Macromolecular Modeling Blog ™

Bob Bateman* and Paul Craig** recently wrote an article entitled, “A Proficiency Rubric for Biomacromolecular 3D Literacy“, for the Education Corner of the PDB newsletter, where they presented three levels of 3D biomolecular literacy: Introductory Biology (novice level), Biochemistry/Cell Biology (amateur level) and Structural Biology Graduate Student (expert level). Together with Lea Michel** their goal is to develop structural biology educational resources for teaching, that will include assessment according to these rubrics.

As a first step, Bob, Paul and Lea are conducting a survey that addresses the uses of molecular visualization in education. The survey includes 11 multiple choice questions and 3 open ended questions. They have included comment boxes on many of the multiple choice questions in case there is not a choice or set of choices that accurately reflects your perspective. In that case, please indicate your choice in the comment box. We anticipate it will take your 15-25 minutes to complete.

Please take this short time to answer the survey (http://www.surveymonkey.com/s/62TL6ZJ) If the response to the survey is good, the plan is to compile the results and submit a manuscript to Biochemistry and Molecular Biology Education.

* Bob Bateman is a professor of biochemistry at the University of Southern Mississippi in Hattiesburg, Mississippi.

** Paul Craig and Lea Michel are professors in the Department of Chemistry at the Rochester Institute of Technology in Rochester, NY.

P.s. while you’re at it, you might want to revisit our poll on your favourite macromolecular viewer.

How do enzymes catalyze reactions? There are countless answers of course, but one answer that has gained much attention and popularity in recent years is – through intrinsic dynamics. Is that so? PNAS recently published a paper by Arieh Warshel entitled: “Enzyme millisecond conformational dynamics do not catalyze the chemical step”. Warshel, an avid assailant of the coupling between dynamics and catalysis was met by Martin Karplus, devoted advocate for catalytic dynamics, to engage in a public dispute over the letters section of PNAS. Who do you find more convincing?

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The importance of secondary structure prediction to bioinformatics, modeling and structure prediction cannot be overstated. In a somewhat recent paper by Palopoli et al., their group found that combining the results of mutliple algorithms (with their JSSPrediction methodology) gave major improvements in prediction accuracy (see table at right). This ‘combine and conquer’ strategy is a tried and true strategy for multiple prediction problems in biological systems.

With this poll, we’d like to see who you go to for your secondary structure predictions and hear your thoughts. Are there algorithms not in the poll that you like? Do you combine algorithms? Do you like the servers or prefer in-house command-line installs? What are the challenges? Is an 88% prediction accuracy the ceiling or will we one day get to 95%?

For Rosetta, the standard is a mix of PsiPred, Jufo, SAM, and Prof.

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Following the success of our previous poll which was followed by an extensive and interesting discussion, and in order to better our Bi(Tri,..)-Weekly Digests, we present a new poll which will determine once and for all, which is the best journal for the computational structural biologist. 

Of course we’re all reading all of the journals below, and certainly each has its own benefits. There is also the question if by “favorite” journal we mean where would you like to publish, or which are you enjoying reading the most? We know the question isn’t adequately defined. Nontheless, we urge you to pick one and try to explain in the comments what makes that journal special.

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Update: additional journals proposed by the readers (which fall under the “other” category): JMB, Biochemistry, Protein science, Protein engineering, Journal of Medicinal Chemistry, Journal of Computer-Aided Molecular Design, Journal of Molecular Modeling, Journal of Chemical Information and Modeling, BMC Structural Biology, Biopolymers & BMC Bioinformatics. Keep ‘em coming.

Did we miss the best Journal of them all? Tell us in the comments and we’ll add it to the poll.

We have recently conducted a poll amongst people interested in learning more about the Rosetta software (as a preliminary step to the Rosetta Academic Training Workshop). One of the questions in that poll was: “Which Rosetta related topics is of the most interest to you?” The results (from ~200 participants) are summarized in the graph below.

Interest trends amongst Rosetta users

Protein-protein docking was chosen as the topic with the highest interest level, with a slight gap from ab-initio structure prediction and protein-ligand docking. This correlates well with another superficial analysis we made a while back (What’s trendy in structural bioinformatics) in which we showed qualitatively that docking receives the most interest from ‘Cite U Like‘ structural bioinformaticians. It is worth noting that Molecular Dynamics was not included, since Rosetta is not intended for MD simulations.

What is your field in structural bioinformatics? Anyone has more evidence for the increase in interest in protein-protein docking?

One of the main tools of the structural biologist (computational or experimental) if not, THE main tool, is the molecular viewer he uses. There are various viewers available, but usually each of us, at a certain point at his career, chooses a specific one and specializes in it. It becomes a way of self expression, to the point that using a “non-native” viewer is like talking in a foreign language. (How do I select only the polar atoms within 4.5A of chain B and color them CPK in this awkward viewer ?!?!?! On MY viewer it would take me a second!)

We want to hear what is YOUR molecular viewer of choice. Each viewer is optimized for a different set of tasks. E.g. VMD is designed for molecular dynamics, UCSF Chimera integrates many molecular analysis tools, Rasmol is a quick and dirty command line operated little fellow, not producing the nicest figures though, PyMOL creates good looking figures and is highly scriptable (as we demonstrated before in PyMOL scripts), while Jmol is web oriented and easy to embed in web pages.

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There are other software available, please tell us in the comments what’s your favorite viewer advantages and what are the disadvantages of the other “lesser” viewers.