From the Departmental newsletter written by Jes Andersen:
Proteins are a bit like keys. By observing the shape of the key you can figure out which lock it will open and observing the shape of a protein allows you to reveal which functions it performs. Today's technique requires the efforts of a small research team for several months to discover the configuration of a just single protein. Read more here
Friday, February 4, 2011
Visualized molecules can make chemistry comprehensible
From the Departmental news letter written by Jes Andersen:
When students experience chemistry as “tough stuff”, it’s often due to the difficulty of fully appreciating molecules that are too small and reactions that are too fast. Chemistry professor Jan Jensen has a method of making chemistry much more visible. Using computer-based simulations and visualisations, he offers his students a more intuitive and less abstract gateway into the field of chemistry. Now, Jensen and his colleagues at the Centre for Computational Molecular Science have received 200,000 kroner from the University’s “Education at its Best” project to develop and diffuse visualisation tools throughout the courses of his fellow chemistry instructors. He is convinced that these tools will make it a bit easier to become a chemist.
“In the oral examinations, I’ve noticed that even those who are quite poor at equations have improved in their ability to explain what is going on at the molecular level,” explains Jensen. In the coming months Jensen and his collaborators in the Centre for Computational Molecular Science will adapt the visualisation tools to courses at the University of Copenhagen.
The hope is that ambassadors from the Centre will ensure that the visualisation methods spread like ripples across water. While Jensen expects to employ student interns to create finished “instruction packages”, he encourages curious professors to sign up for the development phase. “I am confident that this will make it easier to teach chemistry. But here at the beginning, it would be more fun to test ideas in collaboration with professors who actually have a desire to use these tools,” affirms Jensen who is confident that these methods will benefit instructors as well. “I have a few lectures where I do not use the visualisations and I can clearly see that I lose the students’ attention,” concludes Jensen.
Read Jan Jensen’s blog post about visualisation.
Read about the grant.
When students experience chemistry as “tough stuff”, it’s often due to the difficulty of fully appreciating molecules that are too small and reactions that are too fast. Chemistry professor Jan Jensen has a method of making chemistry much more visible. Using computer-based simulations and visualisations, he offers his students a more intuitive and less abstract gateway into the field of chemistry. Now, Jensen and his colleagues at the Centre for Computational Molecular Science have received 200,000 kroner from the University’s “Education at its Best” project to develop and diffuse visualisation tools throughout the courses of his fellow chemistry instructors. He is convinced that these tools will make it a bit easier to become a chemist.
“In the oral examinations, I’ve noticed that even those who are quite poor at equations have improved in their ability to explain what is going on at the molecular level,” explains Jensen. In the coming months Jensen and his collaborators in the Centre for Computational Molecular Science will adapt the visualisation tools to courses at the University of Copenhagen.
The hope is that ambassadors from the Centre will ensure that the visualisation methods spread like ripples across water. While Jensen expects to employ student interns to create finished “instruction packages”, he encourages curious professors to sign up for the development phase. “I am confident that this will make it easier to teach chemistry. But here at the beginning, it would be more fun to test ideas in collaboration with professors who actually have a desire to use these tools,” affirms Jensen who is confident that these methods will benefit instructors as well. “I have a few lectures where I do not use the visualisations and I can clearly see that I lose the students’ attention,” concludes Jensen.
Read Jan Jensen’s blog post about visualisation.
Read about the grant.
The Japanese taught him focus… and how to enjoy the weekend
From the Departmental news letter written by Jes Andersen:
Most equate study abroad with academic challenges. But For Kasper Steinmann, the chance to study in the Tokyo suburb of Tsukuba offered lessons which proved to be very human.
Read entire story here.
Most equate study abroad with academic challenges. But For Kasper Steinmann, the chance to study in the Tokyo suburb of Tsukuba offered lessons which proved to be very human.
Read entire story here.
QM/MM course: notes from week 1
View more presentations from molmodbasics.
Many questions and discussions during the first lecture. The course is off to a great start.
We started by drawing dates and papers and a list of who does what can now be found on the course web page. You are free to switch dates and papers if you can find someone to switch with.
I also briefly introduced the requirements for the extra credit assignment. There are now more details here.
Points of discussion not covered by the slides
Size of QM region and QM/MM interactions
The most practical way to gauge the whether the division into QM and MM regions is correct is to enlarge the QM region and see if the results change significantly. This is rarely done. If I find some references I will put them here.
Covalent QM/MM boundary
Enlarging the QM/MM region will introduce more cuts across covalent bonds, which could increase the error. However, the cuts will be further away from the active site, so the effect on relative energies should be smaller, and they should converge as the QM region is increased.
I am not aware of any QM/MM study that does not place the cut between the alpha and beta carbon on the side chain (I would very much like to hear about exceptions). This is due to the fact that QM/MM boundaries are heavily parameterized, and need to be redone for other types of cuts. This places restrictions on how much the QM region can be enlarged.
Slide 12: In the link atom method, there are interactions between the MM charges and the link H atom. It is not really feasible to remove the “H-part” of the density.
QM/MM packages
Tuesday, February 1, 2011
Predicting protein pKa values: problem solved?
Quantum chemistry has reached a point where some gas phase properties of small molecules can be computed more accurately than they can be measured. For these systems science can say "problem solved", give itself a light pad on the back, and move on to other things - like how to do the same for molecules twice the size. Or in solution.
I don't believe there is an equivalent success story in biomolecular modeling, but, as I'll argue here, the prediction of the pKa values of ionizable residues in proteins be a close contender!
Jens Erik Nielsen and co-worker recently published a very interesting paper entitled "Remeasuring HEWL pKa values by NMR spectroscopy: Methods, analysis, accuracy, and implications for theoretical pKa calculations" in Proteins.
One of the most interesting implications of the paper, in my opinion, can be found at the bottom of Table III. Here the authors compare Asp, Glu, and His pKa values in HEWL measured using the NMR chemical shifts of backbone nuclei to those obtained from side chain 13C atoms.
The RMSD values are 1.3, 1.2. and 0.6 pH units for 15N, 1HN, and 1Hα, respectively (note that the latter could only be obtained for 7 of the 11 residues). Now compare this to the corresponding values for pKa values predicted with the pKD and PROPKA servers: 0.9 and 0.8.
Thus, if you are not willing to go through the considerable trouble that is assigning side chain chemical shifts for a protein, you are better off predicting the pKa values than measuring them! At least for this protein ... and only if you can't live without knowing all the pKa values ... but still.
Nielsen and co-workers estimate the accuracy of the 13C-derived to be accurate to within 0.1 - 0.2 pH units, so, no, the general problem is not solved. But if more people do as careful a job measuring pKa values as Jens, we may find that we are closer than we think.
I don't believe there is an equivalent success story in biomolecular modeling, but, as I'll argue here, the prediction of the pKa values of ionizable residues in proteins be a close contender!
Jens Erik Nielsen and co-worker recently published a very interesting paper entitled "Remeasuring HEWL pKa values by NMR spectroscopy: Methods, analysis, accuracy, and implications for theoretical pKa calculations" in Proteins.
One of the most interesting implications of the paper, in my opinion, can be found at the bottom of Table III. Here the authors compare Asp, Glu, and His pKa values in HEWL measured using the NMR chemical shifts of backbone nuclei to those obtained from side chain 13C atoms.
The RMSD values are 1.3, 1.2. and 0.6 pH units for 15N, 1HN, and 1Hα, respectively (note that the latter could only be obtained for 7 of the 11 residues). Now compare this to the corresponding values for pKa values predicted with the pKD and PROPKA servers: 0.9 and 0.8.
Thus, if you are not willing to go through the considerable trouble that is assigning side chain chemical shifts for a protein, you are better off predicting the pKa values than measuring them! At least for this protein ... and only if you can't live without knowing all the pKa values ... but still.
Nielsen and co-workers estimate the accuracy of the 13C-derived to be accurate to within 0.1 - 0.2 pH units, so, no, the general problem is not solved. But if more people do as careful a job measuring pKa values as Jens, we may find that we are closer than we think.
Three chemistry bloggers on why you should blog
Noel O'Boyle: Don't keep your cool - share it
Henry Rzepa: Is there a difference between a scientific blog and scientific journal?
Steven Bachrach: Why blog?
Henry Rzepa: Is there a difference between a scientific blog and scientific journal?
Steven Bachrach: Why blog?
Jmol integrated with chemCanvas
From Jmol Users list via Blue Obelisk discussion list (cool idea, but still many bugs):
I though that some people may find interesting the integration of Jmol
with chemCanvas, an open source chemical diagram editor.
http://chemcanvas.sourceforge.net/chemWin.html
chemCanvas is a javascript library (a jQuery plugin) using HTML 5 canvas.
For more information you may visit:
http://chemcanvas.sourceforge.net/
and
http://sourceforge.net/projects/chemcanvas/
I though that some people may find interesting the integration of Jmol
with chemCanvas, an open source chemical diagram editor.
http://chemcanvas.sourceforge.net/chemWin.html
chemCanvas is a javascript library (a jQuery plugin) using HTML 5 canvas.
For more information you may visit:
http://chemcanvas.sourceforge.net/
and
http://sourceforge.net/projects/chemcanvas/
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