Posts Tagged 3D
Utopia is a collection of interactive tools for analysing protein sequence and structure. Up front are user-friendly and responsive visualisation applications, behind the scenes a sophisticated model that allows these to work together and hides much of the tedious work of dealing with file formats and web services.
The installation package (provided by the AdvancedInterfacesGroup AIG) includes
- CINEMA – multiple sequence alignment editor
- Ambrosia – molecular structure viewer
- UTOPIA – support libraries and plugins
After a quick & painless installation, it seems to work out of the box. More in-depth info when I get to grips with more of the functionality.
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.
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.
Biophilia is an extraordinary and innovative multimedia exploration of music, nature and technology by the musician Björk. Comprising a suite of original music and interactive, educational artworks and musical artifacts, Biophilia is released as ten in-app experiences that are accessed as you fly through a three-dimensional galaxy
I still haven’t downloaded and checked out the app myself in detail, the price-tag is a bit hefty for my taste. So far I have never spend over 10 bucks on a single app, and personally find it very hard to digest more than 2 Björk-songs in a row. OK, my ears aren’t bleeding, and in this case my eyes are very much tempted by the visuals. Biophilia contains several subsections (in-apps), so one could argue it’s more than just a single app, comparable to an entire (concept?-)album. On the app-store reviews there’s some criticism of the pricing-policy, however content-wise one reviewer goes as far as claiming that “we will eventually see Biophilia as the Sergeant Peppers of music apps“. A steep claim indeed to liken it to the fab four… but even though the music is not exactly my cup of tea, I am thrilled by the unique combination of contemporary art, science and technology.
As for the scientific content, the spring 2012 issue of the quarterly newsletter published by the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB-PDB for short) features a snapshot of the video for Björk’s title “hollow”:
To accompany the song “Hollow,” Björk’s meditation on biological ancestry, [Biomedical animator Drew] Berry
created a lush landscape for DNA to replicate (and sparkle) to the music. Molecular
machines work at real-time speed, culminating in the appearance of Björk as a complex
protein structure. Many of the molecular shapes, illustrated with great depth and rich
color, were created with the help of crystal structure data from the PDB.
More of these stunning, educational and award-winning 3D animations by Drew Berry and his colleagues are available on WEHI.TV at the Walter+Elisabeth Hall Institute of Medical Research. Enjoy!
The VizBi-2012 Conference took place in Heidelberg this week – unfortunately I couldn’t attend it. Nevertheless, I received a bit of summary and feedback: The talks will be made available online, I am looking forward to check out a few of them (i.e. Jim Robinson, Jernej Ule). Ivet Bahar (ProDy) and Valerie Daggett (Dynameomics) gave an interesting overview on Molecular Dynamics.
Thanks to to Corinna Vehlow for feedback!
Last week CGAL-4.0-beta1 was released – as with most X.0 and beta releases of any kind of sofware, this is not yet intended for use in production. Howevever, previous releases look quite stable.
The goal of the CGAL Open Source Project is to provide easy access to efficient and reliable geometric algorithms in the form of a C++ library. CGAL is used in various areas needing geometric computation, such as: computer graphics, scientific visualization, computer aided design and modeling, geographic information systems, molecular biology, medical imaging, robotics and motion planning, mesh generation, numerical methods… CGAL can be used together with Open Source software free of charge.
Also, a Book on “CGAL Arrangements and Their Applications” just became available (Springer).
The list of features packed into the kernels is impressive and too long to be summed up in a few lines – see here for the Package Overview – I am sure you’ll find quite a few items of interest. Especially the spatial sorting functions and matrix searches sound very useful to me. In addition, there is support for 3rd party software such as the Boost Graph Library. So much to check out – here are some tutorials, manuals and videos on CGAL … For example the dynamic 3D Voronoi demo below. Have fun!
Thanks for hints to Kasthuri Kannan and Chris Sander.
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 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!
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.
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.
- 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
- 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
- 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