regulated by DNA Breathing Dynamics
DNA Breathing Dynamics • Is mainly researched in quantum physics • time-dependent perturbations • Fokker-Planck equation quantum time-dependent Schrödinger equation with imaginary time • Have been experimentally observed through • Fluctuations of fluorescence (fluorophore, quencher) • Single molecular spectroscopy • Connection with biological questions • Boian S Alexandrov, Vladimir Gelev, Sang WookYoo et al. (2010) DNA dynamics play a role as a basal transcription factor in the positioning and regulation of gene transcription initiation., 1790-5. In Nucleic acids research 38 (6).
Definition of Characteristic opening profile Dataset: 58 Fis binding sites with binding affinity (Kd coefficient) 58 binding sites has been classified into 2 groups: 1. high-affinity group (Kd<1) 10 sequences 2. low-affinity group 48 sequences Use the average breathing pattern of high affinity group as characteristic opening profile(COP) Pearson correlation coefficient > 0.52, is a prerequisite for strong Fis binding sites
1st Technical drawback What we suggest: The reason why <=1nM Kd is used as the criterion to define the high binding affinity (<=1nM Kd) is missing. We suggest performing a clustering analysis of all 58 average opening profiles to see if all binding sites with Kd lower than 1nM from a clade. What the author responds: • It makes little difference to the number of sequences in high-affinity groups if they use 0.5nM or 2nM • All sequences with Kd lower than 1nM have similar profiles • They did the clustering but cannot find the 1nM clade. It is because Kdof a sequence depends on factors besides its opening profile.
2ndTechnical drawback • Dataset: Chip-Seq data • Long DNA fragments with true binding site somewhere • What the author did: • For each DNA fragment, choose one subsequence with highest correlation with COP as true binding site • What we suggest: • Plot the distribution of person correlation with COP for all subsequence of DNA fragments without assuming “true” binding site. Compare it with Random Sequences • What they respond: • Performed two-sample T2 test and two-sample Kolmogorov-Smirnov test. Difference is significant. Histogram is not provided. Does not have a true positive group
SVM Model • What we suggest: • The author had better use average opening profiles (vector) instead of average opening profiles similarities to the COP in training the support vector machine • What they responds: • The said it was due to a misstatement. They did use COP in training their SVM model. • What I would like to suggest this time? • Train SVM model by using both sequence and average opening profiles
