Posts Tagged Models

3D Modelling of Proteins and DNA


Judging from the gallery and videos, the Graphite – LifeExplorer is a great tool to model protein and DNA :

The Graphite-LifeExplorer modeling tool to build 3D molecular assemblies of proteins and DNA from Protein Database (PDB) files. Atomic DNA can be modeled from scratch or reconstructed from simulation.

Unfortunately, I didn’t get the Mac-Version to work on my machine (Mac OS X 10.6.8) it works only for OS X 10.7.+ (got it running on 10.7.3) – it’s definitely worth keeping an eye on:

shared by Damien Larivière via LinkedIn/Molecular Modeling in Life Sciences.

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Nobel Prize in Computing


Pearl‘s book on “Causality” has been on my shelf for a while now. I also read it, a few times, but never managed to get through it in one go, cover to cover. Consequently, I haven’t come to grips with all details, implications and equations yet. No reason to worry about my intellectual capabilities, it’s quite fundamental and takes time to sink in. Now Judea Pearl has been awarded the 2011 ACM Turing Award – Congratulations!

The annual Association for Computing Machinery (ACM) A.M. Turing Award, sometimes called the “Nobel Prize in Computing,” recognizes Pearl for his advances in probabilistic and causal reasoning. His work has enabled creation of thinking machines that can cope with uncertainty, making decisions even when answers aren’t black or white. […]
The UCLA computer science professor is widely credited with coining the term “Bayesian Network,” which refers to a statistical model ACM describes as mimicking “the neural activities of the human brain, constantly exchanging messages without benefit of a supervisor.” Bayesian networks have been used to, among other things, analyze biological data for studies of medicine and diseases.

Here is a chance to see him talk for yourself:

“I compute, therefore I understand” – More videos are here on theScienceNetwork.

found via networkworld.com: Judea Pearl, a big brain behind artificial intelligence, wins Turing Award. See also on the ACM NEWS “Judea Pearl Wins 2011 ACM Turing Award“.

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The Network that (likes to think it) runs the World


Scientists at the ETH Zurich analysed the international ownership network of multi-national companies. If you had a look at the intrinsic properties of real-world and biological networks, the 80-20 rule comes as no surprise: in biological networks, usually over 80% of the edges are covered by less than 20% of the nodes. A related phenomenon is called the Pareto Principle in economics. The core of this network contained 1318 companies, which

… represented 20 per cent of global operating revenues, the 1318 appeared to collectively own through their shares the majority of the world’s large blue chip and manufacturing firms – the “real” economy – representing a further 60 per cent of global revenues …

“Reality is so complex, we must move away from dogma, whether it’s conspiracy theories or free-market,” says James Glattfelder.

It took me some time to find the original paper on PLoS, which isn’t linked from this article on the NewScientist – probably because it wasn’t out yet at the time:

Reference: The Network of Global Corporate Control by Stefania Vitali, James B. Glattfelder, Stefano Battiston (2011) PLoS ONE 6(10): e25995. doi:10.1371/journal.pone.0025995

How these findings relate to the error and attack tolerance of scale-free networks in the context of the current economic situation is further food for thought. But I urge caution to naïvely transfer insights from one domain to another, there are no simple (mono-causal) answers to complex problems. Especially when dealing with the emergent properties of networks, there is only one constant: they tend to work out quite differently from what we initially thought.

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(molecular) Happy New Year !

The PSI Structural Biology Knowledgebase released their annual Calendar. Similar to the Cal” by Pirelli, the 2012 issue is featuring tantalizing renderings of some of the finest models around.

… a very sophisticated concept of beauty, mid-way between fashion and glamour. And every year the Cal offers a collection of images that interpret the concept of beauty in an original way, different to the previous year.

In some (aeehh, broad sense, admittedly) this applies to the PDB version as well, I guess it’s a a must have for the structural biologist! The .PDF file is available here, the card on the right is from the corresponding RCSB PDB News.

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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 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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Free Ivy-League Education

Stanford University offers free courses, mainly in Computer Science. Most closely related to the topics of this blog and the heart of yours truely probably are some of the following :

From the FAQs:

How much does it cost to take the course? Nothing: it’s free!
Will I get university credit for taking this course? No.

In a nutshell, I very much like the idea to attend courses because one is interested in the topic per se, not for grabbing a title. That’s the spirit.

Thanks for hints to Esther Wojcicki (teacher and journalist) via google+.

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Free Science Books

A collection of free science books is available (in .pdf format) at INTECHopen – among them are the following ones on experimental / computational aspects of systems biology and on HMMs which might be of interest:



(thanks to Zana Kadric for sharing via the group “Systems Biology” at LinkedIn)

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