Sunday, August 23, 2015

Thermodynamics for Biochemists: a YouTube textbook



As you may know I don't use textbooks in my courses anymore.  Instead I make my own video lectures and make the corresponding slides accessible.  Inspired by Engineering Mathematics: YouTube Workbook I have now organized the slides from one of my courses into a "YouTube textbook".

It's mostly in Danish but a few subsections are in English.  I am working on a fully English version where the bottleneck is re-recording the videos.  You can see the progress here.


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Tuesday, August 18, 2015

Writing an informed teaching statement for a university faculty position


In the US new open faculty positions are starting to be announced and ChemBark and Chemjobber are curating a list for chemistry this year.  Most of these positions will require a teaching statement. When I wrote my teaching statement back in 1996 I really didn't know what to write. You find a textbook, make some lecture notes, show up 3 hours a week and write on the blackboard, and assign some problems in the book. How do you fluff that up so it fills a page?

The main point of this blog post is that in 2015 the traditional lecture model is just one of many teaching styles you can choose and you should make an informed decision, i.e. even if you choose to lecture you now really have to argue why you choose that.  What follows is a very, very brief overview (CliffsNotes) that is mainly intended to introduce you to terms that you have not have heard of but that you really need to know in order to make an informed decision.

Alternatives to the lecture approach
The main argument against the lecture model is that students only real learn by actively doing, do the the most general umbrella term for these new approaches is active learning. One fairly popular variant of active learning is project based learning which can be combined with inquiry-based learning.  These approaches can be hard to implement for large-enrollment courses. Another, increasingly popular, variant of active learning is the flipped classroom approach. The flipped classroom is often equated with blended-learning and video lectures, but the flipped classroom approach can also be based on a textbook.

There are several variants of the flipped classroom that differ on how the "lecture" time is used.  The most basic implementation of flipped classroom is simply to use the lecture time as help sessions for homework. The flipped classroom approach can also be combined with inquiry based learning using the POGIL approach.  Perhaps the most popular variant of the flipped classroom approach is the peer instruction or "clicker" approach, which scales very nicely to very large courses.

Another interesting new idea in education (that can also be used with the standard lecture model) is specification grading which is part of a relatively new movement within higher education called competency-based learning.

Finally, here are two recent peer-reviewed studies that document improvements in learning compared to the traditional lecture approach: Active learning increases student performance in science, engineering, and mathematics and Improved Learning in a Large-Enrollment Physics Class

Some pedagogical terms and concepts
Here are some  pedagogical terms and concepts that should inform your teaching no matter which style you choose.
Cognitive load - you can learn up to 7 new things at a time
Spaced learning - it doesn’t stick until you’ve seen it 3-4 times over a period of time
Formative assessment - you learn by answering questions if you get immediate feedback
Just in case vs just in time teaching - “You’ll need to know this later” is not a good motivator

Video Lectures and Web clickers
Though not strictly required, many teachers who use the flipped classroom approach make video lectures. This can be done relatively cheaply and easily using screencasting software such as Camtasia or Screenflow together with Powerpoint. One can also make pencasts (handwritten video lectures) using, for example, iPad apps such as Explain Everything or the Livescribe pen but such pencasts often appear too slow when watched online.

Traditional clickers are increasingly being replaced by "web-clickers" such as Socrative or Poll Everywhere on smartphones and laptops

More information
Active learning: tools and tips
My flipped classroom: what I did and how I did it
Why Not Try A Scientific Approach To Science Education?
Psychological insights for improved physics teaching
Carl Wieman Science Education Initiative - Resources
Confessions of a converted lecturer (Youtube) (abbreviated version)


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Sunday, August 16, 2015

Finding the melting temperature by coexistence simulations

While reviewing a paper I came across the idea of finding melting temperatures by coexistence simulations.  The idea is very simple:

1. Run an $NVE$ MD simulation starting from a configuration where half the molecules are solid and the other half is liquid.
2. If the choice of $E$ is such that both phases exist after equilibrium then the average temperature will of the system will converge to the melting temperature.
3. In practice you determine $E$ by running a short $NVT$ MD simulation, where $T$ is reasonably close to the suspected melting temperature, and using the final position and velocities as initial conditions for the $NVE$ simulation.  It's probably best to use a range of temperatures.
4. If you want the melting temperature at constant $P$ run an $NPH$ MD simulation instead.

I traced the approach back as far as this paper, which also has a nice explanation of why this works:
A more direct approach (Ref) to finding the transition temperature is to avoid the nucleation problem altogether, i.e., by simulating coexisting phases and allowing the system to evolve to equilibrium. If the equilibrium system contains both solid and liquid phases, then the system will be at a melting point. This approach is suitable for both experimental and theoretical studies. Molecular dynamic (MD) techniques are particularly useful for this approach, due to the fact that total energy is conserved in conventional MD schemes. To understand how this helps the system evolve toward equilibrium, consider a system with a phase boundary. If the system as a whole is at a temperature slightly below the melting point, then some portion of the liquid phase will solidify, generating the appropriate latent heat. Because the system is closed, this heats up the system towards the melting point. Similarly, if the system is above the melting temperature, the latent heat required to melt the solid will cool the system. The pressure of the system will also tend to equilibrate; thus, the system will evolve toward an equilibrium phase. There is no difficulty in nucleating either the liquid or solid phases, as the interface assists in the nucleation for the melting or solidification process.
The paper references an earlier book chapter, which I didn't bother to get a hold of,* that might reference even earlier works.  (*yet another demonstration of why publishing original work as a book chapter is equivalent to burying it in your backyard).


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Saturday, August 15, 2015

New preprint: ProCS15: A DFT-based chemical shift predictor for backbone and Cβ atoms in proteins

2015.08.24 Update: the paper has now been submitted to PeerJ

Here is a new manuscript that just appeared on PeerJ Preprints today.  I plan to submit the paper in a week or so and am very interested in feedback.  There are several ways of providing feedback: here, comments below, on PeerJ, on Authorea (where I wrote the paper), and on PubPeer (which allows for anonymous comments).

I am thinking of submitting it to PeerJ but other ideas are also welcome.

Here's the abstract
We present ProCS15: A program that computes the isotropic chemical shielding values of backbone and Cβ atoms given a protein structure in less than a second. ProCS15 is based on around 2.35 million OPBE/6-31G(d,p)//PM6 calculations on tripeptides and small structural models of hydrogen-bonding. The ProCS15-predicted chemical shielding values are compared to experimentally measured chemical shifts for Ubiquitin and the third IgG-binding domain of Protein G through linear regression and yield RMSD values of up to 2.2, 0.7, and 4.8 ppm for carbon, hydrogen, and nitrogen atoms. These RMSD values are very similar to corresponding RMSD values computed using OPBE/6-31G(d,p) for the entire structure for each proteins. These maximum RMSD values can be reduced by using NMR-derived structural ensembles of Ubiquitin. For example, for the largest ensemble the largest RMSD values are 1.7, 0.5, and 3.5 ppm for carbon, hydrogen, and nitrogen. The corresponding RMSD values predicted by several empirical chemical shift predictors range between 0.7 - 1.1, 0.2 - 0.4, and 1.8 - 2.8 ppm for carbon, hydrogen, and nitrogen atoms, respectively.


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Friday, July 17, 2015

Planned papers for 2015 - six months in

In January I wrote about the papers I plan to publish and made this list:

Submitted
1. Søs Torpenholt, Leonardo De Maria, Mats H. M. Olsson, Lars H. Christensen, Michael Skjøt, Peter Westh, Jan H. Jensen and Leila Lo Leggio "Effect of mutations on the thermostability of Aspergillus aculeatus β-1,4-galactanase" Computational and Structural Biotechnology Journal, submitted
2. Lars A. Bratholm, Anders S. Christensen, Thomas Hamelryck, and Jan H. Jensen "Bayesian inference of protein structure from chemical shift data" PeerJ, submitted. Preprint

Probable
3. Automated prediction of the NMR structure of the protein CI-2.
4. Linear scaling HF-3c calculations by interface to FMO2 in GAMESS
5. Thermodynamics of binding. I plan to turn my recent blogposts (with 2 more to come) on this topic into a perspective article.
6. ProCS14. I need to turn this masters thesis into a paper (how will I find the time?).
7. NMR structure of the protein AKMT.
8. Benchmarking of PM6 and DFTB3 for barrier heights computed using enzyme active site models.
9. Predicting binding free energies for CB7
10. Probabilistic treatment of distance restraints in protein structure determination

Six months later this list is now:

Published
1. Søs Torpenholt, Leonardo De Maria, Mats H. M. Olsson, Lars H. Christensen, Michael Skjøt, Peter Westh, Jan H. Jensen and Leila Lo Leggio "Effect of mutations on the thermostability of Aspergillus aculeatus β-1,4-galactanase" Computational and Structural Biotechnology Journal 2015, 13, 256–264. DOI
2. Lars A. Bratholm, Anders S. Christensen, Thomas Hamelryck, and Jan H. Jensen "Bayesian inference of protein structure from chemical shift data" PeerJ 2015, 3:e861. DOI
5. Jan H. Jensen "Predicting accurate absolute binding energies in aqueous solution: thermodynamic considerations for electronic structure methods" PCCP 2015, 17, 12441-12451. DOI 

Actively being worked on
3. Automated prediction of the NMR structure of the protein CI-2.
We found a bug in the CamShift implementation.  This took a while to fix and to re-run a bunch of stuff, including the calculations in Paper 2.  The bug doesn't seem to affect the results much. Lars sent me the first rough draft of the paper this week.  There is still some calculations missing but it's coming together. My goal is a finished manuscript by the end of August.

6. ProCS15
As per usual, when writing up we found a bunch of small things that needed to be fixed/rerun/checked and since the MS student is now a PhD student in another group this, of course, takes time. I think we are at the point where we have all the data we need and know what to say, but enough has changed that it's a matter of writing the paper from scratch.  I'm working on this now. My goal is a finished manuscript by the end of August.

Very likely
8. Benchmarking of PM6 and DFTB3 for barrier heights computed using enzyme active site models.
Jimmy is now actively working on this and generating data at a rapid pace.  Current goal is to submit in early October at the latest, so that it will be published in 2015.  That means a first rough draft in early September, at the latest.

Probably next year
4. Linear scaling HF-3c calculations by interface to FMO2 in GAMESS
Jimmy basically needs to find a bug related to basis set normalization for heavier elements and run some benchmarks, once he is done with "paper 8".  I would like to submit this in December.

7. NMR structure of the protein AKMT//10. Probabilistic treatment of distance restraints in protein structure determination
These two papers are actually related and may be combined into 1.  However, there is some fundamental work with regard to "10" that still needs to be worked out and tested.

9. Predicting binding free energies for CB7
An undergraduate is now working on this, so there is a good chance we'll have something to publish in 2016.

New: 11. Protein structure refinement using ProCS15
We are getting some undergrads to work in this in September, so there is a good chance we'll have something to publish in 2016.



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Wednesday, July 15, 2015

Does this experiment measure ΔG° or ΔA°?

I recently came across this interesting paper from 1974 in which they measure the equilibrium constants for hydrogen bond formation between methanol and amines in the gas phase.  I find it interesting because it's a concrete example the practical considerations that underlying thermodynamics - in particular whether the experiment best approximates constant pressure or constant volume.

Adapted from Millen & Mines J. Chem. Soc., Faraday Trans. 2, 1974,70, 693-699. (c) Royal Society of Chemistry,  Reproduced with permission.


Here's a picture of the experimental set-up.  What you don't see is that "The entire apparatus was immersed in a glass-fronted thermostat controlled within $\pm$0.1 $^\circ$C."  In essence you have two containers A and B connected by a valve C, which, initially is closed.  You know the volume of container A ($V_{\rm{A}}$) and B ($V_{\rm{B}}$) from calibration experiments using nitrogen gas for some level of mercury in the manometers.

To start an experiment you fill container A with some gas (e.g. methanol) by briefly opening valve D and you do the same for some amine in container B but you make sure that the pressure in B is higher than that in A. (I'll call the molecules in container A, $A$ and similarly for B.) Then you let the system sit a while so that the temperature of the gas is the same as the thermostat.  Then you record the equilibrium pressures in container A ($p_{\rm{A,i}}$) and B ($p_{\rm{B,i}}$) by reading off the heights of the mercury columns (to within $\pm$0.05 mm Hg) using a cathetomer.  The increase in volume due to the mercury moving can be calculated using the radius of the manometer tube and added to the measure volumes to yield $V_{\rm{A,i}}$ and $V_{\rm{B,i}}$

Then you open valve C briefly and let some gas flow from B into A, wait for equilibration, and re-measure the pressure in A ($p$) and B ($p_{\rm{B, f}}$).  The pressure in volume A
$$ p=p_A+p_B+p_{A_2}+p_{B_2}+p_{AB}$$
is a sum of the pressures of the individual species now present in container A, such as molecule $A$, dimers of $A$ ($A_2$), etc. This expression can be rewritten as
$$ p=p_A+p_B+K_{A_2}p_A^2+K_{B_2}p_B^2+K_{AB}p_Ap_B$$
where $K_{AB}$ is the equilibrium constant we are after.

$K_{A_2}$ and $K_{B_2}$ can be measured by similar experiments on pure $A$ and and $B$, but we have two additional unknowns $p_A$ and  $p_B$ so we need two additional equations:
$$ \pi_A=p_A+2K_{A_2}p_A^2 +K_{AB}p_Ap_B$$
$$ \pi_B=p_B+2K_{B_2}p_B^2 +K_{AB}p_Ap_B$$
Here $\pi_A$ is the "formal" pressure of $A$ which is the "the pressure the compound would exert if present in the vapour phase solely as monomer obeying the ideal gas law", and similarly for $B$.

The initial formal pressure of $A$ can be computed from the initial pressure measurement ($p_{\rm{A,i}}$) and the second virial coefficient of $A$ ($B_A(T)$)
$$\pi_{A,\rm{i}} =  \frac{p_{\rm{A,i}}}{1+B_A(T)/V_{\rm{A,i}}}$$
and from this we can compute the final formal pressure of $A$ using Boyle's law.
$$\pi_A =  \frac{\pi_{A,\rm{i}}V_{\rm{A,i}}}{V_{\rm{A,f}}} $$
$V_{\rm{A,f}} \ne V_{\rm{A,i}}$ because the level of mercury in the manometer changes but $\Delta V_{\rm{A}}$ can easily be computed knowing the radius of the manometer tube.

To get the formal pressure of $B$ in container A we convert the initial and final pressures measured for container B to formal pressures and compute the number of moles of $B$ transferred to volume A
$$\Delta n_B = \frac{\pi_{\rm{B,i}}V_{\rm{B,i}}-\pi_{\rm{B,f}}V_{\rm{B,f}}}{RT}$$
and use this value to compute the formal pressure of $B$ in volume A, $\pi_B$
$$ \pi_B = \frac{\Delta n_B RT}{V_{\rm{A,f}}} $$
What about the standard free energy change?
Colloquially speaking, if we use the raw pressure data $K_{AB}$ will have units of (mm Hg)$^{-1}$ but after we convert it to bar$^{-1}$ we can compute a standard free energy change from $K_{AB}$. The question is whether this free energy change corresponds to a Gibbs ($\Delta G^\circ$) or Helmholtz free energy ($\Delta A^\circ$) change or something in between.  The Gibbs free energy corresponds to constant pressure while the Helmholtz free energy corresponds to constant volume, and neither seem to strictly apply. The short answer is "I don't know" while the long answer is:

I think the standard free energy change most closely represents $\Delta A^\circ$
While volumes A and B are connected under equilibrium conditions, $K_{AB}$ represents equilibrium measurements done on both volumes A and B, so the thermodynamic system is comprised of both volumes.  The manometer tubes most likely have the same radius to maximize error cancellation, so the decrease in volume B would be quite similar to the increase in volume A, leading to a small net volume change for the system.

What do you think?


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Friday, May 15, 2015

#twitterquiz: an educational experiment

I recently came across this tweet which makes very innovative use of pictures to ask a multiple choice question.  After some experimentation I made this quiz yesterday



So far, the tweet has received 2,568 impressions and 741 engagements, which is twitter-speak for number of people who saw the tweet and clicked on it, respectively.  This is in large parts thanks to retweets by RealTimeChem, A-Level Chemistry and EiC, each with several thousand followers.

The question is taken straight from my teaching material and 741 is far more students that I reach in a year of teaching at the University of Copenhagen.  Did I just teach my first (Nano)MOCC?

Anyway, since I use peer instruction in all my courses I have tons more questions so this won't be the last #twitterquiz I post.

If you would like to make your own #twitterquiz you can find the images here.


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