Transcript Slides

A System Approach to Measuring
the Binding Energy Landscapes of
Transcription Factors
Authors: Sebastian J. et. al
Presenter: Hongliang Fei
Outline
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Motivation
Terminologies
Problem statement and significance
Challenges
Method
Result
Conclusion
Motivation
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Quantitatively characterize interactions
of network elements;
Predict the function of genes in
biological networks.
Terminologies
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Affinity: The tendency of a molecule to
associate with another;
DNA binding domain: any protein motif
that binds to double or single-stranded
DNA;
Terminologies continues
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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.
Problem Statement
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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.
Significance
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Predict basic function of TF
Test basic assumptions of TF (e.g. base
additivity )
Test other hypothesis of TF
Understand biological network better
Challenges
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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.
Method
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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.
Data sources
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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
Tool
processing
Result for binding affinities (N_3 to N_1)
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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.
Position weight Matrix (PWM)
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Describes changes in the Gibbs free
energy for all 16 possible single-base
substitutions.
Each isoform has a PWM;
Used to test additivity assumption.
Comparisons of predicted energy changes
with measured values
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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.
Recognition of flanking bases for
pho4p and Cbflp
Comparison of A and B
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Pho4p prefers CC as
N_5N_4 and GG as
N4N5, extending the
motif to
5’-CCCACGTGGG-3’.
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Cbflp prefers GT as
N_5N_4 and AC as
N4N5, extending the
motif to 5’GTCACGTGAC-3’.
Hypothesis testing
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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.
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Except for a few
outliers, the basic
region is sufficient to
transform original
isoform B pattern to
patterns resembling
Pho4p and Cbflp.
Hypothesis testing
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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
Function distribution related with
Pho4p and Cbf1p data sets
Prediction Result Comparison
Conclusion
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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.
Acknowledgment
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Thanks for Dr. Huan’s guidance;
Thanks to Google, Wiki.