There was a time in structural biology when solving protein structures using NMR was received with considerable skepticism. In addition to the normal experimental uncertainty, the technique generated structures with additional uncertainty due to the vibrational motions of proteins in solution. That’s part of the reason standard NMR entries in the PDB contain ~20 structures while x-ray structures have just one. However modern NMR methods have advanced to the point that few skeptics are left. The two techniques together were essential in the rapid increase of structural information that’s available today.

According to Dr. Wah Chiu, electron cryomicroscopy today looks a lot like NMR 20 years ago. On his first slide he showed was a definition of Cryo-EM, which looked a lot like a definition of NMR. In bold he emphasized that Cryo-EM is solving structures without crystals. I’ve often heard protein crystallization called ‘black art’ or ‘trying to hold a stack of bowling balls together with tape’. I’m not a practitioner so I’ll assume it’s hard for at least the interesting cases. Getting good crystals is not required but sample preparation rules still apply to Cryo-EM. Wah stresses how diligent and often labor intensive work at the bench yields much better results further along in the pipeline. Once they have a sample though, Wah has a playground full of high-end instruments…

JEOL 3200FSC electron cryomicroscope

1,000-core Linux cluster

images via [http://ncmi.bcm.tmc.edu]

Cryo-EM techniques have been very successful in determining structures of nanomachines (or macromolecular assemblies if you are frustrated with nano-fications) and looks to continue improving over the next 20 years. Large assemblies like capsids, phages, pores, and channels are all possible with Cryo-EM. The resolution is still quite far away from the < 2 Angstroms typical of good x-ray structures. If I remember correctly, Wah said they are currently achieving around 4.7 Angstrom res. and better depending on the system. But resolution isn’t just a number, it’s all about what you can actually see. And what they can actually see now is things like secondary structure and even side-chains. Complete atomic detail is not very far off.

Software and computational techniques influenced by image processing and protein structure prediction efforts are providing atomic details even sooner than expected. Wah’s group has developed SSEHunter to detect secondary structures from the Cryo-EM data and programs like MODELLER are used to characterize each component [paper].

My expectations are quite high. How long before we can see the entire cell and all of it’s components in atomic detail? 5? 10? 20 years?
Futher reading:

• De Novo Backbone Trace of GroEL from Single Particle Electron Cryomicroscopy
• Protein Structure Fitting and Refinement Guided by Cryo-EM Density
• Identification of Secondary Structure Elements in Intermediate-Resolution Density Maps

David Baker is one of my favorite scientists. His group performs the best at CASP. He started the Rosetta protein folding and design software and Rosetta@HOME a distributed computing network to run it. And now he’s behind one of the coolest projects I’ve ever seen. Fold.it is an amazing community-based game where players can compete by folding proteins in a graphical point and click manner. Fold.it has a beautiful UI and molecular graphics not unlike the ones you’ve come to expect from VMD, PyMOL, and UCSF Chimera. Most importantly, this highly addictive puzzle game has real scientific value. Each time you solve a folding puzzle, the software sends your results back to FoldIt. With that data they hope to gain insight into the powerful human capacity to recognize patterns and apply that to new protein structure prediction methods. Players can create and join groups to compete against other players for high-scores.

After playing FoldIt for about an hour the game is actually very fun and addicting! Any game with actions like “Shake Sidechains” and “Wiggle Backbone” is guaranteed to make any bioche/biophysicist smile. While it may compete with GTA4, this game is a huge step in educating students in protein structure. It’s truly brilliant. Thanks to Andrew Perry for pointing this out.

FoldIt – Crowdsourcing to solve the protein folding problem

Around the web, week of March 21, 2008

Journals
Big science from Andrei Sali and David Baker
• The molecular architecture of the nuclear pore complex
• De Novo Computational Design of Retro-Aldol Enzymes

Blogs

• Nature archive visualized – a Processing sketch to visualize the keywords from Nature over the last 30 years. Some of the more spurious terms could probably be cleaned up but even as a draft the effect is pretty neat.
• Research streaming is born. Mike from Bioinformatics Zen is auto-publishing his svn commit messages and uploading figures he generates to Flikr. This would be well suited to someone like me who has too many projects going on to stop and dedicate time to blog about them here.
• Universal Parallel Computing Research Centers are being heavily funded by Microsoft and Intel. One at University of Illinois at Urbana-Champaign, well known for the CHARMM++ parallel library and the super-scalable NAMD molecular dynamics package built on top of it. The other will be located at UC Berkeley.
• The End of the Relational era, is SQL dying? Bill McColl of Computing at Scale says it is. I would argue that relational databases have received the golden hammer treatment over the years. But I totally agree with his prediction that SQL will ultimately be replaced by DSL’s having implicit data-parallelism.
• The Youtube API has been updated with some significant improvements for developers. Uploads, comments, and video playlists can all be manipulated outside of youtube. This makes a convincing case to leverage the massive youtube userbase if your site deals with video content.

Tech

• I’ve finally moved most of my projects from SVN to Git. I’m now a ‘branch-a-holic’ and git definitely fits my workflow better than subversion now that I’m used to it.
• Capistrano is typically used for Rails deployment, but I’m finding it’s good for just about anything you want to run across multiple remote hosts. This is a great mini-language for cluster admins who don’t want to struggle with something like mpirun

A fundamental process of life is the selective and efficient catalysis of chemical reactions by enzymes. Enzymes are usually proteins (ribozymes are one exception), and when these catalysts are chained together they form pathways. Enzyme pathways can be loosely described by their inputs and outputs. An even better abstraction than pathways though is to think in terms of networks. Networks have hubs which are critical to the operation of the network. [Vidal Lab is doing great work in this area of cancer proteomics]

In biology, allosteric enzymes are typically the regulatory elements in a catalytic network. More importantly, interactions distant from the catalytic site can induce changes in activity. One of the first examples of regulated enzyme networks is a system of 5 enzymes in bacteria which catalyze the conversion of L-Threonine to L-Isoleucine. Threonine dehydratase, the first enzyme in the pathway, is specifically inhibited by the end product of the pathway. This is simple feedback-inhibition, where buildup of the end product regulates and slows down the entire pathway by modulating the first step. This simple model illustrates an important aspect of protein interaction. It’s not good enough to simply say that enzyme A “interacts with” enzyme B. We need models that can express things like feedback, messaging, and other more abstracted language about protein relationships.

Compared to genomics, the proteomics universe appears to be pretty messy. Proteins interact in networks with enormous complexity. The challenges for a reverse engineering approach are overwhelming. There is no high-throughput method for reliably characterizing protein functions. Systems Biology is applying simplistic network models, and the Gene Ontology Consortium is working to develop a language for cellular functions. Both of these efforts have much to gain from structural biology.

Further Reading:

Ligand binding and allostery can emerge simultaneously

Is allostery an intrinsic property of all dynamic proteins?

The changing landscape of protein allostery

-image taken from viperdb.scripps.edu

I remember Matthew Baker, one of the authors on this paper, spoke at CASP7 last year. This is an exciting application of modeling since CryoEM can provide the structure of large virus assemblies and membranes. Where comparative modeling becomes important is increasing the resolution to pseudo-atom structures so you can really see what’s happening in terms of chemistry. With simulated electron density maps, Sali et al benchmarked a comparative modeling pipeline which added a density fit score to the DOPE potential.

The genetic algorithm runs for 15 hours on a 50 node dual-PIII cluster.