Posts Tagged Interaction

Play more …

The Federation of American Scientists (FAS) Learning Technologies Program (pre-)launched the Science Game Centerto demonstrate to teachers, scientists, museums, and parents the myriad ways games can be used to improve education in math and science“. Next to Phylo and (which I mentioned around here before) are several entries listed I haven’t seen yet. It may be due to the movie “Fantastic Voyage” that made a lasting impression on me as a kid that “Immune attack” immediately caught my attention. After all, I’d rather kick some pathogenic butt than blowing up poor aliens in space. Good hunting!

P.S.: Reminds me of this quote by 137th Gebirg on

“I may appear unoccupied to you, but at the molecular level, I’m really quite busy.”

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Visualizing your Social Network

LinkedIn offers a visualisation of your network connections. While browsing and looking at the persons represented by the nodes and their proximity, directly hypothesis form in the head as to the common context (when and where one met). To me the accuracy of the layout and coloring is amazing! That the different clusters actually delineate different institutes and departments I had the pleasure to work with/at is a nice “proof-of-concept”, albeit a bit terrifying as to how much the network knows about us … if you already are at LinkedIn give it a spin to see “the community” emerging around yourself. Makes you also wonder what the guys running the social networks actually can do with the entirety of network data we dump on them. Anyway, have fun exploring your local network!

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Molecular Interaction Exchange

The International Molecular Exchange Consortium IMEx is the latest effort of data-providers to integrate Protein-Interaction Data –

IMEx provides

  • A non-redundant set of protein-protein interaction data from a broad taxonomic range of organisms
  • the data in standards compliant download formats (MITAB or PSI-MI XML 2.5)
  • Expertly curated from direct submissions or peer-reviewed journals to a consistent high standard.
[ … aiming to … ]
  • Develop and work to a single set of curation rules when capturing data from both directly deposited interaction data or from publications in peer-reviewed journals
  • Make these interaction available in a single search interface on a common website
  • Make all IMEx records freely accessible under the Creative Commons Attribution License

If you’ve been looking for that one-stop shop for getting a representative dataset of Protein-Protein Interactions, this just looks like it. There is an overview available on youtube (see below)

… and a training course on “Networks and Pathways Bioinformatics for Biologists” will take place at EMBL-EBI in May.

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Goodbye to hairballs?

You probably have seen the hairballs resulting from a force-directed layout of complex biological networks. What do they tell you? Well, that the networks are rather complex. But for much more detailed analysis the classical visualizations are actually quite useless. The hiveplot  is an attempt to provide

“A scalable, computationally fast, and straight-forward network visualization method that makes possible visual interpretation of network structure and evolution.”

A laudable goal, if it works in practice for you and your data – check it out. In addition there is an R package available for creating hive plots in 2D and 3D called HiveR.

Also see Krzywinski M, Birol I, Jones S, Marra M (2011). Hive Plots — Rational Approach to Visualizing Networks. Briefings in Bioinformatics (doi: 10.1093/bib/bbr069).

Thanks to Lucy Colwell for the hint!

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Complexity in the natural world is fascinating, don’t you think? In the most complex systems, we can look deeper to find a network of interacting elements. Little beings loving and dancing scientific hobscotch(?) using their tiny little brains to make the most wonderful things happen – TOGETHER.

Emergent properties in Complex systems and robotics explained by a hilly-billy guy with a funny (dutch?) accent – just made my day:

Read the rest of this entry »

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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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