Sean Eddy - Washington University - St. Louis: "Biological Sequence Analysis with Probabilistic Models." Talk at the UCSD Bioinformatics Symposium

Sean Eddy had a very interesting talk, obviously departing from his normal presentation to one that addressed the history of his career in science. It was very interesting to hear from a pioneer in the field of probabilistic models in bioinformatics.

He got his start looking for similar intron/RNAs. These are aligned using paired bases. What he needed was a way to simultaneously align and score (structurally) a sequence to a template. Originally he was working on a purely wet-lab RNA project and was scooped so went with what he was moonlighting as computational biology. Luckily, his postdoc committee didn't care that he completely dropped what he originally started with. Talking to people in other departments helped him crystallize his ideas. Talked to speech people at Cambrige, England to get good feedback.

After thinking about the need for a better RNA alignment, he got handed the Karplus, Haussler SAM paper about aligning sequences with HMMs. After reading the paper he was inspired to apply this to his RNA problem. It started with a complete reimplementation of SAM, in a package that he named hmmer. He said that this was a good lesson, if you really want to understand something just go and reimplement it. Working out the bugs and nuances will teach you more than just thinking about the problem.

It was a very interesting talk and although I had never met Sean Eddy before, I found him to be a very nice guy. I never would have guessed from his standoffish website, which has undoubtedly put off many a potential student.

Ruben Abagyan - The Scripps Research Institute: "Structures, Drugs and Computing". Talk at the UCSD Bioinformatics Symposium

The focus of this symposium was "most important open problems in bioinformatics." Dr. Abagyan represented the structure community and discussed docking and modeling.

He apparently has an affinity for -omes. There is a need to characterize the "pocketome", meaning a method for finding meaningful pockets in the PDB for docking small molecules. Using an approach to do this they create the "deorphanome" which can find the native substrate of a ligand in silico. This uses known receptor structures in the PDB and matches them to a particular substrate from KEGG. They find the approach works pretty well with a couple limiting factors: the prediction accuracy depends largely on the resolution of the atomic structure they are docking to and the speed of docking. Currently, it takes about 20 seconds to dock per compound for structure; they need intelligent ways to filter out ligand substrate pairs that have no chance of working.
They have also made an attempt to keep their work from becoming simply theoretical. When looking for receptors for the androgen molecule they found a dock to a known anti-psychotic protein: they found that it binds to the androgen receptor in silico (this was not known) and indeed it was an inhibitor experimentally. Then changed the molecule a bit to not have this antipsychotic effect. This was a nice example of getting close to rational drug design.
He also described work in predicting loop regions (the most disordered regions) of protein structure. They find that up to 12 residues they can model under .5 A which is within crystal accuracy. In predicting loop regions using a monte carlo approach they find that at 120 hours they get the right dock. It's just a matter of devoting enough computational time to the problem...

There are two special features in this month's issue of Science about careers in systems biology. One is "Working the Systems" by Jim Kling and the other "A Meeting of Minds, Expertise, and Imagination" by Anne Forde.
Both articles say that the burgeoning field of systems biology is so new and unique that people haven't been able to really get their heads around it. Although the definition of the field is pretty vague, people who have excelled in it seem to have diverse talents. The movement in this field is integrating different experiments and computational approaches to really push the boundaries of traditional science. This is tough to do, however, because breaking the paradigms of traditional science is hard to accomplish. The former article cites the importance of building teams in systems biology, a practice that is hard to push in academia.

As far as job opportunities go, both articles say that in academics, the mood is optimistic and there is funding available for new groups and group leaders in this area. However, in industry the current situation isn't as good, as many drug companies focus biotech funding on later stages of the drug development pipeline and are waiting to see the real benefit of these approaches before investing in them too heavily.

All in all a couple of short, interesting letters that reinforce what most of us readers think about bioinformatics and systems biology: they are young and promising but really have a lot to prove in the future.

Hopefully, some of these issues will be addressed at the International symposium on systems biology and medicine coming up in April. We invite registrants from that conference to post their reports!









tt: Systems Biology, Biotech

Pubmed - Forde; Pubmed-Kling