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A System Approach to Measuring the Binding Energy Landscapes of Transcription Factors

A System Approach to Measuring the Binding Energy Landscapes of Transcription Factors. Authors: Sebastian J. et. al Presenter: Hongliang Fei. Outline. Motivation Terminologies Problem statement and significance Challenges Method Result Conclusion. Motivation.

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A System Approach to Measuring the Binding Energy Landscapes of Transcription Factors

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  1. A System Approach to Measuring the Binding Energy Landscapes ofTranscription Factors Authors: Sebastian J. et. al Presenter: Hongliang Fei

  2. Outline • Motivation • Terminologies • Problem statement and significance • Challenges • Method • Result • Conclusion

  3. Motivation • Quantitatively characterize interactions of network elements; • Predict the function of genes in biological networks.

  4. Terminologies • Affinity: The tendency of a molecule to associate with another; • DNA binding domain: any protein motif that binds to double or single-stranded DNA;

  5. Terminologies continues • Transcription factor: a protein that binds to specific parts of DNA using DNA binding domains. • Isoform: A protein that has the same function as another protein but which is encoded by a different gene. • Flanking bases: Immediate Neighbors of a mutated base.

  6. Problem Statement • Given a set of transition factors (TF for short) belonging to a certain basic protein structure family, the problem of measuring Binding Energy Landscapes is to quantify the affinities of molecular interactions.

  7. Significance • Predict basic function of TF • Test basic assumptions of TF (e.g. base additivity ) • Test other hypothesis of TF • Understand biological network better

  8. Challenges • A large number of variables in biological interaction lead to so many assays; • Many molecular interactions are transient and exhibit nanomolar to micromolar affinities. • Low affinity binding events are hard to capture.

  9. Method • step 1: Use a high-throughput micro fluidic platform to measure affinities of four eukaryotic transcription factors; • step 2: results from the platform were used to test hypotheses about transcription factor binding and to predict their in vivo function.

  10. Data sources • Four eukaryotic TFs belonging to the basic helix-loop-helix (bHLH) family, including Isoforms A and B of Human TF MAX, the yeast TFs Pho4p and Cbf1p. TFs generally bind to a consensus sequence of 5’-CANNTG-3’ • 38 genes bound by Pho4p • 24 genes bound by Cbf1pb

  11. Tool

  12. processing

  13. Result for binding affinities (N_3 to N_1)

  14. From N_3 to N_1, select CAC; • From N1 to N3, select GTG (refer to supporting materials) • The optimal binding sequence for four TFs is CACGTG for N_3 to N3.

  15. Position weight Matrix (PWM) • Describes changes in the Gibbs free energy for all 16 possible single-base substitutions. • Each isoform has a PWM; • Used to test additivity assumption.

  16. Comparisons of predicted energy changes with measured values

  17. To address the question of how Pho4p and Cbf1p serve distinct biological functions while recognizing seemingly identical consensus motifs, we measured the extent to which these TFs recognize flanking bases.

  18. Recognition of flanking bases for pho4p and Cbflp

  19. Pho4p prefers CC as N_5N_4 and GG as N4N5, extending the motif to 5’-CCCACGTGGG-3’. Cbflp prefers GT as N_5N_4 and AC as N4N5, extending the motif to 5’-GTCACGTGAC-3’. Comparison of A and B

  20. Hypothesis testing • Whether the sequence-specific binding of bHLH TFs is determined entirely by basic region? • Test whether the basic region itself is sufficient to produce the observed flanking base sequence specificity by cloning the basic regions of Pho4p and Cbf1p into the MAX isoform B backbone.

  21. Except for a few outliers, the basic region is sufficient to transform original isoform B pattern to patterns resembling Pho4p and Cbflp.

  22. Hypothesis testing • Whether the binding energy landscapes are sufficient to predict which genes these TFs physically bind. • Using a simple model based on calculating a probability of occupancy to generate genes • Test these gene’s functions

  23. Function distribution related with Pho4p and Cbf1p data sets

  24. Prediction Result Comparison

  25. Conclusion • This platform can measure DNA binding energy very well even in transient and low-affinity interactions; • We can successfully predict biological function by pure biophysical measurements.

  26. Acknowledgment • Thanks for Dr. Huan’s guidance; • Thanks to Google, Wiki.

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