Posts Tagged Evolution

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 http://EVfold.org.

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.

Enjoy!

References:

  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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A Multiverse of Exploration

The Institute for the Future  explored at a recent Technology Horizons Program conference the “Future of Science.

As a result, they identified 6 main areas and visualized them on this map. Most fascinating and closely related to my own interests are the topics on the right side of it:

  • data intensive science spawns new disciplines
  • science becomes gameified (i.e. fold.it)
  • scientific papers are executable as code
  • massively linked data becomes a public utility
  • wikipedia of science models is created
  • human microbiome is mapped
  • organisms become programmable
  • epigenetics informs real-time genome tweaking
  • new lifeforms created from scratch (synthetic biology?!)
  • engineered evolution

I am not an expert on the rest of the pack, “space-time cloaks” and “teleportation” still sound far too much like science fiction to me to bet on it. However, for the above-mentioned items, I can see they get realized (and have a major impact) during the next decade – some aspects of the list have already been covered in previous posts. Some of these “scientific projects” are already almost there, considering for example the advances in directed evolution, crowd-sourcing and open linked data. As any strange map, it conveys a certain set of ideas and contains some build-in bias – but going through them and discovering where it (mis-)matches with ones own views of the multiverse I always find illuminating.

Found via this article on boingboing.

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A beginners guide to the galaxy

Admittedly, I have no idea about Quantum Gravity, apart that some physicists like their universe to be rather bubbly than stringy ( or just the other way around: more stringy than loopy, like Leonhard in Series 2 Episode 2 of “Big Bang Theory“).

But the caption of the above movie from the Max-Planck Institute for Gravitational Physics in Potsdam/Golm says (quote) “The following sequence visualises the quantum evolution of geometry in Loop Quantum Gravity. The colours of the faces of the tetrahedra indicate where and how much area exists at a given moment of time. The movie illustrates how these excitations of geometry change as dictated by the Quantum Einstein Equations. Technically, the faces form a complex dual to the graph of a spin network state and the colour shows the amount of spin (area) with which the edges of the graph area are charged.” (end quote).
Wow – a combination of words like evolution, network, graph, spin, state, geometry and tetrahedra in a few lines and you have my full attention! Although it was bound to appear on my radar at some point, I don’t quite see the exact connection with biomolecular networks and structures clearly – yet. Nevertheless, it’s either watching the visualisation for mere aesthetic reasons or digging deeper with the aid of “Loop and Spin Foam Quantum Gravity: A Brief Guide for Beginners” – by Hermann Nicolai, a string theorist and director of the Quantum Gravity and Unified Theories department at the MPI for Gravitational Physics / Albert Einstein Institute. The visualisation is available for download on their pages.
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From Mendel to Bioinformatics in 3 minutes

– a wonderful animation to communicate the foundations of molecular genetics :


by David Murawsky

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