Single TF

$$\newcommand{\kon}{k_{\mathrm{on}}} \newcommand{\koff}{k_{\mathrm{off}}}$$

There exist two mathematical formalisms to describe the probabilities of molecular configurations at equilibrium: thermodynamics and kinetics. In the kinetics formalism, the system transits between configurational states according to rate parameters contained in differential equations, which can describe not just the system's equilibrium but also its trajectory towards it, or the kinetics of non-equilibrium systems. In the thermodynamics/statistical mechanics formalism, we posit that a system will occupy configurations with lower energy, and use the Boltzmann distribution to estimate the proportion of time the system spends in each state, at equilibrium. The thermodynamics formalism is limited to describing equilibrium state probabilities, but it does so with fewer parameters.

We'll derive an expression for the probability of a single TFBS' occupancy with both formalisms, but proceed with the thermodynamic description for more elaborate configurations. It will become clear why that is preferable.

Kinetics

Most derivations of the probability of single TFBS occupancy at equilibrium employ a kinetics formalism, so we'll walk through that first, and then explore the analog in the thermodynamics description. In the kinetics description, the parameters are rates.

$$ \mathrm{TF} + \mathrm{TFBS} \underset{\koff}{\overset{\kon}{\rightleftarrows}} \mathrm{TF\colon TFBS} $$

The natural rates are the rate of TF binding $\kon$ and unbinding $\koff$. Equilibrium is reached when binding and unbinding are balanced:

$$\frac{d[\mathrm{TF\colon TFBS}]}{dt} = k_{\mathrm{on}}[\mathrm{TF}][\mathrm{TFBS}] - k_{\mathrm{off}}[\mathrm{TF\colon TFBS}] = 0 \text{ at equilibrium}$$ $$k_{\mathrm{on}}[\mathrm{TF}]_{\mathrm{eq}}[\mathrm{TFBS}]_{\mathrm{eq}} = k_{\mathrm{off}}[\mathrm{TF\colon TFBS}]_{\mathrm{eq}}$$ $$\text{(dropping eq subscript) }[\mathrm{TF\colon TFBS}] = \frac{k_{\mathrm{on}}[\mathrm{TF}][\mathrm{TFBS}]}{k_{\mathrm{off}}} = \frac{[\mathrm{TF}][\mathrm{TFBS}]}{k_{d}}$$

where $k_{d} = \frac{\koff}{\kon}$ is called the dissociation constant (or equilibrium constant). We'd like to determine the probability of finding the TFBS occupied, i.e. the fraction of time it spends in the bound state. That fraction is $\frac{[\mathrm{bound}]}{([\mathrm{unbound}] + [\mathrm{bound}])} = \frac{[\mathrm{TF\colon TFBS}]}{([\mathrm{TFBS}] + [\mathrm{TF\colon TFBS}])}$. Define the denominator as $[\mathrm{TFBS}]_{0} = [\mathrm{TFBS}] + [\mathrm{TF\colon TFBS}]$ so that $[\mathrm{TFBS}] = [\mathrm{TFBS}]_{0} - [\mathrm{TF\colon TFBS}]$ and substitute:

$$[\mathrm{TF\colon TFBS}] = \frac{[\mathrm{TF}]([\mathrm{TFBS}]_{0} - [\mathrm{TF\colon TFBS}])}{k_{d}}$$ $$[\mathrm{TF\colon TFBS}](k_d + [\mathrm{TF}]) = [\mathrm{TF}][\mathrm{TFBS}]_{0}$$ $$\frac{[\mathrm{TF\colon TFBS}]}{[\mathrm{TFBS}]_{0}} = \frac{[\mathrm{TF}]}{k_d + [\mathrm{TF}]}$$

Note: We could also ask for this expression in terms of $[\mathrm{TF}]_0 = [\mathrm{TF}] + [\mathrm{TF\colon TFBS}]$ however, since we’re considering a single TFBS, $[\mathrm{TF\colon TFBS}]$ is at most 1, and so $[\mathrm{TF}]_0 \approx [\mathrm{TF}]$. In instances of ligand-receptor binding in which that approximation cannot be made, the fraction bound is a messy quadratic. Derivation here.

Thermodynamics

In the thermodynamics description, the parameters are Gibbs free energies $\Delta G$. Let's follow the derivation from Physical Biology of the Cell (pp. 242) and consider the number microstates underlying each of the of bound and unbound macrostates, and their energies.

In order to count microstates, we imagine distributing $L$ TF molecules across a space-filling lattice with $\Omega$ sites. The energy of a TF in solution is $\varepsilon_{\mathrm{solution}}$ and the energy of a bound TF is $\varepsilon_{\mathrm{bound}}$. $\beta$ is the constant $1/k_b T$ where $k_b$ is Boltzmann's constant and $T$ is the temperature.

<!--

StateEnergyMultiplicityWeight
$A \cdot A_s$ $\frac{\Omega!}{(\Omega - A)!A!} \approx \frac{\Omega^{A}}{A!}$ $\frac{\Omega^{A}}{A!} \cdot e^{-\beta \left[ A \cdot A_s \right]}$
$(A - 1) A_s + A_b$ $\frac{\Omega!}{(\Omega - (A - 1))!(A-1)!B!} \approx \frac{\Omega^{A-1}}{(A-1)!}$ $\frac{\Omega^{A-1}}{(A-1)!} \cdot e^{-\beta \left[ (A - 1) A_s + A_b \right]}$
-->

In our case, the number of microstates in the unbound macrostate is $\frac{\Omega !}{L!(\Omega -L)!}\approx \frac{\Omega^L}{L!}$ and they each have energy $L \cdot \varepsilon_s$. The number of microstates in the bound macrostate is $\frac{\Omega !}{(L-1)!(\Omega -(L+1))}\approx \frac{\Omega^{(L-1)}}{(L-1)!}$ and they each have energy $(L-1) \varepsilon_s + \varepsilon_b$.

The Boltzmann distribution describes the probability of a microstate as a function of its energy: $p(E_i) = e^{-\beta E_i}/Z$ where $Z$ is the "partition function" or simply $\sum_i e^{-\beta E_i}$ the sum of the weights of the microstates, which normalizes the distribution. In our case:

$$Z(L,\Omega)=\left(\colorbox{LightCyan}{$ \frac{\Omega^L}{L!} e^{-\beta L \varepsilon_s}$}\right) + \left(\colorbox{Seashell}{$\frac{\Omega^{(L-1)}}{(L-1)!} e^{-\beta [(L-1) \varepsilon_s + \varepsilon_b]}$}\right)$$

With that expression in hand, we can express the probability of the bound macrostate, $p_b$:

$$p_b=\frac{ \colorbox{Seashell}{$\frac{\Omega^{(L-1)}}{(L-1)!} e^{-\beta [(L-1) \varepsilon_s + \varepsilon_b]}$}}{\colorbox{LightCyan}{$\frac{\Omega^L}{L!} e^{-\beta L \varepsilon_s}$} + \colorbox{Seashell}{$\frac{\Omega^{(L-1)}}{(L-1)!} e^{-\beta [(L-1) \varepsilon_s + \varepsilon_b]}$}} \cdot \color{DarkRed}\frac{\frac{\Omega^L}{L!}e^{\beta L \varepsilon_s}}{\frac{\Omega^L}{L!}e^{\beta L \varepsilon_s}} \color{black} = \frac{(L/\Omega)e^{- \beta \Delta \varepsilon}}{1+(L/\Omega)e^{- \beta \Delta \varepsilon}} $$

Where we have defined $\Delta \varepsilon = \varepsilon_b - \varepsilon_s$. $L/\Omega$ is really just a dimensionless TF concentration, which we'll hand-wave as being equivalent to $[\mathrm{TF}]$, which leaves us with an expression suspiciously similar to the one we derived from the kinetics formalism:

$$p_b = \frac{[\mathrm{TF}]e^{-\beta \Delta \varepsilon}}{1+[\mathrm{TF}]e^{-\beta \Delta \varepsilon}} \cdot \color{DarkRed}\frac{e^{\beta \Delta \varepsilon}}{e^{\beta \Delta \varepsilon}} \color{black} = \frac{[\mathrm{TF}]}{e^{\beta \Delta \varepsilon}+[\mathrm{TF}]}$$

From which we recapitulate an important correspondence between kinetics and thermodynamics at equilibrium: $ k_d = e^{\beta \Delta \varepsilon} = e^{\Delta \varepsilon / k_bT} $ more commonly written for different units as $k = e^{-\Delta G / RT}$.

The takeaway is that both the kinetics and thermodynamics formalisms produce an equivalent expression for the probabilities of each of the bound and unbound configurations, parameterized respectively by $k_d$ and $\Delta G$.

References:

Sample Values

In order to compute probabilities like $p_b$, we need concrete TF concentrations $[\mathrm{TF}]$ and binding affinities (either $k_d$ or $\Delta G$). What are typical intranuclear TF concentrations and binding affinities?

Concentrations

A typical human cell line, K562s, have a cellular diameter of 17 microns. (BioNumbers)

def sphere_volume(d):
    return 4/3*np.pi*(d/2)**3

K562_diameter_microns = 17
K562_volume_micron_cubed = sphere_volume(K562_diameter_microns)
print(f'K562 volume: {round(K562_volume_micron_cubed)} μm^3')

K562 volume: 2572 μm^3

A typical expressed TF has a per-cell copy number range from $10^3$ - $10^6$. (BioNumbers)

copy_number_range = [1e3, 1e6]
N_A = 6.02214076e23

def copy_number_and_cubic_micron_volume_to_molar_concentration(copy_number, volume=K562_volume_micron_cubed):
    return (copy_number / N_A) / (volume * (1e3 / 1e18))  # 1000 Liters / m^3; 1e18 μm^3 / m^3

lower_end_molar = copy_number_and_cubic_micron_volume_to_molar_concentration(copy_number_range[0], K562_volume_micron_cubed)
upper_end_molar = copy_number_and_cubic_micron_volume_to_molar_concentration(copy_number_range[1], K562_volume_micron_cubed)

lower_end_nanomolar = lower_end_molar / 1e-9
upper_end_nanomolar = upper_end_molar / 1e-9

print('If TF copy numbers range from 1,000-1,000,000, then TF concentrations range from', str(round(lower_end_nanomolar))+'nM', 'to', str(round(upper_end_nanomolar))+'nM')

If TF copy numbers range from 1,000-1,000,000, then TF concentrations range from 1nM to 646nM

We might also like a distribution over this range. Let's posit a lognormal, where $10^3$ and $10^6$ are the 3σ from the mean, which is $10^{4.5}$. Then $\sigma = 10^{0.5}$

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Affinities

What are typical TF ΔG's of binding? How about the $\koff$ rates and half lives?

  • We can use the prior knowledge that dissociation constants should be in the nanomolar regime (BioNumbers).
  • We can use the relation that $\Delta G = -k_b T \cdot \ln(k_d)$ (Plugging in 310°K (human body temp) and the Boltzmann constant $k_b$ in kcal/Mol)
  • We use the approximation that $\kon$ is ~$10^5 / $ Molar $ \times $ sec (Wittrup)
T = 310
k_b = 3.297623483e-24 * 1e-3  ## cal/K * kcal/cal
kbT = k_b*T*N_A
kbT  ## in 1/Mol -- an unusual format

k_on = 1e5

def nanomolar_kd_from_kcal_ΔG(ΔG): return exp(-ΔG/kbT) * 1e9
def kcal_ΔG_from_nanomolar_kd(K_d): return -kbT*ln(K_d*1e-9)
def k_off_from_nanomolar_kd(k_d): return (k_d*1e-9) * k_on
def half_life_from_kd(k_d): return ln(2) / ((k_d*1e-9) * k_on)
$\Delta G$ $\kon$ $\koff$ $t_{1/2}$
$k_d$
1 12.757685 1e5 / (M * s) 0.0001 0 days 01:55:31.471805599
10 11.340164 1e5 / (M * s) 0.0010 0 days 00:11:33.147180560
100 9.922644 1e5 / (M * s) 0.0100 0 days 00:01:09.314718056
1000 8.505123 1e5 / (M * s) 0.1000 0 days 00:00:06.931471806

We learn that an order of magnitude residence time difference results from just 1.4 extra kcal/Mol, and that TF half lives range from about 5s to about 2h. Let's once again posit a distribution of affinities to sample from (defined on $k_d$):

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With those concrete TF concentrations and dissociation constants, we can finally plot our function $p_b = \frac{[\mathrm{TF}]}{e^{\beta \Delta \varepsilon}+[\mathrm{TF}]}$.

@np.vectorize
def fraction_bound(TF, ΔG):
    '''TF in nanomolar'''
    return TF / (TF + nanomolar_kd_from_kcal_ΔG(ΔG))

(Note that both $[\mathrm{TF}]$ and $k_d$ are plotted in log space, but $p_b$ is linear.)

Multiple TFs: Direct Cooperativity & Competition

Suppose now that two TFs bind adjacent segments of DNA in such a way that the binding of either facilitates (or hinders) the binding of the other. We call this direct cooperativity (and competition). We'll focus first on cooperativity.

Cooperativity

As before, the statistical mechanics formalism entails enumerating the configurations, their multiplicities, and their energies. We'll call the TFs A and B. We'll denote their counts as $A$ and $B$. The energy of a TF in solution will once more be $A_s$ and bound to its cognate TFBS $A_b$. The energy of cooperativity will be $C_{AB}$.

StateEnergyMultiplicityWeight
$A \cdot A_s + B \cdot B_s$ $\frac{\Omega!}{(\Omega - A - B)!A!B!} \approx \frac{\Omega^{A+B}}{A!B!}$ $\frac{\Omega^{A+B}}{A!B!} \cdot e^{-\beta \left[ A \cdot A_s + B \cdot B_s \right]}$
$(A - 1) A_s + A_b + B \cdot B_s$ $\frac{\Omega!}{(\Omega - (A - 1) - B)!(A-1)!B!} \approx \frac{\Omega^{A+B-1}}{(A-1)!B!}$ $\frac{\Omega^{A+B-1}}{(A-1)!B!} \cdot e^{-\beta \left[ (A - 1) A_s + A_b + B \cdot B_s \right]}$
$A \cdot A_s + (B - 1) B_s + B_b$ $\frac{\Omega!}{(\Omega - A - (B - 1))!A!(B-1)!} \approx \frac{\Omega^{A+B-1}}{A!(B-1)!}$ $\frac{\Omega^{A+B-1}}{A!(B-1)!} \cdot e^{-\beta \left[ A \cdot A_s + (B - 1) B_s + B_b \right]}$
$(A - 1) A_s + A_b + (B - 1) B_s + B_b + C_{AB}$ $\frac{\Omega!}{(\Omega - (A - 1) - (B-1))!(A-1)!(B-1)!} \approx \frac{\Omega^{A+B-2}}{(A-1)!(B-1)!}$ $\frac{\Omega^{A+B-2}}{(A-1)!(B-1)!} \cdot e^{-\beta \left[ (A - 1) A_s + A_b + (B - 1) B_s + B_b + C_{AB} \right]}$

The partition function is the sum of the weights:

$$ Z = \frac{\Omega^{A+B}}{A!B!} \cdot e^{-\beta \left[ A \cdot A_s + B \cdot B_s \right]} + \frac{\Omega^{A+B-1}}{(A-1)!B!} \cdot e^{-\beta \left[ (A - 1) A_s + A_b + B \cdot B_s \right]} + \frac{\Omega^{A+B-1}}{A!(B-1)!} \cdot e^{-\beta \left[ A \cdot A_s + (B - 1) B_s + B_b \right]} + \frac{\Omega^{A+B-2}}{(A-1)!(B-1)!} \cdot e^{-\beta \left[ (A - 1) A_s + A_b + (B - 1) B_s + B_b + C_{AB} \right]}$$

Which we can greatly simplify by multiplying the entire expression by the reciprocal of the "base state" weight, $\color{DarkRed}\frac{A!B!}{\Omega^{A+B}} \cdot e^{\beta \left[ A \cdot A_s + B \cdot B_s \right]}$, normalizing that weight to 1:

$$ Z = 1 + \frac{A}{\Omega} \cdot e^{-\beta \left[ A_b-A_s \right]} + \frac{B}{\Omega} \cdot e^{-\beta \left[ B_b-B_s \right]} + \frac{A \cdot B}{\Omega^2} \cdot e^{-\beta \left[ A_b-A_s+B_b-B_s+C_{AB} \right]}$$

Taking the definition $[A] = A/\Omega$ and $\Delta G_A = A_b-A_s$ produces:

$$ Z = 1 + [A] e^{-\beta \left[ \Delta G_A \right]} + [B] e^{-\beta \left[ \Delta G_B \right]} + [A][B] e^{-\beta \left[ \Delta G_A+\Delta G_B+C_{AB} \right]}$$

Then the probability of any state is just the weight of that state (scaled by the weight of the base state) divided by the partition function expression $Z$.

From the above, we notice the form of the expression for the weight of a configuration of N TFBSs:

$$ p_{\mathrm{config}} = \prod_{i \in \, \mathrm{bound \,TBFS}} \left( [\mathrm{TF}_{\mathrm{cognate}(i)}] \cdot e^{-\beta \left[ \Delta G_i + \sum_j c_{ij} \right]} \right) / Z$$

For numerical stability, we take the log of the unnormalized probability (that is, the weight) of configurations:

$$ \log(\tilde{p}_{\mathrm{config}}) = \sum_{i \in \, \mathrm{bound \,TBFS}} \left( \log([\mathrm{TF}_{\mathrm{cognate}(i)}]) - \beta \left[ \Delta G_i + \sum_j c_{ij} \right] \right) $$

Competition

Incorporating competition into our thermodynamic model is slightly more subtle than cooperativity, because we can imagine two types of competition

  • Two TFs which may both bind DNA at adjacent sites, causing a free energy penalty due to some unfavorable interaction.
  • Two TFs with overlapping DNA binding sites, which cannot physically be bound at the same time.

In the former case, the expression for $p_\mathrm{config}$ we had written before suffices, with values of $C_{AB}$ having both signs to represent cooperativity and competition. Nominally, the latter case also fits this formalism if we allow $C_{AB}$ to reach $-\infty$, but that would cause us headaches in the implementation. Instead, the weights of all those configurations which are not physical attainable, due to "strict" competitive binding between TFs vying for overlapping binding sites, are merely omitted from the denominator $Z$.

References:

Sample cooperativity values

In order to compute concrete probabilities of configurations, accounting for cooperativity and competition, we will need concrete energies. We'll take $C_{AB}$ to be distributed exponentially with a mean at 2.2kcal/Mol. (Forsén & Linse).

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Let's check our derivation (and our implementation) of $p_\mathrm{config}$ by comparing it to our direct computation of $p_b$ from §1. Single TF.

def enumerate_configs(TFBSs): return list(map(np.array, itertools.product([0,1], repeat=len(TFBSs))))

def p_configs(TFBSs, TF_conc, cooperativities):

    configs = enumerate_configs(TFBSs)
    weights = []

    for config in configs:

        weights.append(np.exp(log_P_config(config, TFBSs=TFBSs, TF_conc=TF_conc, cooperativities=cooperativities)))

    return list(zip(configs, np.array(weights) / sum(weights)))

p_bound (prev method): 	 0.010806914902521452
p_config (new method): 	 [(array([0]), 0.9891930850974786), (array([1]), 0.010806914902521435)]
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With that sanity check, let's now consider the scenario of two transcription factors with cooperative binding, and compute the probabilities of each of the 4 configurations:

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Low-Affinity binding

In reality, transcription factors' binding sites are not so discretely present or absent: transcription factors may bind anywhere along the DNA polymer, with an affinity dependent on the interaction surface provided by the sequence of nucleotides at each locus. There are a variety of approaches to model the sequence-to-affinity function for each TF. The simplest is to consider each nucleotide independently, and list the preferences for each nucleotide at each sequential position in a matrix. This type of "mono-nucleotide position weight matrix" (PWM) model is commonly used, and frequently represented by a "sequence logo" plot.

Mono-Nucleotide Position Weight Matrices: Construction

I have curated a list of PWMs for ~1000 human TFs from the HumanTFs database.

tfdb = pd.read_json('../../AChroMap/data/processed/TF.json', orient='index').fillna(np.nan)

base_index = 'ACGT'

As an example, consider the Transcription Factor SPI1 / PU.1, who's PWM looks like:

ax = plot_pwm(tfdb.loc['SPI1'].pwm)
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This representation of this matrix visualizes the probability of the occurrence of a base at a particular position in SPI1 binding sites.

These PWM models can be fit from any experiment measuring PU.1 binding to a large variety of sequences. In this case, we're looking at a model fit from High-Throughput SELEX data (HT-SELEX), but other models for PU.1 binding are catalogued at its entry in the HumanTFs database.

An information-theoretic interpretation of this matrix produces a method to score sequences for possible binding sites. The information content of a base $j$ at a particular position $i$ is

$$IC_{ij} = -p_{ij} * log_2(\frac{p_{ij}}{\mathrm{bg}_j})$$

where $\mathrm{bg}_j$ is the background frequency of nucleotide $j$ in the (in our case, human) genome. Negative $IC_{ij}$ values are flattened to 0.

bg = [0.2955, 0.2045, 0.2045, 0.2955]

def freq_pwm_to_info_pwm(freq_pwm):
    return [[max(pos * np.log2((pos+1e-8)/bg[i]),0) for i, pos in enumerate(row)] for row in freq_pwm]

ax = plot_pwm(freq_pwm_to_info_pwm(tfdb.loc['SPI1'].pwm))
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Unfortunately, both of these formalisms miss the biophysical interpretation we have carried throughout our analysis thus far. We can coax a biophysical interpretation from these PWMs as follows (following the approach from Foat, Morozov, Bussemaker, 2006):

If we consider the nucleotides independently, then the ratio of equilibrium binding to a given nucleotide $j$ versus the highest-affinity nucleotide $\mathrm{best}$ at each position is related to the difference in energies of binding:

$$ \frac{[TF:N_j]}{[TF:N_{best}]} = \frac{K_{a_j}}{K_{a_{best}}} = \exp \{\Delta G_j - \Delta G_{best} \} $$

With this expression, we can compute the energy of binding of a TF to a sequence relative to that TF's energy of binding to its best sequence of nucleotides. However, we need to know an absolute energy of binding to anchor this relative measure.

We can visualize this "energy PWM" as well. Visualizing energies of particular bases relative to SPI1's best sequence does not produce a visual representation that's easy to reason about:

ax = plot_energy_pwm(tfdb.loc['SPI1'].energy_pwm)
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We can rescale this matrix to the average sequence:

SPI1_energy_pwm = tfdb.loc['SPI1'].energy_pwm

ax = plot_energy_pwm(SPI1_energy_pwm - np.expand_dims(SPI1_energy_pwm.mean(axis=1), axis=1))
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However, the ideal rescaling of this PWM would indicate whether each nucleotide contributes positively or negatively towards the energy of binding. After all, SPI1 likely doesn't bind the "average" sequence -- which is to say it's off rate is higher than it's on rate.

We chose SPI1 / PU.1 as our prototype in part because its absolute binding energies to various sequences have been measured, allowing us to anchor our relative energies PWM model. Pham et al measured PU.1 binding to a variety of high-affinity sequences by microscale thermophoresis and recorded a $k_d$ of 156nm for the best one.

SPI1_best_nanomolar_kd = 156
SPI1_best_ΔG = kcal_ΔG_from_nanomolar_kd(SPI1_best_nanomolar_kd)
SPI1_best_ΔG

9.648885505793956

However, the question remains of how to allocate those ~9.6 kcal/mol across the 14 bases of the SPI1 motif. The naive option is to uniformly spread the binding energy across the bases (9.6/14). Instead, we'll distribute it proportionally to the variance at each position to capture the intuition that the flanking nucleotides contribute less.

SPI1_energy_pwm_scaled = make_abs_pwm(SPI1_energy_pwm, SPI1_best_ΔG)

plot_energy_pwm(SPI1_energy_pwm_scaled)

<AxesSubplot:ylabel='ΔΔG/RT'>
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At last we have a visual representation of of a mono-nucleotide sequence preference model which captures our intuitions about the energetics of TF binding.

For a restricted set of TFs with high-quality HT-SELEX data, Rube et al produced relative energy PWMs with higher accuracy in the low-affinity regime. SPI1 is among that privileged set, so we'll take a look at that PWM for comparison.

ProBound_PWMs = pd.read_json('../../AChroMap/data/processed/TF_ProBound.json', orient='index')

probound_SPI1_pwms_scaled = ProBound_PWMs[ProBound_PWMs.TF_name == 'SPI1'].pwm.apply(make_abs_pwm, args=(SPI1_best_ΔG,))

ax = plot_energy_pwm(probound_SPI1_pwms_scaled.iloc[1])
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tfdb['abs_pwm'] = tfdb.energy_pwm.apply(make_abs_pwm, args=(10,))

probound_SPI1_pwm_scaled = probound_SPI1_pwms_scaled.iloc[1]

Position-Weight Matrices: Scanning sequences

Since we now have sequence-dependent transcription factor binding models, we will select a sequence which harbors known binding sites for select transcription factors, and see whether the models predict binding of those transcription factors.

As a specific sequence, let's use the SPI1 promoter.

gene_name = 'SPI1'
hg38_reference_annotation = pd.read_csv('../../AChroMap/data/processed/GRCh38_V29.csv')
reference_transcript = hg38_reference_annotation[(hg38_reference_annotation.gene_name == gene_name) & (hg38_reference_annotation.canonicity == 0)]
tss = (reference_transcript.txStart if all(reference_transcript.strand == 1) else reference_transcript.txEnd).iat[0]

chrom = reference_transcript.chr.iat[0]
start = tss-400
stop = tss+400

def get_hg38_bases(chrom, start, stop):

    url = f'http://genome.ucsc.edu/cgi-bin/das/hg38/dna?segment={chrom}:{start},{stop}'

    response = requests.get(url)
    root = ElementTree.fromstring(response.content)
    dna = root.find('SEQUENCE').find('DNA').text.replace('\n', '')

    return dna

SPI1_promoter_dna_sequence = get_hg38_bases(chrom, start, stop)

SPI1_promoter_dna_sequence

'ttccagggaggaaaccctgacttcccactgatagcaagccaggagggcagtgggtgggctggcgggttcgtgggcaggcaggcaggcgtccgagggccacgggttgggctggtggaggagtcccggtactcacaggggggacgaggggaaacccttccattttgcacgcctgtaacatccagccgggctccgagtcggtcagatcccctgcctcggtgggggccaatgcagagcccctcaggatggggtgccccgtcaggggctggacggtcgtggggcgggtgcagggctcaggcctgccccctgagctacaggagccctgggtgagccccctcccttgacattgcagggccagcacaagttcctgattttatcgaagggcctgccgctgggagatagtccccttggggtgacatcaccgccccaacccgtttgcataaatctcttgcgctacatacaggaagtctctggccggctggggcaggtggtgctcaaagggctggcctgggaagccatggggtccaggccccctgcccagaggaagctgggactgagagggatgactttgggggctaagctggggagggaggatgggagggagaacgtgtagctctgccacaccactgggaggcttttgctctaacccaacaaatgcctgcttcttttgagatccctatgtagccaacagtcacctcattggggtcagagctggaaggggtggcctcttggggcctccactttctggagtcagccttcctgggtgagggctctgatctagcaggctatcaggcctggctcttc'
base_index = {'a':0,'c':1,'g':2,'t':3,'A':0,'C':1,'G':2,'T':3}

def scan_sequence(dna, pwm):
    binding_energies_at_starting_positions = []

    for i in range(len(dna)):
        cumulative_energy_of_binding = 0
        for j, row in enumerate(pwm):
            if i+j < len(dna):
                cumulative_energy_of_binding += row[base_index[dna[i+j]]]

        binding_energies_at_starting_positions.append(cumulative_energy_of_binding)

    return np.array(binding_energies_at_starting_positions)

SPI1_binding_energy_track = scan_sequence(SPI1_promoter_dna_sequence, probound_SPI1_pwm_scaled)
SPI1_binding_energy_track = pd.Series(np.where(SPI1_binding_energy_track > 0, SPI1_binding_energy_track, np.nan))
SPI1_binding_energy_track.to_frame().reset_index().plot.scatter(x='index', y=0)

<AxesSubplot:xlabel='index', ylabel='0'>
<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd"> 2022-03-26T15:12:21.616743 image/svg+xml Matplotlib v3.5.1, https://matplotlib.org/

Scanning 

TF_scanned = pd.DataFrame({tf: scan_sequence(SPI1_promoter_dna_sequence, pwm) for tf, pwm in tfdb.abs_pwm.iteritems() if str(pwm) != 'nan'})
TF_scanned = pd.DataFrame(np.where(TF_scanned > 0, TF_scanned, np.nan), index=TF_scanned.index, columns=TF_scanned.columns)

TF_scanned.iloc[:, :15].plot.line(lw=0, markersize=1, marker='.')

<AxesSubplot:>
<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd"> 2022-03-26T15:12:28.134206 image/svg+xml Matplotlib v3.5.1, https://matplotlib.org/