Open source
single molecule
force spectroscopy
Protein unfolding in varying salt concentrations
Trevor King
Open source SMFS
Proteins: What are they?
Proteins: Where are they?
Proteins: Titin
Adapted from Wikipedia
Proteins: Titin's I27
Proteins: I27
Proteins: What's the problem?
MHHHHHHSSLIEV EKPLYGVEVFVGE TAHFEIELSEPDV HGQWKLKGQPLTA SPDCEIIEDGKKH ILILHNCQLGMTG EVSFQAANAKSAA NLKVKEL |
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Open source SMFS
Atomic force microscopy
AFM: Cantilever geometry
Olympus TR800PSA
, images from
Asylum Research
We use the thinner
TR400PSA
AFM: Laser deflection
AFM: Piezo positioning
The piezoelectric effect
Lead zirconium titanate (PZT) from Wikipedia
AFM: Tubular piezos
Open source SMFS
Single molecule force spectroscopy
SMFS: Sawtooth curve
SMFS: What's going on?
Carrion-Vazquez, et al., 2000; adapted from Baljon and Robbins, 1996
SMFS: Unfolding one domain
Open source SMFS
Experiment control
Control: Quick-and-dirty
Control: Modular stack
Open source: Existing layers
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| Linux | GNU | Gentoo | Python | SciPy |
| Comedi | matplotlib | pymodbus | Cython | NumPy |
| h5py | … |
Open source: Teamwork
Control: Example code
class Unfolder (object):
…
def run(self):
"""Approach-bind-unfold-save[-plot] cycle.
"""
ret = {}
ret['timestamp'] = _email_utils.formatdate(localtime=True)
ret['temperature'] = self.afm.get_temperature()
ret['approach'] = self._approach()
self._bind()
ret['unfold'] = self._unfold()
self._save(**ret)
if _package_config['matplotlib']:
self._plot(**ret)
return ret
Archival: HDF5 and h5config
GROUP "/" GROUP "approach" … GROUP "config" GROUP "afm" … GROUP "approach" … DATASET "bind time" … GROUP "unfold" … DATASET "velocity" GROUP "environment" DATASET "temperature" DATASET "timestamp" … GROUP "unfold" DATASET "deflection" DATASET "frequency" DATASET "z"
Archival: Version control
commit 32bfbf98d79c73eba50b77d0917df100e0e33bcf Author: W. Trevor King <wking@tremily.us> Date: Fri Jan 18 22:54:49 2013 -0500 afm: Optionally return stepper_approach data with `record_data` Sometimes these approach curves are pretty funky, so I'll start recording them by default in calibcant-calibrate.py. diff --git a/pyafm/afm.py b/pyafm/afm.py index 60741c6..e76b118 100644 --- a/pyafm/afm.py +++ b/pyafm/afm.py @@ -460,10 +460,11 @@ class AFM (object): _LOG.warn(e) raise e - def stepper_approach(self, target_deflection): + def stepper_approach(self, target_deflection, record_data=None): …
Open source SMFS
Cantilever calibration
Calibration: Geometry
Olympus TR800PSA
, images from
Asylum Research
We use the thinner
TR400PSA
Calibration: Equipartition
For a damped harmonic oscillator
\[ -\kappa x_c - \gamma \frac{\mathrm{d}\! x_c}{\mathrm{d}\! t} + F_\text{ext}(t) = m\frac{\mathrm{d}^2\! x}{\mathrm{d}\! t^2} \;, \]the energy in each degree of freedom is $\frac{1}{2}k_B T$.
\[ \frac{1}{2} \kappa \left\langle x_c^2 \right\rangle = \frac{1}{2}k_B T \;, \]where $k_B$ is Boltzmann's constant and $T$ is the temperature.
Calibration: Vibration
Calibration: Photodiode calibration
Calibration: Results
\[ \begin{aligned} T &= 298.15 \pm 0.03 \; \text{K} & \sigma_p &= 35.7 \pm 0.9 \; \text{mV/nm} \\ \left\langle V_p^2 \right\rangle &= 97 \pm 1 \; \text{mV}^2 & \sqrt{\left\langle x_c^2 \right\rangle} &= \sqrt{\frac{\left\langle V_p^2 \right\rangle}{\sigma_p^2}} = 0.28 \; \text{nm} \\ \kappa &= \frac{k_B T \sigma_p^2}{\left\langle V_p^2 \right\rangle} = 54 \pm 3 \; \text{pN/nm} \end{aligned} \]Calibration: Stability
| Quant. | Units | Day 1 | Day 2 | ||||
|---|---|---|---|---|---|---|---|
| $T$ | K | 296.30 | ±0.02 | 294.27 | ±0.02 | ||
| $\sigma_p$ | mV/nm | 46.2 | ±0.8 | 41.3 | ±0.2 | ||
| $\left\langle V_p^2 \right\rangle$ | mV$^2$ | 108 | ±1 | 105 | ±2 | ||
| $\kappa$ | pN/nm | 67 | ±2 | 66 | ±2 | ||
Calibration: Inconsistency
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Open source SMFS
Monte Carlo unfolding simulations
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Hooke: Experimental histograms
Sawsim: State model
Sawsim: Simulation loop
- Calculate piezo-induced gap $x_t(t)=v t$
- Find tension model parameters for each state
- Distribute per-state stretching ($x_c$, $x_u$, …) to balance the tension
- Calculate the transition rates between states
- Roll the dice to determine if transitions take place as you step forward in time
Sawsim: Monte Carlo
Sawsim: Unfolding models
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Sawsim: Kramers' model
\[ \frac{1}{k_u} = \frac{1}{D} \int_{-\infty}^{\infty} \mathrm{d}\! x \; e^{\frac{U_F(x)}{k_B T}} \int_{-\infty}^{x} \mathrm{d}\! x' \; e^{\frac{-U_F(x')}{k_B T}} \]
Sawsim: Tension models
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Sawsim: Fitting models
Open source SMFS
Unfolding in salty buffers
Salt: Glutamic acid
Salt: Reduced stability in CaCl₂
Salt: Sawsim fits
| Buffer | $\Delta x$ (Å) | $k_{u0}$ (s$^{-1}$) |
|---|---|---|
| PBS | 1.32 | 0.222 |
| PBS + 0.5 M CaCl₂ | 1.23 | 0.450 |
Ca²⁺ radius ∼1.1 Å, H-bond ∼2 Å.
Open source SMFS
Conclusions: Salt
- Prelimiary results show the expected destabilizing effect of CaCl₂
- More contextual data!
- Pulling speeds
- Salt concentrations (physiological levels)
- Salt species
- Mutated proteins?
- Is glutamic acid special?
Conclusions: Hardware
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For automatic control, it would be nice to have…
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Conclusions: Software
Everything works for me, and I expect it will work for others… but no software (except maybe TeX) is without bugs. Testers welcome!
THE END
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