Posts Tagged Protein

Happy Holidays!

OMG (OhMightyGraph)! This year is almost over, so I’d like to thank you, my dear reader, for your kind interest. WordPress reports well over 3200 clicks for “cistronic”, which has now been up here for about a year. The rendering of the nucleosome (PDB:1KX5) in the style of a bubbly xmas candle wrapped in green DNA is meant as a virtual seasons greeting card from yours truly, courtesy of the organisation in the core of your cells.

I’ll be travelling a couple of days and hence updates and new posts will probably occur quite infrequently until January. But with more than 120 posts in total now there is plenty to dig through! In the meantime, if you’d like to make my day, take a couple of minutes for a brief comment or email: What’s your (most/least) favourite post, which topics deserve more depth and coverage, what’s missing? Any hints, criticism, praise, questions and interesting additions are very much welcome.

I wish you a happy and healthy 2012, stay curious and tuned for more to come. Cheers!

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Kendrew’s prediction – are we there yet?

Indeed, in the very long run, it should only be necessary to
determine the amino acid sequence of a protein, and its three-dimensional
structure could then be predicted; in my view this day will not come soon,
but when it does come the X-ray crystallographers can go out of business,
perhaps with a certain sense of relief, and it will also be possible to discuss
the structures of many important proteins which cannot be crystallized and
therefore lie outside the crystallographer’s purview.

(JOHN C. KENDREWMyoglobin and the structure of proteins” Nobel Lecture, December 11, 1962)

If you are into (structural) molecular biology, you will probably have seen this before. Honestly, I don’t get tired of reading this statement. That was 49  years (and 11 days, to be precise) ago – where are we now, almost half a century later? Are we there yet? (sounds like the little ones nagging on a long-distance journey – daddy told you it would take a while!) Seems we might be there soon, since we have made quite some headway recently.

First of all, the above statement displays some amazing farsightedness combined with a humble self-perception. He is not overstating it, indicating that not all will be crystallized. If you read on in his speech, he was already talking about larger assemblies and complexes, and that’s where we are now, and that’s where things get REALLY interesting. Besides the picture with him modeling a 3D structure (on the sticks for z axis) is by no means old-fashioned, to me it means he just took what was available at the time to get the 3D model constructed. Today we have sophisticated ComputerGraphics, yet nothing beats the experience of building a physical model – an art that should not be forgotten and developed further (thinking of 3D printing here). I am convinced that even in the age of the high-throughput techniques, interaction data etc. we ultimately need a structural view to truly understand the molecular mechanisms.

But the main point – or prediction – is that ultimately, we should be able to compute structure and function from sequence alone.

If you think about it, that’s a very bold statement indeed, with wide ramifications. By now our sequencing capabilities are growing at a pace beyond Moore’s law (see here). I probably don’t have to remind ourselves that experimental structure determination is difficult and time-consuming, to say the least. And computer predictions in the absence of a related solved structure in the PDB are usually no match for the real thing (a.k.a. experimental 3D structure).

But there is a fresh breeze in the field: Recently a number of groups report that the ancient dream (from the mid-nineties and even before, “ancient” in bioinformatics = over 15 yrs) of using patterns of correlated mutations to derive useful spatial constraints for structure prediction does work indeed. Properly. Finally!
Given enough information content, seems there are no limits to the size of the proteins, and even notoriously difficult ones like transmembrane structures seem to work. All you need is sequences. And lots of them. Properly aligned, of course. (That’s what a lot of bioinformatics was all about, wasn’t it?) But massive amounts of sequences is what we get anyway these days, more than you ever wanted (to analyze) from next-gen sequencing projects. That’s off-topic, delving deeper into that mania is a topic for different post to explore.

If you are interested to check it out in depth: One of the methods is called EVfold, see

Of course, there is still some room for optimization, cross-fertilization and improvement in the methods, I think. Simply by looking at some of the predicted contact maps, it’s fairly obvious to me these methods are not only better than what was available so far, but they are also not identical. Seeing their performance and following the competition in this field hotting up on next years CASP will be jolly exciting.

I’m sure I’ll keep you posted on further developments and deeper analysis – for the moment I’ll leave you with a few references to get started. As a final word, I am so glad most of them (at least the ones I list below) are not hidden behind a payhedge but open access, free to check-out by anyone who cares.



  1. Marks DS, Colwell LJ, Sheridan R, Hopf TA, Pagnani A, Zecchina R, Sander C. (2011) Protein 3D Structure Computed from Evolutionary Sequence Variation. PLoS ONE 6(12): e28766. doi:10.1371/journal.pone.0028766
  2. Taylor WR, Sadowski MI (2011) Structural Constraints on the Covariance Matrix Derived from Multiple Aligned Protein Sequences. PLoS ONE 6(12): e28265. doi:10.1371/journal.pone.0028265
  3. Burger L, van Nimwegen E (2010) Disentangling Direct from Indirect Co-Evolution of Residues in Protein Alignments. PLoS Comput Biol 6(1): e1000633. doi:10.1371/journal.pcbi.1000633

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Water – Oil – Alphabet

As a bioinformatician, the computer scientist in me has a certain affinity to (finite) alphabets. Having been exposed to experiments with micro-droplets (in the context of high-throughput interaction screening and directed evolution using in-vitro compartmentalization), finding this video that combines characters with a water/oil emulsion is just great! Not to mention hydrophobicity as a force in protein folding – anyway, I hope you find it also aesthetically as appealing as I do, even if the experimental or biophysical ideas that I project onto it might not be exactly your cup of tea. Enjoy!

Isn’t that what art is meant to be, building a stimulating projection surface for more abstract concepts beyond superficial beauty – even if these concepts are not really what the artist had in mind originally?

video by Jesse Zanzinger, found via Glaserei.

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ATP Synthase movie

The proton driven turbine cranking out ATP – beautifully animated.
More background info at

shared by Rajini Rao via google+.

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R-omes aren’t build in a day

where R ∈ {Prote, Interact, Gen, …}

Tommy Carstensen

[…] built a model of DNA out of LEGO bricks (of PDB entry 2DAU, to be precise) to celebrate the 40th anniversary of the PDB. The clip also has educational value, explaining some of the basics of DNA structure and more.

[posted by Gerard J. Kleywegt via pdb-l mailing list (] For all those which are under the jurisdiction (=curse?) of the GEMA: the video (mp4) can also be found here.
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New Modeller release – 9.10

Modeller – One of the best approaches to structure prediction – just got an overhaul:

(Modeller) 9.10 is primarily a bugfix release relative to the last public release
(9.9). Major user-visible changes include:

  • Add Python 3 support
  • Add support for Mac OS X 10.7 (Lion)
  • Modeller on 64-bit Macs is now built with Intel Fortran, resulting in a roughly 2x speedup compared to 9.9.
  • Add Unicode support; all filenames should be UTF-8 encoded.

See the Modeller manual for a full change log:

by email from Ben Webb, Modeller Caretaker (

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Free web-based learning resource for life scientists

The EMBL-EBI just announced a beta version of Train online and will launch it publicly at the EMBO meeting over the weekend.

The PDBe: Quick tour and the UniProt: Quick tour seem like good primers on using these resources.

We’re delighted to announce that EMBL- EBI has launched the beta release of Train online.

What is Train online? Train online is a free, web-based learning resource for life scientists.

How can Train online help you? Train online helps you make the most of the huge amount of biological data that the EMBL-EBI makes publicly available for the research community. Using a combination of tutorials, guided examples, exercises and quizzes, Train online guides you towards becoming a confident user of open-access data resources. Current topics include data resources for genomics, functional genomics and proteomics. More will be added soon.

Who is Train online for? Train online is there for you to learn in your own time and at your own pace. You do not need previous experience in bioinformatics to benefit from our courses.

Want to know more? Visit Here you can access all our courses, as many times as you like, free of charge. We warmly invite you to register and sign up for Train online updates, which will alert you when we add new courses and features.

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