Inner space

Donald Rumsfeld famously said, "There are known knowns.  These are things we know that we know.  There are known unknowns.  That is to say, there are things that we know we don't know.  But there are also unknown unknowns.  There are things we don't know we don't know."

At the time, much fun was made of his choice of words.  But although I wouldn't choose this as an exemplar of clarity, I have to admit the point he was making is valid enough.  Sometimes discovery starts with determining exactly what it is we don't yet know, with sketching out what astrophysicist Neil deGrasse Tyson (more eloquently) called "the perimeter of our ignorance."

This is the point of the Unknome Project, which is an effort to take our own genome and figure out what parts of it are, at present, unstudied and unexplained.  Cellular biologist Seth Munro and his colleagues at the Medical Research Council Laboratory of Molecular Biology in Cambridge, England, have developed a catalogue of thirteen thousand gene families found in humans (or other mammals that have been sequenced), coding for over two million proteins, and assigned each a "knownness score" -- a number describing to what extent the function of each is understood.  And three thousand of the families -- a little less than a quarter of them -- have a knownness score of zero.

That's a lot of genes that were (at least before Munro et al.) unknown unknowns.

[Image licensed under the Creative Commons Christoph Bock, Max Planck Institute for Informatics, DNA methylation, CC BY-SA 3.0]

What's even cooler is that the group is working to chip away at this bit of the perimeter of our ignorance, and to learn something about the mysteries of our own genetic inner space.  They found 260 genes with low knownness scores that are also present in fruit flies -- a much easier species to study -- and used a technique to suppress the expression of those genes.

Astonishingly, reducing the expression of sixty of these hitherto-unknown genes killed the flies outright.  Dampening others inhibited such important functions as reproduction, growth, mobility, and resistance to stress.

If these poorly-studied genes have analogous effects in humans -- and it's suspected that they do, given that they were evolutionarily conserved since the last common ancestor of humans and fruit flies, something like a half a billion years ago -- that's a lot of critical parts of our genome we don't yet understand.

What it got me wondering is how many of these are involved in diseases for which we haven't yet determined the causes.  There are so many disorders -- like, unfortunately, most mental illnesses -- for which the treatments are erratic at best, in part because we don't know for sure what the underlying origin of the condition is.  In my own case, I know for sure that depression and anxiety run in both sides of my family -- my mother and maternal grandmother both suffered from major depression, and a paternal great-grandmother committed suicide after (according to the newspaper article that reported it) "becoming mentally unbalanced by the illness of her husband."  Part of the problem with these sorts of things is, of course, that it's hard to tease apart the genetic from the environmental factors.  Growing up with mental illness in the family certainly doesn't make for an easy childhood; as my wise grandmother once said, "Hurt people hurt people" -- something that was certainly true enough within her own family.

It's fantastic that Munro and his colleagues are working to try and elucidate the functions of these mysterious genes, and I hope that perhaps some of them might turn out to be good targets for medications to alleviate conditions that have heretofore been resistant to treatment.  Certainly, anything we can do to reduce the perimeter of our own ignorance -- to eliminate some of those unknown unknowns -- is a good thing.

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The biochemical symphony

Sometimes I run into a piece of scientific research that's so odd and charming that I just have to tell you about it.

Take, for example, the paper that appeared in ACS Nano that ties together two of my favorite things -- biology and music.  It has the imposing title, "A Self-Consistent Sonification Method to Translate Amino Acid Sequences into Musical Compositions and Application in Protein Design Using Artificial Intelligence," and was authored by Chi-Hua Yu, Zhao Qin, Francisco J. Martin-Martinez, and Markus J. Buehler, all of the Massachusetts Institute of Technology.  Their research uses a fascinating lens to study protein structure: converting the amino acid sequence and structure of a protein into music, then having an AI software study the musical pattern that results as a way of learning more about how proteins function -- and how that function might be altered.

What's cool is that the musical note that represents each amino acid isn't randomly chosen.  It's based on the amino acid's actual quantum vibrational frequency.  So when you listen to it, you're not just hearing a whimsical combination of notes based on something from nature; you're actually hearing the protein itself.

[Image licensed under the Creative Commons © Nevit Dilmen, Music 01754, CC BY-SA 3.0]

In an article about the research in MIT News, written by David L. Chandler, you can hear clips from the Yu et al. study.  I recommend the second one especially -- the one titled "An Orchestra of Amino Acids" -- which is a "sonification" of spider silk protein.  The strange, percussive rhythm is kind of mesmerizing, and if someone had told me that it was a composition by an avant-garde modern composer -- Philip Glass, perhaps, or Steve Reich -- I would have believed it without question.  But what's coolest about this is that the music actually means something beyond the sound.  The AI is now able to discern the difference between some basic protein structures, including two of the most common -- the alpha-helix (shaped like a spring) and the beta-pleated-sheet (shaped like the pleats on a kilt -- because they sound different.  This gives us a lens into protein function that we didn't have before.  "[Proteins] have their own language, and we don’t know how it works," said Markus Buehler, who co-authored the study.  "We don’t know what makes a silk protein a silk protein or what patterns reflect the functions found in an enzyme.  We don’t know the code."

But this is exactly what the AI, and the scientists running it, hope to find out.  "When you look at a molecule in a textbook, it’s static," Buehler said.  "But it’s not static at all.  It’s moving and vibrating.  Every bit of matter is a set of vibrations.  And we can use this concept as a way of describing matter."

This new approach has impressed a lot of people not only for its potential applications, but from how amazingly creative it is.  This is why it drives me nuts when people say that science isn't a creative process. They apparently have the impression that science is pure grunt work, inoculating petri dishes, looking at data from particle accelerators, analyzing rock layers.  But at its heart, the best science is about making connections between disparate ideas -- just like this research does -- and is as deeply creative as writing a symphony.

"Markus Buehler has been gifted with a most creative soul, and his explorations into the inner workings of biomolecules are advancing our understanding of the mechanical response of biological materials in a most significant manner," said Marc Meyers, professor of materials science at the University of California at San Diego, who was not involved in this work.  "The focusing of this imagination to music is a novel and intriguing direction. his is experimental music at its best.  The rhythms of life, including the pulsations of our heart, were the initial sources of repetitive sounds that engendered the marvelous world of music.  Markus has descended into the nanospace to extract the rhythms of the amino acids, the building blocks of life."

What is most amazing about this is the potential for the AI, once trained, to go in reverse -- to be given an altered musical pattern, and to predict from that what the function of a protein engineered from that music would do.  Proteins are perhaps the most fundamental pieces of living things; the majority of genes do what they do by making proteins, which then guide processes within the organism (including frequently affecting other genes).  The idea that we could use music as a lens into how our biochemistry works is kind of stunning.

So that's your science-is-so-freaking-cool moment for the day.  I peruse the science news pretty much daily, looking for intriguing new research, but this one's gonna be hard to top.  Now I think I'm going to go back to the paper and click on the sound links -- and listen to the proteins sing.

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The power of models

I get that scientific terminology can be daunting.  Scientists, and therefore scientific papers, have become so specialized that unless you are an expert, the vocabulary by itself can be an overwhelming barrier to understanding.  That only gets worse in disciplines like physics and chemistry, where complex mathematics throws another spanner into the works.  I have a B.S. in physics, enough credits for a second major in biology, and a minor in math, and am reasonably articulate, but just about every academic paper I've ever seen loses me within a couple of paragraphs, except for the two areas I know best -- population genetics and evolutionary biology.

So I'm not expecting laypeople to become experts in scientific jargon.  But there are two words I really wish everyone would familiarize themselves with -- theory and model.

Confusion over the first one is what gives rise to the "it's only a theory" *shrug* reaction a lot of people have when discussing the theory of evolution.  Theory, in scientific discussions, does not mean "a wild guess that could as easily be wrong as right."  In scientific parlance, a theory is an explanation of a natural phenomenon that has passed repeated tests and makes predictions that are in good accordance with the data.  This is why intelligent design creationism isn't a theory; it makes no predictions.  If things get complex, it defaults to "God did it," and the conversation ends.

In science, a model is a representation of a natural object, system, or phenomenon, often idealized or simplified, that can then be manipulated -- once again, to see if the results are consistent with observed data from the real world.  As an example, the computerized three-dimensional maps of the climate are models, breaking up the atmosphere into thousands of cubical regions and the land and ocean into square blocks of area, with specifications for atmospheric composition, heat absorption capacity for land and water, solar radiation input, and so on.  The software can take the known input parameters and then run a simulation to see if what comes out matches what we actually know of the real climate data (and they have, to a startling degree of accuracy, something that is simultaneously impressive and terrifying).

The problem with the idea of modeling is that to an outside observer, it may look like the scientists are just messing around -- playing Sims with the world, with no particular expectation that what they're doing has anything in common with reality.  This, of course, is the opposite of the truth -- if a model doesn't align very well with the natural world, it's rightly abandoned for one that works better.

Even models that seem to be a little bit out there are only retained because they describe a known part of the universe sufficiently well that their predictions can be useful for describing something not yet understood.  Take, for example, the paper last week in Proceedings of the National Academy of Sciences that used what's known about biochemistry to make a stab at the configuration and composition of the earliest proteins, molecules that were around 3.5 billion years ago -- produced abiotically before there were any living things on Earth.

In "Small Protein Folds at the Root of an Ancient Metabolic Network," Hagai Raanan, Saroj Poudel, Douglas Pike, Vikas Nanda, and Paul Falkowski, of Rutgers University, describe a sophisticated computer simulation that took what we know about the chemistry that is common to all living organisms (such as using oxidation/reduction reactions to power metabolism) and combined it with what is surmised about the conditions on the early Earth, and used it to infer what the earliest energy-transfer proteins looked like.  The authors write:

Life on Earth is driven by electron transfer reactions catalyzed by a suite of enzymes that comprise the superfamily of oxidoreductases (Enzyme Classification EC1).  Most modern oxidoreductases are complex in their structure and chemistry and must have evolved from a small set of ancient folds.  Ancient oxidoreductases from the Archean Eon between ca. 3.5 and 2.5 billion years ago have been long extinct, making it challenging to retrace evolution by sequence-based phylogeny or ancestral sequence reconstruction.  However, three-dimensional topologies of proteins change more slowly than sequences.  Using comparative structure and sequence profile-profile alignments, we quantify the similarity between proximal cofactor-binding folds and show that they are derived from a common ancestor.  We discovered that two recurring folds were central to the origin of metabolism: ferredoxin and Rossmann-like folds.  In turn, these two folds likely shared a common ancestor that, through duplication, recruitment, and diversification, evolved to facilitate electron transfer and catalysis at a very early stage in the origin of metabolism.

Here's one of the ancestral proteins the model generated:

Now, maybe you see this as a bunch of hand-waving in an intellectual vacuum.  After all, we have no way of going back 3.5 million years and checking to see if the model is correct.  But the key thing is that this was created within parameters of how we know proteins work, and what we see in the energy-transfer proteins of current organisms.  This model was very much constrained by reality -- meaning that its results have a really good chance of being accurate.

Further, like any good model (or theory, for that matter), it generates predictions -- in this case, what we might look for as a signature of emerging life on other planets.  "In the realm of deep-time evolutionary inference," the authors write, "we are necessarily limited to deducing what could have happened, rather than proving what did happen...  Ultimately, our goal is for the proposed effort to inform future NASA missions about detection of life on planetary bodies in habitable zones.  Our effort provides a unique window to potential planetary-scale chemical characteristics that might arise from abiotic chemistry, which must be understood if we are to recognize unique biosignatures on other worlds."

So models and theories aren't guesses, they're real-world descriptions, and the best ones give us deep insight into the workings of the universe.  As such, they are part of the scientist's stock-in-trade -- and essential to understand for laypeople who would like to know what's happening on the cutting edge of research.

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Any guesses as to what was the deadliest natural disaster in United States history?

I'd speculate that if a poll was taken on the street, the odds-on favorites would be Hurricane Katrina, Hurricane Camille, and the Great San Francisco Earthquake.  None of these are correct, though -- the answer is the 1900 Galveston hurricane, that killed an estimated nine thousand people and basically wiped the city of Galveston off the map.  (Galveston was on its way to becoming the busiest and fastest-growing city in Texas; the hurricane was instrumental in switching this hub to Houston, a move that was never undone.)

In the wonderful book Isaac's Storm, we read about Galveston Weather Bureau director Isaac Cline, who tried unsuccessfully to warn people about the approaching hurricane -- a failure which led to a massive overhaul of how weather information was distributed around the United States, and also spurred an effort toward more accurate forecasting.  But author Erik Larson doesn't make this simply about meteorology; it's a story about people, and brings into sharp focus how personalities can play a huge role in determining the outcome of natural events.

It's a gripping read, about a catastrophe that remarkably few people know about.  If you have any interest in weather, climate, or history, read Isaac's Storm -- you won't be able to put it down.

[Note: if you purchase this book using the image/link below, part of the proceeds goes to support Skeptophilia!]

Proteins in space

There's always a danger, when you want to believe something really really really badly, that you'll jump to belief on the basis of questionable evidence.

It's called confirmation bias, and it plagues us all, skeptic and non-skeptic alike.  It's why I've always tried to be more critical of claims that line up with what I want to be true -- because I'm more likely to accept such arguments unquestioningly.

Well, I try to.  It was kind of hard to do when I read a paper by Malcolm McGeoch ((PLEX Corporation), Sergei Dikler (Bruker Scientific), and Julie McGeoch (Harvard Univeristy) that appeared in arXiv last Friday, with the unprepossessing name, "Hemolithin: A Meteoric Protein Containing Iron and Lithium," which you'd think would only be of interest to biochemistry nerds, but had me leaping about making excited little squeaking noises.

Maybe you've already figured out why from the title, but if not, here's the salient bit:

This paper characterizes the first protein to be discovered in a meteorite. Amino acid polymers previously observed in Acfer 086 and Allende meteorites have been further characterized in Acfer 086 via high precision MALDI mass spectrometry to reveal a principal unified structure of molecular weight 2320 Daltons that involves chains of glycine and hydroxy-glycine residues terminated by iron atoms, with additional oxygen and lithium atoms...  Analysis of the complete spectrum of isotopes associated with each molecular fragment shows 2H enhancements above terrestrial averaging 25,700 parts per thousand (sigma = 3,500, n=15), confirming extra-terrestrial origin and hence the existence of this molecule within the asteroid parent body of the CV3 meteorite class.  The molecule is tipped by an iron-oxygen-iron grouping that in other terrestrial contexts has been proposed to be capable of absorbing photons and splitting water into hydroxyl and hydrogen moieties...

Analysis via iron and lithium isotope satellites in mass spectrometry reveals a novel protein motif with iron atoms closing out the ends of anti-parallel peptide chains composed of glycine.  Very high 2H content indicates proto-solar disc or molecular cloud origin. FeO3Fe groups at each end are of a type that could split H2O upon absorption of photons.  The existence of a unique chain length suggests that there could be a functionality conferring a replication advantage.

 They found a protein in a meteorite.  An extraterrestrial protein.  An extraterrestrial protein that appears to be able to perform photolysis -- the fracturing of water using the energy from light.

Like, the first step in photosynthesis in plants.

That was the point when my jaw hit the floor.

One of the barriers to estimating the likelihood of extraterrestrial life is that we don't know how common it is for planets to have conditions supporting a biochemistry.  I say "a" biochemistry because there's no particular necessity that extraterrestrial life have the same chemistry we do.  It's long been speculated, for example, that there could be a biochemistry based upon silicon, which (like carbon) has four valence electrons and is capable of bonding into chains, sheets, and rings.  Like many of us, I first ran into this idea with the episode of Star Trek called "The Devil in the Dark" -- where the intrepid space travelers of the U. S. S. Enterprise were confronted with a life form that used silicon instead of carbon as a biochemical scaffolding, and hydrofluoric acid instead of water as a solvent and carrier -- and so initially, it didn't look alive at all, more like some kind of extremely caustic rock.

Here, though, we don't even need to go as way out as silicon-based life.  Here we have a protein based on amino acids found right here on Earth -- glycine and hydroxyglycine -- coupled with attachment to metal ions, much like many terrestrial proteins (hemoglobin being the obvious example).  If the conditions for biochemical reactions to produce such a protein can be achieved in a proto-solar disc or molecular cloud -- as McGeoch et al. claim -- then carbon-based biochemistry, and probably life, might be a lot more common in the universe than we thought.

On the other hand...

The really far-fetched I-Want-To-Believe streak in me has to wonder if the mysterious protein they found isn't an indication that complex biochemicals can form easily and under a great variety of conditions, but an indication of life.

Like, this protein was produced by a living thing, somewhere out there.

I find it extremely suggestive that the meteoric protein looks like it has the same ability as the photosystem-II array in chloroplasts -- using light to break apart water.  In plants this frees electrons that then are used to store chemical energy as ATP and ultimately synthesize glucose, and therefore underpin virtually every energy-demanding reaction in every life form on Earth.  If I had to pick the one reaction that was the most central to the survival of every terrestrial life form, that'd be it.

The discovery of such a protein in a meteorite is somewhere in that rarified environment just past "mind-blowing."

I'm trying to control myself, here.  I know it's easy to leap to the conclusion that this is evidence of extraterrestrial life, or at the very least, that life is all over the place out there in space.  At the moment, we just have a single bit of protein in a single meteorite.

But it's an alien protein.  One that has a function that, even in the careful diction of the scientists who discovered it, would give it a replication advantage.

Okay, I need to stop writing now, because I feel another bout of jumping around making excited squeaking noises coming on.

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This week's Skeptophilia book-of-the-week is brand new -- science journalist Lydia Denworth's brilliant and insightful book Friendship: The Evolution, Biology, and Extraordinary Power of Life's Fundamental Bond.

Denworth looks at the evolutionary basis of our ability to form bonds of friendship -- comparing our capacity to that of other social primates, such as a group of monkeys in a sanctuary in Puerto Rico and a tribe of baboons in Kenya.  Our need for social bonds other than those of mating and pair-bonding is deep in our brains and in our genes, and the evidence is compelling that the strongest correlate to depression is social isolation.

Friendship examines social bonding not only from the standpoint of observational psychology, but from the perspective of neuroscience.  We have neurochemical systems in place -- mediated predominantly by oxytocin, dopamine, and endorphin -- that are specifically devoted to strengthening those bonds.

Denworth's book is both scientifically fascinating and also reassuringly optimistic -- stressing to the reader that we're built to be cooperative.  Something that we could all do with a reminder of during these fractious times.

[Note: if you purchase this book using the image/link below, part of the proceeds goes to support Skeptophilia!]

The biochemical symphony

Sometimes I run into a piece of scientific research that's so odd and charming that I just have to tell you about it.

Take, for example, the paper that appeared in ACS Nano this week, that ties together two of my favorite things -- biology and music.  It has the imposing title,  "A Self-Consistent Sonification Method to Translate Amino Acid Sequences into Musical Compositions and Application in Protein Design Using Artificial Intelligence," and was authored by Chi-Hua Yu, Zhao Qin, Francisco J. Martin-Martinez, and Markus J. Buehler, all of the Massachusetts Institute of Technology.  Their research uses a fascinating lens to study protein structure: converting the amino acid sequence and structure of a protein into music, then having an AI software study the musical pattern that results as a way of learning more about how proteins function -- and how that function might be altered.

What's cool is that the musical note that represents each amino acid isn't randomly chosen.  It's based on the amino acid's actual quantum vibrational frequency.  So when you listen to it, you're not just hearing a whimsical combination of notes based on something from nature; you're actually hearing the protein itself.

[Image licensed under the Creative Commons © Nevit Dilmen, Music 01754, CC BY-SA 3.0]

In an article about the research in MIT News, written by David L. Chandler, you can hear clips from the Yu et al. study.  I recommend the second one especially -- the one titled "An Orchestra of Amino Acids" -- which is a "sonification" of spider silk protein.  The strange, percussive rhythm is kind of mesmerizing, and if someone had told me that it was a composition by an avant-garde modern composer -- Philip Glass, perhaps, or Steve Reich -- I would have believed it without question.  But what's coolest about this is that the music actually means something beyond the sound.  The AI is now able to discern the difference between some basic protein structures, including two of the most common -- the alpha-helix (shaped like a spring) and the beta-pleated-sheet (shaped like the pleats on a kilt -- because they sound different.  This gives us a lens into protein  function that we didn't have before.  "[Proteins] have their own language, and we don’t know how it works," said Markus Buehler, who co-authored the study.  "We don’t know what makes a silk protein a silk protein or what patterns reflect the functions found in an enzyme.  We don’t know the code."

But this is exactly what the AI, and the scientists running it, hope to find out.  "When you look at a molecule in a textbook, it’s static," Buehler said.  "But it’s not static at all. It’s moving and vibrating. Every bit of matter is a set of vibrations.  And we can use this concept as a way of describing matter."

This new approach has impressed a lot of people not only for its potential applications, but from how amazingly creative it is.  This is why it drives me nuts when people say that science isn't a creative process.  They apparently have the impression that science is pure grunt work, inoculating petri dishes, looking at data from particle accelerators, analyzing rock layers.  But at its heart, the best science is about making connections between disparate ideas -- just like this research does -- and is as deeply creative as writing a symphony.

"Markus Buehler has been gifted with a most creative soul, and his explorations into the inner workings of biomolecules are advancing our understanding of the mechanical response of biological materials in a most significant manner," said Marc Meyers, professor of materials science at the University of California at San Diego, who was not involved in this work.  "The focusing of this imagination to music is a novel and intriguing direction.  his is experimental music at its best.  The rhythms of life, including the pulsations of our heart, were the initial sources of repetitive sounds that engendered the marvelous world of music.  Markus has descended into the nanospace to extract the rhythms of the amino acids, the building blocks of life."

What is most amazing about this is the potential for the AI, once trained, to go in reverse -- to be given an altered musical pattern, and to predict from that what the function of a protein engineered from that music would do.  Proteins are perhaps the most fundamental pieces of living things; the majority of genes do what they do by making proteins, which then guide processes within the organism (including frequently affecting other genes).  The idea that we could use music as a lens into how our biochemistry works is kind of stunning.

So that's your science-is-so-freaking-cool moment for the day.  I peruse the science news pretty much daily, looking for intriguing new research, but this one's gonna be hard to top.  Now I think I'm going to go back to the paper and click on the sound links -- and listen to the proteins sing.

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Richard Dawkins is a name that often sets people's teeth on edge.  However, the combative evolutionary biologist, whose no-holds-barred approach to young-Earth creationists has given him a well-deserved reputation for being unequivocally devoted to evidence-based science and an almost-as-well-deserved reputation for being hostile to religion in general, has written a number of books that are must-reads for anyone interested in the history of life on Earth -- The Blind Watchmaker, Unweaving the Rainbow, Climbing Mount Improbable, and (most of all) The Ancestor's Tale.

I recently read a series of essays by Dawkins, collectively called A Devil's Chaplain, and it's well worth checking out, whatever you think of the author's forthrightness.  From the title, I expected a bunch of anti-religious screeds, and I was pleased to see that they were more about science and education, and written in Dawkins's signature lucid, readable style.  They're all good, but a few are sheer brilliance -- his piece, "The Joy of Living Dangerously," about the right way to approach teaching, should be required reading in every teacher-education program in the world, and "The Information Challenge" is an eloquent answer to one of the most persistent claims of creationists and intelligent-design advocates -- that there's no way to "generate new information" in a genome, and thus no way organisms can evolve from less complex forms.

It's an engaging read, and I recommend it even if you don't necessarily agree with Dawkins all the time.  He'll challenge your notions of how science works, and best of all -- he'll make you think.

[If you purchase this book using the image/link below, part of the proceeds will go to support Skeptophilia!]