(Originally published March 14, 2009)

Happy Pi (π) Day! Americans write dates in the MM/DD/YYYY format instead of the DD/MM/YYYY format used by the rest of the world.  Usually a rather painful and confusing format if you did not grow up with it, causing checks to bounce and leases to expire for those who recently moved to the US, but it has a few benefits: you can take the numeric representation of March 14, and you have the first three digits of Pi. This coincidence is good enough to celebrate a day around the uber-celebrity of numbers. (Heh, I said  “around”). Everybody’s welcome.

This is the day all geeky bloggers come out and try to: (1) show how smart they are; (2) connect Pi, usually in some improbable and tenuous fashion, to whatever theme they have in their blogs and (3) try to make an original observation of pi no one else has made before. So that is exactly what I am going to do today.

Sort of.

Well,  probably not.

Smarts

Well, I remembered Pi day, didn’t I? OK, that does not show I’m smart, just shows my brain is a repository of useless trivia. Look at the time of publication of this post:  March14, 1:59am which is 3.14159. Hey, five digit time stamp that’s smart! (Not very original though, also I’m actually up at this time finishing a grant proposal).

A post with a less than tenuous connection to Pi

Some virus capsids are icosahedral. Not really spherical but sort-of. Bacteria have flagella motors that are circular. Micelles are usually spherical.  Microvesicles are spherical. All these are a good start for pi-topics.

Well, too bad. I actually want to write about circular proteins. Only “circular” in this case does not mean “circle shaped”:  hence, we are chucking Pi out the window right now. Stick around though, these proteins are really cool.

You were probably taught that proteins are linear chains of amino acids that fold into a shape that produces their function. The links connecting the chains are peptide bonds. But there is no real reason why the carboxy terminus (right side) and amino terminus (left side) would not bond themselves.  It just has never been observed, or looked for. Well, they do. And some proteins are circular, like a snake biting its own tail.

These cyclotides are very robust. For one, they are almost immune to proteases: enzymes that break up proteins. Many proteases attack the edge of the protein (exoproteases, because they start from the “outside”), but there are no edges to attack here. The disulfide bonds, their short length make them immune to endoproteases as well as to heat, pH, etc.

What do cyclotides do?

They protect the organism that produces them.  All kingdoms of life produce cyclotides, everything from bacteria to Rhesus monkeys. (Actually, I am not sure about Archaea). Cyclotides seem to act in different mechanisms: some form holes in the membrane of the attacking microbe;  plant cyclotides stunt the growth of feeding caterpillars. Interestingly, the same plant peptide, Kalata B1 induces uterine contractions in mammals. This is how it was discovered: a physician working in the Democratic Republic of Congo noticed that laboring women were drinking tea made from Oleanda affinis to induce childbirth. Theactive ingredient was the first cyclotide to be discovered. Since then, cyclotides have been shown to be antibiotic, antiviral and insecticidal.

Do humans produce cyclotides?

I could not find anything about that in the literature. So I took the amino acid sequence of a recently discovered monkey cyclotide, rhesus theta defensin 1 (RTD1) sequence and BLASTed it (TBLASTN: protein vs. nucleotide)  against the human genome. No results. Of course, this 5 minute trial proves very little. TBLASTNing short sequences  (the RTD1 is only 18aa long) is a bit sticky. If you are a beginning bioinformatics student looking for a course or rotation project, finding candidate Cyclotides in humans (or in other genomes) might be a good idea.  There are about 100 known sequences, so quite a bit for a training set to start from.  You can build a profile or an HMM, and do some more sensitive searches.

But what about Pi?

Sigh.. well, here is an XKCD oldie but goldie nerd litmus test… enjoy…

Trabi, M. (2002). Circular proteins — no end in sight Trends in Biochemical Sciences, 27 (3), 132-138 DOI: 10.1016/S0968-0004(02)02057-1

PELEGRINI, P., QUIRINO, B., & FRANCO, O. (2007). Plant cyclotides: An unusual class of defense compounds Peptides, 28 (7), 1475-1481 DOI: 10.1016/j.peptides.2007.04.025

Wang, C., Hu, S., Martin, J., Sjogren, T., Hajdu, J., Bohlin, L., Claeson, P., Goransson, U., Rosengren, K., Tang, J., Tan, N., & Craik, D. (2009). Combined X-ray and NMR analysis of the stability of the cyclotide cystine knot fold that underpins its insecticidal activity and potential use as drug scaffold Journal of Biological Chemistry DOI: 10.1074/jbc.M900021200

A quick post for International Women’s Day: how did the gender symbols originate in biology? What do ♀ and ♂ actually stand for?

The answer starts in antiquity, when planets and gods were almost synonymous. Religious rites (at least in Europe) were also associated with the working of metals. Thus, each heavenly body was associated with a metal, a god and provided with a proper symbol, thus:

1. Sun (gold) 2. Moon (silver) 3. Saturn (lead) 4. Jupiter (tin) 5. Mars (iron) 6. Mercury (mercury, duh) 7. Venus (copper) After woodcuts by Friz Kredel, published in Stearn 1962.

But how did the symbols of Mars (iron) and Venus (copper) migrate to describe sex in biology? It seems obvious to us that of all symbols, that of the god of war be assigned to male, and the goddess of love to female (stereotypes nonwithstanding), but who was the first who did that?

The answer can be traced to one of the greatest biologists of all times: Carl Linnaeus. He is better known for being the father of modern taxonomy: Linnaeus  is the reason that we uniquely identify organisms using genus and species names in Latin grammatical form, a system known as Linneael binomial nomnclature. From Homo sapiens to Escherichia coli, we all owe our scientific names to Linnaeus.

But Linnaeus was also the one to appropriate the planet symbols to biology. In his notes, he used the Venus symbol as shorthand for female and the Mars symbol as shorthand for male. He also used Saturn to denote woody plants, the Sun for annual plants and Jupiter for perennials. As for gender, the Mercury symbol was used by Linnaeus for hermaphrodite plants. However, that symbol’s meaning has changed over the years, at least in scientific shorthand, and is now used to denote virgin female (e.g. in genetic analysis).  Mars was also used by Linnaeus, somewhat confusingly, for biennial plants.

But how did the symbols actually originate? The accepted thought now is that they were derived by the Roman from the Greek initial letters for the planets / deities. So Phosphoros  Φωσφόρος (Greek: “Morning Star” or later the planet Venus) was abbreviated to Φκ and Thouros (Mars) to θρ further contracted over the years, by metal workers, astrologers and alchemists to the modern symbols.

Kronos (saturn); Zeus (Jupiter); Thouros (Mars); Phosphoros (Venus) Stilbon (Mercury). After Stearn 1962

William T. Stearn (1962). The Origin of the Male and Female Symbols of Biology Taxon, 11 (4), 109-113

Hats off to Jonathan Eisen for hosting this activity on his blog. (I’ll keep mine on, thank you. It’s raining cats and dogs here right now).

A couple of weeks ago I posted a discussion about two papers that challenged the ortholog conjecture. Briefly, both papers stated that orthologs may not be such great predictors for molecular function. One study from  Indiana University by  has shown that paralogs may be better predictors than orthologs for molecular function. Or, at the very least, paralogs should not be excluded as predictors. This paper has generated quite a bit of interest and controversy. Consequently, Eisen has invited Matthew Hahn, the lead author to write about “the story behind the story” in Eisen’s well-read blog. The post is a great read, and has generated an animated discussion in the comments area. You do need to clear quite a bit of time to go through both Hahn’s guest post and the comment thread: the topic is a rather complex one, and as explained in the comments thread, one problem is that the ‘ortholog conjecture’ itself seems to be not well-defined.

I kept checking in to Eisen’s blog to read the elongating comment thread. It seems that now a special session on the topic may be in the works for the 2012 annual meeting of the Society for Molecular Biology and Evolution following this discussion. So great to see such an involved community getting together.

The trouble with genomic sequencing, is that it is too cheap. Anyone that has a bit of extra cash laying around, you can scrape the bugs off your windshield, sequence them, and write a paper. Seriously?

Yes, seriously now: as we sequence more and more genomes, our annotation tools cannot keep up with them. It’s like unearthing thousands of books at some vast archaeological dig of an ancient library, but being able to read only a few pages here and there. Simply put: what do all these genes do? The gap between what we do know and what we do not know is constantly growing. We are unearthing more and more books (genomes) at an ever-increasing pace, but we cannot keep up with the influx of new and strange words (genes) of this ancient language. Many genes are being tested for their function experimentally in laboratories. But the number of genes whose function we are determining using experiments is but a drop in the ocean compared to the number of genes we have sequenced and whose whose function is not known We may be sitting on the next drug target for cancer or Alzheimer’s disease, but those proteins are labeled as “unknown function” in the databases.

The red line is the growth of protein sequences deposited in TrEMBL, a comprehensive protein sequence database. The blue line illustrates the growth proteins in TrEMBL whose function is know, or at least can be predicted with some reasonable accuracy. The green line is the growth in the proteins whose 3D structure has been solved. Note the logarithmically increasing gap between what we know (blue) and what we do not know (red). Image courtesy of Predrag Radivojac.

Enter bioinformatics. CPU hours are cheaper than high throughput screening assays. And if the algorithms are good, software can do the work of determining function much cheaper than experiments. But therein lies the rub: how do we know how well function prediction algorithms perform? How do we compare their accuracy? Which method performs best, and are different methods better for different types of function predictions? This is important because most of the functional annotations in the databases come from bioinformatic prediction tools, not from experimental evidence. We need to know how accurate these tools are. Think about it this way: even an increase of 1% in accuracy  would means that hundreds of thousands of sequence database entries are better annotated, which in turn means a lot less time in the lab or in high throughput screening labs going after false drug leads.

So a few of us got together and decided to run an experiment to compare the performance of different function prediction software tools.  We call our initiative the CAFA challenge: Critical Assessment of Function Annotation. There are many research groups that are developing algorithms for gene and protein function prediction, but those have not been compared on a large scale, yet. OK then: let’s have some fun. We, the CAFA challenge organizers, will release the sequences of some 50,000 proteins whose functions are unknown. The various research groups will predict their functions using their own software. By January 2011 all the predictions should be submitted to the CAFA experiment website. Over the net few months, some of these proteins will get annotated experimentally. Not many, probably no more than a few hundred judging by the slow growth of the experimental annotations in the databases. But we don’t need that many to score the predictions. A few dozen will do.

On July 15, 2011 we will all meet in Vienna, and hold the first-ever CAFA meeting as a satellite meeting of ISMB 2011. This will be the fifth Automated Function Prediction meeting we have been holding since 2005. Only this time, there won’t just be the usual talks and posters, there will be the results of a very interesting experiment. The International Society for Computational Biology is generously hosting our meeting, and judging by the response we are getting so far, we will need one of the larger halls.

Learn more at http://biofunctionprediction.org If computational protein function prediction is your thing, join the CAFA challenge. If you are just an interested observer, keep an eye on the site. In any case, please spread the word.  Finally, if your company wants some publicity, get in touch! We could use the sponsorship ^_^

Acknowledgements: I would like to thank the CAFA co-organizers, Michal Linial and Predrag Radivojac. The CAFA steeering committee: Burkhard Rost, Steven Brenner, Patsy Babbitt and Christine Orengo for supporting us, keeping us on the straight and narrow and for incredibly useful and insightful suggestions.  Sean Mooney and Amos Bairoch for hashing out the assessment.  Tal Ronnen-Oron and the rest of Sean Mooney’s group for setting up the CAFA website. The International Society for Computational Biology for sponsoring us. The community of computational function predictors that have participated in and supported past meetings on computational function prediction, the research groups that have registered to CAFA so far, and those that will register soon   Finally, Inbal Halperin-Landsberg for coining the name CAFA. I apologize in advance if I left someone out.

GO CAFA!

Fast forward to July 1, 2011: the meeting is so on. We have 38 teams participating in the challenge, with over 50 algorithms. We have about 600 proteins whose functions have been predicted. We have a great line up of speakers including Janet Thornton the Director of the European Bioinformatics Institute and Amos Bairoch, the founder of SwissProt. The full program is available. The Radivojac, Mooney and Friedberg labs have been working very hard, and we do have some really great results to report, and some surprising activities. We are also grateful to the NIH and the US Department of Energy for their support.

So if you are planning on being at ISMB this year, drop by  the AFP/CAFA SIG. We are messing with something brand-new, which is what science is all about.

The National Academies Press are offering all their books in PDF format for free. The announcement yesterday created a serious traffic surge on their site. But the books are still there, and are still free. Got to buy that new 5Tb external disk now….

A woman in Chesterfield, Ohio robbed a convenience store using her MRSA-infected arm as a weapon.  Warning: graphic picture of MRSA infected arm. Or a zombie limb. You can never know in northwestern Ohio.

Scanning Electron Microscope image of Methicillin-Resistant Staphylococcus aureus or MRSA. Credit: Janice Carr. Source: wikipedia

A US vector biologist got infected with Zika virus which is  a mosquito-borne pathogen causing joint pain and fatigue. He then passed in on to his wife during sexual intercourse. This would be the first documented case of sexual transmission of an insect-borne pathogen. Also, “Honey, Iswear, I got it from a mosquito bite” is the new excuse for two-timing spouses everywhere.

April’s fool came and went on the intertubes.  My favorite was the arsenic based sea-monkeys.

Great news for geeky kids of the 1980′s: the Commodore 64 is back!  Same nice bulky keyboard-in computer, with just a little bit more than 64K of RAM underneath:

“The upcoming Commodore 64 looks exactly like the ancient model which reshaped the home computer sector decades ago, though underneath the bonnet things have been brought well into the 21st century.

“Even though the computer refrains from relinquishing its old charm, it will come packed with modern computer hardware. The new Commodore 64 model will contain a mini-ITC motherboard, a dual core Intel 525 Atom processor, DDR3 RAM between 2GB and 4GB and Nvidia Ion2 graphics unit.

“Commodore says that the device will run on the Ubuntu 10.04 LTS OS and will come with a DVD drive, with support for Blu-Ray, a multi-format memory card reader and five USB slots. The new Commodore 64 will also come with Wi-Fi support and will come pre-loaded with 3D games and other applications including a Microsoft Office compatible productivity suite.
Read more: http://www.itproportal.com/2011/04/06/iconic-commodore-64-all-set-comeback/#ixzz1IqxQ1LXX

 

Finally, remember the name: Zhuchengtyrannus magnus (zoo-CHENG TEER-ah-nus MAG-nus) or Z. magnus. A new dinosaur discovered in Zucheng, China. Related to Tyrannosaurus rex it is 4m tall, 11m long, 6 tons of carnivorous mass. The original paper.

Credit: xkcd.com

But now the science in the article itself is coming under fire. Several blog posts by notable microbiologists and biochemists  have questioned the claims made in the paper. To sum those up: yes, the microbes contain arsenate, the can grow on arsenic-rich media but there is no convincing evidence that arsenic gets incorporated into DNA, much less other molecules that use phosphate. Because this research is so much in the spotlight, the comments on it are in the spotlight too. I believe we will see some very interesting correspondence on the website and in the upcoming issues of Science.

Which brings me to the point of this post: is the peer-review publication culture undergoing a reform?  The arsenate bacteria article itself went through the peer-review mill, which means that at least three scientists which are credited as experts in the field have looked at it and given it a clean bill of health. But once it got published, hundreds of microbiologists and biochemists had a look, and many were less than convinced of some of its claims.  So which is better for the process of peer-review: three anonymous referees before publication, or 100 after? Or maybe we should use both?

A personal example: I recently  published a paper  in PLoS Computational Biology, which went through two pre-publication review cycles making it much better. However, even after those revisions an error (minor, fortunately) slipped through. A reader emailed me about it, and I immediately went to PLoS-CB‘s site and addressed that error as an inline comment in the paper. This mechanism provided by PLoS is laudable: I wish it were used more, and that other journals could provide it.

So, post-publication peer-review seems to be a good thing: it quickly identifies issues with the science, and helps to fix them.  So why is it not done more? Well, for one, there is the lack of anonymity. Post-publication commentators do not have the luxury of the official peer-reviewers of hiding their identity. Another is lack of credit: while some credit is given for pre-publication review, which is recognized as service rendered to the community, none is given yet for post-publication review. But why not? It is scientists like Rosie Redfield, Larry Moran, Jim Hu and others who did a great public service by taking the time to carefully read and then publicly critique the paper.  And in case there are still doubters of the value of science blogging, please read this piece by Larry Moran and for blogging as a career enhancer in science, “10 benefits for my career of blogging/ tweeting etc.) #fb” by Jonathan Eisen.

Where am I going with this? I’m not sure. But it seems like the fallout from the arsenate bacteria paper brings to light a new kind of science culture, in which post-publication critiques in expert science blogs are given. Perhaps all this energy could be harnessed to provide a better publication environment for research papers.  This has been going on for some time, as many science bloggers emphasize paper critique. But high profile incidents like the arsenate bacteria bring the value of post-publication review to light. To paraphrase a quote by Carl Sagan which was mentioned at the press conference held when the paper was published: “extraordinary claims attract extraordinary blogging”.

Wolfe-Simon F, Blum JS, Kulp TR, Gordon GW, Hoeft SE, Pett-Ridge J, Stolz JF, Webb SM, Weber PK, Davies PC, Anbar AD, & Oremland RS (2010). A Bacterium That Can Grow by Using Arsenic Instead of Phosphorus. Science (New York, N.Y.) PMID: 21127214

I would have put Botany in Fangorn (because of the Ents), Microbiology in the Sea of Rhûn for beyond it are “wide uncharted lands, nameless plains, and forests unexplored” and machine learning in the Misty Mountains (close enough to Bioinformatics, statistics and Computer Science).

Also, if the social sciences are in Mordor, what does that say about Sauron?